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	WEBVTT

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	The following is a conversation with Jeff Hawkins.

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	He's the founder of the Redwood Center

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	for Theoretical and Neuroscience in 2002

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	and New Menta in 2005.

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	In his 2004 book titled On Intelligence

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	and in the research before and after,

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	he and his team have worked to reverse engineer

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	the New York Cortex and propose artificial intelligence

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	architectures approaches and ideas

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	that are inspired by the human brain.

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	These ideas include hierarchical temporal memory,

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	HTM from 2004, and New Work, The Thousand's Brain's Theory

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	of Intelligence from 2017, 18, and 19.

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	Jeff's ideas have been an inspiration

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	to many who have looked for progress beyond the current

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	machine learning approaches, but they have also received

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	criticism for lacking a body of empirical evidence

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	supporting the models.

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	This is always a challenge when seeking more than small

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	incremental steps forward in AI.

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	Jeff is a brilliant mind and many of the ideas

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	he has developed and aggregated from neuroscience

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	are worth understanding and thinking about.

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	There are limits to deep learning

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	as it is currently defined.

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	Forward progress in AI is shrouded in mystery.

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	My hope is that conversations like this

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	can help provide an inspiring spark for new ideas.

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	This is the Artificial Intelligence Podcast.

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	If you enjoy it, subscribe on YouTube,

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	iTunes, or simply connect with me on Twitter

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	at Lex Freedman spelled F R I D.

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	And now here's my conversation with Jeff Hawkins.

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	Are you more interested in understanding the human brain

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	or in creating artificial systems

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	that have many of the same qualities,

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	but don't necessarily require that you actually understand

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	the underpinning workings of our mind?

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	So there's a clear answer to that question.

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	My primary interest is understanding the human brain.

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	No question about it, but I also firmly believe

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	that we will not be able to create

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	fully intelligent machines

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	until we understand how the human brain works.

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	So I don't see those as separate problems.

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	I think there's limits to what can be done

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	with machine intelligence

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	if you don't understand the principles

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	by which the brain works.

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	And so I actually believe that studying the brain

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	is actually the fastest way to get to machine intelligence.

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	And within that, let me ask the impossible question.

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	How do you not define, but at least think

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	about what it means to be intelligent?

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	So I didn't try to answer that question first.

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	We said, let's just talk about how the brain works.

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	And let's figure out how certain parts of the brain,

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	mostly the neocortex, but some other parts too,

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	the parts of the brain most associated with intelligence.

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	And let's discover the principles by how they work.

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	Because intelligence isn't just like some mechanism

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	and it's not just some capabilities.

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	It's like, okay, we don't even know where to begin

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	on this stuff.

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	And so now that we've made a lot of progress on this,

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	after we've made a lot of progress

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	on how the neocortex works, and we can talk about that,

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	I now have a very good idea

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	what's gonna be required to make intelligent machines.

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	I can tell you today, some of the things

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	are gonna be necessary, I believe,

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	to create intelligent machines.

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	Well, so we'll get there.

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	We'll get to the neocortex and some of the theories

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	of how the whole thing works.

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	And you're saying, as we understand more and more

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	about the neocortex, about our own human mind,

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	we'll be able to start to more specifically define

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	what it means to be intelligent.

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	It's not useful to really talk about that until...

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	I don't know if it's not useful.

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	Look, there's a long history of AI, as you know.

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	And there's been different approaches taken to it.

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	And who knows?

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	Maybe they're all useful.

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	So the good old fashioned AI, the expert systems,

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	the current convolutional neural networks,

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	they all have their utility.

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	They all have a value in the world.

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	But I would think almost everyone agreed

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	that none of them are really intelligent

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	in a sort of a deep way that humans are.

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	And so it's just the question of how do you get

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	from where those systems were or are today

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	to where a lot of people think we're gonna go.

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	And there's a big, big gap there, a huge gap.

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	And I think the quickest way of bridging that gap

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	is to figure out how the brain does that.

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	And then we can sit back and look and say,

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	oh, what are these principles that the brain works on

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	are necessary and which ones are not?

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	Clearly, we don't have to build this in,

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	and tellage machines aren't gonna be built

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	out of organic living cells.

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	But there's a lot of stuff that goes on the brain

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	that's gonna be necessary.

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	So let me ask maybe, before we get into the fun details,

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	let me ask maybe a depressing or a difficult question.

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	Do you think it's possible that we will never

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	be able to understand how our brain works,

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	that maybe there's aspects to the human mind

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	like we ourselves cannot introspectively get to the core,

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	that there's a wall you eventually hit?

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	Yeah, I don't believe that's the case.

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	I have never believed that's the case.

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	There's not been a single thing we've ever,

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	humans have ever put their minds to that we've said,

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	oh, we reached the wall, we can't go any further.

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	People keep saying that.

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	People used to believe that about life, you know,

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	Alain Vitao, right?

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	There's like, what's the difference in living matter

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	and nonliving matter?

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	Something special you never understand.

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	We no longer think that.

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	So there's no historical evidence that suggests this is the case

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	and I just never even consider that's a possibility.

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	I would also say today, we understand so much

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	about the neocortex.

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	We've made tremendous progress in the last few years

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	that I no longer think of as an open question.

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	The answers are very clear to me and the pieces

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	that we don't know are clear to me,

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	but the framework is all there and it's like, oh, okay,

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	we're gonna be able to do this.

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	This is not a problem anymore.

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	It just takes time and effort, but there's no mystery,

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	a big mystery anymore.

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	So then let's get into it for people like myself

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	who are not very well versed in the human brain,

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	except my own.

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	Can you describe to me at the highest level,

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	what are the different parts of the human brain

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	and then zooming in on the neocortex,

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	the parts of the neocortex and so on, a quick overview.

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	Yeah, sure.

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	The human brain, we can divide it roughly into two parts.

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	There's the old parts, lots of pieces,

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	and then there's the new part.

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	The new part is the neocortex.

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	It's new because it didn't exist before mammals.

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	The only mammals have a neocortex and in humans

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	and primates is very large.

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	In the human brain, the neocortex occupies about 70 to 75%

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	of the volume of the brain.

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	It's huge.

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	And the old parts of the brain are,

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	there's lots of pieces there.

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	There's a spinal cord and there's the brainstem

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	and the cerebellum and the different parts

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	of the basal ganglion and so on.

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	In the old parts of the brain,

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	you have the autonomic regulation,

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	like breathing and heart rate.

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	You have basic behaviors.

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	So like walking and running are controlled

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	by the old parts of the brain.

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	All the emotional centers of the brain

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	are in the old part of the brain.

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	So when you feel anger or hungry,

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	lust or things like that,

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	those are all in the old parts of the brain.

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	And we associate with the neocortex

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	all the things we think about

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	as sort of high level perception.

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	And cognitive functions, anything from seeing and hearing

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	and touching things to language, to mathematics

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	and engineering and science and so on.

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	Those are all associated with the neocortex.

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	And they're certainly correlated.

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	Our abilities in those regards are correlated

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	with the relative size of our neocortex

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	compared to other mammals.

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	So that's like the rough division.

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	And you obviously can't understand

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	the neocortex completely isolated,

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	but you can understand a lot of it

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	with just a few interfaces

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	to the old parts of the brain.

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	And so it gives you a system to study.

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	The other remarkable thing about the neocortex

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	compared to the old parts of the brain

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	is the neocortex is extremely uniform.

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	It's not visually or anatomically,

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	or it's very, it's like a,

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	I always like to say it's like the size

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	of a dinner napkin, about two and a half millimeters thick.

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	And it looks remarkably the same everywhere.

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	Everywhere you look in that two and a half millimeters

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	is this detailed architecture.

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	And it looks remarkably the same everywhere.

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	And that's a cross species,

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	a mouse versus a cat and a dog and a human.

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	Where if you look at the old parts of the brain,

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	there's lots of little pieces do specific things.

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	So it's like the old parts of a brain evolved,

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	like this is the part that controls heart rate

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	and this is the part that controls this

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	and this is this kind of thing.

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	And that's this kind of thing.

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	And these evolve for eons of a long, long time

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	and they have those specific functions.

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	And all of a sudden mammals come along

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	and they got this thing called the neocortex

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	and it got large by just replicating the same thing

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	over and over and over again.

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	This is like, wow, this is incredible.

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	So all the evidence we have,

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	and this is an idea that was first articulated

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	in a very cogent and beautiful argument

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	by a guy named Vernon Malkassel in 1978, I think it was,

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	that the neocortex all works on the same principle.

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	So language, hearing, touch, vision, engineering,

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	all these things are basically underlying

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	or all built in the same computational substrate.

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	They're really all the same problem.

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	So the low level of the building blocks all look similar.

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	Yeah, and they're not even that low level.

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	We're not talking about like neurons.

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	We're talking about this very complex circuit

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	that exists throughout the neocortex is remarkably similar.

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	It is, it's like, yes, you see variations of it here

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	and they're more of the cell, that's not old and so on.

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	But what Malkassel argued was,

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	it says, you know, if you take a section on neocortex,

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	why is one a visual area and one is a auditory area?

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	Or why is, and his answer was,

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	it's because one is connected to eyes

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	and one is connected to ears.

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	Literally, you mean just as most closest

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	in terms of the number of connections to the sensor?

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	Literally, if you took the optic nerve

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	and attached it to a different part of the neocortex,

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	that part would become a visual region.

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	This actually, this experiment was actually done

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	by Murgankasur in developing, I think it was lemurs,

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	I can't remember what it was, it's some animal.

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	And there's a lot of evidence to this.

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	You know, if you take a blind person,

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	a person is born blind at birth,

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	they're born with a visual neocortex.

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	It doesn't, may not get any input from the eyes

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	because of some congenital defect or something.

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	And that region becomes, does something else.

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	It picks up another task.

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	So, and it's, so it's this very complex thing.

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	It's not like, oh, they're all built on neurons.

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	No, they're all built in this very complex circuit.

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	And somehow that circuit underlies everything.

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	And so this is, it's called the common cortical algorithm,

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	if you will, some scientists just find it hard to believe.

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	And they just say, I can't believe that's true.

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	But the evidence is overwhelming in this case.

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	And so a large part of what it means

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	to figure out how the brain creates intelligence

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	and what is intelligence in the brain

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	is to understand what that circuit does.

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	If you can figure out what that circuit does,

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	as amazing as it is, then you can,

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	then you understand what all these other cognitive functions are.

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	So if you were to sort of put neocortex

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	outside of your book on intelligence,

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	you look, if you wrote a giant tome,

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	a textbook on the neocortex,

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	and you look maybe a couple of centuries from now,

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	how much of what we know now would still be accurate

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	two centuries from now.

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	So how close are we in terms of understanding?

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	I have to speak from my own particular experience here.

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	So I run a small research lab here.

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	It's like any other research lab.

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	I'm sort of the principal investigator.

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	There's actually two of us,

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	and there's a bunch of other people.

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	And this is what we do.

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	We started the neocortex,

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	and we publish our results and so on.

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	So about three years ago,

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	we had a real breakthrough in this field.

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	Just tremendous breakthrough.

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	We started, we now publish, I think, three papers on it.

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	And so I have a pretty good understanding

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	of all the pieces and what we're missing.

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	I would say that almost all the empirical data

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	we've collected about the brain, which is enormous.

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	If you don't know the neuroscience literature,

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	it's just incredibly big.

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	And it's, for the most part, all correct.

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	It's facts and experimental results

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	and measurements and all kinds of stuff.

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	But none of that has been really assimilated

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	into a theoretical framework.

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	It's data without, in the language of Thomas Kuhn,

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	the historian, it would be sort of a preparadigm science.

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	Lots of data, but no way to fit it in together.

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	I think almost all of that's correct.

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	There's gonna be some mistakes in there.

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	And for the most part,

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	there aren't really good cogent theories

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	about how to put it together.

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	It's not like we have two or three competing good theories,

	12:50.040 --> 12:51.520
	which ones are right and which ones are wrong.

	12:51.520 --> 12:53.720
	It's like, yeah, people just scratching their heads

	12:53.720 --> 12:55.560
	throwing things, you know, some people giving up

	12:55.560 --> 12:57.560
	on trying to figure out what the whole thing does.

	12:57.560 --> 13:00.960
	In fact, there's very, very few labs that we do

	13:00.960 --> 13:03.280
	that focus really on theory

	13:03.280 --> 13:06.760
	and all this unassimilated data and trying to explain it.

	13:06.760 --> 13:08.880
	So it's not like we've got it wrong.

	13:08.880 --> 13:11.120
	It's just that we haven't got it at all.

	13:11.120 --> 13:15.040
	So it's really, I would say, pretty early days

	13:15.040 --> 13:18.360
	in terms of understanding the fundamental theories,

	13:18.360 --> 13:20.240
	forces of the way our mind works.

	13:20.240 --> 13:21.080
	I don't think so.

	13:21.080 --> 13:23.760
	I would have said that's true five years ago.

	13:25.360 --> 13:28.600
	So as I said, we had some really big breakthroughs

	13:28.600 --> 13:30.800
	on this recently and we started publishing papers on this.

	13:30.800 --> 13:34.240
	So you can get to that.

	13:34.240 --> 13:36.760
	But so I don't think it's, you know, I'm an optimist

	13:36.760 --> 13:38.280
	and from where I sit today,

	13:38.280 --> 13:39.440
	most people would disagree with this,

	13:39.440 --> 13:41.640
	but from where I sit today, from what I know,

	13:43.240 --> 13:44.920
	it's not super early days anymore.

	13:44.920 --> 13:46.840
	We are, you know, the way these things go

	13:46.840 --> 13:48.200
	is it's not a linear path, right?

	13:48.200 --> 13:49.840
	You don't just start accumulating

	13:49.840 --> 13:50.800
	and get better and better and better.

	13:50.800 --> 13:52.920
	No, you got all the stuff you've collected.

	13:52.920 --> 13:53.760
	None of it makes sense.

	13:53.760 --> 13:55.640
	All these different things are just sort of around.

	13:55.640 --> 13:57.120
	And then you're going to have some breaking points

	13:57.120 --> 13:59.400
	all of a sudden, oh my God, now we got it right.

	13:59.400 --> 14:01.120
	That's how it goes in science.

	14:01.120 --> 14:04.480
	And I personally feel like we passed that little thing

	14:04.480 --> 14:06.320
	about a couple of years ago.

	14:06.320 --> 14:07.560
	All that big thing a couple of years ago.

	14:07.560 --> 14:09.600
	So we can talk about that.

	14:09.600 --> 14:11.000
	Time will tell if I'm right,

	14:11.000 --> 14:12.640
	but I feel very confident about it.

	14:12.640 --> 14:15.120
	That's when we'll just say it on tape like this.

	14:15.120 --> 14:18.040
	At least very optimistic.

	14:18.040 --> 14:20.160
	So let's, before those few years ago,

	14:20.160 --> 14:23.200
	let's take a step back to HTM,

	14:23.200 --> 14:25.960
	the hierarchical temporal memory theory,

	14:25.960 --> 14:27.480
	which you first proposed on intelligence

	14:27.480 --> 14:29.280
	and went through a few different generations.

	14:29.280 --> 14:31.200
	Can you describe what it is,

	14:31.200 --> 14:33.560
	how it would evolve through the three generations

	14:33.560 --> 14:35.360
	since you first put it on paper?

	14:35.360 --> 14:39.240
	Yeah, so one of the things that neuroscientists

	14:39.240 --> 14:42.920
	just sort of missed for many, many years.

	14:42.920 --> 14:45.720
	And especially people were thinking about theory

	14:45.720 --> 14:47.720
	was the nature of time in the brain.

	14:47.720 --> 14:50.440
	Brain's process, information through time,

	14:50.440 --> 14:53.280
	the information coming into the brain is constantly changing.

	14:53.280 --> 14:56.160
	The patterns from my speech right now,

	14:56.160 --> 14:58.520
	if you're listening to it at normal speed,

	14:58.520 --> 15:00.080
	would be changing on your ears

	15:00.080 --> 15:02.680
	about every 10 milliseconds or so, you'd have a change.

	15:02.680 --> 15:05.320
	This constant flow, when you look at the world,

	15:05.320 --> 15:06.800
	your eyes are moving constantly,

	15:06.800 --> 15:08.240
	three to five times a second,

	15:08.240 --> 15:09.920
	and the input's completely, completely.

	15:09.920 --> 15:11.800
	If I were to touch something like a coffee cup

	15:11.800 --> 15:13.880
	as I move my fingers, the input changes.

	15:13.880 --> 15:16.840
	So this idea that the brain works on time

	15:16.840 --> 15:19.640
	changing patterns is almost completely,

	15:19.640 --> 15:21.080
	or was almost completely missing

	15:21.080 --> 15:23.520
	from a lot of the basic theories like fears of vision

	15:23.520 --> 15:24.360
	and so on.

	15:24.360 --> 15:26.280
	It's like, oh no, we're gonna put this image in front of you

	15:26.280 --> 15:28.360
	and flash it and say, what is it?

	15:28.360 --> 15:31.120
	A convolutional neural network's worked that way today, right?

	15:31.120 --> 15:33.280
	Classified this picture.

	15:33.280 --> 15:35.120
	But that's not what vision is like.

	15:35.120 --> 15:37.760
	Vision is this sort of crazy time based pattern

	15:37.760 --> 15:39.080
	that's going all over the place,

	15:39.080 --> 15:40.920
	and so is touch and so is hearing.

	15:40.920 --> 15:42.880
	So the first part of a hierarchical temporal memory

	15:42.880 --> 15:44.280
	was the temporal part.

	15:44.280 --> 15:47.680
	It's to say, you won't understand the brain,

	15:47.680 --> 15:49.360
	nor will you understand intelligent machines

	15:49.360 --> 15:51.720
	unless you're dealing with time based patterns.

	15:51.720 --> 15:54.760
	The second thing was, the memory component of it was,

	15:54.760 --> 15:59.760
	is to say that we aren't just processing input,

	15:59.760 --> 16:02.000
	we learn a model of the world.

	16:02.000 --> 16:04.000
	And the memory stands for that model.

	16:04.000 --> 16:06.640
	The point of the brain, part of the neocortex,

	16:06.640 --> 16:07.840
	it learns a model of the world.

	16:07.840 --> 16:10.840
	We have to store things that are experiences

	16:10.840 --> 16:13.520
	in a form that leads to a model of the world.

	16:13.520 --> 16:15.080
	So we can move around the world,

	16:15.080 --> 16:16.240
	we can pick things up and do things

	16:16.240 --> 16:17.520
	and navigate and know how it's going on.

	16:17.520 --> 16:19.320
	So that's what the memory referred to.

	16:19.320 --> 16:22.320
	And many people just, they were thinking about like,

	16:22.320 --> 16:24.480
	certain processes without memory at all.

	16:24.480 --> 16:26.120
	They're just like processing things.

	16:26.120 --> 16:28.320
	And then finally, the hierarchical component

	16:28.320 --> 16:31.640
	was a reflection to that the neocortex,

	16:31.640 --> 16:33.920
	although it's just a uniform sheet of cells,

	16:33.920 --> 16:36.920
	different parts of it project to other parts,

	16:36.920 --> 16:38.680
	which project to other parts.

	16:38.680 --> 16:42.400
	And there is a sort of rough hierarchy in terms of that.

	16:42.400 --> 16:46.000
	So the hierarchical temporal memory is just saying,

	16:46.000 --> 16:47.720
	look, we should be thinking about the brain

	16:47.720 --> 16:52.720
	as time based, model memory based and hierarchical processing.

	16:54.760 --> 16:58.160
	And that was a placeholder for a bunch of components

	16:58.160 --> 17:00.720
	that we would then plug into that.

	17:00.720 --> 17:02.600
	We still believe all those things I just said,

	17:02.600 --> 17:06.960
	but we now know so much more that I'm stopping to use

	17:06.960 --> 17:08.200
	the word hierarchical temporal memory yet

	17:08.200 --> 17:11.320
	because it's insufficient to capture the stuff we know.

	17:11.320 --> 17:12.960
	So again, it's not incorrect,

	17:12.960 --> 17:15.800
	but I now know more and I would rather describe it

	17:15.800 --> 17:16.800
	more accurately.

	17:16.800 --> 17:20.360
	Yeah, so you're basically, we could think of HTM

	17:20.360 --> 17:24.800
	as emphasizing that there's three aspects of intelligence

	17:24.800 --> 17:25.920
	that are important to think about

	17:25.920 --> 17:28.880
	whatever the eventual theory converges to.

	17:28.880 --> 17:32.480
	So in terms of time, how do you think of nature of time

	17:32.480 --> 17:33.880
	across different time scales?

	17:33.880 --> 17:36.800
	So you mentioned things changing,

	17:36.800 --> 17:39.160
	sensory inputs changing every 10, 20 minutes.

	17:39.160 --> 17:40.520
	What about every few minutes?

	17:40.520 --> 17:42.120
	Every few months and years?

	17:42.120 --> 17:44.840
	Well, if you think about a neuroscience problem,

	17:44.840 --> 17:49.640
	the brain problem, neurons themselves can stay active

	17:49.640 --> 17:51.560
	for certain periods of time.

	17:51.560 --> 17:53.280
	They're parts of the brain where they stay active

	17:53.280 --> 17:56.680
	for minutes, so you could hold a certain perception

	17:56.680 --> 18:01.320
	or an activity for a certain period of time,

	18:01.320 --> 18:04.480
	but not most of them don't last that long.

	18:04.480 --> 18:07.160
	And so if you think about your thoughts

	18:07.160 --> 18:09.080
	or the activity neurons,

	18:09.080 --> 18:10.680
	if you're gonna wanna involve something

	18:10.680 --> 18:11.920
	that happened a long time ago,

	18:11.920 --> 18:14.400
	even just this morning, for example,

	18:14.400 --> 18:16.360
	the neurons haven't been active throughout that time.

	18:16.360 --> 18:17.800
	So you have to store that.

	18:17.800 --> 18:20.720
	So by I ask you, what did you have for breakfast today?

	18:20.720 --> 18:22.000
	That is memory.

	18:22.000 --> 18:24.160
	That is, you've built it into your model of the world now.

	18:24.160 --> 18:27.880
	You remember that and that memory is in the synapses,

	18:27.880 --> 18:30.080
	it's basically in the formation of synapses.

	18:30.080 --> 18:35.080
	And so you're sliding into what used to different time scales.

	18:36.760 --> 18:38.280
	There's time scales of which we are

	18:38.280 --> 18:40.440
	like understanding my language and moving about

	18:40.440 --> 18:41.840
	and seeing things rapidly and over time.

	18:41.840 --> 18:44.280
	That's the time scales of activities of neurons.

	18:44.280 --> 18:46.200
	But if you wanna get in longer time scales,

	18:46.200 --> 18:48.840
	then it's more memory and we have to invoke those memories

	18:48.840 --> 18:50.960
	to say, oh, yes, well, now I can remember

	18:50.960 --> 18:54.160
	what I had for breakfast because I stored that someplace.

	18:54.160 --> 18:58.200
	I may forget it tomorrow, but I'd store it for now.

	18:58.200 --> 19:01.600
	So does memory also need to have,

	19:02.880 --> 19:06.240
	so the hierarchical aspect of reality

	19:06.240 --> 19:07.720
	is not just about concepts,

	19:07.720 --> 19:08.800
	it's also about time.

	19:08.800 --> 19:10.280
	Do you think of it that way?

	19:10.280 --> 19:12.840
	Yeah, time is infused in everything.

	19:12.840 --> 19:15.560
	It's like, you really can't separate it out.

	19:15.560 --> 19:19.560
	If I ask you, what is your, how's the brain

	19:19.560 --> 19:21.360
	learn a model of this coffee cup here?

	19:21.360 --> 19:23.200
	I have a coffee cup, then I met the coffee cup.

	19:23.200 --> 19:26.000
	I said, well, time is not an inherent property

	19:26.000 --> 19:28.520
	of the model I have of this cup,

	19:28.520 --> 19:31.440
	whether it's a visual model or tactile model.

	19:31.440 --> 19:32.600
	I can sense it through time,

	19:32.600 --> 19:34.880
	but the model itself doesn't really have much time.

	19:34.880 --> 19:36.560
	If I asked you, if I say, well,

	19:36.560 --> 19:39.000
	what is the model of my cell phone?

	19:39.000 --> 19:41.480
	My brain has learned a model of the cell phones.

	19:41.480 --> 19:43.360
	If you have a smartphone like this,

	19:43.360 --> 19:45.680
	and I said, well, this has time aspects to it.

	19:45.680 --> 19:48.040
	I have expectations when I turn it on,

	19:48.040 --> 19:49.480
	what's gonna happen, what water,

	19:49.480 --> 19:51.960
	how long it's gonna take to do certain things,

	19:51.960 --> 19:54.040
	if I bring up an app, what sequences,

	19:54.040 --> 19:56.520
	and so I have instant, it's like melodies in the world,

	19:56.520 --> 19:58.560
	you know, melody has a sense of time.

	19:58.560 --> 20:01.200
	So many things in the world move and act,

	20:01.200 --> 20:03.720
	and there's a sense of time related to them.

	20:03.720 --> 20:08.280
	Some don't, but most things do actually.

	20:08.280 --> 20:12.120
	So it's sort of infused throughout the models of the world.

	20:12.120 --> 20:13.720
	You build a model of the world,

	20:13.720 --> 20:16.400
	you're learning the structure of the objects in the world,

	20:16.400 --> 20:17.840
	and you're also learning

	20:17.840 --> 20:19.760
	how those things change through time.

	20:20.760 --> 20:23.920
	Okay, so it really is just a fourth dimension

	20:23.920 --> 20:25.280
	that's infused deeply,

	20:25.280 --> 20:26.760
	and you have to make sure

	20:26.760 --> 20:30.960
	that your models of intelligence incorporate it.

	20:30.960 --> 20:34.840
	So, like you mentioned, the state of neuroscience

	20:34.840 --> 20:36.000
	is deeply empirical.

	20:36.000 --> 20:40.120
	A lot of data collection, it's, you know,

	20:40.120 --> 20:43.120
	that's where it is, you mentioned Thomas Kuhn, right?

	20:43.120 --> 20:44.560
	Yeah.

	20:44.560 --> 20:48.040
	And then you're proposing a theory of intelligence,

	20:48.040 --> 20:50.520
	and which is really the next step,

	20:50.520 --> 20:52.920
	the really important step to take,

	20:52.920 --> 20:57.920
	but why is HTM, or what we'll talk about soon,

	20:57.920 --> 21:01.160
	the right theory?

	21:01.160 --> 21:05.160
	So is it more in this, is it backed by intuition,

	21:05.160 --> 21:09.160
	is it backed by evidence, is it backed by a mixture of both?

	21:09.160 --> 21:12.800
	Is it kind of closer to where string theory is in physics,

	21:12.800 --> 21:15.800
	where there's mathematical components

	21:15.800 --> 21:18.160
	which show that, you know what,

	21:18.160 --> 21:20.160
	it seems that this,

	21:20.160 --> 21:23.560
	it fits together too well for it not to be true,

	21:23.560 --> 21:25.360
	which is where string theory is.

	21:25.360 --> 21:28.080
	Is that where you're kind of thinking?

	21:28.080 --> 21:30.080
	It's a mixture of all those things,

	21:30.080 --> 21:32.080
	although definitely where we are right now,

	21:32.080 --> 21:34.080
	it's definitely much more on the empirical side

	21:34.080 --> 21:36.080
	than, let's say, string theory.

	21:36.080 --> 21:39.080
	The way this goes about, we're theorists, right?

	21:39.080 --> 21:41.080
	So we look at all this data,

	21:41.080 --> 21:43.080
	and we're trying to come up with some sort of model

	21:43.080 --> 21:45.080
	that explains it, basically,

	21:45.080 --> 21:47.080
	and there's, unlike string theory,

	21:47.080 --> 21:50.080
	there's vast more amounts of empirical data here

	21:50.080 --> 21:54.080
	than I think that most physicists deal with.

	21:54.080 --> 21:57.080
	And so our challenge is to sort through that

	21:57.080 --> 22:01.080
	and figure out what kind of constructs would explain this.

	22:01.080 --> 22:04.080
	And when we have an idea,

	22:04.080 --> 22:06.080
	you come up with a theory of some sort,

	22:06.080 --> 22:08.080
	you have lots of ways of testing it.

	22:08.080 --> 22:10.080
	First of all, I am, you know,

	22:10.080 --> 22:14.080
	there are 100 years of assimilated,

	22:14.080 --> 22:16.080
	und assimilated empirical data from neuroscience.

	22:16.080 --> 22:18.080
	So we go back and repapers, and we say,

	22:18.080 --> 22:20.080
	oh, did someone find this already?

	22:20.080 --> 22:23.080
	We can predict X, Y, and Z,

	22:23.080 --> 22:25.080
	and maybe no one's even talked about it

	22:25.080 --> 22:27.080
	since 1972 or something,

	22:27.080 --> 22:29.080
	but we go back and find that, and we say,

	22:29.080 --> 22:31.080
	oh, either it can support the theory

	22:31.080 --> 22:33.080
	or it can invalidate the theory.

	22:33.080 --> 22:35.080
	And then we say, okay, we have to start over again.

	22:35.080 --> 22:37.080
	Oh, no, it's support. Let's keep going with that one.

	22:37.080 --> 22:40.080
	So the way I kind of view it,

	22:40.080 --> 22:43.080
	when we do our work, we come up,

	22:43.080 --> 22:45.080
	we look at all this empirical data,

	22:45.080 --> 22:47.080
	and it's what I call it is a set of constraints.

	22:47.080 --> 22:49.080
	We're not interested in something that's biologically inspired.

	22:49.080 --> 22:52.080
	We're trying to figure out how the actual brain works.

	22:52.080 --> 22:55.080
	So every piece of empirical data is a constraint on a theory.

	22:55.080 --> 22:57.080
	If you have the correct theory,

	22:57.080 --> 22:59.080
	it needs to explain every pin, right?

	22:59.080 --> 23:02.080
	So we have this huge number of constraints on the problem,

	23:02.080 --> 23:05.080
	which initially makes it very, very difficult.

	23:05.080 --> 23:07.080
	If you don't have many constraints,

	23:07.080 --> 23:09.080
	you can make up stuff all the day.

	23:09.080 --> 23:11.080
	You can say, oh, here's an answer to how you can do this,

	23:11.080 --> 23:13.080
	you can do that, you can do this.

	23:13.080 --> 23:15.080
	But if you consider all biology as a set of constraints,

	23:15.080 --> 23:17.080
	all neuroscience as a set of constraints,

	23:17.080 --> 23:19.080
	and even if you're working in one little part of the Neocortex,

	23:19.080 --> 23:21.080
	for example, there are hundreds and hundreds of constraints.

	23:21.080 --> 23:23.080
	There are a lot of empirical constraints

	23:23.080 --> 23:25.080
	that it's very, very difficult initially

	23:25.080 --> 23:27.080
	to come up with a theoretical framework for that.

	23:27.080 --> 23:31.080
	But when you do, and it solves all those constraints at once,

	23:31.080 --> 23:33.080
	you have a high confidence

	23:33.080 --> 23:36.080
	that you got something close to correct.

	23:36.080 --> 23:39.080
	It's just mathematically almost impossible not to be.

	23:39.080 --> 23:43.080
	So that's the curse and the advantage of what we have.

	23:43.080 --> 23:47.080
	The curse is we have to meet all these constraints,

	23:47.080 --> 23:49.080
	which is really hard.

	23:49.080 --> 23:51.080
	But when you do meet them,

	23:51.080 --> 23:53.080
	then you have a great confidence

	23:53.080 --> 23:55.080
	that you've discovered something.

	23:55.080 --> 23:58.080
	In addition, then we work with scientific labs.

	23:58.080 --> 24:00.080
	So we'll say, oh, there's something we can't find,

	24:00.080 --> 24:02.080
	we can predict something,

	24:02.080 --> 24:04.080
	but we can't find it anywhere in the literature.

	24:04.080 --> 24:07.080
	So we will then, we have people we collaborated with,

	24:07.080 --> 24:09.080
	we'll say, sometimes they'll say, you know what,

	24:09.080 --> 24:11.080
	I have some collected data, which I didn't publish,

	24:11.080 --> 24:13.080
	but we can go back and look at it

	24:13.080 --> 24:15.080
	and see if we can find that,

	24:15.080 --> 24:17.080
	which is much easier than designing a new experiment.

	24:17.080 --> 24:20.080
	You know, neuroscience experiments take a long time, years.

	24:20.080 --> 24:23.080
	So although some people are doing that now too.

	24:23.080 --> 24:27.080
	So, but between all of these things,

	24:27.080 --> 24:29.080
	I think it's a reasonable,

	24:29.080 --> 24:32.080
	it's actually a very, very good approach.

	24:32.080 --> 24:35.080
	We are blessed with the fact that we can test our theories

	24:35.080 --> 24:37.080
	out to yin and yang here,

	24:37.080 --> 24:39.080
	because there's so much on a similar data,

	24:39.080 --> 24:41.080
	and we can also falsify our theories very easily,

	24:41.080 --> 24:43.080
	which we do often.

	24:43.080 --> 24:46.080
	So it's kind of reminiscent to whenever that was with Copernicus,

	24:46.080 --> 24:49.080
	you know, when you figure out that the sun is at the center,

	24:49.080 --> 24:53.080
	the solar system as opposed to Earth,

	24:53.080 --> 24:55.080
	the pieces just fall into place.

	24:55.080 --> 24:59.080
	Yeah, I think that's the general nature of the Ha moments,

	24:59.080 --> 25:02.080
	is in Copernicus, it could be,

	25:02.080 --> 25:05.080
	you could say the same thing about Darwin,

	25:05.080 --> 25:07.080
	you could say the same thing about, you know,

	25:07.080 --> 25:09.080
	about the double helix,

	25:09.080 --> 25:13.080
	that people have been working on a problem for so long,

	25:13.080 --> 25:14.080
	and have all this data,

	25:14.080 --> 25:15.080
	and they can't make sense of it, they can't make sense of it.

	25:15.080 --> 25:17.080
	But when the answer comes to you,

	25:17.080 --> 25:19.080
	and everything falls into place,

	25:19.080 --> 25:21.080
	it's like, oh my gosh, that's it.

	25:21.080 --> 25:23.080
	That's got to be right.

	25:23.080 --> 25:28.080
	I asked both Jim Watson and Francis Crick about this.

	25:28.080 --> 25:30.080
	I asked them, you know,

	25:30.080 --> 25:33.080
	when you were working on trying to discover the structure

	25:33.080 --> 25:35.080
	of the double helix,

	25:35.080 --> 25:38.080
	and when you came up with the sort of,

	25:38.080 --> 25:42.080
	the structure that ended up being correct,

	25:42.080 --> 25:44.080
	but it was sort of a guess, you know,

	25:44.080 --> 25:46.080
	it wasn't really verified yet.

	25:46.080 --> 25:48.080
	I said, did you know that it was right?

	25:48.080 --> 25:50.080
	And they both said, absolutely.

	25:50.080 --> 25:52.080
	We absolutely knew it was right.

	25:52.080 --> 25:55.080
	And it doesn't matter if other people didn't believe it or not,

	25:55.080 --> 25:57.080
	we knew it was right, they'd get around to thinking it

	25:57.080 --> 25:59.080
	and agree with it eventually anyway.

	25:59.080 --> 26:01.080
	And that's the kind of thing you hear a lot with scientists

	26:01.080 --> 26:04.080
	who really are studying a difficult problem,

	26:04.080 --> 26:07.080
	and I feel that way too, about our work.

	26:07.080 --> 26:10.080
	Have you talked to Crick or Watson about the problem

	26:10.080 --> 26:15.080
	you're trying to solve, the, of finding the DNA of the brain?

	26:15.080 --> 26:16.080
	Yeah.

	26:16.080 --> 26:19.080
	In fact, Francis Crick was very interested in this,

	26:19.080 --> 26:21.080
	in the latter part of his life.

	26:21.080 --> 26:23.080
	And in fact, I got interested in brains

	26:23.080 --> 26:26.080
	by reading an essay he wrote in 1979

	26:26.080 --> 26:28.080
	called Thinking About the Brain.

	26:28.080 --> 26:30.080
	And that was when I decided

	26:30.080 --> 26:33.080
	I'm going to leave my profession of computers and engineering

	26:33.080 --> 26:35.080
	and become a neuroscientist.

	26:35.080 --> 26:37.080
	Just reading that one essay from Francis Crick.

	26:37.080 --> 26:39.080
	I got to meet him later in life.

	26:39.080 --> 26:43.080
	I got to, I spoke at the Salk Institute

	26:43.080 --> 26:44.080
	and he was in the audience

	26:44.080 --> 26:47.080
	and then I had a tea with him afterwards.

	26:47.080 --> 26:50.080
	You know, he was interested in a different problem.

	26:50.080 --> 26:52.080
	He was focused on consciousness.

	26:52.080 --> 26:54.080
	The easy problem, right?

	26:54.080 --> 26:58.080
	Well, I think it's the red herring

	26:58.080 --> 27:01.080
	and so we weren't really overlapping a lot there.

	27:01.080 --> 27:05.080
	Jim Watson, who's still alive,

	27:05.080 --> 27:07.080
	is also interested in this problem

	27:07.080 --> 27:11.080
	and when he was director of the Coltsman Harbor Laboratories,

	27:11.080 --> 27:13.080
	he was really sort of behind

	27:13.080 --> 27:16.080
	moving in the direction of neuroscience there.

	27:16.080 --> 27:19.080
	And so he had a personal interest in this field

	27:19.080 --> 27:23.080
	and I have met with him numerous times.

	27:23.080 --> 27:25.080
	And in fact, the last time,

	27:25.080 --> 27:27.080
	a little bit over a year ago,

	27:27.080 --> 27:30.080
	I gave a talk at Coltsman Harbor Labs

	27:30.080 --> 27:34.080
	about the progress we were making in our work.

	27:34.080 --> 27:39.080
	And it was a lot of fun because he said,

	27:39.080 --> 27:41.080
	well, you wouldn't be coming here

	27:41.080 --> 27:42.080
	unless you had something important to say,

	27:42.080 --> 27:44.080
	so I'm going to go attend your talk.

	27:44.080 --> 27:46.080
	So he sat in the very front row.

	27:46.080 --> 27:50.080
	Next to him was the director of the lab, Bruce Stillman.

	27:50.080 --> 27:52.080
	So these guys were in the front row of this auditorium, right?

	27:52.080 --> 27:54.080
	So nobody else in the auditorium wants to sit in the front row

	27:54.080 --> 27:57.080
	because there's Jim Watson there as the director.

	27:57.080 --> 28:03.080
	And I gave a talk and then I had dinner with Jim afterwards.

	28:03.080 --> 28:06.080
	But there's a great picture of my colleague,

	28:06.080 --> 28:08.080
	Subitai Amantik, where I'm up there

	28:08.080 --> 28:11.080
	sort of expiring the basics of this new framework we have.

	28:11.080 --> 28:13.080
	And Jim Watson's on the edge of his chair.

	28:13.080 --> 28:15.080
	He's literally on the edge of his chair,

	28:15.080 --> 28:17.080
	like, internally staring up at the screen.

	28:17.080 --> 28:21.080
	And when he discovered the structure of DNA,

	28:21.080 --> 28:25.080
	the first public talk he gave was at Coltsman Harbor Labs.

	28:25.080 --> 28:27.080
	And there's a picture, there's a famous picture

	28:27.080 --> 28:29.080
	of Jim Watson standing at the whiteboard

	28:29.080 --> 28:31.080
	with an overhead thing pointing at something,

	28:31.080 --> 28:33.080
	pointing at the double helix at this pointer.

	28:33.080 --> 28:35.080
	And it actually looks a lot like the picture of me.

	28:35.080 --> 28:37.080
	So there was a sort of funny, there's an area talking about the brain

	28:37.080 --> 28:39.080
	and there's Jim Watson staring up at the tent.

	28:39.080 --> 28:41.080
	And of course, there was, you know, whatever,

	28:41.080 --> 28:44.080
	60 years earlier he was standing pointing at the double helix.

	28:44.080 --> 28:47.080
	It's one of the great discoveries in all of, you know,

	28:47.080 --> 28:50.080
	whatever, by all the science, all science and DNA.

	28:50.080 --> 28:54.080
	So it's the funny that there's echoes of that in your presentation.

	28:54.080 --> 28:58.080
	Do you think in terms of evolutionary timeline and history,

	28:58.080 --> 29:01.080
	the development of the neocortex was a big leap?

	29:01.080 --> 29:06.080
	Or is it just a small step?

	29:06.080 --> 29:09.080
	So, like, if we ran the whole thing over again,

	29:09.080 --> 29:12.080
	from the birth of life on Earth,

	29:12.080 --> 29:15.080
	how likely would we develop the mechanism of the neocortex?

	29:15.080 --> 29:17.080
	Okay, well, those are two separate questions.

	29:17.080 --> 29:19.080
	One, was it a big leap?

	29:19.080 --> 29:21.080
	And one was how likely it is, okay?

	29:21.080 --> 29:23.080
	They're not necessarily related.

	29:23.080 --> 29:25.080
	Maybe correlated.

	29:25.080 --> 29:28.080
	And we don't really have enough data to make a judgment about that.

	29:28.080 --> 29:30.080
	I would say definitely it was a big leap.

	29:30.080 --> 29:31.080
	And I can tell you why.

	29:31.080 --> 29:34.080
	I don't think it was just another incremental step.

	29:34.080 --> 29:36.080
	I'll get that in a moment.

	29:36.080 --> 29:38.080
	I don't really have any idea how likely it is.

	29:38.080 --> 29:41.080
	If we look at evolution, we have one data point,

	29:41.080 --> 29:43.080
	which is Earth, right?

	29:43.080 --> 29:45.080
	Life formed on Earth billions of years ago,

	29:45.080 --> 29:48.080
	whether it was introduced here or it created it here

	29:48.080 --> 29:50.080
	or someone introduced it we don't really know,

	29:50.080 --> 29:51.080
	but it was here early.

	29:51.080 --> 29:55.080
	It took a long, long time to get to multicellular life.

	29:55.080 --> 29:58.080
	And then from multicellular life,

	29:58.080 --> 30:02.080
	it took a long, long time to get the neocortex.

	30:02.080 --> 30:05.080
	And we've only had the neocortex for a few hundred thousand years.

	30:05.080 --> 30:07.080
	So that's like nothing.

	30:07.080 --> 30:09.080
	Okay, so is it likely?

	30:09.080 --> 30:13.080
	Well, certainly it isn't something that happened right away on Earth.

	30:13.080 --> 30:15.080
	And there were multiple steps to get there.

	30:15.080 --> 30:17.080
	So I would say it's probably not going to something that would happen

	30:17.080 --> 30:20.080
	instantaneously on other planets that might have life.

	30:20.080 --> 30:23.080
	It might take several billion years on average.

	30:23.080 --> 30:24.080
	Is it likely?

	30:24.080 --> 30:25.080
	I don't know.

	30:25.080 --> 30:28.080
	But you'd have to survive for several billion years to find out.

	30:28.080 --> 30:29.080
	Probably.

	30:29.080 --> 30:30.080
	Is it a big leap?

	30:30.080 --> 30:35.080
	Yeah, I think it is a qualitative difference

	30:35.080 --> 30:38.080
	in all other evolutionary steps.

	30:38.080 --> 30:40.080
	I can try to describe that if you'd like.

	30:40.080 --> 30:42.080
	Sure, in which way?

	30:42.080 --> 30:44.080
	Yeah, I can tell you how.

	30:44.080 --> 30:48.080
	Pretty much, let's start with a little preface.

	30:48.080 --> 30:54.080
	Maybe the things that humans are able to do do not have obvious

	30:54.080 --> 30:59.080
	survival advantages precedent.

	30:59.080 --> 31:00.080
	We create music.

	31:00.080 --> 31:03.080
	Is there a really survival advantage to that?

	31:03.080 --> 31:04.080
	Maybe, maybe not.

	31:04.080 --> 31:05.080
	What about mathematics?

	31:05.080 --> 31:09.080
	Is there a real survival advantage to mathematics?

	31:09.080 --> 31:10.080
	You can stretch it.

	31:10.080 --> 31:13.080
	You can try to figure these things out, right?

	31:13.080 --> 31:18.080
	But most of evolutionary history, everything had immediate survival

	31:18.080 --> 31:19.080
	advantages to it.

	31:19.080 --> 31:22.080
	I'll tell you a story, which I like.

	31:22.080 --> 31:25.080
	It may not be true.

	31:25.080 --> 31:29.080
	But the story goes as follows.

	31:29.080 --> 31:34.080
	Organisms have been evolving since the beginning of life here on Earth.

	31:34.080 --> 31:37.080
	Adding this sort of complexity onto that and this sort of complexity onto that.

	31:37.080 --> 31:40.080
	And the brain itself is evolved this way.

	31:40.080 --> 31:44.080
	There's an old part, an older part, an older, older part to the brain that kind of just

	31:44.080 --> 31:47.080
	keeps calming on new things and we keep adding capabilities.

	31:47.080 --> 31:52.080
	When we got to the neocortex, initially it had a very clear survival advantage

	31:52.080 --> 31:56.080
	in that it produced better vision and better hearing and better touch and maybe

	31:56.080 --> 31:58.080
	a new place and so on.

	31:58.080 --> 32:04.080
	But what I think happens is that evolution took a mechanism, and this is in our

	32:04.080 --> 32:08.080
	recent theory, but it took a mechanism that evolved a long time ago for

	32:08.080 --> 32:10.080
	navigating in the world, for knowing where you are.

	32:10.080 --> 32:14.080
	These are the so called grid cells and place cells of that old part of the brain.

	32:14.080 --> 32:21.080
	And it took that mechanism for building maps of the world and knowing where you are

	32:21.080 --> 32:26.080
	on those maps and how to navigate those maps and turns it into a sort of a slim

	32:26.080 --> 32:29.080
	down idealized version of it.

	32:29.080 --> 32:32.080
	And that idealized version could now apply to building maps of other things,

	32:32.080 --> 32:36.080
	maps of coffee cups and maps of phones, maps of mathematics.

	32:36.080 --> 32:40.080
	Concepts, yes, and not just almost, exactly.

	32:40.080 --> 32:44.080
	And it just started replicating this stuff.

	32:44.080 --> 32:46.080
	You just think more and more and more.

	32:46.080 --> 32:51.080
	So we went from being sort of dedicated purpose neural hardware to solve certain

	32:51.080 --> 32:56.080
	problems that are important to survival to a general purpose neural hardware

	32:56.080 --> 33:02.080
	that could be applied to all problems and now it's escaped the orbit of survival.

	33:02.080 --> 33:08.080
	It's, we are now able to apply it to things which we find enjoyment, you know,

	33:08.080 --> 33:13.080
	but aren't really clearly survival characteristics.

	33:13.080 --> 33:19.080
	And that it seems to only have happened in humans to the large extent.

	33:19.080 --> 33:24.080
	And so that's what's going on where we sort of have, we've sort of escaped the

	33:24.080 --> 33:28.080
	gravity of evolutionary pressure in some sense in the near cortex.

	33:28.080 --> 33:32.080
	And it now does things which are not, that are really interesting,

	33:32.080 --> 33:36.080
	discovering models of the universe, which may not really help us.

	33:36.080 --> 33:37.080
	It doesn't matter.

	33:37.080 --> 33:41.080
	How does it help us surviving knowing that there might be multiple verses or that

	33:41.080 --> 33:44.080
	there might be, you know, the age of the universe or how do, you know,

	33:44.080 --> 33:46.080
	various stellar things occur?

	33:46.080 --> 33:47.080
	It doesn't really help us survive at all.

	33:47.080 --> 33:50.080
	But we enjoy it and that's what happened.

	33:50.080 --> 33:53.080
	Or at least not in the obvious way, perhaps.

	33:53.080 --> 33:58.080
	It is required, if you look at the entire universe in an evolutionary way,

	33:58.080 --> 34:03.080
	it's required for us to do interplanetary travel and therefore survive past our own fun.

	34:03.080 --> 34:05.080
	But you know, let's not get too quick.

	34:05.080 --> 34:07.080
	Yeah, but, you know, evolution works at one time frame.

	34:07.080 --> 34:11.080
	It's survival, if you think of survival of the phenotype,

	34:11.080 --> 34:13.080
	survival of the individual.

	34:13.080 --> 34:16.080
	What you're talking about there is spans well beyond that.

	34:16.080 --> 34:22.080
	So there's no genetic, I'm not transferring any genetic traits to my children.

	34:22.080 --> 34:25.080
	That are going to help them survive better on Mars.

	34:25.080 --> 34:27.080
	Totally different mechanism.

	34:27.080 --> 34:32.080
	So let's get into the new, as you've mentioned, this idea,

	34:32.080 --> 34:35.080
	I don't know if you have a nice name, thousand.

	34:35.080 --> 34:37.080
	We call it the thousand brain theory of intelligence.

	34:37.080 --> 34:38.080
	I like it.

	34:38.080 --> 34:44.080
	So can you talk about this idea of spatial view of concepts and so on?

	34:44.080 --> 34:45.080
	Yeah.

	34:45.080 --> 34:49.080
	So can I just describe sort of the, there's an underlying core discovery,

	34:49.080 --> 34:51.080
	which then everything comes from that.

	34:51.080 --> 34:55.080
	That's a very simple, this is really what happened.

	34:55.080 --> 35:00.080
	We were deep into problems about understanding how we build models of stuff in the world

	35:00.080 --> 35:03.080
	and how we make predictions about things.

	35:03.080 --> 35:07.080
	And I was holding a coffee cup just like this in my hand.

	35:07.080 --> 35:10.080
	And I had my finger was touching the side, my index finger.

	35:10.080 --> 35:15.080
	And then I moved it to the top and I was going to feel the rim at the top of the cup.

	35:15.080 --> 35:18.080
	And I asked myself a very simple question.

	35:18.080 --> 35:22.080
	I said, well, first of all, let's say I know that my brain predicts what it's going to feel

	35:22.080 --> 35:23.080
	before it touches it.

	35:23.080 --> 35:25.080
	You can just think about it and imagine it.

	35:25.080 --> 35:28.080
	And so we know that the brain's making predictions all the time.

	35:28.080 --> 35:31.080
	So the question is, what does it take to predict that?

	35:31.080 --> 35:33.080
	And there's a very interesting answer.

	35:33.080 --> 35:36.080
	First of all, it says the brain has to know it's touching a coffee cup.

	35:36.080 --> 35:38.080
	It has to have a model of a coffee cup.

	35:38.080 --> 35:43.080
	It needs to know where the finger currently is on the cup, relative to the cup.

	35:43.080 --> 35:46.080
	Because when I make a movement, it needs to know where it's going to be on the cup

	35:46.080 --> 35:50.080
	after the movement is completed, relative to the cup.

	35:50.080 --> 35:53.080
	And then it can make a prediction about what it's going to sense.

	35:53.080 --> 35:56.080
	So this told me that the neocortex, which is making this prediction,

	35:56.080 --> 35:59.080
	needs to know that it's sensing it's touching a cup.

	35:59.080 --> 36:02.080
	And it needs to know the location of my finger relative to that cup

	36:02.080 --> 36:04.080
	in a reference frame of the cup.

	36:04.080 --> 36:06.080
	It doesn't matter where the cup is relative to my body.

	36:06.080 --> 36:08.080
	It doesn't matter its orientation.

	36:08.080 --> 36:09.080
	None of that matters.

	36:09.080 --> 36:13.080
	It's where my finger is relative to the cup, which tells me then that the neocortex

	36:13.080 --> 36:17.080
	has a reference frame that's anchored to the cup.

	36:17.080 --> 36:19.080
	Because otherwise, I wouldn't be able to say the location

	36:19.080 --> 36:21.080
	and I wouldn't be able to predict my new location.

	36:21.080 --> 36:24.080
	And then we quickly, very instantly, you can say,

	36:24.080 --> 36:26.080
	well, every part of my skin could touch this cup

	36:26.080 --> 36:28.080
	and therefore every part of my skin is making predictions

	36:28.080 --> 36:30.080
	and every part of my skin must have a reference frame

	36:30.080 --> 36:33.080
	that it's using to make predictions.

	36:33.080 --> 36:39.080
	So the big idea is that throughout the neocortex,

	36:39.080 --> 36:47.080
	there are, everything is being stored and referenced in reference frames.

	36:47.080 --> 36:49.080
	You can think of them like XYZ reference frames,

	36:49.080 --> 36:50.080
	but they're not like that.

	36:50.080 --> 36:52.080
	We know a lot about the neural mechanisms for this.

	36:52.080 --> 36:55.080
	But the brain thinks in reference frames.

	36:55.080 --> 36:58.080
	And as an engineer, if you're an engineer, this is not surprising.

	36:58.080 --> 37:01.080
	You'd say, if I were to build a CAD model of the coffee cup,

	37:01.080 --> 37:03.080
	well, I would bring it up in some CAD software

	37:03.080 --> 37:05.080
	and I would assign some reference frame and say,

	37:05.080 --> 37:07.080
	this features at this location and so on.

	37:07.080 --> 37:10.080
	But the fact that this, the idea that this is occurring

	37:10.080 --> 37:14.080
	throughout the neocortex everywhere, it was a novel idea.

	37:14.080 --> 37:20.080
	And then a zillion things fell into place after that, a zillion.

	37:20.080 --> 37:23.080
	So now we think about the neocortex as processing information

	37:23.080 --> 37:25.080
	quite differently than we used to do it.

	37:25.080 --> 37:28.080
	We used to think about the neocortex as processing sensory data

	37:28.080 --> 37:30.080
	and extracting features from that sensory data

	37:30.080 --> 37:32.080
	and then extracting features from the features

	37:32.080 --> 37:35.080
	very much like a deep learning network does today.

	37:35.080 --> 37:36.080
	But that's not how the brain works at all.

	37:36.080 --> 37:39.080
	The brain works by assigning everything,

	37:39.080 --> 37:41.080
	every input, everything to reference frames,

	37:41.080 --> 37:44.080
	and there are thousands, hundreds of thousands of them

	37:44.080 --> 37:47.080
	active at once in your neocortex.

	37:47.080 --> 37:49.080
	It's a surprising thing to think about,

	37:49.080 --> 37:51.080
	but once you sort of internalize this,

	37:51.080 --> 37:54.080
	you understand that it explains almost every,

	37:54.080 --> 37:57.080
	almost all the mysteries we've had about this structure.

	37:57.080 --> 38:00.080
	So one of the consequences of that is that

	38:00.080 --> 38:04.080
	every small part of the neocortex, say a millimeter square,

	38:04.080 --> 38:06.080
	and there's 150,000 of those.

	38:06.080 --> 38:08.080
	So it's about 150,000 square millimeters.

	38:08.080 --> 38:11.080
	If you take every little square millimeter of the cortex,

	38:11.080 --> 38:13.080
	it's got some input coming into it,

	38:13.080 --> 38:15.080
	and it's going to have reference frames

	38:15.080 --> 38:17.080
	where it's assigning that input to.

	38:17.080 --> 38:21.080
	And each square millimeter can learn complete models of objects.

	38:21.080 --> 38:22.080
	So what do I mean by that?

	38:22.080 --> 38:23.080
	If I'm touching the coffee cup,

	38:23.080 --> 38:25.080
	well, if I just touch it in one place,

	38:25.080 --> 38:27.080
	I can't learn what this coffee cup is

	38:27.080 --> 38:29.080
	because I'm just feeling one part.

	38:29.080 --> 38:32.080
	But if I move it around the cup and touch it in different areas,

	38:32.080 --> 38:34.080
	I can build up a complete model of the cup

	38:34.080 --> 38:36.080
	because I'm now filling in that three dimensional map,

	38:36.080 --> 38:37.080
	which is the coffee cup.

	38:37.080 --> 38:39.080
	I can say, oh, what am I feeling in all these different locations?

	38:39.080 --> 38:40.080
	That's the basic idea.

	38:40.080 --> 38:42.080
	It's more complicated than that.

	38:42.080 --> 38:46.080
	But so through time, and we talked about time earlier,

	38:46.080 --> 38:48.080
	through time, even a single column,

	38:48.080 --> 38:50.080
	which is only looking at, or a single part of the cortex,

	38:50.080 --> 38:52.080
	which is only looking at a small part of the world,

	38:52.080 --> 38:54.080
	can build up a complete model of an object.

	38:54.080 --> 38:57.080
	And so if you think about the part of the brain,

	38:57.080 --> 38:59.080
	which is getting input from all my fingers,

	38:59.080 --> 39:01.080
	so they're spread across the top of your head here.

	39:01.080 --> 39:03.080
	This is the somatosensory cortex.

	39:03.080 --> 39:07.080
	There's columns associated with all the different areas of my skin.

	39:07.080 --> 39:10.080
	And what we believe is happening is that

	39:10.080 --> 39:12.080
	all of them are building models of this cup,

	39:12.080 --> 39:15.080
	every one of them, or things.

	39:15.080 --> 39:18.080
	Not every column or every part of the cortex

	39:18.080 --> 39:19.080
	builds models of everything,

	39:19.080 --> 39:21.080
	but they're all building models of something.

	39:21.080 --> 39:26.080
	And so when I touch this cup with my hand,

	39:26.080 --> 39:29.080
	there are multiple models of the cup being invoked.

	39:29.080 --> 39:30.080
	If I look at it with my eyes,

	39:30.080 --> 39:32.080
	there are again many models of the cup being invoked,

	39:32.080 --> 39:34.080
	because each part of the visual system,

	39:34.080 --> 39:36.080
	the brain doesn't process an image.

	39:36.080 --> 39:38.080
	That's a misleading idea.

	39:38.080 --> 39:40.080
	It's just like your fingers touching the cup,

	39:40.080 --> 39:43.080
	so different parts of my retina are looking at different parts of the cup.

	39:43.080 --> 39:45.080
	And thousands and thousands of models of the cup

	39:45.080 --> 39:47.080
	are being invoked at once.

	39:47.080 --> 39:49.080
	And they're all voting with each other,

	39:49.080 --> 39:50.080
	trying to figure out what's going on.

	39:50.080 --> 39:52.080
	So that's why we call it the thousand brains theory of intelligence,

	39:52.080 --> 39:54.080
	because there isn't one model of a cup.

	39:54.080 --> 39:56.080
	There are thousands of models of this cup.

	39:56.080 --> 39:58.080
	There are thousands of models of your cell phone,

	39:58.080 --> 40:01.080
	and about cameras and microphones and so on.

	40:01.080 --> 40:03.080
	It's a distributed modeling system,

	40:03.080 --> 40:05.080
	which is very different than what people have thought about it.

	40:05.080 --> 40:07.080
	So that's a really compelling and interesting idea.

	40:07.080 --> 40:09.080
	I have two first questions.

	40:09.080 --> 40:12.080
	So one, on the ensemble part of everything coming together,

	40:12.080 --> 40:14.080
	you have these thousand brains.

	40:14.080 --> 40:19.080
	How do you know which one has done the best job of forming the cup?

	40:19.080 --> 40:20.080
	Great question. Let me try to explain.

	40:20.080 --> 40:23.080
	There's a problem that's known in neuroscience

	40:23.080 --> 40:25.080
	called the sensor fusion problem.

	40:25.080 --> 40:26.080
	Yes.

	40:26.080 --> 40:28.080
	And so the idea is something like,

	40:28.080 --> 40:29.080
	oh, the image comes from the eye.

	40:29.080 --> 40:30.080
	There's a picture on the retina.

	40:30.080 --> 40:32.080
	And it gets projected to the neocortex.

	40:32.080 --> 40:35.080
	Oh, by now it's all sped out all over the place,

	40:35.080 --> 40:37.080
	and it's kind of squirrely and distorted,

	40:37.080 --> 40:39.080
	and pieces are all over the, you know,

	40:39.080 --> 40:41.080
	it doesn't look like a picture anymore.

	40:41.080 --> 40:43.080
	When does it all come back together again?

	40:43.080 --> 40:44.080
	Right?

	40:44.080 --> 40:46.080
	Or you might say, well, yes, but I also,

	40:46.080 --> 40:48.080
	I also have sounds or touches associated with the cup.

	40:48.080 --> 40:50.080
	So I'm seeing the cup and touching the cup.

	40:50.080 --> 40:52.080
	How do they get combined together again?

	40:52.080 --> 40:54.080
	So this is called the sensor fusion problem.

	40:54.080 --> 40:57.080
	As if all these disparate parts have to be brought together

	40:57.080 --> 40:59.080
	into one model someplace.

	40:59.080 --> 41:01.080
	That's the wrong idea.

	41:01.080 --> 41:03.080
	The right idea is that you get all these guys voting.

	41:03.080 --> 41:05.080
	There's auditory models of the cup,

	41:05.080 --> 41:07.080
	there's visual models of the cup,

	41:07.080 --> 41:09.080
	there's tactile models of the cup.

	41:09.080 --> 41:11.080
	In the vision system, there might be ones

	41:11.080 --> 41:13.080
	that are more focused on black and white,

	41:13.080 --> 41:14.080
	ones versioned on color.

	41:14.080 --> 41:15.080
	It doesn't really matter.

	41:15.080 --> 41:17.080
	There's just thousands and thousands of models of this cup.

	41:17.080 --> 41:18.080
	And they vote.

	41:18.080 --> 41:20.080
	They don't actually come together in one spot.

	41:20.080 --> 41:22.080
	Just literally think of it this way.

	41:22.080 --> 41:25.080
	Imagine you have, each columns are like about the size

	41:25.080 --> 41:26.080
	of a little piece of spaghetti.

	41:26.080 --> 41:27.080
	Okay?

	41:27.080 --> 41:28.080
	Like a two and a half millimeters tall

	41:28.080 --> 41:30.080
	and about a millimeter in white.

	41:30.080 --> 41:33.080
	They're not physical like, but you can think of them that way.

	41:33.080 --> 41:36.080
	And each one's trying to guess what this thing is or touching.

	41:36.080 --> 41:38.080
	Now they can, they can do a pretty good job

	41:38.080 --> 41:40.080
	if they're allowed to move over time.

	41:40.080 --> 41:42.080
	So I can reach my hand into a black box and move my finger

	41:42.080 --> 41:44.080
	around an object and if I touch enough space,

	41:44.080 --> 41:46.080
	it's like, okay, I know what it is.

	41:46.080 --> 41:48.080
	But often we don't do that.

	41:48.080 --> 41:50.080
	Often I can just reach and grab something with my hand

	41:50.080 --> 41:51.080
	all at once and I get it.

	41:51.080 --> 41:53.080
	Or if I had to look through the world through a straw,

	41:53.080 --> 41:55.080
	so I'm only invoking one little column,

	41:55.080 --> 41:57.080
	I can only see part of something because I have to move

	41:57.080 --> 41:58.080
	the straw around.

	41:58.080 --> 42:00.080
	But if I open my eyes to see the whole thing at once.

	42:00.080 --> 42:02.080
	So what we think is going on is all these little pieces

	42:02.080 --> 42:05.080
	of spaghetti, all these little columns in the cortex

	42:05.080 --> 42:08.080
	are all trying to guess what it is that they're sensing.

	42:08.080 --> 42:10.080
	They'll do a better guess if they have time

	42:10.080 --> 42:11.080
	and can move over time.

	42:11.080 --> 42:13.080
	So if I move my eyes and move my fingers.

	42:13.080 --> 42:16.080
	But if they don't, they have a, they have a poor guess.

	42:16.080 --> 42:19.080
	It's a, it's a probabilistic guess of what they might be touching.

	42:19.080 --> 42:22.080
	Now imagine they can post their probability

	42:22.080 --> 42:24.080
	at the top of little piece of spaghetti.

	42:24.080 --> 42:25.080
	Each one of them says, I think,

	42:25.080 --> 42:27.080
	and it's not really a probability distribution.

	42:27.080 --> 42:29.080
	It's more like a set of possibilities in the brain.

	42:29.080 --> 42:31.080
	It doesn't work as a probability distribution.

	42:31.080 --> 42:33.080
	It works as more like what we call a union.

	42:33.080 --> 42:35.080
	You could say, and one column says,

	42:35.080 --> 42:39.080
	I think it could be a coffee cup, a soda can or a water bottle.

	42:39.080 --> 42:42.080
	And another column says, I think it could be a coffee cup

	42:42.080 --> 42:45.080
	or a, you know, telephone or camera or whatever.

	42:45.080 --> 42:46.080
	Right.

	42:46.080 --> 42:49.080
	And all these guys are saying what they think it might be.

	42:49.080 --> 42:51.080
	And there's these long range connections

	42:51.080 --> 42:53.080
	in certain layers in the cortex.

	42:53.080 --> 42:57.080
	So there's some layers in some cell types in each column

	42:57.080 --> 42:59.080
	send the projections across the brain.

	42:59.080 --> 43:01.080
	And that's the voting occurs.

	43:01.080 --> 43:04.080
	And so there's a simple associative memory mechanism.

	43:04.080 --> 43:07.080
	We've described this in a recent paper and we've modeled this

	43:07.080 --> 43:11.080
	that says they can all quickly settle on the only

	43:11.080 --> 43:14.080
	or the one best answer for all of them.

	43:14.080 --> 43:17.080
	If there is a single best answer, they all vote and say,

	43:17.080 --> 43:19.080
	yep, it's got to be the coffee cup.

	43:19.080 --> 43:21.080
	And at that point, they all know it's a coffee cup.

	43:21.080 --> 43:23.080
	And at that point, everyone acts as if it's a coffee cup.

	43:23.080 --> 43:24.080
	Yeah, we know it's a coffee.

	43:24.080 --> 43:26.080
	Even though I've only seen one little piece of this world,

	43:26.080 --> 43:28.080
	I know it's a coffee cup I'm touching or I'm seeing or whatever.

	43:28.080 --> 43:31.080
	And so you can think of all these columns are looking

	43:31.080 --> 43:33.080
	at different parts and different places,

	43:33.080 --> 43:35.080
	different sensory input, different locations.

	43:35.080 --> 43:36.080
	They're all different.

	43:36.080 --> 43:40.080
	But this layer that's doing the voting, it solidifies.

	43:40.080 --> 43:43.080
	It crystallizes and says, oh, we all know what we're doing.

	43:43.080 --> 43:46.080
	And so you don't bring these models together in one model,

	43:46.080 --> 43:49.080
	you just vote and there's a crystallization of the vote.

	43:49.080 --> 43:50.080
	Great.

	43:50.080 --> 43:56.080
	That's at least a compelling way to think about the way you

	43:56.080 --> 43:58.080
	form a model of the world.

	43:58.080 --> 44:00.080
	Now, you talk about a coffee cup.

	44:00.080 --> 44:04.080
	Do you see this as far as I understand that you were proposing

	44:04.080 --> 44:07.080
	this as well, that this extends to much more than coffee cups?

	44:07.080 --> 44:09.080
	Yeah, it does.

	44:09.080 --> 44:11.080
	Or at least the physical world.

	44:11.080 --> 44:14.080
	It expands to the world of concepts.

	44:14.080 --> 44:15.080
	Yeah, it does.

	44:15.080 --> 44:18.080
	And well, the first, the primary phase of evidence for that

	44:18.080 --> 44:21.080
	is that the regions of the neocortex that are associated

	44:21.080 --> 44:24.080
	with language or high level thought or mathematics or things

	44:24.080 --> 44:26.080
	like that, they look like the regions of the neocortex

	44:26.080 --> 44:28.080
	that process vision and hearing and touch.

	44:28.080 --> 44:31.080
	They don't look any different or they look only marginally

	44:31.080 --> 44:32.080
	different.

	44:32.080 --> 44:36.080
	And so one would say, well, if Vernon Mountcastle,

	44:36.080 --> 44:39.080
	who proposed that all the parts of the neocortex

	44:39.080 --> 44:42.080
	are the same thing, if he's right, then the parts

	44:42.080 --> 44:44.080
	that are doing language or mathematics or physics

	44:44.080 --> 44:46.080
	are working on the same principle.

	44:46.080 --> 44:48.080
	They must be working on the principle of reference frames.

	44:48.080 --> 44:51.080
	So that's a little odd thought.

	44:51.080 --> 44:55.080
	But of course, we had no prior idea how these things happen.

	44:55.080 --> 44:57.080
	So let's go with that.

	44:57.080 --> 45:01.080
	And in our recent paper, we talked a little bit about that.

	45:01.080 --> 45:03.080
	I've been working on it more since.

	45:03.080 --> 45:05.080
	I have better ideas about it now.

	45:05.080 --> 45:08.080
	I'm sitting here very confident that that's what's happening.

	45:08.080 --> 45:11.080
	And I can give you some examples to help you think about that.

	45:11.080 --> 45:13.080
	It's not that we understand it completely,

	45:13.080 --> 45:15.080
	but I understand it better than I've described it in any paper

	45:15.080 --> 45:16.080
	so far.

	45:16.080 --> 45:18.080
	But we did put that idea out there.

	45:18.080 --> 45:22.080
	It's a good place to start.

	45:22.080 --> 45:25.080
	And the evidence would suggest it's how it's happening.

	45:25.080 --> 45:27.080
	And then we can start tackling that problem one piece at a time.

	45:27.080 --> 45:29.080
	What does it mean to do high level thought?

	45:29.080 --> 45:30.080
	What does it mean to do language?

	45:30.080 --> 45:34.080
	How would that fit into a reference framework?

	45:34.080 --> 45:38.080
	I don't know if you could tell me if there's a connection,

	45:38.080 --> 45:42.080
	but there's an app called Anki that helps you remember different concepts.

	45:42.080 --> 45:46.080
	And they talk about like a memory palace that helps you remember

	45:46.080 --> 45:50.080
	completely random concepts by trying to put them in a physical space

	45:50.080 --> 45:52.080
	in your mind and putting them next to each other.

	45:52.080 --> 45:54.080
	It's called the method of loci.

	45:54.080 --> 45:57.080
	For some reason, that seems to work really well.

	45:57.080 --> 46:00.080
	Now that's a very narrow kind of application of just remembering some facts.

	46:00.080 --> 46:03.080
	But that's a very, very telling one.

	46:03.080 --> 46:04.080
	Yes, exactly.

	46:04.080 --> 46:09.080
	So this seems like you're describing a mechanism why this seems to work.

	46:09.080 --> 46:13.080
	So basically the way what we think is going on is all things you know,

	46:13.080 --> 46:17.080
	all concepts, all ideas, words, everything, you know,

	46:17.080 --> 46:20.080
	are stored in reference frames.

	46:20.080 --> 46:24.080
	And so if you want to remember something,

	46:24.080 --> 46:27.080
	you have to basically navigate through a reference frame the same way

	46:27.080 --> 46:28.080
	a rat navigates to a man.

	46:28.080 --> 46:31.080
	Even the same way my finger rat navigates to this coffee cup.

	46:31.080 --> 46:33.080
	You are moving through some space.

	46:33.080 --> 46:37.080
	And so if you have a random list of things you would ask to remember

	46:37.080 --> 46:39.080
	by assigning them to a reference frame,

	46:39.080 --> 46:42.080
	you've already know very well to see your house, right?

	46:42.080 --> 46:44.080
	And the idea of the method of loci is you can say,

	46:44.080 --> 46:46.080
	okay, in my lobby, I'm going to put this thing.

	46:46.080 --> 46:48.080
	And then the bedroom, I put this one.

	46:48.080 --> 46:49.080
	I go down the hall, I put this thing.

	46:49.080 --> 46:51.080
	And then you want to recall those facts.

	46:51.080 --> 46:52.080
	So recall those things.

	46:52.080 --> 46:53.080
	You just walk mentally.

	46:53.080 --> 46:54.080
	You walk through your house.

	46:54.080 --> 46:57.080
	You're mentally moving through a reference frame that you already had.

	46:57.080 --> 47:00.080
	And that tells you there's two things that are really important about that.

	47:00.080 --> 47:03.080
	It tells us the brain prefers to store things in reference frames.

	47:03.080 --> 47:08.080
	And the method of recalling things or thinking, if you will,

	47:08.080 --> 47:11.080
	is to move mentally through those reference frames.

	47:11.080 --> 47:13.080
	You could move physically through some reference frames,

	47:13.080 --> 47:16.080
	like I could physically move through the reference frame of this coffee cup.

	47:16.080 --> 47:18.080
	I can also mentally move through the reference frame of the coffee cup,

	47:18.080 --> 47:19.080
	imagining me touching it.

	47:19.080 --> 47:22.080
	But I can also mentally move my house.

	47:22.080 --> 47:26.080
	And so now we can ask ourselves, are all concepts stored this way?

	47:26.080 --> 47:32.080
	There was some recent research using human subjects in fMRI.

	47:32.080 --> 47:36.080
	And I'm going to apologize for not knowing the name of the scientists who did this.

	47:36.080 --> 47:41.080
	But what they did is they put humans in this fMRI machine,

	47:41.080 --> 47:42.080
	which was one of these imaging machines.

	47:42.080 --> 47:46.080
	And they gave the humans tasks to think about birds.

	47:46.080 --> 47:49.080
	So they had different types of birds, and birds that looked big and small

	47:49.080 --> 47:51.080
	and long necks and long legs, things like that.

	47:51.080 --> 47:56.080
	And what they could tell from the fMRI was a very clever experiment.

	47:56.080 --> 48:00.080
	You get to tell when humans were thinking about the birds,

	48:00.080 --> 48:05.080
	that the birds, the knowledge of birds was arranged in a reference frame

	48:05.080 --> 48:08.080
	similar to the ones that are used when you navigate in a room.

	48:08.080 --> 48:10.080
	These are called grid cells.

	48:10.080 --> 48:14.080
	And there are grid cell like patterns of activity in the neocortex when they do this.

	48:14.080 --> 48:18.080
	So that, it's a very clever experiment.

	48:18.080 --> 48:22.080
	And what it basically says is that even when you're thinking about something abstract

	48:22.080 --> 48:24.080
	and you're not really thinking about it as a reference frame,

	48:24.080 --> 48:27.080
	it tells us the brain is actually using a reference frame.

	48:27.080 --> 48:29.080
	And it's using the same neural mechanisms.

	48:29.080 --> 48:32.080
	These grid cells are the basic same neural mechanisms that we propose

	48:32.080 --> 48:36.080
	that grid cells, which exist in the old part of the brain, the entomonic cortex,

	48:36.080 --> 48:40.080
	that that mechanism is now similar mechanism, is used throughout the neocortex.

	48:40.080 --> 48:44.080
	It's the same nature to preserve this interesting way of creating reference frames.

	48:44.080 --> 48:49.080
	And so now they have empirical evidence that when you think about concepts like birds

	48:49.080 --> 48:53.080
	that you're using reference frames that are built on grid cells.

	48:53.080 --> 48:55.080
	So that's similar to the method of loci.

	48:55.080 --> 48:57.080
	But in this case, the birds are related so that makes,

	48:57.080 --> 49:01.080
	they create their own reference frame, which is consistent with bird space.

	49:01.080 --> 49:03.080
	And when you think about something, you go through that.

	49:03.080 --> 49:04.080
	You can make the same example.

	49:04.080 --> 49:06.080
	Let's take a math mathematics.

	49:06.080 --> 49:08.080
	Let's say you want to prove a conjecture.

	49:08.080 --> 49:09.080
	Okay.

	49:09.080 --> 49:10.080
	What is a conjecture?

	49:10.080 --> 49:13.080
	A conjecture is a statement you believe to be true,

	49:13.080 --> 49:15.080
	but you haven't proven it.

	49:15.080 --> 49:17.080
	And so it might be an equation.

	49:17.080 --> 49:19.080
	I want to show that this is equal to that.

	49:19.080 --> 49:21.080
	And you have some places you start with.

	49:21.080 --> 49:23.080
	You say, well, I know this is true and I know this is true.

	49:23.080 --> 49:26.080
	And I think that maybe to get to the final proof,

	49:26.080 --> 49:28.080
	I need to go through some intermediate results.

	49:28.080 --> 49:33.080
	What I believe is happening is literally these equations

	49:33.080 --> 49:36.080
	or these points are assigned to a reference frame,

	49:36.080 --> 49:38.080
	a mathematical reference frame.

	49:38.080 --> 49:40.080
	And when you do mathematical operations,

	49:40.080 --> 49:42.080
	a simple one might be multiply or divide,

	49:42.080 --> 49:44.080
	maybe a little plus transform or something else.

	49:44.080 --> 49:47.080
	That is like a movement in the reference frame of the math.

	49:47.080 --> 49:50.080
	And so you're literally trying to discover a path

	49:50.080 --> 49:56.080
	from one location to another location in a space of mathematics.

	49:56.080 --> 49:58.080
	And if you can get to these intermediate results,

	49:58.080 --> 50:00.080
	then you know your map is pretty good

	50:00.080 --> 50:03.080
	and you know you're using the right operations.

	50:03.080 --> 50:06.080
	Much of what we think about is solving hard problems

	50:06.080 --> 50:09.080
	is designing the correct reference frame for that problem,

	50:09.080 --> 50:12.080
	how to organize the information, and what behaviors

	50:12.080 --> 50:15.080
	I want to use in that space to get me there.

	50:15.080 --> 50:19.080
	Yeah, so if you dig in on an idea of this reference frame,

	50:19.080 --> 50:21.080
	whether it's the math, you start a set of axioms

	50:21.080 --> 50:24.080
	to try to get to proving the conjecture.

	50:24.080 --> 50:27.080
	Can you try to describe, maybe take a step back,

	50:27.080 --> 50:30.080
	how you think of the reference frame in that context?

	50:30.080 --> 50:35.080
	Is it the reference frame that the axioms are happy in?

	50:35.080 --> 50:38.080
	Is it the reference frame that might contain everything?

	50:38.080 --> 50:41.080
	Is it a changing thing as you...

	50:41.080 --> 50:43.080
	You have many, many reference frames.

	50:43.080 --> 50:45.080
	In fact, the way the thousand brain theories of intelligence

	50:45.080 --> 50:48.080
	says that every single thing in the world has its own reference frame.

	50:48.080 --> 50:50.080
	So every word has its own reference frames.

	50:50.080 --> 50:52.080
	And we can talk about this.

	50:52.080 --> 50:55.080
	The mathematics work out this is no problem for neurons to do this.

	50:55.080 --> 50:58.080
	But how many reference frames does the coffee cup have?

	50:58.080 --> 51:03.080
	Well, let's say you ask how many reference frames

	51:03.080 --> 51:07.080
	could the column in my finger that's touching the coffee cup have

	51:07.080 --> 51:10.080
	because there are many, many models of the coffee cup.

	51:10.080 --> 51:12.080
	So there is no model of the coffee cup.

	51:12.080 --> 51:14.080
	There are many models of the coffee cup.

	51:14.080 --> 51:17.080
	And you can say, well, how many different things can my finger learn?

	51:17.080 --> 51:19.080
	Is this the question you want to ask?

	51:19.080 --> 51:21.080
	Imagine I say every concept, every idea,

	51:21.080 --> 51:23.080
	everything you've ever know about that you can say,

	51:23.080 --> 51:28.080
	I know that thing has a reference frame associated with it.

	51:28.080 --> 51:30.080
	And what we do when we build composite objects,

	51:30.080 --> 51:34.080
	we assign reference frames to point another reference frame.

	51:34.080 --> 51:37.080
	So my coffee cup has multiple components to it.

	51:37.080 --> 51:38.080
	It's got a limb.

	51:38.080 --> 51:39.080
	It's got a cylinder.

	51:39.080 --> 51:40.080
	It's got a handle.

	51:40.080 --> 51:43.080
	And those things have their own reference frames.

	51:43.080 --> 51:45.080
	And they're assigned to a master reference frame,

	51:45.080 --> 51:46.080
	which is called this cup.

	51:46.080 --> 51:48.080
	And now I have this mental logo on it.

	51:48.080 --> 51:50.080
	Well, that's something that exists elsewhere in the world.

	51:50.080 --> 51:51.080
	It's its own thing.

	51:51.080 --> 51:52.080
	So it has its own reference frame.

	51:52.080 --> 51:56.080
	So we now have to say, how can I assign the mental logo reference frame

	51:56.080 --> 51:59.080
	onto the cylinder or onto the coffee cup?

	51:59.080 --> 52:04.080
	So we talked about this in the paper that came out in December

	52:04.080 --> 52:06.080
	of this last year.

	52:06.080 --> 52:09.080
	The idea of how you can assign reference frames to reference frames,

	52:09.080 --> 52:10.080
	how neurons could do this.

	52:10.080 --> 52:14.080
	So my question is, even though you mentioned reference frames a lot,

	52:14.080 --> 52:18.080
	I almost feel it's really useful to dig into how you think

	52:18.080 --> 52:20.080
	of what a reference frame is.

	52:20.080 --> 52:22.080
	It was already helpful for me to understand that you think

	52:22.080 --> 52:26.080
	of reference frames as something there is a lot of.

	52:26.080 --> 52:29.080
	OK, so let's just say that we're going to have some neurons

	52:29.080 --> 52:32.080
	in the brain, not many actually, 10,000, 20,000,

	52:32.080 --> 52:34.080
	are going to create a whole bunch of reference frames.

	52:34.080 --> 52:35.080
	What does it mean?

	52:35.080 --> 52:37.080
	What is a reference frame?

	52:37.080 --> 52:40.080
	First of all, these reference frames are different than the ones

	52:40.080 --> 52:42.080
	you might be used to.

	52:42.080 --> 52:43.080
	We know lots of reference frames.

	52:43.080 --> 52:45.080
	For example, we know the Cartesian coordinates,

	52:45.080 --> 52:47.080
	XYZ, that's a type of reference frame.

	52:47.080 --> 52:50.080
	We know longitude and latitude.

	52:50.080 --> 52:52.080
	That's a different type of reference frame.

	52:52.080 --> 52:55.080
	If I look at a printed map, it might have columns,

	52:55.080 --> 52:59.080
	A through M and rows, 1 through 20,

	52:59.080 --> 53:01.080
	that's a different type of reference frame.

	53:01.080 --> 53:04.080
	It's kind of a Cartesian reference frame.

	53:04.080 --> 53:07.080
	The interesting thing about the reference frames in the brain,

	53:07.080 --> 53:09.080
	and we know this because these have been established

	53:09.080 --> 53:12.080
	through neuroscience studying the entorhinal cortex.

	53:12.080 --> 53:13.080
	So I'm not speculating here.

	53:13.080 --> 53:16.080
	This is known neuroscience in an old part of the brain.

	53:16.080 --> 53:18.080
	The way these cells create reference frames,

	53:18.080 --> 53:20.080
	they have no origin.

	53:20.080 --> 53:24.080
	So what it's more like, you have a point,

	53:24.080 --> 53:26.080
	a point in some space,

	53:26.080 --> 53:29.080
	and you, given a particular movement,

	53:29.080 --> 53:32.080
	you can then tell what the next point should be.

	53:32.080 --> 53:34.080
	And you can then tell what the next point would be.

	53:34.080 --> 53:35.080
	And so on.

	53:35.080 --> 53:40.080
	You can use this to calculate how to get from one point to another.

	53:40.080 --> 53:43.080
	So how do I get from my house to my home,

	53:43.080 --> 53:45.080
	or how do I get my finger from the side of my cup

	53:45.080 --> 53:46.080
	to the top of the cup?

	53:46.080 --> 53:52.080
	How do I get from the axioms to the conjecture?

	53:52.080 --> 53:54.080
	So it's a different type of reference frame.

	53:54.080 --> 53:57.080
	And I can, if you want, I can describe in more detail.

	53:57.080 --> 53:59.080
	I can paint a picture how you might want to think about that.

	53:59.080 --> 54:00.080
	It's really helpful to think.

	54:00.080 --> 54:02.080
	It's something you can move through.

	54:02.080 --> 54:03.080
	Yeah.

	54:03.080 --> 54:08.080
	But is it helpful to think of it as spatial in some sense,

	54:08.080 --> 54:09.080
	or is there something?

	54:09.080 --> 54:11.080
	No, it's definitely spatial.

	54:11.080 --> 54:13.080
	It's spatial in a mathematical sense.

	54:13.080 --> 54:14.080
	How many dimensions?

	54:14.080 --> 54:16.080
	Can it be a crazy number of dimensions?

	54:16.080 --> 54:17.080
	Well, that's an interesting question.

	54:17.080 --> 54:20.080
	In the old part of the brain, the entorhinal cortex,

	54:20.080 --> 54:22.080
	they studied rats.

	54:22.080 --> 54:24.080
	And initially, it looks like, oh, this is just two dimensional.

	54:24.080 --> 54:27.080
	It's like the rat is in some box in a maze or whatever,

	54:27.080 --> 54:29.080
	and they know whether the rat is using these two dimensional

	54:29.080 --> 54:32.080
	reference frames and know where it is in the maze.

	54:32.080 --> 54:35.080
	We say, OK, well, what about bats?

	54:35.080 --> 54:38.080
	That's a mammal, and they fly in three dimensional space.

	54:38.080 --> 54:39.080
	How do they do that?

	54:39.080 --> 54:41.080
	They seem to know where they are, right?

	54:41.080 --> 54:44.080
	So this is a current area of active research,

	54:44.080 --> 54:47.080
	and it seems like somehow the neurons in the entorhinal cortex

	54:47.080 --> 54:50.080
	can learn three dimensional space.

	54:50.080 --> 54:55.080
	We just, two members of our team, along with Ilefet from MIT,

	54:55.080 --> 54:59.080
	just released a paper this literally last week,

	54:59.080 --> 55:03.080
	it's on bioarchive, where they show that you can,

	55:03.080 --> 55:06.080
	the way these things work, and unless you want to,

	55:06.080 --> 55:10.080
	I won't get into the detail, but grid cells

	55:10.080 --> 55:12.080
	can represent any n dimensional space.

	55:12.080 --> 55:15.080
	It's not inherently limited.

	55:15.080 --> 55:18.080
	You can think of it this way, if you had two dimensional,

	55:18.080 --> 55:21.080
	the way it works is you had a bunch of two dimensional slices.

	55:21.080 --> 55:22.080
	That's the way these things work.

	55:22.080 --> 55:24.080
	There's a whole bunch of two dimensional models,

	55:24.080 --> 55:27.080
	and you can slice up any n dimensional space

	55:27.080 --> 55:29.080
	with two dimensional projections.

	55:29.080 --> 55:31.080
	And you could have one dimensional models.

	55:31.080 --> 55:34.080
	So there's nothing inherent about the mathematics

	55:34.080 --> 55:36.080
	about the way the neurons do this,

	55:36.080 --> 55:39.080
	which constrained the dimensionality of the space,

	55:39.080 --> 55:41.080
	which I think was important.

	55:41.080 --> 55:44.080
	So obviously, I have a three dimensional map of this cup.

	55:44.080 --> 55:46.080
	Maybe it's even more than that, I don't know.

	55:46.080 --> 55:48.080
	But it's a clearly three dimensional map of the cup.

	55:48.080 --> 55:50.080
	I don't just have a projection of the cup.

	55:50.080 --> 55:52.080
	But when I think about birds,

	55:52.080 --> 55:53.080
	or when I think about mathematics,

	55:53.080 --> 55:55.080
	perhaps it's more than three dimensions.

	55:55.080 --> 55:56.080
	Who knows?

	55:56.080 --> 56:00.080
	So in terms of each individual column

	56:00.080 --> 56:04.080
	building up more and more information over time,

	56:04.080 --> 56:06.080
	do you think that mechanism is well understood?

	56:06.080 --> 56:10.080
	In your mind, you've proposed a lot of architectures there.

	56:10.080 --> 56:14.080
	Is that a key piece, or is it, is the big piece,

	56:14.080 --> 56:16.080
	the thousand brain theory of intelligence,

	56:16.080 --> 56:18.080
	the ensemble of it all?

	56:18.080 --> 56:19.080
	Well, I think they're both big.

	56:19.080 --> 56:21.080
	I mean, clearly the concept, as a theorist,

	56:21.080 --> 56:23.080
	the concept is most exciting, right?

	56:23.080 --> 56:24.080
	A high level concept.

	56:24.080 --> 56:25.080
	A high level concept.

	56:25.080 --> 56:26.080
	This is a totally new way of thinking about

	56:26.080 --> 56:27.080
	how the near characteristics work.

	56:27.080 --> 56:29.080
	So that is appealing.

	56:29.080 --> 56:31.080
	It has all these ramifications.

	56:31.080 --> 56:34.080
	And with that, as a framework for how the brain works,

	56:34.080 --> 56:35.080
	you can make all kinds of predictions

	56:35.080 --> 56:36.080
	and solve all kinds of problems.

	56:36.080 --> 56:38.080
	Now we're trying to work through many of these details right now.

	56:38.080 --> 56:40.080
	Okay, how do the neurons actually do this?

	56:40.080 --> 56:42.080
	Well, it turns out, if you think about grid cells

	56:42.080 --> 56:44.080
	and place cells in the old parts of the brain,

	56:44.080 --> 56:46.080
	there's a lot that's known about them,

	56:46.080 --> 56:47.080
	but there's still some mysteries.

	56:47.080 --> 56:49.080
	There's a lot of debate about exactly the details,

	56:49.080 --> 56:50.080
	how these work, and what are the signs.

	56:50.080 --> 56:52.080
	And we have that same level of detail,

	56:52.080 --> 56:54.080
	that same level of concern.

	56:54.080 --> 56:56.080
	What we spend here, most of our time doing,

	56:56.080 --> 56:59.080
	is trying to make a very good list

	56:59.080 --> 57:02.080
	of the things we don't understand yet.

	57:02.080 --> 57:04.080
	That's the key part here.

	57:04.080 --> 57:05.080
	What are the constraints?

	57:05.080 --> 57:07.080
	It's not like, oh, this seems to work, we're done.

	57:07.080 --> 57:09.080
	It's like, okay, it kind of works,

	57:09.080 --> 57:11.080
	but these are other things we know it has to do,

	57:11.080 --> 57:13.080
	and it's not doing those yet.

	57:13.080 --> 57:15.080
	I would say we're well on the way here.

	57:15.080 --> 57:17.080
	We're not done yet.

	57:17.080 --> 57:20.080
	There's a lot of trickiness to this system,

	57:20.080 --> 57:23.080
	but the basic principles about how different layers

	57:23.080 --> 57:27.080
	in the neocortex are doing much of this, we understand.

	57:27.080 --> 57:29.080
	But there's some fundamental parts

	57:29.080 --> 57:30.080
	that we don't understand as well.

	57:30.080 --> 57:34.080
	So what would you say is one of the harder open problems,

	57:34.080 --> 57:37.080
	or one of the ones that have been bothering you,

	57:37.080 --> 57:39.080
	keeping you up at night the most?

	57:39.080 --> 57:41.080
	Well, right now, this is a detailed thing

	57:41.080 --> 57:43.080
	that wouldn't apply to most people, okay?

	57:43.080 --> 57:44.080
	Sure.

	57:44.080 --> 57:45.080
	But you want me to answer that question?

	57:45.080 --> 57:46.080
	Yeah, please.

	57:46.080 --> 57:49.080
	We've talked about, as if, oh, to predict

	57:49.080 --> 57:51.080
	what you're going to sense on this coffee cup,

	57:51.080 --> 57:54.080
	I need to know where my finger's going to be on the coffee cup.

	57:54.080 --> 57:56.080
	That is true, but it's insufficient.

	57:56.080 --> 57:59.080
	Think about my finger touching the edge of the coffee cup.

	57:59.080 --> 58:02.080
	My finger can touch it at different orientations.

	58:02.080 --> 58:05.080
	I can touch it at my finger around here.

	58:05.080 --> 58:06.080
	And that doesn't change.

	58:06.080 --> 58:09.080
	I can make that prediction, and somehow,

	58:09.080 --> 58:10.080
	so it's not just the location.

	58:10.080 --> 58:13.080
	There's an orientation component of this as well.

	58:13.080 --> 58:15.080
	This is known in the old part of the brain, too.

	58:15.080 --> 58:17.080
	There's things called head direction cells,

	58:17.080 --> 58:18.080
	which way the rat is facing.

	58:18.080 --> 58:20.080
	It's the same kind of basic idea.

	58:20.080 --> 58:23.080
	So if my finger were a rat, you know, in three dimensions,

	58:23.080 --> 58:25.080
	I have a three dimensional orientation,

	58:25.080 --> 58:27.080
	and I have a three dimensional location.

	58:27.080 --> 58:29.080
	If I was a rat, I would have a,

	58:29.080 --> 58:31.080
	I think it was a two dimensional location,

	58:31.080 --> 58:33.080
	or one dimensional orientation, like this,

	58:33.080 --> 58:35.080
	which way is it facing?

	58:35.080 --> 58:38.080
	So how the two components work together,

	58:38.080 --> 58:41.080
	how does it, I combine orientation,

	58:41.080 --> 58:43.080
	the orientation of my sensor,

	58:43.080 --> 58:47.080
	as well as the location,

	58:47.080 --> 58:49.080
	is a tricky problem.

	58:49.080 --> 58:52.080
	And I think I've made progress on it.

	58:52.080 --> 58:55.080
	So at a bigger version of that,

	58:55.080 --> 58:57.080
	the perspective is super interesting,

	58:57.080 --> 58:58.080
	but super specific.

	58:58.080 --> 58:59.080
	Yeah, I warned you.

	58:59.080 --> 59:01.080
	No, no, no, it's really good,

	59:01.080 --> 59:04.080
	but there's a more general version of that.

	59:04.080 --> 59:06.080
	Do you think context matters?

	59:06.080 --> 59:10.080
	The fact that we are in a building in North America,

	59:10.080 --> 59:15.080
	that we, in the day and age where we have mugs,

	59:15.080 --> 59:18.080
	I mean, there's all this extra information

	59:18.080 --> 59:22.080
	that you bring to the table about everything else in the room

	59:22.080 --> 59:24.080
	that's outside of just the coffee cup.

	59:24.080 --> 59:25.080
	Of course it is.

	59:25.080 --> 59:27.080
	How does it get connected, do you think?

	59:27.080 --> 59:30.080
	Yeah, and that is another really interesting question.

	59:30.080 --> 59:32.080
	I'm going to throw that under the rubric

	59:32.080 --> 59:34.080
	or the name of attentional problems.

	59:34.080 --> 59:36.080
	First of all, we have this model.

	59:36.080 --> 59:37.080
	I have many, many models.

	59:37.080 --> 59:39.080
	And also the question, does it matter?

	59:39.080 --> 59:41.080
	Well, it matters for certain things.

	59:41.080 --> 59:42.080
	Of course it does.

	59:42.080 --> 59:44.080
	Maybe what we think about as a coffee cup

	59:44.080 --> 59:47.080
	in another part of the world is viewed as something completely different.

	59:47.080 --> 59:51.080
	Or maybe our logo, which is very benign in this part of the world,

	59:51.080 --> 59:53.080
	it means something very different in another part of the world.

	59:53.080 --> 59:56.080
	So those things do matter.

	59:56.080 --> 1:00:00.080
	I think the way to think about it as the following,

	1:00:00.080 --> 1:00:01.080
	one way to think about it,

	1:00:01.080 --> 1:00:03.080
	is we have all these models of the world.

	1:00:03.080 --> 1:00:06.080
	And we model everything.

	1:00:06.080 --> 1:00:08.080
	And as I said earlier, I kind of snuck it in there.

	1:00:08.080 --> 1:00:12.080
	Our models are actually, we build composite structures.

	1:00:12.080 --> 1:00:15.080
	So every object is composed of other objects,

	1:00:15.080 --> 1:00:16.080
	which are composed of other objects,

	1:00:16.080 --> 1:00:18.080
	and they become members of other objects.

	1:00:18.080 --> 1:00:21.080
	So this room is chairs and a table and a room and walls and so on.

	1:00:21.080 --> 1:00:24.080
	Now we can just arrange these things in a certain way.

	1:00:24.080 --> 1:00:27.080
	And you go, oh, that's in the Nementa conference room.

	1:00:27.080 --> 1:00:32.080
	So, and what we do is when we go around the world,

	1:00:32.080 --> 1:00:34.080
	when we experience the world,

	1:00:34.080 --> 1:00:36.080
	by walking to a room, for example,

	1:00:36.080 --> 1:00:38.080
	the first thing I do is like, oh, I'm in this room.

	1:00:38.080 --> 1:00:39.080
	Do I recognize the room?

	1:00:39.080 --> 1:00:42.080
	Then I can say, oh, look, there's a table here.

	1:00:42.080 --> 1:00:44.080
	And by attending to the table,

	1:00:44.080 --> 1:00:46.080
	I'm then assigning this table in a context of the room.

	1:00:46.080 --> 1:00:48.080
	Then I say, oh, on the table, there's a coffee cup.

	1:00:48.080 --> 1:00:50.080
	Oh, and on the table, there's a logo.

	1:00:50.080 --> 1:00:52.080
	And in the logo, there's the word Nementa.

	1:00:52.080 --> 1:00:54.080
	So if you look in the logo, there's the letter E.

	1:00:54.080 --> 1:00:56.080
	And look, it has an unusual surf.

	1:00:56.080 --> 1:00:59.080
	It doesn't actually, but I pretend it does.

	1:00:59.080 --> 1:01:05.080
	So the point is your attention is kind of drilling deep in and out

	1:01:05.080 --> 1:01:07.080
	of these nested structures.

	1:01:07.080 --> 1:01:09.080
	And I can pop back up and I can pop back down.

	1:01:09.080 --> 1:01:11.080
	I can pop back up and I can pop back down.

	1:01:11.080 --> 1:01:13.080
	So when I attend to the coffee cup,

	1:01:13.080 --> 1:01:15.080
	I haven't lost the context of everything else,

	1:01:15.080 --> 1:01:19.080
	but it's sort of, there's this sort of nested structure.

	1:01:19.080 --> 1:01:22.080
	The attention filters the reference frame formation

	1:01:22.080 --> 1:01:24.080
	for that particular period of time.

	1:01:24.080 --> 1:01:25.080
	Yes.

	1:01:25.080 --> 1:01:28.080
	It basically, a moment to moment, you attend the subcomponents

	1:01:28.080 --> 1:01:30.080
	and then you can attend the subcomponents to subcomponents.

	1:01:30.080 --> 1:01:31.080
	You can move up and down.

	1:01:31.080 --> 1:01:32.080
	You can move up and down.

	1:01:32.080 --> 1:01:33.080
	We do that all the time.

	1:01:33.080 --> 1:01:35.080
	You're not even, now that I'm aware of it,

	1:01:35.080 --> 1:01:37.080
	I'm very conscious of it.

	1:01:37.080 --> 1:01:40.080
	But most people don't even think about this.

	1:01:40.080 --> 1:01:42.080
	You know, you just walk in a room and you don't say,

	1:01:42.080 --> 1:01:43.080
	oh, I looked at the chair and I looked at the board

	1:01:43.080 --> 1:01:44.080
	and looked at that word on the board

	1:01:44.080 --> 1:01:45.080
	and I looked over here.

	1:01:45.080 --> 1:01:46.080
	What's going on?

	1:01:46.080 --> 1:01:47.080
	Right.

	1:01:47.080 --> 1:01:50.080
	So what percentage of your day are you deeply aware of this?

	1:01:50.080 --> 1:01:53.080
	In what part can you actually relax and just be Jeff?

	1:01:53.080 --> 1:01:55.080
	Me personally, like my personal day.

	1:01:55.080 --> 1:01:56.080
	Yeah.

	1:01:56.080 --> 1:02:01.080
	Unfortunately, I'm afflicted with too much of the former.

	1:02:01.080 --> 1:02:03.080
	Well, unfortunately or unfortunately.

	1:02:03.080 --> 1:02:04.080
	Yeah.

	1:02:04.080 --> 1:02:05.080
	You don't think it's useful?

	1:02:05.080 --> 1:02:06.080
	Oh, it is useful.

	1:02:06.080 --> 1:02:07.080
	Totally useful.

	1:02:07.080 --> 1:02:09.080
	I think about this stuff almost all the time.

	1:02:09.080 --> 1:02:13.080
	And one of my primary ways of thinking is

	1:02:13.080 --> 1:02:14.080
	when I'm asleep at night,

	1:02:14.080 --> 1:02:16.080
	I always wake up in the middle of the night

	1:02:16.080 --> 1:02:19.080
	and I stay awake for at least an hour with my eyes shut

	1:02:19.080 --> 1:02:21.080
	in sort of a half sleep state thinking about these things.

	1:02:21.080 --> 1:02:23.080
	I come up with answers to problems very often

	1:02:23.080 --> 1:02:25.080
	in that sort of half sleeping state.

	1:02:25.080 --> 1:02:27.080
	I think about on my bike ride, I think about on walks.

	1:02:27.080 --> 1:02:29.080
	I'm just constantly thinking about this.

	1:02:29.080 --> 1:02:34.080
	I have to almost schedule time to not think about this stuff

	1:02:34.080 --> 1:02:37.080
	because it's very, it's mentally taxing.

	1:02:37.080 --> 1:02:39.080
	Are you, when you're thinking about this stuff,

	1:02:39.080 --> 1:02:41.080
	are you thinking introspectively,

	1:02:41.080 --> 1:02:43.080
	like almost taking a step outside of yourself

	1:02:43.080 --> 1:02:45.080
	and trying to figure out what is your mind doing right now?

	1:02:45.080 --> 1:02:48.080
	I do that all the time, but that's not all I do.

	1:02:48.080 --> 1:02:50.080
	I'm constantly observing myself.

	1:02:50.080 --> 1:02:52.080
	So as soon as I started thinking about grid cells,

	1:02:52.080 --> 1:02:54.080
	for example, and getting into that,

	1:02:54.080 --> 1:02:57.080
	I started saying, oh, well, grid cells can have my place of sense

	1:02:57.080 --> 1:02:58.080
	in the world.

	1:02:58.080 --> 1:02:59.080
	That's where you know where you are.

	1:02:59.080 --> 1:03:01.080
	And it's interesting, we always have a sense of where we are

	1:03:01.080 --> 1:03:02.080
	unless we're lost.

	1:03:02.080 --> 1:03:05.080
	And so I started at night when I got up to go to the bathroom,

	1:03:05.080 --> 1:03:07.080
	I would start trying to do it completely with my eyes closed

	1:03:07.080 --> 1:03:09.080
	all the time and I would test my sense of grid cells.

	1:03:09.080 --> 1:03:13.080
	I would walk five feet and say, okay, I think I'm here.

	1:03:13.080 --> 1:03:14.080
	Am I really there?

	1:03:14.080 --> 1:03:15.080
	What's my error?

	1:03:15.080 --> 1:03:17.080
	And then I would calculate my error again and see how the errors

	1:03:17.080 --> 1:03:18.080
	accumulate.

	1:03:18.080 --> 1:03:20.080
	So even something as simple as getting up in the middle of the

	1:03:20.080 --> 1:03:22.080
	night to go to the bathroom, I'm testing these theories out.

	1:03:22.080 --> 1:03:23.080
	It's kind of fun.

	1:03:23.080 --> 1:03:25.080
	I mean, the coffee cup is an example of that too.

	1:03:25.080 --> 1:03:30.080
	So I think I find that these sort of everyday introspections

	1:03:30.080 --> 1:03:32.080
	are actually quite helpful.

	1:03:32.080 --> 1:03:34.080
	It doesn't mean you can ignore the science.

	1:03:34.080 --> 1:03:38.080
	I mean, I spend hours every day reading ridiculously complex

	1:03:38.080 --> 1:03:39.080
	papers.

	1:03:39.080 --> 1:03:41.080
	That's not nearly as much fun,

	1:03:41.080 --> 1:03:44.080
	but you have to sort of build up those constraints and the knowledge

	1:03:44.080 --> 1:03:47.080
	about the field and who's doing what and what exactly they think

	1:03:47.080 --> 1:03:48.080
	is happening here.

	1:03:48.080 --> 1:03:51.080
	And then you can sit back and say, okay, let's try to have pieces

	1:03:51.080 --> 1:03:52.080
	all together.

	1:03:52.080 --> 1:03:56.080
	Let's come up with some, you know, I'm very in this group here

	1:03:56.080 --> 1:03:58.080
	and people, they know they do this.

	1:03:58.080 --> 1:03:59.080
	I do this all the time.

	1:03:59.080 --> 1:04:01.080
	I come in with these introspective ideas and say, well,

	1:04:01.080 --> 1:04:02.080
	there we ever thought about this.

	1:04:02.080 --> 1:04:04.080
	Now watch, well, let's all do this together.

	1:04:04.080 --> 1:04:06.080
	And it's helpful.

	1:04:06.080 --> 1:04:10.080
	It's not, as long as you don't, if all you did was that,

	1:04:10.080 --> 1:04:12.080
	then you're just making up stuff, right?

	1:04:12.080 --> 1:04:15.080
	But if you're constraining it by the reality of the neuroscience,

	1:04:15.080 --> 1:04:17.080
	then it's really helpful.

	1:04:17.080 --> 1:04:22.080
	So let's talk a little bit about deep learning and the successes

	1:04:22.080 --> 1:04:28.080
	in the applied space of neural networks, ideas of training model

	1:04:28.080 --> 1:04:31.080
	on data and these simple computational units,

	1:04:31.080 --> 1:04:37.080
	artificial neurons that with back propagation have statistical

	1:04:37.080 --> 1:04:42.080
	ways of being able to generalize from the training set on to

	1:04:42.080 --> 1:04:44.080
	data that similar to that training set.

	1:04:44.080 --> 1:04:48.080
	So where do you think are the limitations of those approaches?

	1:04:48.080 --> 1:04:52.080
	What do you think are strengths relative to your major efforts

	1:04:52.080 --> 1:04:55.080
	of constructing a theory of human intelligence?

	1:04:55.080 --> 1:04:56.080
	Yeah.

	1:04:56.080 --> 1:04:58.080
	Well, I'm not an expert in this field.

	1:04:58.080 --> 1:04:59.080
	I'm somewhat knowledgeable.

	1:04:59.080 --> 1:05:00.080
	So, but I'm not.

	1:05:00.080 --> 1:05:02.080
	A little bit in just your intuition.

	1:05:02.080 --> 1:05:04.080
	Well, I have a little bit more than intuition,

	1:05:04.080 --> 1:05:07.080
	but I just want to say like, you know, one of the things that you asked me,

	1:05:07.080 --> 1:05:09.080
	do I spend all my time thinking about neuroscience?

	1:05:09.080 --> 1:05:10.080
	I do.

	1:05:10.080 --> 1:05:12.080
	That's to the exclusion of thinking about things like convolutional neural

	1:05:12.080 --> 1:05:13.080
	networks.

	1:05:13.080 --> 1:05:15.080
	But I try to stay current.

	1:05:15.080 --> 1:05:18.080
	So look, I think it's great the progress they've made.

	1:05:18.080 --> 1:05:19.080
	It's fantastic.

	1:05:19.080 --> 1:05:23.080
	And as I mentioned earlier, it's very highly useful for many things.

	1:05:23.080 --> 1:05:27.080
	The models that we have today are actually derived from a lot of

	1:05:27.080 --> 1:05:28.080
	neuroscience principles.

	1:05:28.080 --> 1:05:31.080
	They are distributed processing systems and distributed memory systems,

	1:05:31.080 --> 1:05:33.080
	and that's how the brain works.

	1:05:33.080 --> 1:05:36.080
	And they use things that we might call them neurons,

	1:05:36.080 --> 1:05:37.080
	but they're really not neurons at all.

	1:05:37.080 --> 1:05:39.080
	So we can just, they're not really neurons.

	1:05:39.080 --> 1:05:42.080
	So they're distributed processing systems.

	1:05:42.080 --> 1:05:47.080
	And nature of hierarchy that came also from neuroscience.

	1:05:47.080 --> 1:05:50.080
	And so there's a lot of things, the learning rules, basically,

	1:05:50.080 --> 1:05:52.080
	not backprop, but other, you know, sort of heavy entire learning.

	1:05:52.080 --> 1:05:55.080
	I'll be curious to say they're not neurons at all.

	1:05:55.080 --> 1:05:56.080
	Can you describe in which way?

	1:05:56.080 --> 1:06:00.080
	I mean, some of it is obvious, but I'd be curious if you have specific

	1:06:00.080 --> 1:06:02.080
	ways in which you think are the biggest differences.

	1:06:02.080 --> 1:06:06.080
	Yeah, we had a paper in 2016 called Why Neurons of Thousands of Synapses.

	1:06:06.080 --> 1:06:11.080
	And if you read that paper, you'll know what I'm talking about here.

	1:06:11.080 --> 1:06:14.080
	A real neuron in the brain is a complex thing.

	1:06:14.080 --> 1:06:18.080
	Let's just start with the synapses on it, which is a connection between neurons.

	1:06:18.080 --> 1:06:24.080
	Real neurons can everywhere from five to 30,000 synapses on them.

	1:06:24.080 --> 1:06:30.080
	The ones near the cell body, the ones that are close to the soma, the cell body,

	1:06:30.080 --> 1:06:33.080
	those are like the ones that people model in artificial neurons.

	1:06:33.080 --> 1:06:35.080
	There's a few hundred of those.

	1:06:35.080 --> 1:06:37.080
	Maybe they can affect the cell.

	1:06:37.080 --> 1:06:39.080
	They can make the cell become active.

	1:06:39.080 --> 1:06:43.080
	95% of the synapses can't do that.

	1:06:43.080 --> 1:06:44.080
	They're too far away.

	1:06:44.080 --> 1:06:47.080
	So if you activate one of those synapses, it just doesn't affect the cell body

	1:06:47.080 --> 1:06:49.080
	enough to make any difference.

	1:06:49.080 --> 1:06:50.080
	Any one of them individually.

	1:06:50.080 --> 1:06:53.080
	Any one of them individually, or even if you do a mass of them.

	1:06:53.080 --> 1:06:57.080
	What real neurons do is the following.

	1:06:57.080 --> 1:07:04.080
	If you activate, or you get 10 to 20 of them active at the same time,

	1:07:04.080 --> 1:07:06.080
	meaning they're all receiving an input at the same time,

	1:07:06.080 --> 1:07:10.080
	and those 10 to 20 synapses or 40 synapses are within a very short distance

	1:07:10.080 --> 1:07:13.080
	on the dendrite, like 40 microns, a very small area.

	1:07:13.080 --> 1:07:17.080
	So if you activate a bunch of these right next to each other at some distant place,

	1:07:17.080 --> 1:07:21.080
	what happens is it creates what's called the dendritic spike.

	1:07:21.080 --> 1:07:24.080
	And dendritic spike travels through the dendrites

	1:07:24.080 --> 1:07:27.080
	and can reach the soma or the cell body.

	1:07:27.080 --> 1:07:31.080
	Now, when it gets there, it changes the voltage,

	1:07:31.080 --> 1:07:33.080
	which is sort of like going to make the cell fire,

	1:07:33.080 --> 1:07:35.080
	but never enough to make the cell fire.

	1:07:35.080 --> 1:07:38.080
	It's sort of what we call, it says we depolarize the cell.

	1:07:38.080 --> 1:07:41.080
	You raise the voltage a little bit, but not enough to do anything.

	1:07:41.080 --> 1:07:42.080
	It's like, well, what good is that?

	1:07:42.080 --> 1:07:44.080
	And then it goes back down again.

	1:07:44.080 --> 1:07:50.080
	So we proposed a theory, which I'm very confident in basics are,

	1:07:50.080 --> 1:07:54.080
	is that what's happening there is those 95% of the synapses

	1:07:54.080 --> 1:07:58.080
	are recognizing dozens to hundreds of unique patterns.

	1:07:58.080 --> 1:08:01.080
	They can write, you know, about 10, 20 synapses at a time,

	1:08:01.080 --> 1:08:04.080
	and they're acting like predictions.

	1:08:04.080 --> 1:08:07.080
	So the neuron actually is a predictive engine on its own.

	1:08:07.080 --> 1:08:11.080
	It can fire when it gets enough, what they call proximal input from those ones

	1:08:11.080 --> 1:08:15.080
	near the cell fire, but it can get ready to fire from dozens to hundreds

	1:08:15.080 --> 1:08:17.080
	of patterns that it recognizes from the other guys.

	1:08:17.080 --> 1:08:22.080
	And the advantage of this to the neuron is that when it actually does produce

	1:08:22.080 --> 1:08:27.080
	a spike in action potential, it does so slightly sooner than it would have otherwise.

	1:08:27.080 --> 1:08:29.080
	And so what could just slightly sooner?

	1:08:29.080 --> 1:08:33.080
	Well, the slightly sooner part is it, there's all the neurons in the,

	1:08:33.080 --> 1:08:36.080
	the excited throwing neurons in the brain are surrounded by these inhibitory neurons,

	1:08:36.080 --> 1:08:40.080
	and they're very fast, the inhibitory neurons, these baskets all.

	1:08:40.080 --> 1:08:44.080
	And if I get my spike out a little bit sooner than someone else,

	1:08:44.080 --> 1:08:46.080
	I inhibit all my neighbors around me, right?

	1:08:46.080 --> 1:08:49.080
	And what you end up with is a different representation.

	1:08:49.080 --> 1:08:52.080
	You end up with a representation that matches your prediction.

	1:08:52.080 --> 1:08:55.080
	It's a sparser representation, meaning fewer neurons are active,

	1:08:55.080 --> 1:08:57.080
	but it's much more specific.

	1:08:57.080 --> 1:09:04.080
	And so we showed how networks of these neurons can do very sophisticated temporal prediction, basically.

	1:09:04.080 --> 1:09:10.080
	So this summarizes real neurons in the brain are time based prediction engines,

	1:09:10.080 --> 1:09:17.080
	and there's no concept of this at all in artificial, what we call point neurons.

	1:09:17.080 --> 1:09:19.080
	I don't think you can mail the brain without them.

	1:09:19.080 --> 1:09:25.080
	I don't think you can build intelligence without them because it's where a large part of the time comes from.

	1:09:25.080 --> 1:09:31.080
	These are predictive models and the time is, there's a prior prediction and an action,

	1:09:31.080 --> 1:09:34.080
	and it's inherent through every neuron in the neocortex.

	1:09:34.080 --> 1:09:38.080
	So I would say that point neurons sort of model a piece of that,

	1:09:38.080 --> 1:09:45.080
	and not very well at that either, but, you know, like, for example, synapses are very unreliable,

	1:09:45.080 --> 1:09:49.080
	and you cannot assign any precision to them.

	1:09:49.080 --> 1:09:52.080
	So even one digit of precision is not possible.

	1:09:52.080 --> 1:09:57.080
	So the way real neurons work is they don't add these, they don't change these weights accurately,

	1:09:57.080 --> 1:09:59.080
	like artificial neural networks do.

	1:09:59.080 --> 1:10:03.080
	They basically form new synapses, and so what you're trying to always do is

	1:10:03.080 --> 1:10:09.080
	detect the presence of some 10 to 20 active synapses at the same time as opposed,

	1:10:09.080 --> 1:10:11.080
	and they're almost binary.

	1:10:11.080 --> 1:10:14.080
	It's like, because you can't really represent anything much finer than that.

	1:10:14.080 --> 1:10:18.080
	So these are the kind of, and I think that's actually another essential component

	1:10:18.080 --> 1:10:24.080
	because the brain works on sparse patterns, and all that mechanism is based on sparse patterns,

	1:10:24.080 --> 1:10:28.080
	and I don't actually think you could build real brains or machine intelligence

	1:10:28.080 --> 1:10:30.080
	without incorporating some of those ideas.

	1:10:30.080 --> 1:10:34.080
	It's hard to even think about the complexity that emerges from the fact that

	1:10:34.080 --> 1:10:40.080
	the timing of the firing matters in the brain, the fact that you form new synapses,

	1:10:40.080 --> 1:10:44.080
	and everything you just mentioned in the past couple minutes.

	1:10:44.080 --> 1:10:47.080
	Trust me, if you spend time on it, you can get your mind around it.

	1:10:47.080 --> 1:10:49.080
	It's not like it's no longer a mystery to me.

	1:10:49.080 --> 1:10:53.080
	No, but sorry, as a function in a mathematical way,

	1:10:53.080 --> 1:10:58.080
	can you start getting an intuition about what gets it excited, what not,

	1:10:58.080 --> 1:11:00.080
	and what kind of representation?

	1:11:00.080 --> 1:11:04.080
	Yeah, it's not as easy as there are many other types of neural networks

	1:11:04.080 --> 1:11:10.080
	that are more amenable to pure analysis, especially very simple networks.

	1:11:10.080 --> 1:11:12.080
	You know, oh, I have four neurons, and they're doing this.

	1:11:12.080 --> 1:11:16.080
	Can we describe them mathematically what they're doing type of thing?

	1:11:16.080 --> 1:11:19.080
	Even the complexity of convolutional neural networks today,

	1:11:19.080 --> 1:11:23.080
	it's sort of a mystery. They can't really describe the whole system.

	1:11:23.080 --> 1:11:25.080
	And so it's different.

	1:11:25.080 --> 1:11:31.080
	My colleague, Subitain Ahmad, he did a nice paper on this.

	1:11:31.080 --> 1:11:34.080
	You can get all the stuff on our website if you're interested.

	1:11:34.080 --> 1:11:38.080
	Talking about sort of mathematical properties of sparse representations,

	1:11:38.080 --> 1:11:42.080
	and so what we can do is we can show mathematically, for example,

	1:11:42.080 --> 1:11:46.080
	why 10 to 20 synapses to recognize a pattern is the correct number,

	1:11:46.080 --> 1:11:48.080
	is the right number you'd want to use.

	1:11:48.080 --> 1:11:50.080
	And by the way, that matches biology.

	1:11:50.080 --> 1:11:55.080
	We can show mathematically some of these concepts about the show

	1:11:55.080 --> 1:12:01.080
	why the brain is so robust to noise and error and fallout and so on.

	1:12:01.080 --> 1:12:05.080
	We can show that mathematically as well as empirically in simulations.

	1:12:05.080 --> 1:12:08.080
	But the system can't be analyzed completely.

	1:12:08.080 --> 1:12:12.080
	Any complex system can, and so that's out of the realm.

	1:12:12.080 --> 1:12:19.080
	But there is mathematical benefits and intuitions that can be derived from mathematics.

	1:12:19.080 --> 1:12:21.080
	And we try to do that as well.

	1:12:21.080 --> 1:12:23.080
	Most of our papers have a section about that.

	1:12:23.080 --> 1:12:28.080
	So I think it's refreshing and useful for me to be talking to you about deep neural networks,

	1:12:28.080 --> 1:12:36.080
	because your intuition basically says that we can't achieve anything like intelligence with artificial neural networks.

	1:12:36.080 --> 1:12:37.080
	Well, not in the current form.

	1:12:37.080 --> 1:12:38.080
	Not in the current form.

	1:12:38.080 --> 1:12:40.080
	I'm sure we can do it in the ultimate form, sure.

	1:12:40.080 --> 1:12:43.080
	So let me dig into it and see what your thoughts are there a little bit.

	1:12:43.080 --> 1:12:49.080
	So I'm not sure if you read this little blog post called Bitter Lesson by Rich Sutton recently.

	1:12:49.080 --> 1:12:51.080
	He's a reinforcement learning pioneer.

	1:12:51.080 --> 1:12:53.080
	I'm not sure if you're familiar with him.

	1:12:53.080 --> 1:13:02.080
	His basic idea is that all the stuff we've done in AI in the past 70 years, he's one of the old school guys.

	1:13:02.080 --> 1:13:10.080
	The biggest lesson learned is that all the tricky things we've done don't, you know, they benefit in the short term.

	1:13:10.080 --> 1:13:20.080
	But in the long term, what wins out is a simple general method that just relies on Moore's law on computation getting faster and faster.

	1:13:20.080 --> 1:13:21.080
	This is what he's saying.

	1:13:21.080 --> 1:13:23.080
	This is what has worked up to now.

	1:13:23.080 --> 1:13:25.080
	This is what has worked up to now.

	1:13:25.080 --> 1:13:31.080
	If you're trying to build a system, if we're talking about, he's not concerned about intelligence.

	1:13:31.080 --> 1:13:38.080
	He's concerned about a system that works in terms of making predictions on applied, narrow AI problems.

	1:13:38.080 --> 1:13:41.080
	That's what the discussion is about.

	1:13:41.080 --> 1:13:50.080
	That you just try to go as general as possible and wait years or decades for the computation to make it actually possible.

	1:13:50.080 --> 1:13:54.080
	Is he saying that as a criticism or is he saying this is a prescription of what we ought to be doing?

	1:13:54.080 --> 1:13:55.080
	Well, it's very difficult.

	1:13:55.080 --> 1:13:57.080
	He's saying this is what has worked.

	1:13:57.080 --> 1:14:03.080
	And yes, a prescription, but it's a difficult prescription because it says all the fun things you guys are trying to do.

	1:14:03.080 --> 1:14:05.080
	We are trying to do.

	1:14:05.080 --> 1:14:07.080
	He's part of the community.

	1:14:07.080 --> 1:14:11.080
	He's saying it's only going to be short term gains.

	1:14:11.080 --> 1:14:19.080
	This all leads up to a question, I guess, on artificial neural networks and maybe our own biological neural networks.

	1:14:19.080 --> 1:14:24.080
	Do you think if we just scale things up significantly?

	1:14:24.080 --> 1:14:28.080
	Take these dumb artificial neurons, the point neurons.

	1:14:28.080 --> 1:14:30.080
	I like that term.

	1:14:30.080 --> 1:14:36.080
	If we just have a lot more of them, do you think some of the elements that we see in the brain

	1:14:36.080 --> 1:14:38.080
	may start emerging?

	1:14:38.080 --> 1:14:39.080
	No, I don't think so.

	1:14:39.080 --> 1:14:43.080
	We can do bigger problems of the same type.

	1:14:43.080 --> 1:14:50.080
	I mean, it's been pointed out by many people that today's convolutional neural networks aren't really much different than the ones we had quite a while ago.

	1:14:50.080 --> 1:14:56.080
	We just, they're bigger and train more and we have more labeled data and so on.

	1:14:56.080 --> 1:15:03.080
	But I don't think you can get to the kind of things I know the brain can do and that we think about as intelligence by just scaling it up.

	1:15:03.080 --> 1:15:12.080
	So that may be, it's a good description of what's happened in the past, what's happened recently with the reemergence of artificial neural networks.

	1:15:12.080 --> 1:15:17.080
	It may be a good prescription for what's going to happen in the short term.

	1:15:17.080 --> 1:15:19.080
	But I don't think that's the path.

	1:15:19.080 --> 1:15:20.080
	I've said that earlier.

	1:15:20.080 --> 1:15:21.080
	There's an alternate path.

	1:15:21.080 --> 1:15:29.080
	I should mention to you, by the way, that we've made sufficient progress on our, the whole cortical theory in the last few years.

	1:15:29.080 --> 1:15:40.080
	But last year, we decided to start actively pursuing how we get these ideas embedded into machine learning.

	1:15:40.080 --> 1:15:45.080
	That's, again, being led by my colleague, and he's more of a machine learning guy.

	1:15:45.080 --> 1:15:47.080
	I'm more of an neuroscience guy.

	1:15:47.080 --> 1:15:58.080
	So this is now our, I wouldn't say our focus, but it is now an equal focus here because we need to proselytize what we've learned.

	1:15:58.080 --> 1:16:03.080
	And we need to show how it's beneficial to the machine learning.

	1:16:03.080 --> 1:16:05.080
	So we're putting, we have a plan in place right now.

	1:16:05.080 --> 1:16:07.080
	In fact, we just did our first paper on this.

	1:16:07.080 --> 1:16:09.080
	I can tell you about that.

	1:16:09.080 --> 1:16:15.080
	But, you know, one of the reasons I want to talk to you is because I'm trying to get more people in the machine learning community to say,

	1:16:15.080 --> 1:16:17.080
	I need to learn about this stuff.

	1:16:17.080 --> 1:16:21.080
	And maybe we should just think about this a bit more about what we've learned about the brain.

	1:16:21.080 --> 1:16:23.080
	And what are those team, what have they done?

	1:16:23.080 --> 1:16:25.080
	Is that useful for us?

	1:16:25.080 --> 1:16:32.080
	Yeah, so is there elements of all the, the cortical theory that things we've been talking about that may be useful in the short term?

	1:16:32.080 --> 1:16:34.080
	Yes, in the short term, yes.

	1:16:34.080 --> 1:16:41.080
	This is the, sorry to interrupt, but the, the open question is it, it certainly feels from my perspective that in the long term,

	1:16:41.080 --> 1:16:44.080
	some of the ideas we've been talking about will be extremely useful.

	1:16:44.080 --> 1:16:46.080
	The question is whether in the short term.

	1:16:46.080 --> 1:16:51.080
	Well, this is a, always what we, I would call the entrepreneur's dilemma.

	1:16:51.080 --> 1:16:59.080
	You have this long term vision, oh, we're going to all be driving electric cars or we're all going to have computers or we're all going to whatever.

	1:16:59.080 --> 1:17:03.080
	And, and you're at some point in time and you say, I can see that long term vision.

	1:17:03.080 --> 1:17:04.080
	I'm sure it's going to happen.

	1:17:04.080 --> 1:17:07.080
	How do I get there without killing myself, you know, without going out of business?

	1:17:07.080 --> 1:17:09.080
	That's the challenge.

	1:17:09.080 --> 1:17:10.080
	That's the dilemma.

	1:17:10.080 --> 1:17:11.080
	That's the really difficult thing to do.

	1:17:11.080 --> 1:17:13.080
	So we're facing that right now.

	1:17:13.080 --> 1:17:17.080
	So ideally what you'd want to do is find some steps along the way that you can get there incrementally.

	1:17:17.080 --> 1:17:20.080
	You don't have to like throw it all out and start over again.

	1:17:20.080 --> 1:17:25.080
	The first thing that we've done is we focus on these sparse representations.

	1:17:25.080 --> 1:17:30.080
	So just in case you don't know what that means or some of the listeners don't know what that means.

	1:17:30.080 --> 1:17:37.080
	In the brain, if I have like 10,000 neurons, what you would see is maybe 2% of them active at a time.

	1:17:37.080 --> 1:17:41.080
	You don't see 50%, you don't see 30%, you might see 2%.

	1:17:41.080 --> 1:17:42.080
	And it's always like that.

	1:17:42.080 --> 1:17:44.080
	For any set of sensory inputs.

	1:17:44.080 --> 1:17:45.080
	It doesn't matter anything.

	1:17:45.080 --> 1:17:47.080
	It doesn't matter any part of the brain.

	1:17:47.080 --> 1:17:51.080
	But which neurons differs?

	1:17:51.080 --> 1:17:52.080
	Which neurons are active?

	1:17:52.080 --> 1:17:53.080
	Yeah.

	1:17:53.080 --> 1:17:54.080
	So let me put this.

	1:17:54.080 --> 1:17:56.080
	Let's say I take 10,000 neurons that are representing something.

	1:17:56.080 --> 1:17:58.080
	They're sitting there in a little block together.

	1:17:58.080 --> 1:18:00.080
	It's a teeny little block of neurons, 10,000 neurons.

	1:18:00.080 --> 1:18:01.080
	And they're representing a location.

	1:18:01.080 --> 1:18:02.080
	They're representing a cop.

	1:18:02.080 --> 1:18:04.080
	They're representing the input from my sensors.

	1:18:04.080 --> 1:18:05.080
	I don't know.

	1:18:05.080 --> 1:18:06.080
	It doesn't matter.

	1:18:06.080 --> 1:18:07.080
	It's representing something.

	1:18:07.080 --> 1:18:10.080
	The way the representations occur, it's always a sparse representation.

	1:18:10.080 --> 1:18:12.080
	Meaning it's a population code.

	1:18:12.080 --> 1:18:15.080
	So which 200 cells are active tells me what's going on.

	1:18:15.080 --> 1:18:18.080
	It's not individual cells aren't that important at all.

	1:18:18.080 --> 1:18:20.080
	It's the population code that matters.

	1:18:20.080 --> 1:18:23.080
	And when you have sparse population codes,

	1:18:23.080 --> 1:18:26.080
	then all kinds of beautiful properties come out of them.

	1:18:26.080 --> 1:18:29.080
	So the brain uses sparse population codes that we've written

	1:18:29.080 --> 1:18:32.080
	and described these benefits in some of our papers.

	1:18:32.080 --> 1:18:37.080
	So they give this tremendous robustness to the systems.

	1:18:37.080 --> 1:18:39.080
	You know, brains are incredibly robust.

	1:18:39.080 --> 1:18:42.080
	Neurons are dying all the time and spasming and synapses falling apart.

	1:18:42.080 --> 1:18:45.080
	And, you know, all the time and it keeps working.

	1:18:45.080 --> 1:18:52.080
	So what Subitai and Louise, one of our other engineers here have done,

	1:18:52.080 --> 1:18:56.080
	have shown that they're introducing sparseness into convolutional neural networks.

	1:18:56.080 --> 1:18:58.080
	Now other people are thinking along these lines,

	1:18:58.080 --> 1:19:00.080
	but we're going about it in a more principled way, I think.

	1:19:00.080 --> 1:19:06.080
	And we're showing that if you enforce sparseness throughout these convolutional neural networks,

	1:19:06.080 --> 1:19:13.080
	in both the sort of which neurons are active and the connections between them,

	1:19:13.080 --> 1:19:15.080
	that you get some very desirable properties.

	1:19:15.080 --> 1:19:20.080
	So one of the current hot topics in deep learning right now are these adversarial examples.

	1:19:20.080 --> 1:19:23.080
	So, you know, you give me any deep learning network

	1:19:23.080 --> 1:19:27.080
	and I can give you a picture that looks perfect and you're going to call it, you know,

	1:19:27.080 --> 1:19:30.080
	you're going to say the monkey is, you know, an airplane.

	1:19:30.080 --> 1:19:32.080
	So that's a problem.

	1:19:32.080 --> 1:19:36.080
	And DARPA just announced some big thing and we're trying to, you know, have some contests for this.

	1:19:36.080 --> 1:19:40.080
	But if you enforce sparse representations here,

	1:19:40.080 --> 1:19:41.080
	many of these problems go away.

	1:19:41.080 --> 1:19:45.080
	They're much more robust and they're not easy to fool.

	1:19:45.080 --> 1:19:48.080
	So we've already shown some of those results,

	1:19:48.080 --> 1:19:53.080
	just literally in January or February, just like last month we did that.

	1:19:53.080 --> 1:19:59.080
	And you can, I think it's on bioarchive right now or on iCry, you can read about it.

	1:19:59.080 --> 1:20:02.080
	But so that's like a baby step.

	1:20:02.080 --> 1:20:04.080
	Okay. That's a take something from the brain.

	1:20:04.080 --> 1:20:05.080
	We know, we know about sparseness.

	1:20:05.080 --> 1:20:06.080
	We know why it's important.

	1:20:06.080 --> 1:20:08.080
	We know what it gives the brain.

	1:20:08.080 --> 1:20:09.080
	So let's try to enforce that onto this.

	1:20:09.080 --> 1:20:12.080
	What's your intuition why sparsity leads to robustness?

	1:20:12.080 --> 1:20:14.080
	Because it feels like it would be less robust.

	1:20:14.080 --> 1:20:17.080
	Why would you feel the rest robust to you?

	1:20:17.080 --> 1:20:24.080
	So it, it just feels like if the fewer neurons are involved,

	1:20:24.080 --> 1:20:26.080
	the more fragile the representation.

	1:20:26.080 --> 1:20:28.080
	Yeah, but I didn't say there was lots of few.

	1:20:28.080 --> 1:20:30.080
	I said, let's say 200.

	1:20:30.080 --> 1:20:31.080
	That's a lot.

	1:20:31.080 --> 1:20:32.080
	There's still a lot.

	1:20:32.080 --> 1:20:33.080
	Yeah.

	1:20:33.080 --> 1:20:35.080
	So here's an intuition for it.

	1:20:35.080 --> 1:20:37.080
	This is a bit technical.

	1:20:37.080 --> 1:20:41.080
	So for, you know, for engineers, machine learning people this be easy,

	1:20:41.080 --> 1:20:44.080
	but God's listeners, maybe not.

	1:20:44.080 --> 1:20:46.080
	If you're trying to classify something,

	1:20:46.080 --> 1:20:50.080
	you're trying to divide some very high dimensional space into different pieces, A and B.

	1:20:50.080 --> 1:20:55.080
	And you're trying to create some point where you say all these points in this high dimensional space are A

	1:20:55.080 --> 1:20:57.080
	and all these points in this high dimensional space are B.

	1:20:57.080 --> 1:21:03.080
	And if you have points that are close to that line, it's not very robust.

	1:21:03.080 --> 1:21:07.080
	It works for all the points you know about, but it's, it's not very robust

	1:21:07.080 --> 1:21:10.080
	because you can just move a little bit and you've crossed over the line.

	1:21:10.080 --> 1:21:14.080
	When you have sparse representations, imagine I pick, I have,

	1:21:14.080 --> 1:21:18.080
	I'm going to pick 200 cells active out of, out of 10,000.

	1:21:18.080 --> 1:21:19.080
	Okay.

	1:21:19.080 --> 1:21:20.080
	So I have 200 cells active.

	1:21:20.080 --> 1:21:24.080
	Now let's say I pick randomly another, a different representation, 200.

	1:21:24.080 --> 1:21:27.080
	The overlap between those is going to be very small, just a few.

	1:21:27.080 --> 1:21:36.080
	I can pick millions of samples randomly of 200 neurons and not one of them will overlap more than just a few.

	1:21:36.080 --> 1:21:43.080
	So one way to think about it is if I want to fool one of these representations to look like one of those other representations,

	1:21:43.080 --> 1:21:46.080
	I can't move just one cell or two cells or three cells or four cells.

	1:21:46.080 --> 1:21:48.080
	I have to move 100 cells.

	1:21:48.080 --> 1:21:52.080
	And that makes them robust.

	1:21:52.080 --> 1:21:56.080
	In terms of further, so you mentioned sparsity.

	1:21:56.080 --> 1:21:57.080
	Will we be the next thing?

	1:21:57.080 --> 1:21:58.080
	Yeah.

	1:21:58.080 --> 1:21:59.080
	Okay.

	1:21:59.080 --> 1:22:00.080
	So we have, we picked one.

	1:22:00.080 --> 1:22:02.080
	We don't know if it's going to work well yet.

	1:22:02.080 --> 1:22:08.080
	So again, we're trying to come up incremental ways of moving from brain theory to add pieces to machine learning,

	1:22:08.080 --> 1:22:12.080
	current machine learning world in one step at a time.

	1:22:12.080 --> 1:22:20.080
	So the next thing we're going to try to do is, is sort of incorporate some of the ideas of the, the thousand brains theory that you have many,

	1:22:20.080 --> 1:22:22.080
	many models and that are voting.

	1:22:22.080 --> 1:22:23.080
	Now that idea is not new.

	1:22:23.080 --> 1:22:26.080
	There's a mixture of models that's been around for a long time.

	1:22:26.080 --> 1:22:29.080
	But the way the brain does it is a little different.

	1:22:29.080 --> 1:22:36.080
	And, and the way it votes is different and the kind of way it represents uncertainty is different.

	1:22:36.080 --> 1:22:43.080
	So we're just starting this work, but we're going to try to see if we can sort of incorporate some of the principles of voting

	1:22:43.080 --> 1:22:53.080
	or principles of a thousand brain theory, like lots of simple models that talk to each other in a, in a very certain way.

	1:22:53.080 --> 1:23:07.080
	And can we build more machines and systems that learn faster and, and also, well, mostly are multimodal and robust to multimodal type of issues.

	1:23:07.080 --> 1:23:15.080
	So one of the challenges there is, you know, the machine learning computer vision community has certain sets of benchmarks.

	1:23:15.080 --> 1:23:18.080
	So it's a test based on which they compete.

	1:23:18.080 --> 1:23:29.080
	And I would argue, especially from your perspective, that those benchmarks aren't that useful for testing the aspects that the brain is good at or intelligent.

	1:23:29.080 --> 1:23:31.080
	They're not really testing intelligence.

	1:23:31.080 --> 1:23:41.080
	They're very fine and has been extremely useful for developing specific mathematical models, but it's not useful in the long term for creating intelligence.

	1:23:41.080 --> 1:23:46.080
	So do you think you also have a role in proposing better tests?

	1:23:46.080 --> 1:23:50.080
	Yeah, this is a very, you've identified a very serious problem.

	1:23:50.080 --> 1:23:57.080
	First of all, the tests that they have are the tests that they want, not the tests of the other things that we're trying to do.

	1:23:57.080 --> 1:24:01.080
	Right. You know, what are the, so on.

	1:24:01.080 --> 1:24:10.080
	The second thing is sometimes these to be competitive in these tests, you have to have huge data sets and huge computing power.

	1:24:10.080 --> 1:24:13.080
	And so, you know, and we don't have that here.

	1:24:13.080 --> 1:24:18.080
	We don't have it as well as other big teams that big companies do.

	1:24:18.080 --> 1:24:20.080
	So there's numerous issues there.

	1:24:20.080 --> 1:24:26.080
	You know, we come at it, you know, we're our approach to this is all based on, in some sense, you might argue elegance.

	1:24:26.080 --> 1:24:30.080
	You know, we're coming at it from like a theoretical base that we think, oh my God, this is so clearly elegant.

	1:24:30.080 --> 1:24:31.080
	This is how brains work.

	1:24:31.080 --> 1:24:32.080
	This is what intelligence is.

	1:24:32.080 --> 1:24:35.080
	But the machine learning world has gotten in this phase where they think it doesn't matter.

	1:24:35.080 --> 1:24:39.080
	Doesn't matter what you think, as long as you do, you know, 0.1% better on this benchmark.

	1:24:39.080 --> 1:24:41.080
	That's what that's all that matters.

	1:24:41.080 --> 1:24:43.080
	And that's a problem.

	1:24:43.080 --> 1:24:46.080
	You know, we have to figure out how to get around that.

	1:24:46.080 --> 1:24:47.080
	That's a challenge for us.

	1:24:47.080 --> 1:24:50.080
	That's one of the challenges we have to deal with.

	1:24:50.080 --> 1:24:53.080
	So I agree you've identified a big issue.

	1:24:53.080 --> 1:24:55.080
	It's difficult for those reasons.

	1:24:55.080 --> 1:25:02.080
	But, you know, part of the reasons I'm talking to you here today is I hope I'm going to get some machine learning people to say,

	1:25:02.080 --> 1:25:03.080
	I'm going to read those papers.

	1:25:03.080 --> 1:25:04.080
	Those might be some interesting ideas.

	1:25:04.080 --> 1:25:08.080
	I'm tired of doing this 0.1% improvement stuff, you know.

	1:25:08.080 --> 1:25:21.080
	Well, that's why I'm here as well, because I think machine learning now as a community is at a place where the next step is needs to be orthogonal to what has received success in the past.

	1:25:21.080 --> 1:25:27.080
	You see other leaders saying this, machine learning leaders, you know, Jeff Hinton with his capsules idea.

	1:25:27.080 --> 1:25:33.080
	Many people have gotten up saying, you know, we're going to hit road, maybe we should look at the brain, you know, things like that.

	1:25:33.080 --> 1:25:37.080
	So hopefully that thinking will occur organically.

	1:25:37.080 --> 1:25:43.080
	And then we're in a nice position for people to come and look at our work and say, well, what can we learn from these guys?

	1:25:43.080 --> 1:25:49.080
	Yeah, MIT is just launching a billion dollar computing college that's centered around this idea.

	1:25:49.080 --> 1:25:51.080
	On this idea of what?

	1:25:51.080 --> 1:25:59.080
	Well, the idea that, you know, the humanities, psychology, neuroscience have to work all together to get to build the S.

	1:25:59.080 --> 1:26:02.080
	Yeah, I mean, Stanford just did this human center today, I think.

	1:26:02.080 --> 1:26:10.080
	I'm a little disappointed in these initiatives because, you know, they're focusing on sort of the human side of it,

	1:26:10.080 --> 1:26:17.080
	and it can very easily slip into how humans interact with intelligent machines, which is nothing wrong with that.

	1:26:17.080 --> 1:26:20.080
	But that's not, that is orthogonal to what we're trying to do.

	1:26:20.080 --> 1:26:22.080
	We're trying to say, like, what is the essence of intelligence?

	1:26:22.080 --> 1:26:23.080
	I don't care.

	1:26:23.080 --> 1:26:31.080
	In fact, I want to build intelligent machines that aren't emotional, that don't smile at you, that, you know, that aren't trying to tuck you in at night.

	1:26:31.080 --> 1:26:38.080
	Yeah, there is that pattern that you, when you talk about understanding humans is important for understanding intelligence.

	1:26:38.080 --> 1:26:47.080
	You start slipping into topics of ethics or, yeah, like you said, the interactive elements as opposed to, no, no, no, let's zoom in on the brain,

	1:26:47.080 --> 1:26:51.080
	study what the human brain, the baby, the...

	1:26:51.080 --> 1:26:53.080
	Let's study what a brain does.

	1:26:53.080 --> 1:26:57.080
	And then we can decide which parts of that we want to recreate in some system.

	1:26:57.080 --> 1:27:00.080
	But until you have that theory about what the brain does, what's the point?

	1:27:00.080 --> 1:27:03.080
	You know, it's just, you're going to be wasting time, I think.

	1:27:03.080 --> 1:27:09.080
	Just to break it down on the artificial neural network side, maybe you can speak to this on the, on the biologic neural network side,

	1:27:09.080 --> 1:27:13.080
	the process of learning versus the process of inference.

	1:27:13.080 --> 1:27:22.080
	Maybe you can explain to me, what, is there a difference between, you know, in artificial neural networks, there's a difference between the learning stage and the inference stage?

	1:27:22.080 --> 1:27:23.080
	Yeah.

	1:27:23.080 --> 1:27:25.080
	Do you see the brain as something different?

	1:27:25.080 --> 1:27:33.080
	One of the big distinctions that people often say, I don't know how correct it is, is artificial neural networks need a lot of data.

	1:27:33.080 --> 1:27:34.080
	They're very inefficient learning.

	1:27:34.080 --> 1:27:35.080
	Yeah.

	1:27:35.080 --> 1:27:42.080
	Do you see that as a correct distinction from the biology of the human brain, that the human brain is very efficient?

	1:27:42.080 --> 1:27:44.080
	Or is that just something we deceive ourselves with?

	1:27:44.080 --> 1:27:45.080
	No, it is efficient, obviously.

	1:27:45.080 --> 1:27:47.080
	We can learn new things almost instantly.

	1:27:47.080 --> 1:27:50.080
	And so what elements do you think...

	1:27:50.080 --> 1:27:51.080
	Yeah, I can talk about that.

	1:27:51.080 --> 1:27:52.080
	You brought up two issues there.

	1:27:52.080 --> 1:28:00.080
	So remember I talked early about the constraints, we always feel, well, one of those constraints is the fact that brains are continually learning.

	1:28:00.080 --> 1:28:03.080
	That's not something we said, oh, we can add that later.

	1:28:03.080 --> 1:28:11.080
	That's something that was upfront, had to be there from the start, made our problems harder.

	1:28:11.080 --> 1:28:19.080
	But we showed, going back to the 2016 paper on sequence memory, we showed how that happens, how the brains infer and learn at the same time.

	1:28:19.080 --> 1:28:22.080
	And our models do that.

	1:28:22.080 --> 1:28:26.080
	They're not two separate phases or two separate sets of time.

	1:28:26.080 --> 1:28:33.080
	I think that's a big, big problem in AI, at least for many applications, not for all.

	1:28:33.080 --> 1:28:34.080
	So I can talk about that.

	1:28:34.080 --> 1:28:37.080
	It gets detailed.

	1:28:37.080 --> 1:28:46.080
	There are some parts of the neocortex in the brain where actually what's going on, there's these cycles of activity in the brain.

	1:28:46.080 --> 1:28:54.080
	And there's very strong evidence that you're doing more of inference on one part of the phase and more of learning on the other part of the phase.

	1:28:54.080 --> 1:28:58.080
	So the brain can actually sort of separate different populations of cells that are going back and forth like this.

	1:28:58.080 --> 1:29:01.080
	But in general, I would say that's an important problem.

	1:29:01.080 --> 1:29:05.080
	We have all of our networks that we've come up with do both.

	1:29:05.080 --> 1:29:08.080
	They're continuous learning networks.

	1:29:08.080 --> 1:29:10.080
	And you mentioned benchmarks earlier.

	1:29:10.080 --> 1:29:12.080
	Well, there are no benchmarks about that.

	1:29:12.080 --> 1:29:13.080
	Exactly.

	1:29:13.080 --> 1:29:19.080
	So we have to like, we get in our little soapbox and hey, by the way, this is important.

	1:29:19.080 --> 1:29:21.080
	And here's the mechanism for doing that.

	1:29:21.080 --> 1:29:26.080
	But until you can prove it to someone in some commercial system or something, it's a little harder.

	1:29:26.080 --> 1:29:33.080
	So one of the things I had to linger on that is in some ways to learn the concept of a coffee cup.

	1:29:33.080 --> 1:29:38.080
	You only need this one coffee cup and maybe some time alone in a room with it.

	1:29:38.080 --> 1:29:43.080
	So the first thing is I imagine I reach my hand into a black box and I'm reaching, I'm trying to touch something.

	1:29:43.080 --> 1:29:47.080
	I don't know up front if it's something I already know or if it's a new thing.

	1:29:47.080 --> 1:29:50.080
	And I have to, I'm doing both at the same time.

	1:29:50.080 --> 1:29:53.080
	I don't say, oh, let's see if it's a new thing.

	1:29:53.080 --> 1:29:55.080
	Oh, let's see if it's an old thing.

	1:29:55.080 --> 1:29:56.080
	I don't do that.

	1:29:56.080 --> 1:29:59.080
	As I go, my brain says, oh, it's new or it's not new.

	1:29:59.080 --> 1:30:02.080
	And if it's new, I start learning what it is.

	1:30:02.080 --> 1:30:06.080
	And by the way, it starts learning from the get go, even if it's going to recognize it.

	1:30:06.080 --> 1:30:09.080
	So they're not separate problems.

	1:30:09.080 --> 1:30:10.080
	And so that's the thing there.

	1:30:10.080 --> 1:30:13.080
	The other thing you mentioned was the fast learning.

	1:30:13.080 --> 1:30:17.080
	So I was just talking about continuous learning, but there's also fast learning.

	1:30:17.080 --> 1:30:20.080
	Literally, I can show you this coffee cup and I say, here's a new coffee cup.

	1:30:20.080 --> 1:30:21.080
	It's got the logo on it.

	1:30:21.080 --> 1:30:22.080
	Take a look at it.

	1:30:22.080 --> 1:30:23.080
	Done.

	1:30:23.080 --> 1:30:24.080
	You're done.

	1:30:24.080 --> 1:30:27.080
	You can predict what it's going to look like, you know, in different positions.

	1:30:27.080 --> 1:30:29.080
	So I can talk about that too.

	1:30:29.080 --> 1:30:30.080
	Yes.

	1:30:30.080 --> 1:30:34.080
	In the brain, the way learning occurs.

	1:30:34.080 --> 1:30:36.080
	I mentioned this earlier, but I mentioned it again.

	1:30:36.080 --> 1:30:40.080
	The way learning occurs, I imagine I have a section of a dendrite of a neuron.

	1:30:40.080 --> 1:30:43.080
	And I want to learn, I'm going to learn something new.

	1:30:43.080 --> 1:30:44.080
	It just doesn't matter what it is.

	1:30:44.080 --> 1:30:46.080
	I'm just going to learn something new.

	1:30:46.080 --> 1:30:48.080
	I need to recognize a new pattern.

	1:30:48.080 --> 1:30:52.080
	So what I'm going to do is I'm going to form new synapses.

	1:30:52.080 --> 1:30:57.080
	New synapses, we're going to rewire the brain onto that section of the dendrite.

	1:30:57.080 --> 1:31:02.080
	Once I've done that, everything else that neuron has learned is not affected by it.

	1:31:02.080 --> 1:31:06.080
	Now, it's because it's isolated to that small section of the dendrite.

	1:31:06.080 --> 1:31:09.080
	They're not all being added together, like a point neuron.

	1:31:09.080 --> 1:31:13.080
	So if I learn something new on this segment here, it doesn't change any of the learning

	1:31:13.080 --> 1:31:15.080
	that occur anywhere else in that neuron.

	1:31:15.080 --> 1:31:18.080
	So I can add something without affecting previous learning.

	1:31:18.080 --> 1:31:20.080
	And I can do it quickly.

	1:31:20.080 --> 1:31:24.080
	Now, let's talk, we can talk about the quickness, how it's done in real neurons.

	1:31:24.080 --> 1:31:27.080
	You might say, well, doesn't it take time to form synapses?

	1:31:27.080 --> 1:31:30.080
	Yes, it can take maybe an hour to form a new synapse.

	1:31:30.080 --> 1:31:32.080
	We can form memories quicker than that.

	1:31:32.080 --> 1:31:35.080
	And I can explain that happens too, if you want.

	1:31:35.080 --> 1:31:38.080
	But it's getting a bit neurosciencey.

	1:31:38.080 --> 1:31:40.080
	That's great.

	1:31:40.080 --> 1:31:43.080
	But is there an understanding of these mechanisms at every level?

	1:31:43.080 --> 1:31:48.080
	So from the short term memories and the forming new connections.

	1:31:48.080 --> 1:31:51.080
	So this idea of synaptogenesis, the growth of new synapses,

	1:31:51.080 --> 1:31:54.080
	that's well described, as well understood.

	1:31:54.080 --> 1:31:56.080
	And that's an essential part of learning.

	1:31:56.080 --> 1:31:57.080
	That is learning.

	1:31:57.080 --> 1:31:58.080
	That is learning.

	1:31:58.080 --> 1:32:00.080
	Okay.

	1:32:00.080 --> 1:32:04.080
	You know, back, you know, going back many, many years, people, you know,

	1:32:04.080 --> 1:32:08.080
	was, what's his name, the psychologist proposed,

	1:32:08.080 --> 1:32:10.080
	Hebb, Donald Hebb.

	1:32:10.080 --> 1:32:13.080
	He proposed that learning was the modification of the strength

	1:32:13.080 --> 1:32:15.080
	of a connection between two neurons.

	1:32:15.080 --> 1:32:19.080
	People interpreted that as the modification of the strength of a synapse.

	1:32:19.080 --> 1:32:21.080
	He didn't say that.

	1:32:21.080 --> 1:32:24.080
	He just said there's a modification between the effect of one neuron and another.

	1:32:24.080 --> 1:32:28.080
	So synaptogenesis is totally consistent with Donald Hebb said.

	1:32:28.080 --> 1:32:31.080
	But anyway, there's these mechanisms, the growth of new synapse.

	1:32:31.080 --> 1:32:34.080
	You can go online, you can watch a video of a synapse growing in real time.

	1:32:34.080 --> 1:32:36.080
	It's literally, you can see this little thing going.

	1:32:36.080 --> 1:32:38.080
	It's pretty impressive.

	1:32:38.080 --> 1:32:40.080
	So that those mechanisms are known.

	1:32:40.080 --> 1:32:43.080
	Now, there's another thing that we've speculated and we've written about,

	1:32:43.080 --> 1:32:48.080
	which is consistent with no neuroscience, but it's less proven.

	1:32:48.080 --> 1:32:49.080
	And this is the idea.

	1:32:49.080 --> 1:32:51.080
	How do I form a memory really, really quickly?

	1:32:51.080 --> 1:32:53.080
	Like instantaneous.

	1:32:53.080 --> 1:32:56.080
	If it takes an hour to grow a synapse, like that's not instantaneous.

	1:32:56.080 --> 1:33:01.080
	So there are types of synapses called silent synapses.

	1:33:01.080 --> 1:33:04.080
	They look like a synapse, but they don't do anything.

	1:33:04.080 --> 1:33:05.080
	They're just sitting there.

	1:33:05.080 --> 1:33:10.080
	It's like if an action potential comes in, it doesn't release any neurotransmitter.

	1:33:10.080 --> 1:33:12.080
	Some parts of the brain have more of these than others.

	1:33:12.080 --> 1:33:14.080
	For example, the hippocampus has a lot of them,

	1:33:14.080 --> 1:33:18.080
	which is where we associate most short term memory with.

	1:33:18.080 --> 1:33:22.080
	So what we speculated, again, in that 2016 paper,

	1:33:22.080 --> 1:33:26.080
	we proposed that the way we form very quick memories,

	1:33:26.080 --> 1:33:29.080
	very short term memories, or quick memories,

	1:33:29.080 --> 1:33:34.080
	is that we convert silent synapses into active synapses.

	1:33:34.080 --> 1:33:38.080
	It's like saying a synapse has a zero weight and a one weight.

	1:33:38.080 --> 1:33:41.080
	But the long term memory has to be formed by synaptogenesis.

	1:33:41.080 --> 1:33:43.080
	So you can remember something really quickly

	1:33:43.080 --> 1:33:46.080
	by just flipping a bunch of these guys from silent to active.

	1:33:46.080 --> 1:33:49.080
	It's not from 0.1 to 0.15.

	1:33:49.080 --> 1:33:52.080
	It doesn't do anything until it releases transmitter.

	1:33:52.080 --> 1:33:56.080
	If I do that over a bunch of these, I've got a very quick short term memory.

	1:33:56.080 --> 1:34:01.080
	So I guess the lesson behind this is that most neural networks today are fully connected.

	1:34:01.080 --> 1:34:04.080
	Every neuron connects every other neuron from layer to layer.

	1:34:04.080 --> 1:34:06.080
	That's not correct in the brain.

	1:34:06.080 --> 1:34:07.080
	We don't want that.

	1:34:07.080 --> 1:34:08.080
	We actually don't want that.

	1:34:08.080 --> 1:34:09.080
	It's bad.

	1:34:09.080 --> 1:34:13.080
	You want a very sparse connectivity so that any neuron connects

	1:34:13.080 --> 1:34:15.080
	to some subset of the neurons in the other layer,

	1:34:15.080 --> 1:34:18.080
	and it does so on a dendrite by dendrite segment basis.

	1:34:18.080 --> 1:34:21.080
	So it's a very parcelated out type of thing.

	1:34:21.080 --> 1:34:25.080
	And that then learning is not adjusting all these weights,

	1:34:25.080 --> 1:34:29.080
	but learning is just saying, OK, connect to these 10 cells here right now.

	1:34:29.080 --> 1:34:32.080
	In that process, you know, with artificial neural networks,

	1:34:32.080 --> 1:34:37.080
	it's a very simple process of back propagation that adjusts the weights.

	1:34:37.080 --> 1:34:39.080
	The process of synaptogenesis.

	1:34:39.080 --> 1:34:40.080
	Synaptogenesis.

	1:34:40.080 --> 1:34:41.080
	Synaptogenesis.

	1:34:41.080 --> 1:34:42.080
	It's even easier.

	1:34:42.080 --> 1:34:43.080
	It's even easier.

	1:34:43.080 --> 1:34:44.080
	It's even easier.

	1:34:44.080 --> 1:34:49.080
	Back propagation requires something that really can't happen in brains.

	1:34:49.080 --> 1:34:51.080
	This back propagation of this error signal.

	1:34:51.080 --> 1:34:52.080
	They really can't happen.

	1:34:52.080 --> 1:34:55.080
	People are trying to make it happen in brains, but it doesn't happen in brain.

	1:34:55.080 --> 1:34:57.080
	This is pure Hebbian learning.

	1:34:57.080 --> 1:34:59.080
	Well, synaptogenesis is pure Hebbian learning.

	1:34:59.080 --> 1:35:03.080
	It's basically saying there's a population of cells over here that are active right now,

	1:35:03.080 --> 1:35:05.080
	and there's a population of cells over here active right now.

	1:35:05.080 --> 1:35:08.080
	How do I form connections between those active cells?

	1:35:08.080 --> 1:35:11.080
	And it's literally saying this guy became active.

	1:35:11.080 --> 1:35:15.080
	These 100 neurons here became active before this neuron became active.

	1:35:15.080 --> 1:35:17.080
	So form connections to those ones.

	1:35:17.080 --> 1:35:18.080
	That's it.

	1:35:18.080 --> 1:35:20.080
	There's no propagation of error, nothing.

	1:35:20.080 --> 1:35:26.080
	All the networks we do, all models we have work on almost completely on Hebbian learning,

	1:35:26.080 --> 1:35:33.080
	but on dendritic segments and multiple synaptoses at the same time.

	1:35:33.080 --> 1:35:36.080
	So now let's turn the question that you already answered,

	1:35:36.080 --> 1:35:38.080
	and maybe you can answer it again.

	1:35:38.080 --> 1:35:43.080
	If you look at the history of artificial intelligence, where do you think we stand?

	1:35:43.080 --> 1:35:45.080
	How far are we from solving intelligence?

	1:35:45.080 --> 1:35:47.080
	You said you were very optimistic.

	1:35:47.080 --> 1:35:48.080
	Yeah.

	1:35:48.080 --> 1:35:49.080
	Can you elaborate on that?

	1:35:49.080 --> 1:35:55.080
	Yeah, it's always the crazy question to ask, because no one can predict the future.

	1:35:55.080 --> 1:35:56.080
	Absolutely.

	1:35:56.080 --> 1:35:58.080
	So I'll tell you a story.

	1:35:58.080 --> 1:36:02.080
	I used to run a different neuroscience institute called the Redwood Neuroscience Institute,

	1:36:02.080 --> 1:36:07.080
	and we would hold these symposiums, and we'd get like 35 scientists from around the world to come together.

	1:36:07.080 --> 1:36:09.080
	And I used to ask them all the same question.

	1:36:09.080 --> 1:36:13.080
	I would say, well, how long do you think it'll be before we understand how the New York Cortex works?

	1:36:13.080 --> 1:36:17.080
	And everyone went around the room, and they had introduced the name, and they had to answer that question.

	1:36:17.080 --> 1:36:22.080
	So I got, the typical answer was 50 to 100 years.

	1:36:22.080 --> 1:36:24.080
	Some people would say 500 years.

	1:36:24.080 --> 1:36:25.080
	Some people said never.

	1:36:25.080 --> 1:36:27.080
	I said, why are you a neuroscience institute?

	1:36:27.080 --> 1:36:28.080
	Never.

	1:36:28.080 --> 1:36:30.080
	It's good pay.

	1:36:30.080 --> 1:36:33.080
	It's interesting.

	1:36:33.080 --> 1:36:37.080
	But it doesn't work like that.

	1:36:37.080 --> 1:36:39.080
	As I mentioned earlier, these are step functions.

	1:36:39.080 --> 1:36:41.080
	Things happen, and then bingo, they happen.

	1:36:41.080 --> 1:36:43.080
	You can't predict that.

	1:36:43.080 --> 1:36:45.080
	I feel I've already passed a step function.

	1:36:45.080 --> 1:36:53.080
	So if I can do my job correctly over the next five years, then meaning I can proselytize these ideas.

	1:36:53.080 --> 1:36:55.080
	I can convince other people they're right.

	1:36:55.080 --> 1:37:01.080
	We can show that machine learning people should pay attention to these ideas.

	1:37:01.080 --> 1:37:04.080
	Then we're definitely in an under 20 year time frame.

	1:37:04.080 --> 1:37:09.080
	If I can do those things, if I'm not successful in that, and this is the last time anyone talks to me,

	1:37:09.080 --> 1:37:15.080
	and no one reads our papers, and I'm wrong or something like that, then I don't know.

	1:37:15.080 --> 1:37:17.080
	But it's not 50 years.

	1:37:17.080 --> 1:37:22.080
	It's the same thing about electric cars.

	1:37:22.080 --> 1:37:24.080
	How quickly are they going to populate the world?

	1:37:24.080 --> 1:37:27.080
	It probably takes about a 20 year span.

	1:37:27.080 --> 1:37:28.080
	It'll be something like that.

	1:37:28.080 --> 1:37:31.080
	But I think if I can do what I said, we're starting it.

	1:37:31.080 --> 1:37:35.080
	Of course, there could be other use of step functions.

	1:37:35.080 --> 1:37:42.080
	It could be everybody gives up on your ideas for 20 years, and then all of a sudden somebody picks it up again.

	1:37:42.080 --> 1:37:44.080
	Wait, that guy was onto something.

	1:37:44.080 --> 1:37:46.080
	That would be a failure on my part.

	1:37:46.080 --> 1:37:49.080
	Think about Charles Babbage.

	1:37:49.080 --> 1:37:55.080
	Charles Babbage used to invented the computer back in the 1800s.

	1:37:55.080 --> 1:37:59.080
	Everyone forgot about it until 100 years later.

	1:37:59.080 --> 1:38:03.080
	This guy figured this stuff out a long time ago, but he was ahead of his time.

	1:38:03.080 --> 1:38:09.080
	As I said, I recognize this is part of any entrepreneur's challenge.

	1:38:09.080 --> 1:38:11.080
	I use entrepreneur broadly in this case.

	1:38:11.080 --> 1:38:13.080
	I'm not meaning like I'm building a business trying to sell something.

	1:38:13.080 --> 1:38:15.080
	I'm trying to sell ideas.

	1:38:15.080 --> 1:38:20.080
	This is the challenge as to how you get people to pay attention to you.

	1:38:20.080 --> 1:38:24.080
	How do you get them to give you positive or negative feedback?

	1:38:24.080 --> 1:38:27.080
	How do you get the people to act differently based on your ideas?

	1:38:27.080 --> 1:38:30.080
	We'll see how well we do on that.

	1:38:30.080 --> 1:38:34.080
	There's a lot of hype behind artificial intelligence currently.

	1:38:34.080 --> 1:38:43.080
	Do you, as you look to spread the ideas that are in your cortical theory, the things you're working on,

	1:38:43.080 --> 1:38:47.080
	do you think there's some possibility we'll hit an AI winter once again?

	1:38:47.080 --> 1:38:49.080
	It's certainly a possibility.

	1:38:49.080 --> 1:38:51.080
	That's something you worry about?

	1:38:51.080 --> 1:38:54.080
	I guess, do I worry about it?

	1:38:54.080 --> 1:38:58.080
	I haven't decided yet if that's good or bad for my mission.

	1:38:58.080 --> 1:38:59.080
	That's true.

	1:38:59.080 --> 1:39:04.080
	That's very true because it's almost like you need the winter to refresh the pallet.

	1:39:04.080 --> 1:39:08.080
	Here's what you want to have it.

	1:39:08.080 --> 1:39:15.080
	To the extent that everyone is so thrilled about the current state of machine learning and AI,

	1:39:15.080 --> 1:39:20.080
	and they don't imagine they need anything else, it makes my job harder.

	1:39:20.080 --> 1:39:24.080
	If everything crashed completely and every student left the field,

	1:39:24.080 --> 1:39:26.080
	and there was no money for anybody to do anything,

	1:39:26.080 --> 1:39:29.080
	and it became an embarrassment to talk about machine intelligence and AI,

	1:39:29.080 --> 1:39:31.080
	that wouldn't be good for us either.

	1:39:31.080 --> 1:39:33.080
	You want the soft landing approach, right?

	1:39:33.080 --> 1:39:37.080
	You want enough people, the senior people in AI and machine learning to say,

	1:39:37.080 --> 1:39:39.080
	you know, we need other approaches.

	1:39:39.080 --> 1:39:40.080
	We really need other approaches.

	1:39:40.080 --> 1:39:42.080
	Damn, we need other approaches.

	1:39:42.080 --> 1:39:43.080
	Maybe we should look to the brain.

	1:39:43.080 --> 1:39:44.080
	Okay, let's look to the brain.

	1:39:44.080 --> 1:39:45.080
	Who's got some brain ideas?

	1:39:45.080 --> 1:39:49.080
	Okay, let's start a little project on the side here trying to do brain idea related stuff.

	1:39:49.080 --> 1:39:51.080
	That's the ideal outcome we would want.

	1:39:51.080 --> 1:39:53.080
	So I don't want a total winter,

	1:39:53.080 --> 1:39:57.080
	and yet I don't want it to be sunny all the time either.

	1:39:57.080 --> 1:40:02.080
	So what do you think it takes to build a system with human level intelligence

	1:40:02.080 --> 1:40:06.080
	where once demonstrated, you would be very impressed?

	1:40:06.080 --> 1:40:08.080
	So does it have to have a body?

	1:40:08.080 --> 1:40:18.080
	Does it have to have the C word we used before consciousness as an entirety in a holistic sense?

	1:40:18.080 --> 1:40:23.080
	First of all, I don't think the goal is to create a machine that is human level intelligence.

	1:40:23.080 --> 1:40:24.080
	I think it's a false goal.

	1:40:24.080 --> 1:40:26.080
	Back to Turing, I think it was a false statement.

	1:40:26.080 --> 1:40:28.080
	We want to understand what intelligence is,

	1:40:28.080 --> 1:40:31.080
	and then we can build intelligent machines of all different scales,

	1:40:31.080 --> 1:40:33.080
	all different capabilities.

	1:40:33.080 --> 1:40:35.080
	You know, a dog is intelligent.

	1:40:35.080 --> 1:40:38.080
	You know, that would be pretty good to have a dog, you know,

	1:40:38.080 --> 1:40:41.080
	but what about something that doesn't look like an animal at all in different spaces?

	1:40:41.080 --> 1:40:45.080
	So my thinking about this is that we want to define what intelligence is,

	1:40:45.080 --> 1:40:48.080
	agree upon what makes an intelligent system.

	1:40:48.080 --> 1:40:52.080
	We can then say, okay, we're now going to build systems that work on those principles,

	1:40:52.080 --> 1:40:57.080
	or some subset of them, and we can apply them to all different types of problems.

	1:40:57.080 --> 1:41:00.080
	And the kind, the idea, it's not computing.

	1:41:00.080 --> 1:41:05.080
	We don't ask, if I take a little, you know, little one chip computer,

	1:41:05.080 --> 1:41:08.080
	I don't say, well, that's not a computer because it's not as powerful as this, you know,

	1:41:08.080 --> 1:41:09.080
	big server over here.

	1:41:09.080 --> 1:41:11.080
	No, no, because we know that what the principles are computing on,

	1:41:11.080 --> 1:41:14.080
	and I can apply those principles to a small problem or into a big problem.

	1:41:14.080 --> 1:41:16.080
	And same, intelligence needs to get there.

	1:41:16.080 --> 1:41:17.080
	We have to say, these are the principles.

	1:41:17.080 --> 1:41:19.080
	I can make a small one, a big one.

	1:41:19.080 --> 1:41:20.080
	I can make them distributed.

	1:41:20.080 --> 1:41:21.080
	I can put them on different sensors.

	1:41:21.080 --> 1:41:23.080
	They don't have to be human like at all.

	1:41:23.080 --> 1:41:25.080
	Now, you did bring up a very interesting question about embodiment.

	1:41:25.080 --> 1:41:27.080
	Does it have to have a body?

	1:41:27.080 --> 1:41:30.080
	It has to have some concept of movement.

	1:41:30.080 --> 1:41:33.080
	It has to be able to move through these reference frames.

	1:41:33.080 --> 1:41:35.080
	I talked about earlier, whether it's physically moving,

	1:41:35.080 --> 1:41:38.080
	like I need, if I'm going to have an AI that understands coffee cups,

	1:41:38.080 --> 1:41:42.080
	it's going to have to pick up the coffee cup and touch it and look at it with its eyes

	1:41:42.080 --> 1:41:45.080
	and hands or something equivalent to that.

	1:41:45.080 --> 1:41:51.080
	If I have a mathematical AI, maybe it needs to move through mathematical spaces.

	1:41:51.080 --> 1:41:55.080
	I could have a virtual AI that lives in the Internet

	1:41:55.080 --> 1:42:00.080
	and its movements are traversing links and digging into files,

	1:42:00.080 --> 1:42:04.080
	but it's got a location that it's traveling through some space.

	1:42:04.080 --> 1:42:08.080
	You can't have an AI that just takes some flash thing input,

	1:42:08.080 --> 1:42:10.080
	you know, we call it flash inference.

	1:42:10.080 --> 1:42:13.080
	Here's a pattern done.

	1:42:13.080 --> 1:42:16.080
	No, it's movement pattern, movement pattern, movement pattern.

	1:42:16.080 --> 1:42:18.080
	Attention, digging, building, building structure,

	1:42:18.080 --> 1:42:20.080
	just trying to figure out the model of the world.

	1:42:20.080 --> 1:42:25.080
	So some sort of embodiment, whether it's physical or not, has to be part of it.

	1:42:25.080 --> 1:42:28.080
	So self awareness in the way to be able to answer where am I?

	1:42:28.080 --> 1:42:31.080
	You bring up self awareness, it's a different topic, self awareness.

	1:42:31.080 --> 1:42:37.080
	No, the very narrow definition, meaning knowing a sense of self enough to know

	1:42:37.080 --> 1:42:40.080
	where am I in the space where essentially.

	1:42:40.080 --> 1:42:43.080
	The system needs to know its location,

	1:42:43.080 --> 1:42:48.080
	where each component of the system needs to know where it is in the world at that point in time.

	1:42:48.080 --> 1:42:51.080
	So self awareness and consciousness.

	1:42:51.080 --> 1:42:56.080
	Do you think, one, from the perspective of neuroscience and neurocortex,

	1:42:56.080 --> 1:42:59.080
	these are interesting topics, solvable topics,

	1:42:59.080 --> 1:43:04.080
	do you have any ideas of why the heck it is that we have a subjective experience at all?

	1:43:04.080 --> 1:43:05.080
	Yeah, I have a lot of questions.

	1:43:05.080 --> 1:43:08.080
	And is it useful, or is it just a side effect of us?

	1:43:08.080 --> 1:43:10.080
	It's interesting to think about.

	1:43:10.080 --> 1:43:16.080
	I don't think it's useful as a means to figure out how to build intelligent machines.

	1:43:16.080 --> 1:43:21.080
	It's something that systems do, and we can talk about what it is,

	1:43:21.080 --> 1:43:25.080
	that are like, well, if I build a system like this, then it would be self aware.

	1:43:25.080 --> 1:43:28.080
	Or if I build it like this, it wouldn't be self aware.

	1:43:28.080 --> 1:43:30.080
	So that's a choice I can have.

	1:43:30.080 --> 1:43:32.080
	It's not like, oh my God, it's self aware.

	1:43:32.080 --> 1:43:37.080
	I heard an interview recently with this philosopher from Yale.

	1:43:37.080 --> 1:43:38.080
	I can't remember his name.

	1:43:38.080 --> 1:43:39.080
	I apologize for that.

	1:43:39.080 --> 1:43:41.080
	But he was talking about, well, if these computers were self aware,

	1:43:41.080 --> 1:43:43.080
	then it would be a crime to unplug them.

	1:43:43.080 --> 1:43:45.080
	And I'm like, oh, come on.

	1:43:45.080 --> 1:43:46.080
	I unplug myself every night.

	1:43:46.080 --> 1:43:47.080
	I go to sleep.

	1:43:47.080 --> 1:43:49.080
	Is that a crime?

	1:43:49.080 --> 1:43:51.080
	I plug myself in again in the morning.

	1:43:51.080 --> 1:43:53.080
	There I am.

	1:43:53.080 --> 1:43:56.080
	People get kind of bent out of shape about this.

	1:43:56.080 --> 1:44:02.080
	I have very detailed understanding or opinions about what it means to be conscious

	1:44:02.080 --> 1:44:04.080
	and what it means to be self aware.

	1:44:04.080 --> 1:44:07.080
	I don't think it's that interesting a problem.

	1:44:07.080 --> 1:44:08.080
	You talked about Christoph Koch.

	1:44:08.080 --> 1:44:10.080
	He thinks that's the only problem.

	1:44:10.080 --> 1:44:12.080
	I didn't actually listen to your interview with him.

	1:44:12.080 --> 1:44:15.080
	But I know him, and I know that's the thing.

	1:44:15.080 --> 1:44:18.080
	He also thinks intelligence and consciousness are disjoint.

	1:44:18.080 --> 1:44:21.080
	So I mean, it's not, you don't have to have one or the other.

	1:44:21.080 --> 1:44:23.080
	I just agree with that.

	1:44:23.080 --> 1:44:24.080
	I just totally disagree with that.

	1:44:24.080 --> 1:44:26.080
	So where's your thoughts and consciousness?

	1:44:26.080 --> 1:44:28.080
	Where does it emerge from?

	1:44:28.080 --> 1:44:30.080
	Then we have to break it down to the two parts.

	1:44:30.080 --> 1:44:32.080
	Because consciousness isn't one thing.

	1:44:32.080 --> 1:44:34.080
	That's part of the problem with that term.

	1:44:34.080 --> 1:44:36.080
	It means different things to different people.

	1:44:36.080 --> 1:44:38.080
	And there's different components of it.

	1:44:38.080 --> 1:44:40.080
	There is a concept of self awareness.

	1:44:40.080 --> 1:44:44.080
	That can be very easily explained.

	1:44:44.080 --> 1:44:46.080
	You have a model of your own body.

	1:44:46.080 --> 1:44:48.080
	The neocortex models things in the world.

	1:44:48.080 --> 1:44:50.080
	And it also models your own body.

	1:44:50.080 --> 1:44:53.080
	And then it has a memory.

	1:44:53.080 --> 1:44:55.080
	It can remember what you've done.

	1:44:55.080 --> 1:44:57.080
	So it can remember what you did this morning.

	1:44:57.080 --> 1:44:59.080
	It can remember what you had for breakfast and so on.

	1:44:59.080 --> 1:45:02.080
	And so I can say to you, okay, Lex,

	1:45:02.080 --> 1:45:06.080
	were you conscious this morning when you had your bagel?

	1:45:06.080 --> 1:45:08.080
	And you'd say, yes, I was conscious.

	1:45:08.080 --> 1:45:11.080
	Now, what if I could take your brain and revert all the synapses

	1:45:11.080 --> 1:45:13.080
	back to the state they were this morning?

	1:45:13.080 --> 1:45:15.080
	And then I said to you, Lex,

	1:45:15.080 --> 1:45:17.080
	were you conscious when you ate the bagel?

	1:45:17.080 --> 1:45:18.080
	And he said, no, I wasn't conscious.

	1:45:18.080 --> 1:45:20.080
	I said, here's a video of eating the bagel.

	1:45:20.080 --> 1:45:22.080
	And he said, I wasn't there.

	1:45:22.080 --> 1:45:25.080
	That's not possible because I must have been unconscious at that time.

	1:45:25.080 --> 1:45:27.080
	So we can just make this one to one correlation

	1:45:27.080 --> 1:45:30.080
	between memory of your body's trajectory through the world

	1:45:30.080 --> 1:45:32.080
	over some period of time.

	1:45:32.080 --> 1:45:34.080
	And the ability to recall that memory

	1:45:34.080 --> 1:45:36.080
	is what you would call conscious.

	1:45:36.080 --> 1:45:38.080
	I was conscious of that. It's self awareness.

	1:45:38.080 --> 1:45:41.080
	And any system that can recall,

	1:45:41.080 --> 1:45:43.080
	memorize what it's done recently

	1:45:43.080 --> 1:45:46.080
	and bring that back and invoke it again

	1:45:46.080 --> 1:45:48.080
	would say, yeah, I'm aware.

	1:45:48.080 --> 1:45:51.080
	I remember what I did. All right, I got it.

	1:45:51.080 --> 1:45:54.080
	That's an easy one. Although some people think that's a hard one.

	1:45:54.080 --> 1:45:57.080
	The more challenging part of consciousness

	1:45:57.080 --> 1:45:59.080
	is this is one that's sometimes used

	1:45:59.080 --> 1:46:01.080
	by the word Aqualia,

	1:46:01.080 --> 1:46:04.080
	which is, you know, why does an object seem red?

	1:46:04.080 --> 1:46:06.080
	Or what is pain?

	1:46:06.080 --> 1:46:08.080
	And why does pain feel like something?

	1:46:08.080 --> 1:46:10.080
	Why do I feel redness?

	1:46:10.080 --> 1:46:12.080
	So why do I feel a little painless in a way?

	1:46:12.080 --> 1:46:14.080
	And then I could say, well, why does sight

	1:46:14.080 --> 1:46:16.080
	seems different than hearing? You know, it's the same problem.

	1:46:16.080 --> 1:46:18.080
	It's really, you know, these are all just neurons.

	1:46:18.080 --> 1:46:21.080
	And so how is it that why does looking at you

	1:46:21.080 --> 1:46:24.080
	feel different than, you know, hearing you?

	1:46:24.080 --> 1:46:26.080
	It feels different, but there's just neurons in my head.

	1:46:26.080 --> 1:46:28.080
	They're all doing the same thing.

	1:46:28.080 --> 1:46:30.080
	So that's an interesting question.

	1:46:30.080 --> 1:46:32.080
	The best treatise I've read about this

	1:46:32.080 --> 1:46:34.080
	is by a guy named Oregon.

	1:46:34.080 --> 1:46:38.080
	He wrote a book called Why Red Doesn't Sound Like a Bell.

	1:46:38.080 --> 1:46:42.080
	It's a little, it's not a trade book, easy to read,

	1:46:42.080 --> 1:46:45.080
	but it, and it's an interesting question.

	1:46:45.080 --> 1:46:47.080
	Take something like color.

	1:46:47.080 --> 1:46:49.080
	Color really doesn't exist in the world.

	1:46:49.080 --> 1:46:51.080
	It's not a property of the world.

	1:46:51.080 --> 1:46:54.080
	Property of the world that exists is light frequency,

	1:46:54.080 --> 1:46:57.080
	and that gets turned into we have certain cells

	1:46:57.080 --> 1:46:59.080
	in the retina that respond to different frequencies

	1:46:59.080 --> 1:47:00.080
	different than others.

	1:47:00.080 --> 1:47:02.080
	And so when they enter the brain, you just have a bunch

	1:47:02.080 --> 1:47:04.080
	of axons that are firing at different rates,

	1:47:04.080 --> 1:47:06.080
	and from that we perceive color.

	1:47:06.080 --> 1:47:08.080
	But there is no color in the brain.

	1:47:08.080 --> 1:47:11.080
	I mean, there's no color coming in on those synapses.

	1:47:11.080 --> 1:47:14.080
	It's just a correlation between some axons

	1:47:14.080 --> 1:47:17.080
	and some property of frequency.

	1:47:17.080 --> 1:47:19.080
	And that isn't even color itself.

	1:47:19.080 --> 1:47:21.080
	Frequency doesn't have a color.

	1:47:21.080 --> 1:47:23.080
	It's just what it is.

	1:47:23.080 --> 1:47:25.080
	So then the question is, well, why does it even

	1:47:25.080 --> 1:47:27.080
	appear to have a color at all?

	1:47:27.080 --> 1:47:30.080
	Just as you're describing it, there seems to be a connection

	1:47:30.080 --> 1:47:32.080
	to those ideas of reference frames.

	1:47:32.080 --> 1:47:38.080
	I mean, it just feels like consciousness having the subject,

	1:47:38.080 --> 1:47:42.080
	assigning the feeling of red to the actual color

	1:47:42.080 --> 1:47:47.080
	or to the wavelength is useful for intelligence.

	1:47:47.080 --> 1:47:49.080
	Yeah, I think that's a good way of putting it.

	1:47:49.080 --> 1:47:51.080
	It's useful as a predictive mechanism,

	1:47:51.080 --> 1:47:53.080
	or useful as a generalization idea.

	1:47:53.080 --> 1:47:55.080
	It's a way of grouping things together to say

	1:47:55.080 --> 1:47:58.080
	it's useful to have a model like this.

	1:47:58.080 --> 1:48:02.080
	Think about the well known syndrome that people

	1:48:02.080 --> 1:48:06.080
	who've lost a limb experience called phantom limbs.

	1:48:06.080 --> 1:48:11.080
	And what they claim is they can have their arms removed

	1:48:11.080 --> 1:48:13.080
	but they feel the arm.

	1:48:13.080 --> 1:48:15.080
	Not only feel it, they know it's there.

	1:48:15.080 --> 1:48:16.080
	It's there.

	1:48:16.080 --> 1:48:17.080
	I know it's there.

	1:48:17.080 --> 1:48:19.080
	They'll swear to you that it's there.

	1:48:19.080 --> 1:48:20.080
	And then they can feel pain in their arm.

	1:48:20.080 --> 1:48:22.080
	And they'll feel pain in their finger.

	1:48:22.080 --> 1:48:25.080
	And if they move their non existent arm behind their back,

	1:48:25.080 --> 1:48:27.080
	then they feel the pain behind their back.

	1:48:27.080 --> 1:48:30.080
	So this whole idea that your arm exists

	1:48:30.080 --> 1:48:31.080
	is a model of your brain.

	1:48:31.080 --> 1:48:34.080
	It may or may not really exist.

	1:48:34.080 --> 1:48:38.080
	And just like, but it's useful to have a model of something

	1:48:38.080 --> 1:48:40.080
	that sort of correlates to things in the world

	1:48:40.080 --> 1:48:42.080
	so you can make predictions about what would happen

	1:48:42.080 --> 1:48:43.080
	when those things occur.

	1:48:43.080 --> 1:48:44.080
	It's a little bit of a fuzzy,

	1:48:44.080 --> 1:48:46.080
	but I think you're getting quite towards the answer there.

	1:48:46.080 --> 1:48:51.080
	It's useful for the model to express things certain ways

	1:48:51.080 --> 1:48:53.080
	that we can then map them into these reference frames

	1:48:53.080 --> 1:48:55.080
	and make predictions about them.

	1:48:55.080 --> 1:48:57.080
	I need to spend more time on this topic.

	1:48:57.080 --> 1:48:58.080
	It doesn't bother me.

	1:48:58.080 --> 1:49:00.080
	Do you really need to spend more time on this?

	1:49:00.080 --> 1:49:01.080
	Yeah.

	1:49:01.080 --> 1:49:04.080
	It does feel special that we have subjective experience,

	1:49:04.080 --> 1:49:07.080
	but I'm yet to know why.

	1:49:07.080 --> 1:49:08.080
	I'm just personally curious.

	1:49:08.080 --> 1:49:10.080
	It's not necessary for the work we're doing here.

	1:49:10.080 --> 1:49:12.080
	I don't think I need to solve that problem

	1:49:12.080 --> 1:49:14.080
	to build intelligent machines at all.

	1:49:14.080 --> 1:49:15.080
	Not at all.

	1:49:15.080 --> 1:49:19.080
	But there is sort of the silly notion that you described briefly

	1:49:19.080 --> 1:49:22.080
	that doesn't seem so silly to us humans is,

	1:49:22.080 --> 1:49:26.080
	you know, if you're successful building intelligent machines,

	1:49:26.080 --> 1:49:29.080
	it feels wrong to then turn them off.

	1:49:29.080 --> 1:49:32.080
	Because if you're able to build a lot of them,

	1:49:32.080 --> 1:49:36.080
	it feels wrong to then be able to, you know,

	1:49:36.080 --> 1:49:38.080
	to turn off the...

	1:49:38.080 --> 1:49:39.080
	Well, why?

	1:49:39.080 --> 1:49:41.080
	Let's break that down a bit.

	1:49:41.080 --> 1:49:43.080
	As humans, why do we fear death?

	1:49:43.080 --> 1:49:46.080
	There's two reasons we fear death.

	1:49:46.080 --> 1:49:48.080
	Well, first of all, I'll say when you're dead, it doesn't matter.

	1:49:48.080 --> 1:49:49.080
	Okay.

	1:49:49.080 --> 1:49:50.080
	You're dead.

	1:49:50.080 --> 1:49:51.080
	So why do we fear death?

	1:49:51.080 --> 1:49:53.080
	We fear death for two reasons.

	1:49:53.080 --> 1:49:57.080
	One is because we are programmed genetically to fear death.

	1:49:57.080 --> 1:50:02.080
	That's a survival and propaganda in the genes thing.

	1:50:02.080 --> 1:50:06.080
	And we also are programmed to feel sad when people we know die.

	1:50:06.080 --> 1:50:08.080
	We don't feel sad for someone we don't know dies.

	1:50:08.080 --> 1:50:09.080
	There's people dying right now.

	1:50:09.080 --> 1:50:10.080
	They're only scared to say,

	1:50:10.080 --> 1:50:11.080
	I don't feel bad about them because I don't know them.

	1:50:11.080 --> 1:50:13.080
	But I knew they might feel really bad.

	1:50:13.080 --> 1:50:18.080
	So again, these are old brain genetically embedded things

	1:50:18.080 --> 1:50:20.080
	that we fear death.

	1:50:20.080 --> 1:50:23.080
	Outside of those uncomfortable feelings,

	1:50:23.080 --> 1:50:25.080
	there's nothing else to worry about.

	1:50:25.080 --> 1:50:27.080
	Well, wait, hold on a second.

	1:50:27.080 --> 1:50:30.080
	Do you know the denial of death by Beckard?

	1:50:30.080 --> 1:50:36.080
	You know, there's a thought that death is, you know,

	1:50:36.080 --> 1:50:43.080
	our whole conception of our world model kind of assumes immortality.

	1:50:43.080 --> 1:50:47.080
	And then death is this terror that underlies it all.

	1:50:47.080 --> 1:50:50.080
	So like, well, some people's world model, not mine.

	1:50:50.080 --> 1:50:51.080
	But okay.

	1:50:51.080 --> 1:50:54.080
	So what, what Becker would say is that you're just living in an illusion.

	1:50:54.080 --> 1:50:58.080
	You've constructed illusion for yourself because it's such a terrible terror.

	1:50:58.080 --> 1:51:02.080
	The fact that this illusion, the illusion that death doesn't matter.

	1:51:02.080 --> 1:51:05.080
	You're still not coming to grips with the illusion of what?

	1:51:05.080 --> 1:51:08.080
	That death is going to happen.

	1:51:08.080 --> 1:51:10.080
	Oh, it's not going to happen.

	1:51:10.080 --> 1:51:11.080
	You're, you're actually operating.

	1:51:11.080 --> 1:51:13.080
	You haven't, even though you said you've accepted it,

	1:51:13.080 --> 1:51:16.080
	you haven't really accepted the notion of death is what you say.

	1:51:16.080 --> 1:51:21.080
	So it sounds like it sounds like you disagree with that notion.

	1:51:21.080 --> 1:51:22.080
	I mean, totally.

	1:51:22.080 --> 1:51:27.080
	Like, I literally every night, every night I go to bed, it's like dying.

	1:51:27.080 --> 1:51:28.080
	Little deaths.

	1:51:28.080 --> 1:51:29.080
	Little deaths.

	1:51:29.080 --> 1:51:32.080
	And if I didn't wake up, it wouldn't matter to me.

	1:51:32.080 --> 1:51:35.080
	Only if I knew that was going to happen would it be bothersome.

	1:51:35.080 --> 1:51:37.080
	If I didn't know it was going to happen, how would I know?

	1:51:37.080 --> 1:51:39.080
	Then I would worry about my wife.

	1:51:39.080 --> 1:51:40.080
	Yeah.

	1:51:40.080 --> 1:51:44.080
	So imagine, imagine I was a loner and I lived in Alaska and I lived them out there and there

	1:51:44.080 --> 1:51:45.080
	was no animals.

	1:51:45.080 --> 1:51:46.080
	Nobody knew I existed.

	1:51:46.080 --> 1:51:48.080
	I was just eating these roots all the time.

	1:51:48.080 --> 1:51:50.080
	And nobody knew I was there.

	1:51:50.080 --> 1:51:53.080
	And one day I didn't wake up.

	1:51:53.080 --> 1:51:56.080
	Where, what, what pain in the world would there exist?

	1:51:56.080 --> 1:52:01.080
	Well, so most people that think about this problem would say that you're just deeply enlightened

	1:52:01.080 --> 1:52:04.080
	or are completely delusional.

	1:52:04.080 --> 1:52:05.080
	Wow.

	1:52:05.080 --> 1:52:14.080
	But I would say, I would say that's a very enlightened way to see the world is that that's the rational one.

	1:52:14.080 --> 1:52:15.080
	Well, I think it's rational.

	1:52:15.080 --> 1:52:16.080
	That's right.

	1:52:16.080 --> 1:52:22.080
	But the fact is we don't, I mean, we really don't have an understanding of why the heck

	1:52:22.080 --> 1:52:26.080
	it is we're born and why we die and what happens after we die.

	1:52:26.080 --> 1:52:27.080
	Well, maybe there isn't a reason.

	1:52:27.080 --> 1:52:28.080
	Maybe there is.

	1:52:28.080 --> 1:52:30.080
	So I'm interested in those big problems too, right?

	1:52:30.080 --> 1:52:33.080
	You know, you, you interviewed Max Tagmark, you know, and there's people like that, right?

	1:52:33.080 --> 1:52:35.080
	I'm interested in those big problems as well.

	1:52:35.080 --> 1:52:41.080
	And in fact, when I was young, I made a list of the biggest problems I could think of.

	1:52:41.080 --> 1:52:43.080
	First, why does anything exist?

	1:52:43.080 --> 1:52:46.080
	Second, why did we have the laws of physics that we have?

	1:52:46.080 --> 1:52:49.080
	Third, is life inevitable?

	1:52:49.080 --> 1:52:50.080
	And why is it here?

	1:52:50.080 --> 1:52:52.080
	Fourth, is intelligence inevitable?

	1:52:52.080 --> 1:52:53.080
	And why is it here?

	1:52:53.080 --> 1:52:58.080
	I stopped there because I figured if you can make a truly intelligent system, we'll be,

	1:52:58.080 --> 1:53:03.080
	that'll be the quickest way to answer the first three questions.

	1:53:03.080 --> 1:53:04.080
	I'm serious.

	1:53:04.080 --> 1:53:05.080
	Yeah.

	1:53:05.080 --> 1:53:09.080
	And so I said, my mission, you know, you asked me earlier, my first mission is to understand

	1:53:09.080 --> 1:53:12.080
	the brain, but I felt that is the shortest way to get to true machine intelligence.

	1:53:12.080 --> 1:53:16.080
	And I want to get to true machine intelligence because even if it doesn't occur in my lifetime,

	1:53:16.080 --> 1:53:19.080
	other people will benefit from it because I think it'll occur in my lifetime.

	1:53:19.080 --> 1:53:21.080
	But, you know, 20 years, you never know.

	1:53:21.080 --> 1:53:27.080
	And, but that will be the quickest way for us to, you know, we can make super mathematicians.

	1:53:27.080 --> 1:53:29.080
	We can make super space explorers.

	1:53:29.080 --> 1:53:36.080
	We can make super physicists brains that do these things and that can run experiments

	1:53:36.080 --> 1:53:37.080
	that we can't run.

	1:53:37.080 --> 1:53:40.080
	We don't have the abilities to manipulate things and so on.

	1:53:40.080 --> 1:53:42.080
	But we can build and tell the machines to do all those things.

	1:53:42.080 --> 1:53:48.080
	And with the ultimate goal of finding out the answers to the other questions.

	1:53:48.080 --> 1:53:56.080
	Let me ask, you know, the depressing and difficult question, which is once we achieve that goal,

	1:53:56.080 --> 1:54:03.080
	do you, of creating, no, of understanding intelligence, do you think we would be happier,

	1:54:03.080 --> 1:54:05.080
	more fulfilled as a species?

	1:54:05.080 --> 1:54:08.080
	The understanding intelligence or understanding the answers to the big questions?

	1:54:08.080 --> 1:54:09.080
	Understanding intelligence.

	1:54:09.080 --> 1:54:11.080
	Oh, totally.

	1:54:11.080 --> 1:54:12.080
	Totally.

	1:54:12.080 --> 1:54:14.080
	It would be far more fun place to live.

	1:54:14.080 --> 1:54:15.080
	You think so?

	1:54:15.080 --> 1:54:16.080
	Oh, yeah.

	1:54:16.080 --> 1:54:17.080
	Why not?

	1:54:17.080 --> 1:54:22.080
	I just put aside this, you know, terminator nonsense and, and, and, and just think about,

	1:54:22.080 --> 1:54:26.080
	you can think about the, we can talk about the risk of AI if you want.

	1:54:26.080 --> 1:54:27.080
	I'd love to.

	1:54:27.080 --> 1:54:28.080
	So let's talk about.

	1:54:28.080 --> 1:54:30.080
	But I think the world is far better knowing things.

	1:54:30.080 --> 1:54:32.080
	We're always better than no things.

	1:54:32.080 --> 1:54:33.080
	Do you think it's better?

	1:54:33.080 --> 1:54:38.080
	Is it a better place to live in that I know that our planet is one of many in the solar system

	1:54:38.080 --> 1:54:40.080
	and the solar system is one of many in the galaxy?

	1:54:40.080 --> 1:54:44.080
	I think it's a more, I dread, I used to, I sometimes think like, God, what would be like

	1:54:44.080 --> 1:54:47.080
	the 300 years ago, I'd be looking up the sky, I can't understand anything.

	1:54:47.080 --> 1:54:48.080
	Oh my God.

	1:54:48.080 --> 1:54:50.080
	I'd be like going to bed every night going, what's going on here?

	1:54:50.080 --> 1:54:54.080
	Well, I mean, in some sense, I agree with you, but I'm not exactly sure.

	1:54:54.080 --> 1:54:55.080
	So I'm also a scientist.

	1:54:55.080 --> 1:55:01.080
	So I have, I share your views, but I'm not, we're, we're like rolling down the hill together.

	1:55:01.080 --> 1:55:03.080
	What's down the hill?

	1:55:03.080 --> 1:55:05.080
	I feel for climbing a hill.

	1:55:05.080 --> 1:55:07.080
	Whatever we're getting, we're getting closer to enlightenment.

	1:55:07.080 --> 1:55:08.080
	Whatever.

	1:55:08.080 --> 1:55:12.080
	We're climbing, we're getting pulled up a hill.

	1:55:12.080 --> 1:55:14.080
	Pulled up by our curiosity.

	1:55:14.080 --> 1:55:17.080
	We're pulling ourselves up the hill by our curiosity.

	1:55:17.080 --> 1:55:19.080
	Yeah, sycophers are doing the same thing with the rock.

	1:55:19.080 --> 1:55:21.080
	Yeah, yeah, yeah.

	1:55:21.080 --> 1:55:29.080
	But okay, our happiness aside, do you have concerns about, you know, you talk about Sam Harris, Elon Musk,

	1:55:29.080 --> 1:55:32.080
	of existential threats of intelligence systems?

	1:55:32.080 --> 1:55:34.080
	No, I'm not worried about existential threats at all.

	1:55:34.080 --> 1:55:36.080
	There are some things we really do need to worry about.

	1:55:36.080 --> 1:55:38.080
	Even today's AI, we have things we have to worry about.

	1:55:38.080 --> 1:55:43.080
	We have to worry about privacy and about how it impacts false beliefs in the world.

	1:55:43.080 --> 1:55:48.080
	And we have real problems that, and things to worry about with today's AI.

	1:55:48.080 --> 1:55:51.080
	And that will continue as we create more intelligent systems.

	1:55:51.080 --> 1:55:59.080
	There's no question, you know, the whole issue about, you know, making intelligent armament and weapons is something that really we have to think about carefully.

	1:55:59.080 --> 1:56:01.080
	I don't think of those as existential threats.

	1:56:01.080 --> 1:56:09.080
	I think those are the kind of threats we always face and we'll have to face them here and we'll have to deal with them.

	1:56:09.080 --> 1:56:17.080
	We can talk about what people think are the existential threats, but when I hear people talking about them, they all sound hollow to me.

	1:56:17.080 --> 1:56:21.080
	They're based on ideas, they're based on people who really have no idea what intelligence is.

	1:56:21.080 --> 1:56:26.080
	And if they knew what intelligence was, they wouldn't say those things.

	1:56:26.080 --> 1:56:29.080
	So those are not experts in the field, you know.

	1:56:29.080 --> 1:56:31.080
	So there's two, right?

	1:56:31.080 --> 1:56:33.080
	So one is like superintelligence.

	1:56:33.080 --> 1:56:42.080
	So a system that becomes far, far superior in reasoning ability than us humans.

	1:56:42.080 --> 1:56:45.080
	And how is that an existential threat?

	1:56:45.080 --> 1:56:49.080
	So there's a lot of ways in which it could be.

	1:56:49.080 --> 1:57:00.080
	One way is us humans are actually irrational, inefficient and get in the way of not happiness,

	1:57:00.080 --> 1:57:05.080
	but whatever the objective function is of maximizing that objective function and superintelligence.

	1:57:05.080 --> 1:57:07.080
	The paperclip problem and things like that.

	1:57:07.080 --> 1:57:09.080
	So the paperclip problem, but with the superintelligence.

	1:57:09.080 --> 1:57:10.080
	Yeah, yeah, yeah.

	1:57:10.080 --> 1:57:15.080
	So we already faced this threat in some sense.

	1:57:15.080 --> 1:57:17.080
	They're called bacteria.

	1:57:17.080 --> 1:57:21.080
	These are organisms in the world that would like to turn everything into bacteria.

	1:57:21.080 --> 1:57:23.080
	And they're constantly morphing.

	1:57:23.080 --> 1:57:26.080
	They're constantly changing to evade our protections.

	1:57:26.080 --> 1:57:33.080
	And in the past, they have killed huge swaths of populations of humans on this planet.

	1:57:33.080 --> 1:57:38.080
	So if you want to worry about something that's going to multiply endlessly, we have it.

	1:57:38.080 --> 1:57:43.080
	And I'm far more worried in that regard, I'm far more worried that some scientists in the laboratory

	1:57:43.080 --> 1:57:47.080
	will create a super virus or a super bacteria that we cannot control.

	1:57:47.080 --> 1:57:49.080
	That is a more existential threat.

	1:57:49.080 --> 1:57:54.080
	Putting an intelligence thing on top of it actually seems to make it less existential to me.

	1:57:54.080 --> 1:57:56.080
	It's like, it limits its power.

	1:57:56.080 --> 1:57:57.080
	It limits where it can go.

	1:57:57.080 --> 1:57:59.080
	It limits the number of things it can do in many ways.

	1:57:59.080 --> 1:58:02.080
	A bacteria is something you can't even see.

	1:58:02.080 --> 1:58:04.080
	So that's only one of those problems.

	1:58:04.080 --> 1:58:05.080
	Yes, exactly.

	1:58:05.080 --> 1:58:09.080
	So the other one, just in your intuition about intelligence,

	1:58:09.080 --> 1:58:12.080
	when you think about intelligence of us humans,

	1:58:12.080 --> 1:58:14.080
	do you think of that as something,

	1:58:14.080 --> 1:58:18.080
	if you look at intelligence on a spectrum from zero to us humans,

	1:58:18.080 --> 1:58:24.080
	do you think you can scale that to something far superior to all the mechanisms we've been talking about?

	1:58:24.080 --> 1:58:27.080
	I want to make another point here, Alex, before I get there.

	1:58:27.080 --> 1:58:30.080
	Intelligence is the neocortex.

	1:58:30.080 --> 1:58:32.080
	It is not the entire brain.

	1:58:32.080 --> 1:58:36.080
	The goal is not to make a human.

	1:58:36.080 --> 1:58:38.080
	The goal is not to make an emotional system.

	1:58:38.080 --> 1:58:41.080
	The goal is not to make a system that wants to have sex and reproduce.

	1:58:41.080 --> 1:58:42.080
	Why would I build that?

	1:58:42.080 --> 1:58:44.080
	If I want to have a system that wants to reproduce and have sex,

	1:58:44.080 --> 1:58:47.080
	make bacteria, make computer viruses.

	1:58:47.080 --> 1:58:48.080
	Those are bad things.

	1:58:48.080 --> 1:58:49.080
	Don't do that.

	1:58:49.080 --> 1:58:50.080
	Those are really bad.

	1:58:50.080 --> 1:58:51.080
	Don't do those things.

	1:58:51.080 --> 1:58:53.080
	Regulate those.

	1:58:53.080 --> 1:58:56.080
	But if I just say, I want an intelligent system,

	1:58:56.080 --> 1:58:58.080
	why doesn't it have to have any human like emotions?

	1:58:58.080 --> 1:59:00.080
	Why does it even care if it lives?

	1:59:00.080 --> 1:59:02.080
	Why does it even care if it has food?

	1:59:02.080 --> 1:59:04.080
	It doesn't care about those things.

	1:59:04.080 --> 1:59:07.080
	It's just in a trance thinking about mathematics,

	1:59:07.080 --> 1:59:12.080
	or it's out there just trying to build the space for it on Mars.

	1:59:12.080 --> 1:59:15.080
	That's a choice we make.

	1:59:15.080 --> 1:59:17.080
	Don't make human like things.

	1:59:17.080 --> 1:59:18.080
	Don't make replicating things.

	1:59:18.080 --> 1:59:19.080
	Don't make things that have emotions.

	1:59:19.080 --> 1:59:21.080
	Just stick to the neocortex.

	1:59:21.080 --> 1:59:24.080
	That's a view, actually, that I share, but not everybody shares,

	1:59:24.080 --> 1:59:28.080
	in the sense that you have faith and optimism about us as engineers

	1:59:28.080 --> 1:59:31.080
	and systems, humans as builders of systems,

	1:59:31.080 --> 1:59:35.080
	to not put in different stupid things.

	1:59:35.080 --> 1:59:37.080
	This is why I mentioned the bacteria one,

	1:59:37.080 --> 1:59:40.080
	because you might say, well, some person's going to do that.

	1:59:40.080 --> 1:59:42.080
	Well, some person today could create a bacteria

	1:59:42.080 --> 1:59:46.080
	that's resistant to all the known antibacterial agents.

	1:59:46.080 --> 1:59:49.080
	So we already have that threat.

	1:59:49.080 --> 1:59:51.080
	We already know this is going on.

	1:59:51.080 --> 1:59:52.080
	It's not a new threat.

	1:59:52.080 --> 1:59:56.080
	So just accept that, and then we have to deal with it, right?

	1:59:56.080 --> 1:59:59.080
	Yeah, so my point is nothing to do with intelligence.

	1:59:59.080 --> 2:00:02.080
	Intelligence is a separate component that you might apply

	2:00:02.080 --> 2:00:05.080
	to a system that wants to reproduce and do stupid things.

	2:00:05.080 --> 2:00:07.080
	Let's not do that.

	2:00:07.080 --> 2:00:10.080
	Yeah, in fact, it is a mystery why people haven't done that yet.

	2:00:10.080 --> 2:00:14.080
	My dad as a physicist believes that the reason,

	2:00:14.080 --> 2:00:19.080
	for example, nuclear weapons haven't proliferated amongst evil people.

	2:00:19.080 --> 2:00:25.080
	So one belief that I share is that there's not that many evil people in the world

	2:00:25.080 --> 2:00:32.080
	that would use whether it's bacteria or nuclear weapons,

	2:00:32.080 --> 2:00:35.080
	or maybe the future AI systems to do bad.

	2:00:35.080 --> 2:00:37.080
	So the fraction is small.

	2:00:37.080 --> 2:00:40.080
	And the second is that it's actually really hard, technically.

	2:00:40.080 --> 2:00:45.080
	So the intersection between evil and competent is small.

	2:00:45.080 --> 2:00:47.080
	And by the way, to really annihilate humanity,

	2:00:47.080 --> 2:00:51.080
	you'd have to have sort of the nuclear winter phenomenon,

	2:00:51.080 --> 2:00:54.080
	which is not one person shooting or even 10 bombs.

	2:00:54.080 --> 2:00:58.080
	You'd have to have some automated system that detonates a million bombs,

	2:00:58.080 --> 2:01:00.080
	or whatever many thousands we have.

	2:01:00.080 --> 2:01:03.080
	So it's extreme evil combined with extreme competence.

	2:01:03.080 --> 2:01:06.080
	And despite building some stupid system that would automatically,

	2:01:06.080 --> 2:01:10.080
	you know, Dr. Strangelup type of thing, you know,

	2:01:10.080 --> 2:01:14.080
	I mean, look, we could have some nuclear bomb go off in some major city in the world.

	2:01:14.080 --> 2:01:17.080
	I think that's actually quite likely, even in my lifetime.

	2:01:17.080 --> 2:01:20.080
	I don't think that's an unlikely thing, and it would be a tragedy.

	2:01:20.080 --> 2:01:23.080
	But it won't be an existential threat.

	2:01:23.080 --> 2:01:27.080
	And it's the same as, you know, the virus of 1917 or whatever it was,

	2:01:27.080 --> 2:01:29.080
	you know, the influenza.

	2:01:29.080 --> 2:01:33.080
	These bad things can happen and the plague and so on.

	2:01:33.080 --> 2:01:35.080
	We can't always prevent it.

	2:01:35.080 --> 2:01:37.080
	We always try, but we can't.

	2:01:37.080 --> 2:01:41.080
	But they're not existential threats until we combine all those crazy things together.

	2:01:41.080 --> 2:01:45.080
	So on the spectrum of intelligence from zero to human,

	2:01:45.080 --> 2:01:51.080
	do you have a sense of whether it's possible to create several orders of magnitude

	2:01:51.080 --> 2:01:54.080
	or at least double that of human intelligence,

	2:01:54.080 --> 2:01:56.080
	to talk about neural cortex?

	2:01:56.080 --> 2:01:58.080
	I think it's the wrong thing to say, double the intelligence.

	2:01:58.080 --> 2:02:01.080
	Break it down into different components.

	2:02:01.080 --> 2:02:04.080
	Can I make something that's a million times faster than a human brain?

	2:02:04.080 --> 2:02:06.080
	Yes, I can do that.

	2:02:06.080 --> 2:02:10.080
	Could I make something that is, has a lot more storage than a human brain?

	2:02:10.080 --> 2:02:13.080
	Yes, I can do that. More copies come.

	2:02:13.080 --> 2:02:16.080
	Can I make something that attaches to different sensors than a human brain?

	2:02:16.080 --> 2:02:17.080
	Yes, I can do that.

	2:02:17.080 --> 2:02:19.080
	Could I make something that's distributed?

	2:02:19.080 --> 2:02:23.080
	We talked earlier about the departure of neural cortex voting.

	2:02:23.080 --> 2:02:25.080
	They don't have to be co located.

	2:02:25.080 --> 2:02:29.080
	They can be all around the places. I could do that too.

	2:02:29.080 --> 2:02:32.080
	Those are the levers I have, but is it more intelligent?

	2:02:32.080 --> 2:02:35.080
	What depends what I train in on? What is it doing?

	2:02:35.080 --> 2:02:37.080
	So here's the thing.

	2:02:37.080 --> 2:02:46.080
	Let's say larger neural cortex and or whatever size that allows for higher and higher hierarchies

	2:02:46.080 --> 2:02:49.080
	to form, we're talking about reference frames and concepts.

	2:02:49.080 --> 2:02:53.080
	So I could, could I have something that's a super physicist or a super mathematician? Yes.

	2:02:53.080 --> 2:02:59.080
	And the question is, once you have a super physicist, will they be able to understand something?

	2:02:59.080 --> 2:03:03.080
	Do you have a sense that it will be orders, like us compared to ants?

	2:03:03.080 --> 2:03:04.080
	Could we ever understand it?

	2:03:04.080 --> 2:03:05.080
	Yeah.

	2:03:05.080 --> 2:03:11.080
	Most people cannot understand general relativity.

	2:03:11.080 --> 2:03:13.080
	It's a really hard thing to get.

	2:03:13.080 --> 2:03:17.080
	I mean, you can paint it in a fuzzy picture, stretchy space, you know?

	2:03:17.080 --> 2:03:18.080
	Yeah.

	2:03:18.080 --> 2:03:23.080
	But the field equations to do that and the deep intuitions are really, really hard.

	2:03:23.080 --> 2:03:26.080
	And I've tried, I'm unable to do it.

	2:03:26.080 --> 2:03:32.080
	It's easy to get special relative, but general relative, man, that's too much.

	2:03:32.080 --> 2:03:35.080
	And so we already live with this to some extent.

	2:03:35.080 --> 2:03:40.080
	The vast majority of people can't understand actually what the vast majority of other people actually know.

	2:03:40.080 --> 2:03:45.080
	We're just either we don't have the effort to or we can't or we don't have time or just not smart enough, whatever.

	2:03:45.080 --> 2:03:48.080
	So, but we have ways of communicating.

	2:03:48.080 --> 2:03:51.080
	Einstein has spoken in a way that I can understand.

	2:03:51.080 --> 2:03:54.080
	He's given me analogies that are useful.

	2:03:54.080 --> 2:04:00.080
	I can use those analogies for my own work and think about, you know, concepts that are similar.

	2:04:00.080 --> 2:04:02.080
	It's not stupid.

	2:04:02.080 --> 2:04:04.080
	It's not like he's existed in some other plane.

	2:04:04.080 --> 2:04:06.080
	There's no connection to my plane in the world here.

	2:04:06.080 --> 2:04:07.080
	So that will occur.

	2:04:07.080 --> 2:04:09.080
	It already has occurred.

	2:04:09.080 --> 2:04:12.080
	That's when my point at this story is it already has occurred.

	2:04:12.080 --> 2:04:14.080
	We live it every day.

	2:04:14.080 --> 2:04:21.080
	One could argue that with we create machine intelligence that think a million times faster than us that it'll be so far we can't make the connections.

	2:04:21.080 --> 2:04:29.080
	But, you know, at the moment, everything that seems really, really hard to figure out in the world when you actually figure it out is not that hard.

	2:04:29.080 --> 2:04:32.080
	You know, almost everyone can understand the multiverses.

	2:04:32.080 --> 2:04:34.080
	Almost everyone can understand quantum physics.

	2:04:34.080 --> 2:04:38.080
	Almost everyone can understand these basic things, even though hardly any people could figure those things out.

	2:04:38.080 --> 2:04:40.080
	Yeah, but really understand.

	2:04:40.080 --> 2:04:43.080
	But you don't need to really, only a few people really understand.

	2:04:43.080 --> 2:04:49.080
	You need to only understand the projections, the sprinkles of the useful insights from that.

	2:04:49.080 --> 2:04:51.080
	That was my example of Einstein, right?

	2:04:51.080 --> 2:04:55.080
	His general theory of relativity is one thing that very, very, very few people can get.

	2:04:55.080 --> 2:04:59.080
	And what if we just said those other few people are also artificial intelligences?

	2:04:59.080 --> 2:05:01.080
	How bad is that?

	2:05:01.080 --> 2:05:02.080
	In some sense they are, right?

	2:05:02.080 --> 2:05:03.080
	Yeah, they say already.

	2:05:03.080 --> 2:05:05.080
	I mean, Einstein wasn't a really normal person.

	2:05:05.080 --> 2:05:07.080
	He had a lot of weird quirks.

	2:05:07.080 --> 2:05:09.080
	And so the other people who work with him.

	2:05:09.080 --> 2:05:15.080
	So, you know, maybe they already were sort of this actual plane of intelligence that we live with it already.

	2:05:15.080 --> 2:05:17.080
	It's not a problem.

	2:05:17.080 --> 2:05:20.080
	It's still useful and, you know.

	2:05:20.080 --> 2:05:24.080
	So do you think we are the only intelligent life out there in the universe?

	2:05:24.080 --> 2:05:29.080
	I would say that intelligent life has and will exist elsewhere in the universe.

	2:05:29.080 --> 2:05:31.080
	I'll say that.

	2:05:31.080 --> 2:05:39.080
	There is a question about contemporaneous intelligence life, which is hard to even answer when we think about relativity and the nature of space time.

	2:05:39.080 --> 2:05:43.080
	We can't say what exactly is this time someplace else in the world.

	2:05:43.080 --> 2:05:54.080
	But I think it's, you know, I do worry a lot about the filter idea, which is that perhaps intelligent species don't last very long.

	2:05:54.080 --> 2:05:56.080
	And so we haven't been around very long.

	2:05:56.080 --> 2:06:02.080
	As a technological species, we've been around for almost nothing, you know, what, 200 years or something like that.

	2:06:02.080 --> 2:06:08.080
	And we don't have any data, a good data point on whether it's likely that we'll survive or not.

	2:06:08.080 --> 2:06:12.080
	So do I think that there have been intelligent life elsewhere in the universe?

	2:06:12.080 --> 2:06:14.080
	Almost certainly, of course.

	2:06:14.080 --> 2:06:16.080
	In the past, in the future, yes.

	2:06:16.080 --> 2:06:18.080
	Does it survive for a long time?

	2:06:18.080 --> 2:06:19.080
	I don't know.

	2:06:19.080 --> 2:06:25.080
	This is another reason I'm excited about our work, is our work meaning the general world of AI.

	2:06:25.080 --> 2:06:31.080
	I think we can build intelligent machines that outlast us.

	2:06:31.080 --> 2:06:34.080
	You know, they don't have to be tied to Earth.

	2:06:34.080 --> 2:06:39.080
	They don't have to, you know, I'm not saying they're recreating, you know, you know, aliens.

	2:06:39.080 --> 2:06:44.080
	I'm just saying, if I asked myself, and this might be a good point to end on here.

	2:06:44.080 --> 2:06:47.080
	If I asked myself, you know, what's special about our species?

	2:06:47.080 --> 2:06:49.080
	We're not particularly interesting physically.

	2:06:49.080 --> 2:06:51.080
	We're not, we don't fly.

	2:06:51.080 --> 2:06:52.080
	We're not good swimmers.

	2:06:52.080 --> 2:06:53.080
	We're not very fast.

	2:06:53.080 --> 2:06:54.080
	We're not very strong, you know.

	2:06:54.080 --> 2:06:55.080
	It's our brain.

	2:06:55.080 --> 2:06:56.080
	That's the only thing.

	2:06:56.080 --> 2:07:01.080
	And we are the only species on this planet that's built the model of the world that extends beyond what we can actually sense.

	2:07:01.080 --> 2:07:09.080
	We're the only people who know about the far side of the moon and other universes and other galaxies and other stars and what happens in the atom.

	2:07:09.080 --> 2:07:12.080
	That knowledge doesn't exist anywhere else.

	2:07:12.080 --> 2:07:13.080
	It's only in our heads.

	2:07:13.080 --> 2:07:14.080
	Cats don't do it.

	2:07:14.080 --> 2:07:15.080
	Dogs don't do it.

	2:07:15.080 --> 2:07:16.080
	Monkeys don't do it.

	2:07:16.080 --> 2:07:18.080
	That is what we've created that's unique.

	2:07:18.080 --> 2:07:19.080
	Not our genes.

	2:07:19.080 --> 2:07:20.080
	It's knowledge.

	2:07:20.080 --> 2:07:24.080
	And if I ask me, what is the legacy of humanity?

	2:07:24.080 --> 2:07:25.080
	What should our legacy be?

	2:07:25.080 --> 2:07:26.080
	It should be knowledge.

	2:07:26.080 --> 2:07:30.080
	We should preserve our knowledge in a way that it can exist beyond us.

	2:07:30.080 --> 2:07:38.080
	And I think the best way of doing that, in fact, you have to do it, is to have to go along with intelligent machines to understand that knowledge.

	2:07:38.080 --> 2:07:44.080
	It's a very broad idea, but we should be thinking, I call it a state planning for humanity.

	2:07:44.080 --> 2:07:49.080
	We should be thinking about what we want to leave behind when as a species we're no longer here.

	2:07:49.080 --> 2:07:51.080
	And that will happen sometime.

	2:07:51.080 --> 2:07:52.080
	Sooner or later, it's going to happen.

	2:07:52.080 --> 2:07:58.080
	And understanding intelligence and creating intelligence gives us a better chance to prolong.

	2:07:58.080 --> 2:08:00.080
	It does give us a better chance to prolong life.

	2:08:00.080 --> 2:08:01.080
	Yes.

	2:08:01.080 --> 2:08:03.080
	It gives us a chance to live on other planets.

	2:08:03.080 --> 2:08:07.080
	But even beyond that, I mean, our solar system will disappear one day.

	2:08:07.080 --> 2:08:09.080
	It's given enough time.

	2:08:09.080 --> 2:08:10.080
	So I don't know.

	2:08:10.080 --> 2:08:14.080
	I doubt we will ever be able to travel to other things.

	2:08:14.080 --> 2:08:18.080
	But we could tell the stars, but we could send intelligent machines to do that.

	2:08:18.080 --> 2:08:29.080
	Do you have an optimistic, a hopeful view of our knowledge of the echoes of human civilization living through the intelligent systems we create?

	2:08:29.080 --> 2:08:30.080
	Oh, totally.

	2:08:30.080 --> 2:08:32.080
	Well, I think the intelligent systems are greater.

	2:08:32.080 --> 2:08:39.080
	In some sense, the vessel for bringing them beyond Earth or making them last beyond humans themselves.

	2:08:39.080 --> 2:08:41.080
	So how do you feel about that?

	2:08:41.080 --> 2:08:44.080
	That they won't be human, quote unquote.

	2:08:44.080 --> 2:08:48.080
	Human, what is human? Our species are changing all the time.

	2:08:48.080 --> 2:08:52.080
	Human today is not the same as human just 50 years ago.

	2:08:52.080 --> 2:08:54.080
	What is human? Do we care about our genetics?

	2:08:54.080 --> 2:08:56.080
	Why is that important?

	2:08:56.080 --> 2:08:59.080
	As I point out, our genetics are no more interesting than a bacterium's genetics.

	2:08:59.080 --> 2:09:01.080
	It's no more interesting than a monkey's genetics.

	2:09:01.080 --> 2:09:07.080
	What we have, what's unique and what's valuable is our knowledge, what we've learned about the world.

	2:09:07.080 --> 2:09:09.080
	And that is the rare thing.

	2:09:09.080 --> 2:09:11.080
	That's the thing we want to preserve.

	2:09:11.080 --> 2:09:15.080
	Who cares about our genes?

	2:09:15.080 --> 2:09:17.080
	It's the knowledge.

	2:09:17.080 --> 2:09:19.080
	That's a really good place to end.

	2:09:19.080 --> 2:09:42.080
	Thank you so much for talking to me.