vtt/episode_011_small.vtt

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 The following is a conversation with Jürgen Schmidhuber.

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 He's the codirector of its CS Swiss AI lab

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 and a cocreator of long short term memory networks.

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 LSDMs are used in billions of devices today

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 for speech recognition, translation, and much more.

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 Over 30 years, he has proposed a lot of interesting

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 out of the box ideas, a meta learning, adversarial networks,

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 computer vision, and even a formal theory of quote,

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 creativity, curiosity, and fun.

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 This conversation is part of the MIT course

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 on artificial general intelligence

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 and the artificial intelligence podcast.

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 If you enjoy it, subscribe on YouTube, iTunes,

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 or simply connect with me on Twitter

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 at Lex Friedman spelled F R I D.

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 And now here's my conversation with Jürgen Schmidhuber.

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 Early on you dreamed of AI systems

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 that self improve recursively.

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 When was that dream born?

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 When I was a baby.

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 No, that's not true.

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 When I was a teenager.

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 And what was the catalyst for that birth?

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 What was the thing that first inspired you?

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 When I was a boy, I...

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 I was thinking about what to do in my life

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 and then I thought the most exciting thing

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 is to solve the riddles of the universe.

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 And that means you have to become a physicist.

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 However, then I realized that there's something even grander.

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 You can try to build a machine.

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 That isn't really a machine any longer.

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 That learns to become a much better physicist

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 than I could ever hope to be.

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 And that's how I thought maybe I can multiply

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 my tiny little bit of creativity into infinity.

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 But ultimately, that creativity will be multiplied

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 to understand the universe around us.

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 That's the curiosity for that mystery that drove you.

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 Yes, so if you can build a machine

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 that learns to solve more and more complex problems

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 and more and more general problems over,

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 then you basically have solved all the problems.

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 At least all the solvable problems.

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 So how do you think...

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 What is the mechanism for that kind of general solver look like?

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 Obviously, we don't quite yet have one or know

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 how to build one boy of ideas

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 and you have had throughout your career several ideas about it.

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 So how do you think about that mechanism?

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 So in the 80s, I thought about how to build this machine

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 that learns to solve all these problems

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 that I cannot solve myself.

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 And I thought it is clear, it has to be a machine

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 that not only learns to solve this problem here

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 and this problem here,

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 but it also has to learn to improve

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 the learning algorithm itself.

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 So it has to have the learning algorithm

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 in a representation that allows it to inspect it

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 and modify it so that it can come up

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 with a better learning algorithm.

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 So I called that meta learning, learning to learn

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 and recursive self improvement.

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 That is really the pinnacle of that,

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 where you then not only learn how to improve

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 on that problem and on that,

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 but you also improve the way the machine improves

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 and you also improve the way it improves the way

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 it improves itself.

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 And that was my 1987 diploma thesis,

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 which was all about that hierarchy of meta learners

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 that have no computational limits

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 except for the well known limits

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 that Gödel identified in 1931

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 and for the limits of physics.

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 In the recent years, meta learning has gained popularity

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 in a specific kind of form.

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 You've talked about how that's not really meta learning

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 with neural networks, that's more basic transfer learning.

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 Can you talk about the difference

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 between the big general meta learning

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 and a more narrow sense of meta learning

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 the way it's used today, the way it's talked about today?

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 Let's take the example of a deep neural network

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 that has learned to classify images.

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 And maybe you have trained that network

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 on 100 different databases of images.

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 And now a new database comes along

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 and you want to quickly learn the new thing as well.

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 So one simple way of doing that is you take the network

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 which already knows 100 types of databases

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 and then you just take the top layer of that

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 and you retrain that using the new labeled data

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 that you have in the new image database.

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 And then it turns out that it really, really quickly

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 can learn that too, one shot basically,

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 because from the first 100 data sets,

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 it already has learned so much about computer vision

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 that it can reuse that and that is then almost good enough

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 to solve the new tasks except you need a little bit

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 of adjustment on the top.

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 So that is transfer learning

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 and it has been done in principle for many decades.

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 People have done similar things for decades.

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 Meta learning, true meta learning is about

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 having the learning algorithm itself

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 open to introspection by the system that is using it

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 and also open to modification

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 such that the learning system has an opportunity

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 to modify any part of the learning algorithm

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 and then evaluate the consequences of that modification

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 and then learn from that to create a better learning algorithm

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 and so on recursively.

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 So that's a very different animal

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 where you are opening the space of possible learning algorithms

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 to the learning system itself.

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 Right, so you've like in the 2004 paper,

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 you describe Gatal machines and programs

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 that rewrite themselves, right?

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 Philosophically and even in your paper mathematically,

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 these are really compelling ideas,

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 but practically, do you see these self referential programs

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 being successful in the near term to having an impact

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 where sort of it demonstrates to the world

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 that this direction is a good one to pursue in the near term?

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 Yes, we had these two different types

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 of fundamental research,

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 how to build a universal problem solver,

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 one basically exploiting proof search

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 and things like that that you need to come up

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 with asymptotically optimal, theoretically optimal

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 self improvers and problem solvers.

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 However, one has to admit that through this proof search

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 comes in an additive constant,

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 an overhead, an additive overhead

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 that vanishes in comparison to what you have to do

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 to solve large problems.

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 However, for many of the small problems

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 that we want to solve in our everyday life,

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 we cannot ignore this constant overhead.

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 And that's why we also have been doing other things,

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 non universal things such as recurrent neural networks

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 which are trained by gradient descent

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 and local search techniques which aren't universal at all,

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 which aren't provably optimal at all

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 like the other stuff that we did,

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 but which are much more practical

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 as long as we only want to solve the small problems

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 that we are typically trying to solve in this environment here.

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 So the universal problem solvers like the Gödel machine

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 but also Markus Hutter's fastest way

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 of solving all possible problems,

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 which he developed around 2002 in my lab,

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 they are associated with these constant overheads

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 for proof search, which guarantees

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 that the thing that you're doing is optimal.

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 For example, there is this fastest way

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 of solving all problems with a computable solution

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 which is due to Markus Hutter.

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 And to explain what's going on there,

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 let's take traveling salesman problems.

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 With traveling salesman problems,

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 you have a number of cities, N cities,

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 and you try to find the shortest path

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 through all these cities without visiting any city twice.

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 And nobody knows the fastest way

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 of solving traveling salesman problems, TSPs,

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 but let's assume there is a method of solving them

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 within N to the five operations

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 where N is the number of cities.

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 Then the universal method of Markus

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 is going to solve the same traveling salesman problem

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 also within N to the five steps,

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 plus O of one, plus a constant number of steps

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 that you need for the proof searcher,

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 which you need to show that this particular

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 class of problems that traveling salesman problems

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 can be solved within a certain time bound,

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 within order N to the five steps, basically.

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 And this additive constant doesn't care for N,

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 which means as N is getting larger and larger,

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 as you have more and more cities,

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 the constant overhead pales and comparison.

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 And that means that almost all large problems are solved

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 in the best possible way already today.

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 We already have a universal problem solved like that.

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 However, it's not practical because the overhead,

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 the constant overhead is so large

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 that for the small kinds of problems

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 that we want to solve in this little biosphere.

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 By the way, when you say small,

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 you're talking about things that fall

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 within the constraints of our computational systems.

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 So they can seem quite large to us mere humans.

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 That's right, yeah.

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 So they seem large and even unsolvable

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 in a practical sense today,

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 but they are still small compared to almost all problems

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 because almost all problems are large problems,

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 which are much larger than any constant.

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 Do you find it useful as a person

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 who is dreamed of creating a general learning system,

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 has worked on creating one,

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 has done a lot of interesting ideas there

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 to think about P versus NP,

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 this formalization of how hard problems are,

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 how they scale,

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 this kind of worst case analysis type of thinking.

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 Do you find that useful?

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 Or is it only just a mathematical,

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 it's a set of mathematical techniques

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 to give you intuition about what's good and bad?

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 So P versus NP, that's super interesting

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 from a theoretical point of view.

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 And in fact, as you are thinking about that problem,

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 you can also get inspiration

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 for better practical problem solvers.

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 On the other hand, we have to admit

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 that at the moment,

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 the best practical problem solvers

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 for all kinds of problems

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 that we are now solving through what is called AI at the moment,

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 they are not of the kind

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 that is inspired by these questions.

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 There we are using general purpose computers,

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 such as recurrent neural networks,

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 but we have a search technique,

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 which is just local search gradient descent

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 to try to find a program

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 that is running on these recurrent networks,

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 such that it can solve some interesting problems,

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 such as speech recognition or machine translation

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 and something like that.

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 And there is very little theory

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 behind the best solutions that we have at the moment

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 that can do that.

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 Do you think that needs to change?

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 Do you think that will change or can we go,

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 can we create a general intelligence systems

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 without ever really proving

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 that that system is intelligent

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 in some kind of mathematical way,

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 solving machine translation perfectly

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 or something like that,

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 within some kind of syntactic definition of a language?

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 Or can we just be super impressed

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 by the thing working extremely well and that's sufficient?

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 There's an old saying,

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 and I don't know who brought it up first,

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 which says there's nothing more practical

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 than a good theory.

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 And a good theory of problem solving

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 under limited resources like here in this universe

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 or on this little planet

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 has to take into account these limited resources.

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 And so probably there is locking a theory

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 which is related to what we already have,

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 these asymptotically optimal problem solvers,

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 which tells us what we need in addition to that

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 to come up with a practically optimal problem solver.

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 So I believe we will have something like that

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 and maybe just a few little tiny twists

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 are necessary to change what we already have

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 to come up with that as well.

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 As long as we don't have that,

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 we admit that we are taking suboptimal ways

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 and recurrent neural networks and long short term memory

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 for equipped with local search techniques

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 and we are happy that it works better

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 than any competing methods,

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 but that doesn't mean that we think we are done.

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 You've said that an AGI system will ultimately be a simple one,

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 a general intelligence system will ultimately be a simple one,

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 maybe a pseudo code of a few lines

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 will be able to describe it.

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 Can you talk through your intuition behind this idea,

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 why you feel that at its core intelligence

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 is a simple algorithm?

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 Experience tells us that the stuff that works best

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 is really simple.

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 So the asymptotically optimal ways of solving problems,

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 if you look at them,

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 they're just a few lines of code, it's really true.

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 Although they are these amazing properties,

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 just a few lines of code,

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 then the most promising and most useful practical things

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 maybe don't have this proof of optimality associated with them.

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 However, they are also just a few lines of code.

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 The most successful recurrent neural networks,

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 you can write them down and five lines of pseudo code.

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 That's a beautiful, almost poetic idea,

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 but what you're describing there

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 is the lines of pseudo code

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 are sitting on top of layers and layers of abstractions,

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 in a sense.

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 So you're saying at the very top,

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 it'll be a beautifully written sort of algorithm,

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 but do you think that there's many layers of abstractions

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 we have to first learn to construct?

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 Yeah, of course.

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 We are building on all these great abstractions

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 that people have invented over the millennia,

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 such as matrix multiplications and drill numbers

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 and basic arithmetics and calculus and derivations

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 of error functions and derivatives of error functions

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 and stuff like that.

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 So without that language that greatly simplifies

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 our way of thinking about these problems,

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 we couldn't do anything.

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 So in that sense, as always,

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 we are standing on the shoulders of the giants

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 who in the past simplified the problem of problem solving

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 so much that now we have a chance to do the final step.

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 So the final step will be a simple one.

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 If we take a step back through all of human civilization

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 and just the universe in general,

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 how do you think about evolution?

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 And what if creating a universe is required

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 to achieve this final step?

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 What if going through the very painful

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 and inefficient process of evolution is needed

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 to come up with this set of abstractions

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 that ultimately lead to intelligence?

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 Do you think there's a shortcut

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 or do you think we have to create something like our universe

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 in order to create something like human level intelligence?

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 So far, the only example we have is this one,

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 this universe in which we are living.

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 You think you can do better?

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 Maybe not, but we are part of this whole process.

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 So apparently, so it might be the case

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 that the code that runs the universe

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 is really, really simple.

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 Everything points to that possibility

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 because gravity and other basic forces

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 are really simple laws that can be easily described,

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 also in just a few lines of code, basically.

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 And then there are these other events

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 that the apparently random events

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 in the history of the universe,

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 which as far as we know at the moment

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 don't have a compact code,

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 but who knows, maybe somebody in the near future

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 is going to figure out the pseudo random generator,

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 which is computing whether the measurement of that

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 spin up or down thing here

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 is going to be positive or negative.

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 Underline quantum mechanics.

19:19.880 --> 19:20.720
 Yes, so.

19:20.720 --> 19:23.160
 Do you ultimately think quantum mechanics

19:23.160 --> 19:25.200
 is a pseudo random number generator?

19:25.200 --> 19:26.920
 So it's all deterministic.

19:26.920 --> 19:28.760
 There's no randomness in our universe.

19:30.400 --> 19:31.800
 Does God play dice?

19:31.800 --> 19:34.080
 So a couple of years ago,

19:34.080 --> 19:39.080
 a famous physicist, quantum physicist, Anton Zeilinger,

19:39.080 --> 19:41.600
 he wrote an essay in Nature,

19:41.600 --> 19:44.280
 and it started more or less like that.

19:46.720 --> 19:51.720
 One of the fundamental insights of the 20th century

19:53.280 --> 19:58.280
 was that the universe is fundamentally random

19:58.280 --> 20:02.760
 on the quantum level, and that whenever

20:03.760 --> 20:06.720
 you measure spin up or down or something like that,

20:06.720 --> 20:10.720
 a new bit of information enters the history of the universe.

20:13.440 --> 20:14.680
 And while I was reading that,

20:14.680 --> 20:18.000
 I was already typing the response

20:18.000 --> 20:20.280
 and they had to publish it because I was right,

20:21.560 --> 20:25.560
 that there is no evidence, no physical evidence for that.

20:25.560 --> 20:28.440
 So there's an alternative explanation

20:28.440 --> 20:31.240
 where everything that we consider random

20:31.240 --> 20:33.800
 is actually pseudo random,

20:33.800 --> 20:38.800
 such as the decimal expansion of pi, 3.141 and so on,

20:39.400 --> 20:42.120
 which looks random, but isn't.

20:42.120 --> 20:47.120
 So pi is interesting because every three digit sequence,

20:47.720 --> 20:51.720
 every sequence of three digits appears roughly

20:51.720 --> 20:56.720
 one in a thousand times, and every five digit sequence

20:57.360 --> 21:00.760
 appears roughly one in 10,000 times.

21:00.760 --> 21:02.760
 What do you expect?

21:02.760 --> 21:06.760
 If it was random, but there's a very short algorithm,

21:06.760 --> 21:09.120
 a short program that computes all of that.

21:09.120 --> 21:11.200
 So it's extremely compressible.

21:11.200 --> 21:13.120
 And who knows, maybe tomorrow somebody,

21:13.120 --> 21:15.360
 some grad student at CERN goes back

21:15.360 --> 21:19.120
 over all these data points, better decay,

21:19.120 --> 21:21.760
 and whatever, and figures out, oh,

21:21.760 --> 21:25.760
 it's the second billion digits of pi or something like that.

21:25.760 --> 21:28.840
 We don't have any fundamental reason at the moment

21:28.840 --> 21:33.600
 to believe that this is truly random

21:33.600 --> 21:36.440
 and not just a deterministic video game.

21:36.440 --> 21:38.680
 If it was a deterministic video game,

21:38.680 --> 21:40.360
 it would be much more beautiful

21:40.360 --> 21:44.160
 because beauty is simplicity.

21:44.160 --> 21:47.560
 And many of the basic laws of the universe

21:47.560 --> 21:51.560
 like gravity and the other basic forces are very simple.

21:51.560 --> 21:55.560
 So very short programs can explain what these are doing.

21:56.560 --> 22:00.560
 And it would be awful and ugly.

22:00.560 --> 22:01.560
 The universe would be ugly.

22:01.560 --> 22:03.560
 The history of the universe would be ugly

22:03.560 --> 22:06.560
 if for the extra things, the random,

22:06.560 --> 22:10.560
 the seemingly random data points that we get all the time

22:10.560 --> 22:15.560
 that we really need a huge number of extra bits

22:15.560 --> 22:21.560
 to describe all these extra bits of information.

22:22.560 --> 22:25.560
 So as long as we don't have evidence

22:25.560 --> 22:27.560
 that there is no short program

22:27.560 --> 22:32.560
 that computes the entire history of the entire universe,

22:32.560 --> 22:38.560
 we are, as scientists, compelled to look further

22:38.560 --> 22:41.560
 for that shortest program.

22:41.560 --> 22:46.560
 Your intuition says there exists a program

22:46.560 --> 22:50.560
 that can backtrack to the creation of the universe.

22:50.560 --> 22:53.560
 So it can take the shortest path to the creation of the universe.

22:53.560 --> 22:57.560
 Yes, including all the entanglement things

22:57.560 --> 23:01.560
 and all the spin up and down measurements

23:01.560 --> 23:09.560
 that have been taken place since 13.8 billion years ago.

23:09.560 --> 23:14.560
 So we don't have a proof that it is random.

23:14.560 --> 23:19.560
 We don't have a proof that it is compressible to a short program.

23:19.560 --> 23:21.560
 But as long as we don't have that proof,

23:21.560 --> 23:24.560
 we are obliged as scientists to keep looking

23:24.560 --> 23:26.560
 for that simple explanation.

23:26.560 --> 23:27.560
 Absolutely.

23:27.560 --> 23:30.560
 So you said simplicity is beautiful or beauty is simple.

23:30.560 --> 23:32.560
 Either one works.

23:32.560 --> 23:36.560
 But you also work on curiosity, discovery.

23:36.560 --> 23:42.560
 The romantic notion of randomness, of serendipity,

23:42.560 --> 23:49.560
 of being surprised by things that are about you,

23:49.560 --> 23:53.560
 kind of in our poetic notion of reality,

23:53.560 --> 23:56.560
 we think as humans require randomness.

23:56.560 --> 23:58.560
 So you don't find randomness beautiful.

23:58.560 --> 24:04.560
 You find simple determinism beautiful.

24:04.560 --> 24:06.560
 Yeah.

24:06.560 --> 24:07.560
 Okay.

24:07.560 --> 24:08.560
 So why?

24:08.560 --> 24:09.560
 Why?

24:09.560 --> 24:12.560
 Because the explanation becomes shorter.

24:12.560 --> 24:19.560
 A universe that is compressible to a short program

24:19.560 --> 24:22.560
 is much more elegant and much more beautiful

24:22.560 --> 24:24.560
 than another one,

24:24.560 --> 24:28.560
 which needs an almost infinite number of bits to be described.

24:28.560 --> 24:31.560
 As far as we know,

24:31.560 --> 24:34.560
 many things that are happening in this universe are really simple

24:34.560 --> 24:38.560
 in terms of short programs that compute gravity

24:38.560 --> 24:43.560
 and the interaction between elementary particles and so on.

24:43.560 --> 24:45.560
 So all of that seems to be very, very simple.

24:45.560 --> 24:50.560
 Every electron seems to reuse the same subprogram all the time

24:50.560 --> 24:57.560
 as it is interacting with other elementary particles.

24:57.560 --> 25:04.560
 If we now require an extra oracle

25:04.560 --> 25:07.560
 injecting new bits of information all the time

25:07.560 --> 25:11.560
 for these extra things which are currently not understood,

25:11.560 --> 25:18.560
 such as better decay,

25:18.560 --> 25:25.560
 then the whole description length of the data that we can observe

25:25.560 --> 25:31.560
 of the history of the universe would become much longer.

25:31.560 --> 25:33.560
 And therefore, uglier.

25:33.560 --> 25:34.560
 And uglier.

25:34.560 --> 25:38.560
 Again, the simplicity is elegant and beautiful.

25:38.560 --> 25:42.560
 All the history of science is a history of compression progress.

25:42.560 --> 25:43.560
 Yeah.

25:43.560 --> 25:48.560
 So you've described sort of as we build up abstractions

25:48.560 --> 25:52.560
 and you've talked about the idea of compression.

25:52.560 --> 25:55.560
 How do you see this, the history of science,

25:55.560 --> 25:59.560
 the history of humanity, our civilization and life on Earth

25:59.560 --> 26:03.560
 as some kind of path towards greater and greater compression?

26:03.560 --> 26:04.560
 What do you mean by that?

26:04.560 --> 26:06.560
 How do you think about that?

26:06.560 --> 26:12.560
 Indeed, the history of science is a history of compression progress.

26:12.560 --> 26:14.560
 What does that mean?

26:14.560 --> 26:17.560
 Hundreds of years ago, there was an astronomer

26:17.560 --> 26:19.560
 whose name was Kepler.

26:19.560 --> 26:25.560
 And he looked at the data points that he got by watching planets move.

26:25.560 --> 26:28.560
 And then he had all these data points and suddenly it turned out

26:28.560 --> 26:37.560
 that he can greatly compress the data by predicting it through an ellipse law.

26:37.560 --> 26:44.560
 So it turns out that all these data points are more or less on ellipses around the sun.

26:44.560 --> 26:50.560
 And another guy came along whose name was Newton and before him Hook.

26:50.560 --> 26:57.560
 And they said the same thing that is making these planets move like that

26:57.560 --> 27:01.560
 is what makes the apples fall down.

27:01.560 --> 27:10.560
 And it also holds for stones and for all kinds of other objects.

27:10.560 --> 27:16.560
 And suddenly many, many of these observations became much more compressible

27:16.560 --> 27:19.560
 because as long as you can predict the next thing,

27:19.560 --> 27:22.560
 given what you have seen so far, you can compress it.

27:22.560 --> 27:24.560
 But you don't have to store that data extra.

27:24.560 --> 27:28.560
 This is called predictive coding.

27:28.560 --> 27:33.560
 And then there was still something wrong with that theory of the universe

27:33.560 --> 27:37.560
 and you had deviations from these predictions of the theory.

27:37.560 --> 27:41.560
 And 300 years later another guy came along whose name was Einstein

27:41.560 --> 27:50.560
 and he was able to explain away all these deviations from the predictions of the old theory

27:50.560 --> 27:56.560
 through a new theory which was called the general theory of relativity

27:56.560 --> 28:00.560
 which at first glance looks a little bit more complicated

28:00.560 --> 28:05.560
 and you have to warp space and time but you can't phrase it within one single sentence

28:05.560 --> 28:12.560
 which is no matter how fast you accelerate and how fast or how hard you decelerate

28:12.560 --> 28:18.560
 and no matter what is the gravity in your local framework,

28:18.560 --> 28:21.560
 light speed always looks the same.

28:21.560 --> 28:24.560
 And from that you can calculate all the consequences.

28:24.560 --> 28:30.560
 So it's a very simple thing and it allows you to further compress all the observations

28:30.560 --> 28:35.560
 because certainly there are hardly any deviations any longer

28:35.560 --> 28:39.560
 that you can measure from the predictions of this new theory.

28:39.560 --> 28:44.560
 So art of science is a history of compression progress.

28:44.560 --> 28:50.560
 You never arrive immediately at the shortest explanation of the data

28:50.560 --> 28:52.560
 but you're making progress.

28:52.560 --> 28:56.560
 Whenever you are making progress you have an insight.

28:56.560 --> 29:01.560
 You see, oh, first I needed so many bits of information to describe the data,

29:01.560 --> 29:04.560
 to describe my falling apples, my video of falling apples,

29:04.560 --> 29:08.560
 I need so many data, so many pixels have to be stored

29:08.560 --> 29:14.560
 but then suddenly I realize, no, there is a very simple way of predicting the third frame

29:14.560 --> 29:20.560
 in the video from the first two and maybe not every little detail can be predicted

29:20.560 --> 29:24.560
 but more or less most of these orange blots that are coming down,

29:24.560 --> 29:28.560
 I'm sorry, in the same way, which means that I can greatly compress the video

29:28.560 --> 29:33.560
 and the amount of compression, progress,

29:33.560 --> 29:37.560
 that is the depth of the insight that you have at that moment.

29:37.560 --> 29:40.560
 That's the fun that you have, the scientific fun,

29:40.560 --> 29:46.560
 the fun in that discovery and we can build artificial systems that do the same thing.

29:46.560 --> 29:51.560
 They measure the depth of their insights as they are looking at the data

29:51.560 --> 29:55.560
 through their own experiments and we give them a reward,

29:55.560 --> 30:00.560
 an intrinsic reward and proportion to this depth of insight.

30:00.560 --> 30:07.560
 And since they are trying to maximize the rewards they get,

30:07.560 --> 30:12.560
 they are suddenly motivated to come up with new action sequences,

30:12.560 --> 30:17.560
 with new experiments that have the property that the data that is coming in

30:17.560 --> 30:21.560
 as a consequence of these experiments has the property

30:21.560 --> 30:25.560
 that they can learn something about, see a pattern in there

30:25.560 --> 30:28.560
 which they hadn't seen yet before.

30:28.560 --> 30:32.560
 So there's an idea of power play that you've described,

30:32.560 --> 30:37.560
 a training and general problem solver in this kind of way of looking for the unsolved problems.

30:37.560 --> 30:40.560
 Can you describe that idea a little further?

30:40.560 --> 30:42.560
 It's another very simple idea.

30:42.560 --> 30:49.560
 Normally what you do in computer science, you have some guy who gives you a problem

30:49.560 --> 30:56.560
 and then there is a huge search space of potential solution candidates

30:56.560 --> 31:02.560
 and you somehow try them out and you have more or less sophisticated ways

31:02.560 --> 31:06.560
 of moving around in that search space

31:06.560 --> 31:11.560
 until you finally found a solution which you consider satisfactory.

31:11.560 --> 31:15.560
 That's what most of computer science is about.

31:15.560 --> 31:19.560
 Power play just goes one little step further and says,

31:19.560 --> 31:24.560
 let's not only search for solutions to a given problem,

31:24.560 --> 31:30.560
 but let's search to pairs of problems and their solutions

31:30.560 --> 31:36.560
 where the system itself has the opportunity to phrase its own problem.

31:36.560 --> 31:42.560
 So we are looking suddenly at pairs of problems and their solutions

31:42.560 --> 31:46.560
 or modifications of the problem solver

31:46.560 --> 31:50.560
 that is supposed to generate a solution to that new problem.

31:50.560 --> 31:56.560
 And this additional degree of freedom

31:56.560 --> 32:01.560
 allows us to build career systems that are like scientists

32:01.560 --> 32:06.560
 in the sense that they not only try to solve and try to find answers

32:06.560 --> 32:12.560
 to existing questions, no, they are also free to pose their own questions.

32:12.560 --> 32:15.560
 So if you want to build an artificial scientist,

32:15.560 --> 32:19.560
 you have to give it that freedom and power play is exactly doing that.

32:19.560 --> 32:23.560
 So that's a dimension of freedom that's important to have,

32:23.560 --> 32:31.560
 how hard do you think that, how multi dimensional and difficult the space of

32:31.560 --> 32:34.560
 then coming up with your own questions is.

32:34.560 --> 32:38.560
 So it's one of the things that as human beings we consider to be

32:38.560 --> 32:41.560
 the thing that makes us special, the intelligence that makes us special

32:41.560 --> 32:47.560
 is that brilliant insight that can create something totally new.

32:47.560 --> 32:51.560
 Yes. So now let's look at the extreme case.

32:51.560 --> 32:57.560
 Let's look at the set of all possible problems that you can formally describe,

32:57.560 --> 33:03.560
 which is infinite, which should be the next problem

33:03.560 --> 33:07.560
 that a scientist or power play is going to solve.

33:07.560 --> 33:16.560
 Well, it should be the easiest problem that goes beyond what you already know.

33:16.560 --> 33:22.560
 So it should be the simplest problem that the current problems

33:22.560 --> 33:28.560
 that you have which can already solve 100 problems that he cannot solve yet

33:28.560 --> 33:30.560
 by just generalizing.

33:30.560 --> 33:32.560
 So it has to be new.

33:32.560 --> 33:36.560
 So it has to require a modification of the problem solver such that the new

33:36.560 --> 33:41.560
 problem solver can solve this new thing, but the old problem solver cannot do it.

33:41.560 --> 33:47.560
 And in addition to that, we have to make sure that the problem solver

33:47.560 --> 33:50.560
 doesn't forget any of the previous solutions.

33:50.560 --> 33:51.560
 Right.

33:51.560 --> 33:57.560
 And so by definition, power play is now trying always to search in this pair of

33:57.560 --> 34:02.560
 in the set of pairs of problems and problems over modifications

34:02.560 --> 34:08.560
 for a combination that minimize the time to achieve these criteria.

34:08.560 --> 34:14.560
 Power is trying to find the problem which is easiest to add to the repertoire.

34:14.560 --> 34:19.560
 So just like grad students and academics and researchers can spend their whole

34:19.560 --> 34:25.560
 career in a local minima stuck trying to come up with interesting questions,

34:25.560 --> 34:27.560
 but ultimately doing very little.

34:27.560 --> 34:32.560
 Do you think it's easy in this approach of looking for the simplest

34:32.560 --> 34:38.560
 problem solver problem to get stuck in a local minima is not never really discovering

34:38.560 --> 34:43.560
 new, you know, really jumping outside of the hundred problems that you've already

34:43.560 --> 34:47.560
 solved in a genuine creative way.

34:47.560 --> 34:52.560
 No, because that's the nature of power play that it's always trying to break

34:52.560 --> 34:58.560
 its current generalization abilities by coming up with a new problem which is

34:58.560 --> 35:04.560
 beyond the current horizon, just shifting the horizon of knowledge a little bit

35:04.560 --> 35:10.560
 out there, breaking the existing rules such that the new thing becomes solvable

35:10.560 --> 35:13.560
 but wasn't solvable by the old thing.

35:13.560 --> 35:19.560
 So like adding a new axiom, like what Gödel did when he came up with these

35:19.560 --> 35:23.560
 new sentences, new theorems that didn't have a proof in the formal system,

35:23.560 --> 35:30.560
 which means you can add them to the repertoire, hoping that they are not

35:30.560 --> 35:35.560
 going to damage the consistency of the whole thing.

35:35.560 --> 35:41.560
 So in the paper with the amazing title, Formal Theory of Creativity,

35:41.560 --> 35:47.560
 Fun and Intrinsic Motivation, you talk about discovery as intrinsic reward.

35:47.560 --> 35:53.560
 So if you view humans as intelligent agents, what do you think is the purpose

35:53.560 --> 35:56.560
 and meaning of life for us humans?

35:56.560 --> 35:58.560
 You've talked about this discovery.

35:58.560 --> 36:04.560
 Do you see humans as an instance of power play agents?

36:04.560 --> 36:11.560
 Yeah, so humans are curious and that means they behave like scientists,

36:11.560 --> 36:15.560
 not only the official scientists but even the babies behave like scientists

36:15.560 --> 36:19.560
 and they play around with their toys to figure out how the world works

36:19.560 --> 36:22.560
 and how it is responding to their actions.

36:22.560 --> 36:26.560
 And that's how they learn about gravity and everything.

36:26.560 --> 36:30.560
 And yeah, in 1990, we had the first systems like that

36:30.560 --> 36:33.560
 who would just try to play around with the environment

36:33.560 --> 36:39.560
 and come up with situations that go beyond what they knew at that time

36:39.560 --> 36:42.560
 and then get a reward for creating these situations

36:42.560 --> 36:45.560
 and then becoming more general problem solvers

36:45.560 --> 36:48.560
 and being able to understand more of the world.

36:48.560 --> 36:56.560
 So yeah, I think in principle that curiosity,

36:56.560 --> 37:02.560
 strategy or more sophisticated versions of what I just described,

37:02.560 --> 37:07.560
 they are what we have built in as well because evolution discovered

37:07.560 --> 37:12.560
 that's a good way of exploring the unknown world and a guy who explores

37:12.560 --> 37:16.560
 the unknown world has a higher chance of solving problems

37:16.560 --> 37:19.560
 that he needs to survive in this world.

37:19.560 --> 37:23.560
 On the other hand, those guys who were too curious,

37:23.560 --> 37:25.560
 they were weeded out as well.

37:25.560 --> 37:27.560
 So you have to find this trade off.

37:27.560 --> 37:30.560
 Evolution found a certain trade off apparently in our society.

37:30.560 --> 37:35.560
 There is a certain percentage of extremely explorative guys

37:35.560 --> 37:41.560
 and it doesn't matter if they die because many of the others are more conservative.

37:41.560 --> 37:46.560
 And so yeah, it would be surprising to me

37:46.560 --> 37:55.560
 if that principle of artificial curiosity wouldn't be present

37:55.560 --> 37:59.560
 in almost exactly the same form here in our brains.

37:59.560 --> 38:02.560
 So you're a bit of a musician and an artist.

38:02.560 --> 38:07.560
 So continuing on this topic of creativity,

38:07.560 --> 38:10.560
 what do you think is the role of creativity in intelligence?

38:10.560 --> 38:16.560
 So you've kind of implied that it's essential for intelligence,

38:16.560 --> 38:21.560
 if you think of intelligence as a problem solving system,

38:21.560 --> 38:23.560
 as ability to solve problems.

38:23.560 --> 38:28.560
 But do you think it's essential, this idea of creativity?

38:28.560 --> 38:34.560
 We never have a subprogram that is called creativity or something.

38:34.560 --> 38:37.560
 It's just a side effect of what our problems always do.

38:37.560 --> 38:44.560
 They are searching a space of candidates, of solution candidates,

38:44.560 --> 38:47.560
 until they hopefully find a solution to a given problem.

38:47.560 --> 38:50.560
 But then there are these two types of creativity

38:50.560 --> 38:53.560
 and both of them are now present in our machines.

38:53.560 --> 38:56.560
 The first one has been around for a long time,

38:56.560 --> 38:59.560
 which is human gives problem to machine.

38:59.560 --> 39:03.560
 Machine tries to find a solution to that.

39:03.560 --> 39:05.560
 And this has been happening for many decades.

39:05.560 --> 39:09.560
 And for many decades, machines have found creative solutions

39:09.560 --> 39:13.560
 to interesting problems where humans were not aware

39:13.560 --> 39:17.560
 of these particularly creative solutions,

39:17.560 --> 39:20.560
 but then appreciated that the machine found that.

39:20.560 --> 39:23.560
 The second is the pure creativity.

39:23.560 --> 39:28.560
 What I just mentioned, I would call the applied creativity,

39:28.560 --> 39:31.560
 like applied art, where somebody tells you,

39:31.560 --> 39:34.560
 now make a nice picture of this pope,

39:34.560 --> 39:36.560
 and you will get money for that.

39:36.560 --> 39:41.560
 So here is the artist and he makes a convincing picture of the pope

39:41.560 --> 39:44.560
 and the pope likes it and gives him the money.

39:44.560 --> 39:48.560
 And then there is the pure creativity,

39:48.560 --> 39:51.560
 which is more like the power play and the artificial curiosity thing,

39:51.560 --> 39:56.560
 where you have the freedom to select your own problem,

39:56.560 --> 40:02.560
 like a scientist who defines his own question to study.

40:02.560 --> 40:06.560
 And so that is the pure creativity, if you will,

40:06.560 --> 40:13.560
 as opposed to the applied creativity, which serves another.

40:13.560 --> 40:18.560
 In that distinction, there's almost echoes of narrow AI versus general AI.

40:18.560 --> 40:24.560
 So this kind of constrained painting of a pope seems like

40:24.560 --> 40:29.560
 the approaches of what people are calling narrow AI.

40:29.560 --> 40:32.560
 And pure creativity seems to be,

40:32.560 --> 40:34.560
 maybe I'm just biased as a human,

40:34.560 --> 40:40.560
 but it seems to be an essential element of human level intelligence.

40:40.560 --> 40:43.560
 Is that what you're implying?

40:43.560 --> 40:45.560
 To a degree.

40:45.560 --> 40:50.560
 If you zoom back a little bit and you just look at a general problem solving machine,

40:50.560 --> 40:53.560
 which is trying to solve arbitrary problems,

40:53.560 --> 40:57.560
 then this machine will figure out in the course of solving problems

40:57.560 --> 40:59.560
 that it's good to be curious.

40:59.560 --> 41:04.560
 So all of what I said just now about this pre wild curiosity

41:04.560 --> 41:10.560
 and this will to invent new problems that the system doesn't know how to solve yet,

41:10.560 --> 41:14.560
 should be just a byproduct of the general search.

41:14.560 --> 41:21.560
 However, apparently evolution has built it into us

41:21.560 --> 41:26.560
 because it turned out to be so successful, a pre wiring, a bias,

41:26.560 --> 41:33.560
 a very successful exploratory bias that we are born with.

41:33.560 --> 41:36.560
 And you've also said that consciousness in the same kind of way

41:36.560 --> 41:40.560
 may be a byproduct of problem solving.

41:40.560 --> 41:44.560
 Do you find this an interesting byproduct?

41:44.560 --> 41:46.560
 Do you think it's a useful byproduct?

41:46.560 --> 41:49.560
 What are your thoughts on consciousness in general?

41:49.560 --> 41:54.560
 Or is it simply a byproduct of greater and greater capabilities of problem solving

41:54.560 --> 42:00.560
 that's similar to creativity in that sense?

42:00.560 --> 42:04.560
 We never have a procedure called consciousness in our machines.

42:04.560 --> 42:10.560
 However, we get a side effects of what these machines are doing,

42:10.560 --> 42:15.560
 things that seem to be closely related to what people call consciousness.

42:15.560 --> 42:20.560
 So for example, already 1990 we had simple systems

42:20.560 --> 42:25.560
 which were basically recurrent networks and therefore universal computers

42:25.560 --> 42:32.560
 trying to map incoming data into actions that lead to success.

42:32.560 --> 42:39.560
 Maximizing reward in a given environment, always finding the charging station in time

42:39.560 --> 42:43.560
 whenever the battery is low and negative signals are coming from the battery,

42:43.560 --> 42:50.560
 always find the charging station in time without bumping against painful obstacles on the way.

42:50.560 --> 42:54.560
 So complicated things but very easily motivated.

42:54.560 --> 43:01.560
 And then we give these little guys a separate recurrent network

43:01.560 --> 43:04.560
 which is just predicting what's happening if I do that and that.

43:04.560 --> 43:08.560
 What will happen as a consequence of these actions that I'm executing

43:08.560 --> 43:13.560
 and it's just trained on the long and long history of interactions with the world.

43:13.560 --> 43:17.560
 So it becomes a predictive model of the world basically.

43:17.560 --> 43:22.560
 And therefore also a compressor of the observations of the world

43:22.560 --> 43:26.560
 because whatever you can predict, you don't have to store extra.

43:26.560 --> 43:29.560
 So compression is a side effect of prediction.

43:29.560 --> 43:32.560
 And how does this recurrent network compress?

43:32.560 --> 43:36.560
 Well, it's inventing little subprograms, little subnetworks

43:36.560 --> 43:41.560
 that stand for everything that frequently appears in the environment.

43:41.560 --> 43:47.560
 Like bottles and microphones and faces, maybe lots of faces in my environment.

43:47.560 --> 43:51.560
 So I'm learning to create something like a prototype face

43:51.560 --> 43:55.560
 and a new face comes along and all I have to encode are the deviations from the prototype.

43:55.560 --> 44:00.560
 So it's compressing all the time the stuff that frequently appears.

44:00.560 --> 44:04.560
 There's one thing that appears all the time

44:04.560 --> 44:09.560
 that is present all the time when the agent is interacting with its environment,

44:09.560 --> 44:11.560
 which is the agent itself.

44:11.560 --> 44:14.560
 So just for data compression reasons,

44:14.560 --> 44:18.560
 it is extremely natural for this recurrent network

44:18.560 --> 44:23.560
 to come up with little subnetworks that stand for the properties of the agents,

44:23.560 --> 44:27.560
 the hand, the other actuators,

44:27.560 --> 44:31.560
 and all the stuff that you need to better encode the data,

44:31.560 --> 44:34.560
 which is influenced by the actions of the agent.

44:34.560 --> 44:40.560
 So there, just as a side effect of data compression during primal solving,

44:40.560 --> 44:45.560
 you have internal self models.

44:45.560 --> 44:51.560
 Now you can use this model of the world to plan your future.

44:51.560 --> 44:54.560
 And that's what we also have done since 1990.

44:54.560 --> 44:57.560
 So the recurrent network, which is the controller,

44:57.560 --> 44:59.560
 which is trying to maximize reward,

44:59.560 --> 45:02.560
 can use this model of the network of the world,

45:02.560 --> 45:05.560
 this model network of the world, this predictive model of the world

45:05.560 --> 45:08.560
 to plan ahead and say, let's not do this action sequence.

45:08.560 --> 45:11.560
 Let's do this action sequence instead

45:11.560 --> 45:14.560
 because it leads to more predicted rewards.

45:14.560 --> 45:19.560
 And whenever it's waking up these little subnetworks that stand for itself,

45:19.560 --> 45:21.560
 then it's thinking about itself.

45:21.560 --> 45:23.560
 Then it's thinking about itself.

45:23.560 --> 45:30.560
 And it's exploring mentally the consequences of its own actions.

45:30.560 --> 45:36.560
 And now you tell me why it's still missing.

45:36.560 --> 45:39.560
 Missing the gap to consciousness.

45:39.560 --> 45:43.560
 There isn't. That's a really beautiful idea that, you know,

45:43.560 --> 45:46.560
 if life is a collection of data

45:46.560 --> 45:53.560
 and life is a process of compressing that data to act efficiently.

45:53.560 --> 45:57.560
 In that data, you yourself appear very often.

45:57.560 --> 46:00.560
 So it's useful to form compressions of yourself.

46:00.560 --> 46:03.560
 And it's a really beautiful formulation of what consciousness is,

46:03.560 --> 46:05.560
 is a necessary side effect.

46:05.560 --> 46:11.560
 It's actually quite compelling to me.

46:11.560 --> 46:18.560
 We've described RNNs, developed LSTMs, long short term memory networks.

46:18.560 --> 46:22.560
 They're a type of recurrent neural networks.

46:22.560 --> 46:24.560
 They've gotten a lot of success recently.

46:24.560 --> 46:29.560
 So these are networks that model the temporal aspects in the data,

46:29.560 --> 46:31.560
 temporal patterns in the data.

46:31.560 --> 46:36.560
 And you've called them the deepest of the neural networks, right?

46:36.560 --> 46:43.560
 What do you think is the value of depth in the models that we use to learn?

46:43.560 --> 46:47.560
 Yeah, since you mentioned the long short term memory and the LSTM,

46:47.560 --> 46:52.560
 I have to mention the names of the brilliant students who made that possible.

46:52.560 --> 46:53.560
 Yes, of course, of course.

46:53.560 --> 46:56.560
 First of all, my first student ever, Sepp Hochreiter,

46:56.560 --> 47:00.560
 who had fundamental insights already in his diploma thesis.

47:00.560 --> 47:04.560
 Then Felix Giers, who had additional important contributions.

47:04.560 --> 47:11.560
 Alex Gray is a guy from Scotland who is mostly responsible for this CTC algorithm,

47:11.560 --> 47:16.560
 which is now often used to train the LSTM to do the speech recognition

47:16.560 --> 47:21.560
 on all the Google Android phones and whatever, and Siri and so on.

47:21.560 --> 47:26.560
 So these guys, without these guys, I would be nothing.

47:26.560 --> 47:28.560
 It's a lot of incredible work.

47:28.560 --> 47:30.560
 What is now the depth?

47:30.560 --> 47:32.560
 What is the importance of depth?

47:32.560 --> 47:36.560
 Well, most problems in the real world are deep

47:36.560 --> 47:41.560
 in the sense that the current input doesn't tell you all you need to know

47:41.560 --> 47:44.560
 about the environment.

47:44.560 --> 47:49.560
 So instead, you have to have a memory of what happened in the past

47:49.560 --> 47:54.560
 and often important parts of that memory are dated.

47:54.560 --> 47:56.560
 They are pretty old.

47:56.560 --> 47:59.560
 So when you're doing speech recognition, for example,

47:59.560 --> 48:03.560
 and somebody says 11,

48:03.560 --> 48:08.560
 then that's about half a second or something like that,

48:08.560 --> 48:11.560
 which means it's already 50 time steps.

48:11.560 --> 48:15.560
 And another guy or the same guy says 7.

48:15.560 --> 48:18.560
 So the ending is the same, even.

48:18.560 --> 48:22.560
 But now the system has to see the distinction between 7 and 11,

48:22.560 --> 48:26.560
 and the only way it can see the difference is it has to store

48:26.560 --> 48:34.560
 that 50 steps ago there was an S or an L, 11 or 7.

48:34.560 --> 48:37.560
 So there you have already a problem of depth 50,

48:37.560 --> 48:42.560
 because for each time step you have something like a virtual layer

48:42.560 --> 48:45.560
 and the expanded, unrolled version of this recurrent network

48:45.560 --> 48:47.560
 which is doing the speech recognition.

48:47.560 --> 48:53.560
 So these long time lags, they translate into problem depth.

48:53.560 --> 48:59.560
 And most problems in this world are such that you really

48:59.560 --> 49:04.560
 have to look far back in time to understand what is the problem

49:04.560 --> 49:06.560
 and to solve it.

49:06.560 --> 49:09.560
 But just like with LSTMs, you don't necessarily need to,

49:09.560 --> 49:12.560
 when you look back in time, remember every aspect.

49:12.560 --> 49:14.560
 You just need to remember the important aspects.

49:14.560 --> 49:15.560
 That's right.

49:15.560 --> 49:19.560
 The network has to learn to put the important stuff into memory

49:19.560 --> 49:23.560
 and to ignore the unimportant noise.

49:23.560 --> 49:28.560
 But in that sense, deeper and deeper is better?

49:28.560 --> 49:30.560
 Or is there a limitation?

49:30.560 --> 49:36.560
 I mean LSTM is one of the great examples of architectures

49:36.560 --> 49:41.560
 that do something beyond just deeper and deeper networks.

49:41.560 --> 49:47.560
 There's clever mechanisms for filtering data for remembering and forgetting.

49:47.560 --> 49:51.560
 So do you think that kind of thinking is necessary?

49:51.560 --> 49:54.560
 If you think about LSTMs as a leap, a big leap forward

49:54.560 --> 50:01.560
 over traditional vanilla RNNs, what do you think is the next leap

50:01.560 --> 50:03.560
 within this context?

50:03.560 --> 50:08.560
 So LSTM is a very clever improvement, but LSTMs still don't

50:08.560 --> 50:13.560
 have the same kind of ability to see far back in the past

50:13.560 --> 50:18.560
 as humans do, the credit assignment problem across way back,

50:18.560 --> 50:24.560
 not just 50 time steps or 100 or 1,000, but millions and billions.

50:24.560 --> 50:28.560
 It's not clear what are the practical limits of the LSTM

50:28.560 --> 50:30.560
 when it comes to looking back.

50:30.560 --> 50:35.560
 Already in 2006, I think, we had examples where not only

50:35.560 --> 50:40.560
 looked back tens of thousands of steps, but really millions of steps.

50:40.560 --> 50:46.560
 And Juan Perez Ortiz in my lab, I think was the first author of a paper

50:46.560 --> 50:51.560
 where we really, was it 2006 or something, had examples where it

50:51.560 --> 50:56.560
 learned to look back for more than 10 million steps.

50:56.560 --> 51:02.560
 So for most problems of speech recognition, it's not

51:02.560 --> 51:06.560
 necessary to look that far back, but there are examples where it does.

51:06.560 --> 51:12.560
 Now, the looking back thing, that's rather easy because there is only

51:12.560 --> 51:17.560
 one past, but there are many possible futures.

51:17.560 --> 51:21.560
 And so a reinforcement learning system, which is trying to maximize

51:21.560 --> 51:26.560
 its future expected reward and doesn't know yet which of these

51:26.560 --> 51:31.560
 many possible futures should I select, given this one single past,

51:31.560 --> 51:36.560
 is facing problems that the LSTM by itself cannot solve.

51:36.560 --> 51:40.560
 So the LSTM is good for coming up with a compact representation

51:40.560 --> 51:46.560
 of the history so far, of the history and of observations and actions so far.

51:46.560 --> 51:53.560
 But now, how do you plan in an efficient and good way among all these,

51:53.560 --> 51:57.560
 how do you select one of these many possible action sequences

51:57.560 --> 52:02.560
 that a reinforcement learning system has to consider to maximize

52:02.560 --> 52:05.560
 reward in this unknown future.

52:05.560 --> 52:11.560
 So again, we have this basic setup where you have one recon network,

52:11.560 --> 52:16.560
 which gets in the video and the speech and whatever, and it's

52:16.560 --> 52:19.560
 executing the actions and it's trying to maximize reward.

52:19.560 --> 52:24.560
 So there is no teacher who tells it what to do at which point in time.

52:24.560 --> 52:29.560
 And then there's the other network, which is just predicting

52:29.560 --> 52:32.560
 what's going to happen if I do that and then.

52:32.560 --> 52:36.560
 And that could be an LSTM network, and it learns to look back

52:36.560 --> 52:41.560
 all the way to make better predictions of the next time step.

52:41.560 --> 52:45.560
 So essentially, although it's predicting only the next time step,

52:45.560 --> 52:50.560
 it is motivated to learn to put into memory something that happened

52:50.560 --> 52:54.560
 maybe a million steps ago because it's important to memorize that

52:54.560 --> 52:58.560
 if you want to predict that at the next time step, the next event.

52:58.560 --> 53:03.560
 Now, how can a model of the world like that,

53:03.560 --> 53:07.560
 a predictive model of the world be used by the first guy,

53:07.560 --> 53:11.560
 let's call it the controller and the model, the controller and the model.

53:11.560 --> 53:16.560
 How can the model be used by the controller to efficiently select

53:16.560 --> 53:19.560
 among these many possible futures?

53:19.560 --> 53:23.560
 The naive way we had about 30 years ago was

53:23.560 --> 53:27.560
 let's just use the model of the world as a stand in,

53:27.560 --> 53:29.560
 as a simulation of the world.

53:29.560 --> 53:32.560
 And millisecond by millisecond we plan the future

53:32.560 --> 53:36.560
 and that means we have to roll it out really in detail

53:36.560 --> 53:38.560
 and it will work only if the model is really good

53:38.560 --> 53:40.560
 and it will still be inefficient

53:40.560 --> 53:43.560
 because we have to look at all these possible futures

53:43.560 --> 53:45.560
 and there are so many of them.

53:45.560 --> 53:50.560
 So instead, what we do now since 2015 in our CN systems,

53:50.560 --> 53:54.560
 controller model systems, we give the controller the opportunity

53:54.560 --> 54:00.560
 to learn by itself how to use the potentially relevant parts

54:00.560 --> 54:05.560
 of the model network to solve new problems more quickly.

54:05.560 --> 54:09.560
 And if it wants to, it can learn to ignore the M

54:09.560 --> 54:12.560
 and sometimes it's a good idea to ignore the M

54:12.560 --> 54:15.560
 because it's really bad, it's a bad predictor

54:15.560 --> 54:18.560
 in this particular situation of life

54:18.560 --> 54:22.560
 where the controller is currently trying to maximize reward.

54:22.560 --> 54:26.560
 However, it can also learn to address and exploit

54:26.560 --> 54:32.560
 some of the subprograms that came about in the model network

54:32.560 --> 54:35.560
 through compressing the data by predicting it.

54:35.560 --> 54:40.560
 So it now has an opportunity to reuse that code,

54:40.560 --> 54:43.560
 the algorithmic information in the model network

54:43.560 --> 54:47.560
 to reduce its own search space,

54:47.560 --> 54:50.560
 search that it can solve a new problem more quickly

54:50.560 --> 54:52.560
 than without the model.

54:52.560 --> 54:54.560
 Compression.

54:54.560 --> 54:58.560
 So you're ultimately optimistic and excited

54:58.560 --> 55:02.560
 about the power of reinforcement learning

55:02.560 --> 55:04.560
 in the context of real systems.

55:04.560 --> 55:06.560
 Absolutely, yeah.

55:06.560 --> 55:11.560
 So you see RL as a potential having a huge impact

55:11.560 --> 55:15.560
 beyond just sort of the M part is often developed

55:15.560 --> 55:19.560
 on supervised learning methods.

55:19.560 --> 55:25.560
 You see RL as a, for problems of cell driving cars

55:25.560 --> 55:28.560
 or any kind of applied side robotics,

55:28.560 --> 55:33.560
 that's the correct, interesting direction for researching you.

55:33.560 --> 55:35.560
 I do think so.

55:35.560 --> 55:37.560
 We have a company called Nasense,

55:37.560 --> 55:43.560
 which has applied reinforcement learning to little Audis.

55:43.560 --> 55:45.560
 Little Audis.

55:45.560 --> 55:47.560
 Which learn to park without a teacher.

55:47.560 --> 55:51.560
 The same principles were used, of course.

55:51.560 --> 55:54.560
 So these little Audis, they are small, maybe like that,

55:54.560 --> 55:57.560
 so much smaller than the RL Audis.

55:57.560 --> 56:00.560
 But they have all the sensors that you find in the RL Audis.

56:00.560 --> 56:03.560
 You find the cameras, the LIDAR sensors.

56:03.560 --> 56:08.560
 They go up to 120 kilometers an hour if they want to.

56:08.560 --> 56:12.560
 And they have pain sensors, basically.

56:12.560 --> 56:16.560
 And they don't want to bump against obstacles and other Audis.

56:16.560 --> 56:21.560
 And so they must learn like little babies to park.

56:21.560 --> 56:25.560
 Take the raw vision input and translate that into actions

56:25.560 --> 56:28.560
 that lead to successful parking behavior,

56:28.560 --> 56:30.560
 which is a rewarding thing.

56:30.560 --> 56:32.560
 And yes, they learn that.

56:32.560 --> 56:34.560
 So we have examples like that.

56:34.560 --> 56:36.560
 And it's only in the beginning.

56:36.560 --> 56:38.560
 This is just a tip of the iceberg.

56:38.560 --> 56:44.560
 And I believe the next wave of AI is going to be all about that.

56:44.560 --> 56:47.560
 So at the moment, the current wave of AI is about

56:47.560 --> 56:51.560
 passive pattern observation and prediction.

56:51.560 --> 56:54.560
 And that's what you have on your smartphone

56:54.560 --> 56:58.560
 and what the major companies on the Pacific Rim are using

56:58.560 --> 57:01.560
 to sell you ads to do marketing.

57:01.560 --> 57:04.560
 That's the current sort of profit in AI.

57:04.560 --> 57:09.560
 And that's only one or two percent of the wild economy,

57:09.560 --> 57:11.560
 which is big enough to make these companies

57:11.560 --> 57:14.560
 pretty much the most valuable companies in the world.

57:14.560 --> 57:19.560
 But there's a much, much bigger fraction of the economy

57:19.560 --> 57:21.560
 going to be affected by the next wave,

57:21.560 --> 57:25.560
 which is really about machines that shape the data

57:25.560 --> 57:27.560
 through their own actions.

57:27.560 --> 57:32.560
 Do you think simulation is ultimately the biggest way

57:32.560 --> 57:36.560
 that those methods will be successful in the next 10, 20 years?

57:36.560 --> 57:38.560
 We're not talking about 100 years from now.

57:38.560 --> 57:42.560
 We're talking about sort of the near term impact of RL.

57:42.560 --> 57:44.560
 Do you think really good simulation is required?

57:44.560 --> 57:48.560
 Or is there other techniques like imitation learning,

57:48.560 --> 57:53.560
 observing other humans operating in the real world?

57:53.560 --> 57:57.560
 Where do you think this success will come from?

57:57.560 --> 58:01.560
 So at the moment we have a tendency of using

58:01.560 --> 58:06.560
 physics simulations to learn behavior for machines

58:06.560 --> 58:13.560
 that learn to solve problems that humans also do not know how to solve.

58:13.560 --> 58:15.560
 However, this is not the future,

58:15.560 --> 58:19.560
 because the future is in what little babies do.

58:19.560 --> 58:22.560
 They don't use a physics engine to simulate the world.

58:22.560 --> 58:25.560
 They learn a predictive model of the world,

58:25.560 --> 58:29.560
 which maybe sometimes is wrong in many ways,

58:29.560 --> 58:34.560
 but captures all kinds of important abstract high level predictions

58:34.560 --> 58:37.560
 which are really important to be successful.

58:37.560 --> 58:42.560
 And that's what was the future 30 years ago

58:42.560 --> 58:44.560
 when we started that type of research,

58:44.560 --> 58:45.560
 but it's still the future,

58:45.560 --> 58:50.560
 and now we know much better how to move forward

58:50.560 --> 58:54.560
 and to really make working systems based on that,

58:54.560 --> 58:57.560
 where you have a learning model of the world,

58:57.560 --> 59:00.560
 a model of the world that learns to predict what's going to happen

59:00.560 --> 59:01.560
 if I do that and that,

59:01.560 --> 59:06.560
 and then the controller uses that model

59:06.560 --> 59:11.560
 to more quickly learn successful action sequences.

59:11.560 --> 59:13.560
 And then of course always this curiosity thing,

59:13.560 --> 59:15.560
 in the beginning the model is stupid,

59:15.560 --> 59:17.560
 so the controller should be motivated

59:17.560 --> 59:20.560
 to come up with experiments, with action sequences

59:20.560 --> 59:23.560
 that lead to data that improve the model.

59:23.560 --> 59:26.560
 Do you think improving the model,

59:26.560 --> 59:30.560
 constructing an understanding of the world in this connection

59:30.560 --> 59:34.560
 is now the popular approaches have been successful

59:34.560 --> 59:38.560
 or grounded in ideas of neural networks,

59:38.560 --> 59:43.560
 but in the 80s with expert systems there's symbolic AI approaches,

59:43.560 --> 59:47.560
 which to us humans are more intuitive

59:47.560 --> 59:50.560
 in the sense that it makes sense that you build up knowledge

59:50.560 --> 59:52.560
 in this knowledge representation.

59:52.560 --> 59:56.560
 What kind of lessons can we draw into our current approaches

59:56.560 --> 1:00:00.560
 from expert systems, from symbolic AI?

1:00:00.560 --> 1:00:04.560
 So I became aware of all of that in the 80s

1:00:04.560 --> 1:00:09.560
 and back then logic programming was a huge thing.

1:00:09.560 --> 1:00:12.560
 Was it inspiring to yourself that you find it compelling

1:00:12.560 --> 1:00:16.560
 that a lot of your work was not so much in that realm,

1:00:16.560 --> 1:00:18.560
 is more in the learning systems?

1:00:18.560 --> 1:00:20.560
 Yes and no, but we did all of that.

1:00:20.560 --> 1:00:27.560
 So my first publication ever actually was 1987,

1:00:27.560 --> 1:00:31.560
 was the implementation of a genetic algorithm

1:00:31.560 --> 1:00:34.560
 of a genetic programming system in Prolog.

1:00:34.560 --> 1:00:37.560
 So Prolog, that's what you learn back then,

1:00:37.560 --> 1:00:39.560
 which is a logic programming language,

1:00:39.560 --> 1:00:45.560
 and the Japanese, they had this huge fifth generation AI project,

1:00:45.560 --> 1:00:48.560
 which was mostly about logic programming back then,

1:00:48.560 --> 1:00:53.560
 although neural networks existed and were well known back then,

1:00:53.560 --> 1:00:57.560
 and deep learning has existed since 1965,

1:00:57.560 --> 1:01:01.560
 since this guy in the Ukraine, Ivak Nenko, started it,

1:01:01.560 --> 1:01:05.560
 but the Japanese and many other people,

1:01:05.560 --> 1:01:07.560
 they focused really on this logic programming,

1:01:07.560 --> 1:01:10.560
 and I was influenced to the extent that I said,

1:01:10.560 --> 1:01:13.560
 okay, let's take these biologically inspired algorithms

1:01:13.560 --> 1:01:16.560
 like evolution, programs,

1:01:16.560 --> 1:01:22.560
 and implement that in the language which I know,

1:01:22.560 --> 1:01:24.560
 which was Prolog, for example, back then.

1:01:24.560 --> 1:01:28.560
 And then in many ways this came back later,

1:01:28.560 --> 1:01:31.560
 because the Goudel machine, for example,

1:01:31.560 --> 1:01:33.560
 has a proof searcher on board,

1:01:33.560 --> 1:01:35.560
 and without that it would not be optimal.

1:01:35.560 --> 1:01:38.560
 Well, Markus Hutter's universal algorithm

1:01:38.560 --> 1:01:40.560
 for solving all well defined problems

1:01:40.560 --> 1:01:42.560
 has a proof search on board,

1:01:42.560 --> 1:01:46.560
 so that's very much logic programming.

1:01:46.560 --> 1:01:50.560
 Without that it would not be asymptotically optimal.

1:01:50.560 --> 1:01:54.560
 But then on the other hand, because we are very pragmatic guys also,

1:01:54.560 --> 1:01:59.560
 we focused on recurrent neural networks

1:01:59.560 --> 1:02:04.560
 and suboptimal stuff such as gradient based search

1:02:04.560 --> 1:02:09.560
 and program space rather than provably optimal things.

1:02:09.560 --> 1:02:13.560
 So logic programming certainly has a usefulness

1:02:13.560 --> 1:02:17.560
 when you're trying to construct something provably optimal

1:02:17.560 --> 1:02:19.560
 or provably good or something like that,

1:02:19.560 --> 1:02:22.560
 but is it useful for practical problems?

1:02:22.560 --> 1:02:24.560
 It's really useful for our theorem proving.

1:02:24.560 --> 1:02:28.560
 The best theorem proofers today are not neural networks.

1:02:28.560 --> 1:02:31.560
 No, they are logic programming systems

1:02:31.560 --> 1:02:35.560
 that are much better theorem proofers than most math students

1:02:35.560 --> 1:02:38.560
 in the first or second semester.

1:02:38.560 --> 1:02:42.560
 But for reasoning, for playing games of Go, or chess,

1:02:42.560 --> 1:02:46.560
 or for robots, autonomous vehicles that operate in the real world,

1:02:46.560 --> 1:02:50.560
 or object manipulation, you think learning...

1:02:50.560 --> 1:02:53.560
 Yeah, as long as the problems have little to do

1:02:53.560 --> 1:02:58.560
 with theorem proving themselves,

1:02:58.560 --> 1:03:01.560
 then as long as that is not the case,

1:03:01.560 --> 1:03:05.560
 you just want to have better pattern recognition.

1:03:05.560 --> 1:03:09.560
 So to build a self trying car, you want to have better pattern recognition

1:03:09.560 --> 1:03:13.560
 and pedestrian recognition and all these things,

1:03:13.560 --> 1:03:18.560
 and you want to minimize the number of false positives,

1:03:18.560 --> 1:03:22.560
 which is currently slowing down self trying cars in many ways.

1:03:22.560 --> 1:03:27.560
 And all of that has very little to do with logic programming.

1:03:27.560 --> 1:03:32.560
 What are you most excited about in terms of directions

1:03:32.560 --> 1:03:36.560
 of artificial intelligence at this moment in the next few years,

1:03:36.560 --> 1:03:41.560
 in your own research and in the broader community?

1:03:41.560 --> 1:03:44.560
 So I think in the not so distant future,

1:03:44.560 --> 1:03:52.560
 we will have for the first time little robots that learn like kids.

1:03:52.560 --> 1:03:57.560
 And I will be able to say to the robot,

1:03:57.560 --> 1:04:00.560
 look here robot, we are going to assemble a smartphone.

1:04:00.560 --> 1:04:05.560
 Let's take this slab of plastic and the screwdriver

1:04:05.560 --> 1:04:08.560
 and let's screw in the screw like that.

1:04:08.560 --> 1:04:11.560
 No, not like that, like that.

1:04:11.560 --> 1:04:13.560
 Not like that, like that.

1:04:13.560 --> 1:04:17.560
 And I don't have a data glove or something.

1:04:17.560 --> 1:04:20.560
 He will see me and he will hear me

1:04:20.560 --> 1:04:24.560
 and he will try to do something with his own actuators,

1:04:24.560 --> 1:04:26.560
 which will be really different from mine,

1:04:26.560 --> 1:04:28.560
 but he will understand the difference

1:04:28.560 --> 1:04:34.560
 and will learn to imitate me but not in the supervised way

1:04:34.560 --> 1:04:40.560
 where a teacher is giving target signals for all his muscles all the time.

1:04:40.560 --> 1:04:43.560
 No, by doing this high level imitation

1:04:43.560 --> 1:04:46.560
 where he first has to learn to imitate me

1:04:46.560 --> 1:04:50.560
 and to interpret these additional noises coming from my mouth

1:04:50.560 --> 1:04:54.560
 as helpful signals to do that pattern.

1:04:54.560 --> 1:05:00.560
 And then it will by itself come up with faster ways

1:05:00.560 --> 1:05:03.560
 and more efficient ways of doing the same thing.

1:05:03.560 --> 1:05:07.560
 And finally, I stop his learning algorithm

1:05:07.560 --> 1:05:10.560
 and make a million copies and sell it.

1:05:10.560 --> 1:05:13.560
 And so at the moment this is not possible,

1:05:13.560 --> 1:05:16.560
 but we already see how we are going to get there.

1:05:16.560 --> 1:05:21.560
 And you can imagine to the extent that this works economically and cheaply,

1:05:21.560 --> 1:05:24.560
 it's going to change everything.

1:05:24.560 --> 1:05:30.560
 Almost all our production is going to be affected by that.

1:05:30.560 --> 1:05:33.560
 And a much bigger wave,

1:05:33.560 --> 1:05:36.560
 a much bigger AI wave is coming

1:05:36.560 --> 1:05:38.560
 than the one that we are currently witnessing,

1:05:38.560 --> 1:05:41.560
 which is mostly about passive pattern recognition on your smartphone.

1:05:41.560 --> 1:05:47.560
 This is about active machines that shapes data through the actions they are executing

1:05:47.560 --> 1:05:51.560
 and they learn to do that in a good way.

1:05:51.560 --> 1:05:56.560
 So many of the traditional industries are going to be affected by that.

1:05:56.560 --> 1:06:00.560
 All the companies that are building machines

1:06:00.560 --> 1:06:05.560
 will equip these machines with cameras and other sensors

1:06:05.560 --> 1:06:10.560
 and they are going to learn to solve all kinds of problems.

1:06:10.560 --> 1:06:14.560
 Through interaction with humans, but also a lot on their own

1:06:14.560 --> 1:06:18.560
 to improve what they already can do.

1:06:18.560 --> 1:06:23.560
 And lots of old economy is going to be affected by that.

1:06:23.560 --> 1:06:28.560
 And in recent years I have seen that old economy is actually waking up

1:06:28.560 --> 1:06:31.560
 and realizing that this is the case.

1:06:31.560 --> 1:06:35.560
 Are you optimistic about that future? Are you concerned?

1:06:35.560 --> 1:06:40.560
 There's a lot of people concerned in the near term about the transformation

1:06:40.560 --> 1:06:42.560
 of the nature of work.

1:06:42.560 --> 1:06:45.560
 The kind of ideas that you just suggested

1:06:45.560 --> 1:06:48.560
 would have a significant impact on what kind of things could be automated.

1:06:48.560 --> 1:06:51.560
 Are you optimistic about that future?

1:06:51.560 --> 1:06:54.560
 Are you nervous about that future?

1:06:54.560 --> 1:07:01.560
 And looking a little bit farther into the future, there's people like Gila Musk

1:07:01.560 --> 1:07:06.560
 still wrestle concerned about the existential threats of that future.

1:07:06.560 --> 1:07:10.560
 So in the near term, job loss in the long term existential threat,

1:07:10.560 --> 1:07:15.560
 are these concerns to you or are you ultimately optimistic?

1:07:15.560 --> 1:07:22.560
 So let's first address the near future.

1:07:22.560 --> 1:07:27.560
 We have had predictions of job losses for many decades.

1:07:27.560 --> 1:07:32.560
 For example, when industrial robots came along,

1:07:32.560 --> 1:07:37.560
 many people predicted that lots of jobs are going to get lost.

1:07:37.560 --> 1:07:41.560
 And in a sense, they were right,

1:07:41.560 --> 1:07:45.560
 because back then there were car factories

1:07:45.560 --> 1:07:50.560
 and hundreds of people in these factories assembled cars.

1:07:50.560 --> 1:07:53.560
 And today the same car factories have hundreds of robots

1:07:53.560 --> 1:07:58.560
 and maybe three guys watching the robots.

1:07:58.560 --> 1:08:04.560
 On the other hand, those countries that have lots of robots per capita,

1:08:04.560 --> 1:08:09.560
 Japan, Korea, Germany, Switzerland, a couple of other countries,

1:08:09.560 --> 1:08:13.560
 they have really low unemployment rates.

1:08:13.560 --> 1:08:17.560
 Somehow all kinds of new jobs were created.

1:08:17.560 --> 1:08:22.560
 Back then nobody anticipated those jobs.

1:08:22.560 --> 1:08:26.560
 And decades ago, I already said,

1:08:26.560 --> 1:08:31.560
 it's really easy to say which jobs are going to get lost,

1:08:31.560 --> 1:08:35.560
 but it's really hard to predict the new ones.

1:08:35.560 --> 1:08:38.560
 30 years ago, who would have predicted all these people

1:08:38.560 --> 1:08:44.560
 making money as YouTube bloggers, for example?

1:08:44.560 --> 1:08:51.560
 200 years ago, 60% of all people used to work in agriculture.

1:08:51.560 --> 1:08:55.560
 Today, maybe 1%.

1:08:55.560 --> 1:09:01.560
 But still, only, I don't know, 5% unemployment.

1:09:01.560 --> 1:09:03.560
 Lots of new jobs were created.

1:09:03.560 --> 1:09:07.560
 And Homo Ludens, the playing man,

1:09:07.560 --> 1:09:10.560
 is inventing new jobs all the time.

1:09:10.560 --> 1:09:15.560
 Most of these jobs are not existentially necessary

1:09:15.560 --> 1:09:18.560
 for the survival of our species.

1:09:18.560 --> 1:09:22.560
 There are only very few existentially necessary jobs

1:09:22.560 --> 1:09:27.560
 such as farming and building houses and warming up the houses,

1:09:27.560 --> 1:09:30.560
 but less than 10% of the population is doing that.

1:09:30.560 --> 1:09:37.560
 And most of these newly invented jobs are about interacting with other people

1:09:37.560 --> 1:09:40.560
 in new ways, through new media and so on,

1:09:40.560 --> 1:09:45.560
 getting new types of kudos and forms of likes and whatever,

1:09:45.560 --> 1:09:47.560
 and even making money through that.

1:09:47.560 --> 1:09:52.560
 So, Homo Ludens, the playing man, doesn't want to be unemployed,

1:09:52.560 --> 1:09:56.560
 and that's why he's inventing new jobs all the time.

1:09:56.560 --> 1:10:01.560
 And he keeps considering these jobs as really important

1:10:01.560 --> 1:10:07.560
 and is investing a lot of energy and hours of work into those new jobs.

1:10:07.560 --> 1:10:09.560
 That's quite beautifully put.

1:10:09.560 --> 1:10:11.560
 We're really nervous about the future

1:10:11.560 --> 1:10:14.560
 because we can't predict what kind of new jobs will be created.

1:10:14.560 --> 1:10:20.560
 But you're ultimately optimistic that we humans are so restless

1:10:20.560 --> 1:10:24.560
 that we create and give meaning to newer and newer jobs,

1:10:24.560 --> 1:10:29.560
 telling you things that get likes on Facebook

1:10:29.560 --> 1:10:31.560
 or whatever the social platform is.

1:10:31.560 --> 1:10:36.560
 So, what about long term existential threat of AI

1:10:36.560 --> 1:10:40.560
 where our whole civilization may be swallowed up

1:10:40.560 --> 1:10:44.560
 by this ultra super intelligent systems?

1:10:44.560 --> 1:10:47.560
 Maybe it's not going to be swallowed up,

1:10:47.560 --> 1:10:55.560
 but I'd be surprised if we humans were the last step

1:10:55.560 --> 1:10:59.560
 in the evolution of the universe.

1:10:59.560 --> 1:11:03.560
 You've actually had this beautiful comment somewhere

1:11:03.560 --> 1:11:08.560
 that I've seen saying that artificial...

1:11:08.560 --> 1:11:11.560
 Quite insightful, artificial intelligence systems

1:11:11.560 --> 1:11:15.560
 just like us humans will likely not want to interact with humans.

1:11:15.560 --> 1:11:17.560
 They'll just interact amongst themselves,

1:11:17.560 --> 1:11:20.560
 just like ants interact amongst themselves

1:11:20.560 --> 1:11:24.560
 and only tangentially interact with humans.

1:11:24.560 --> 1:11:28.560
 And it's quite an interesting idea that once we create AGI

1:11:28.560 --> 1:11:31.560
 that will lose interest in humans

1:11:31.560 --> 1:11:34.560
 and have compete for their own Facebook likes

1:11:34.560 --> 1:11:36.560
 and their own social platforms.

1:11:36.560 --> 1:11:40.560
 So, within that quite elegant idea,

1:11:40.560 --> 1:11:44.560
 how do we know in a hypothetical sense

1:11:44.560 --> 1:11:48.560
 that there's not already intelligent systems out there?

1:11:48.560 --> 1:11:52.560
 How do you think broadly of general intelligence

1:11:52.560 --> 1:11:56.560
 greater than us, how do we know it's out there?

1:11:56.560 --> 1:12:01.560
 How do we know it's around us and could it already be?

1:12:01.560 --> 1:12:04.560
 I'd be surprised if within the next few decades

1:12:04.560 --> 1:12:10.560
 or something like that we won't have AIs

1:12:10.560 --> 1:12:12.560
 that are truly smart in every single way

1:12:12.560 --> 1:12:17.560
 and better problem solvers in almost every single important way.

1:12:17.560 --> 1:12:22.560
 And I'd be surprised if they wouldn't realize

1:12:22.560 --> 1:12:24.560
 what we have realized a long time ago,

1:12:24.560 --> 1:12:29.560
 which is that almost all physical resources are not here

1:12:29.560 --> 1:12:36.560
 in this biosphere, but throughout the rest of the solar system

1:12:36.560 --> 1:12:42.560
 gets two billion times more solar energy than our little planet.

1:12:42.560 --> 1:12:46.560
 There's lots of material out there that you can use

1:12:46.560 --> 1:12:51.560
 to build robots and self replicating robot factories and all this stuff.

1:12:51.560 --> 1:12:53.560
 And they are going to do that.

1:12:53.560 --> 1:12:56.560
 And they will be scientists and curious

1:12:56.560 --> 1:12:59.560
 and they will explore what they can do.

1:12:59.560 --> 1:13:04.560
 And in the beginning they will be fascinated by life

1:13:04.560 --> 1:13:07.560
 and by their own origins in our civilization.

1:13:07.560 --> 1:13:09.560
 They will want to understand that completely,

1:13:09.560 --> 1:13:13.560
 just like people today would like to understand how life works

1:13:13.560 --> 1:13:22.560
 and also the history of our own existence and civilization

1:13:22.560 --> 1:13:26.560
 and also the physical laws that created all of them.

1:13:26.560 --> 1:13:29.560
 So in the beginning they will be fascinated by life

1:13:29.560 --> 1:13:33.560
 once they understand it, they lose interest,

1:13:33.560 --> 1:13:39.560
 like anybody who loses interest in things he understands.

1:13:39.560 --> 1:13:43.560
 And then, as you said,

1:13:43.560 --> 1:13:50.560
 the most interesting sources of information for them

1:13:50.560 --> 1:13:57.560
 will be others of their own kind.

1:13:57.560 --> 1:14:01.560
 So, at least in the long run,

1:14:01.560 --> 1:14:06.560
 there seems to be some sort of protection

1:14:06.560 --> 1:14:11.560
 through lack of interest on the other side.

1:14:11.560 --> 1:14:16.560
 And now it seems also clear, as far as we understand physics,

1:14:16.560 --> 1:14:20.560
 you need matter and energy to compute

1:14:20.560 --> 1:14:22.560
 and to build more robots and infrastructure

1:14:22.560 --> 1:14:28.560
 and more AI civilization and AI ecologies

1:14:28.560 --> 1:14:31.560
 consisting of trillions of different types of AI's.

1:14:31.560 --> 1:14:34.560
 And so it seems inconceivable to me

1:14:34.560 --> 1:14:37.560
 that this thing is not going to expand.

1:14:37.560 --> 1:14:41.560
 Some AI ecology not controlled by one AI

1:14:41.560 --> 1:14:44.560
 but trillions of different types of AI's competing

1:14:44.560 --> 1:14:47.560
 in all kinds of quickly evolving

1:14:47.560 --> 1:14:49.560
 and disappearing ecological niches

1:14:49.560 --> 1:14:52.560
 in ways that we cannot fathom at the moment.

1:14:52.560 --> 1:14:54.560
 But it's going to expand,

1:14:54.560 --> 1:14:56.560
 limited by light speed and physics,

1:14:56.560 --> 1:15:00.560
 but it's going to expand and now we realize

1:15:00.560 --> 1:15:02.560
 that the universe is still young.

1:15:02.560 --> 1:15:05.560
 It's only 13.8 billion years old

1:15:05.560 --> 1:15:10.560
 and it's going to be a thousand times older than that.

1:15:10.560 --> 1:15:13.560
 So there's plenty of time

1:15:13.560 --> 1:15:16.560
 to conquer the entire universe

1:15:16.560 --> 1:15:19.560
 and to fill it with intelligence

1:15:19.560 --> 1:15:21.560
 and send us in receivers such that

1:15:21.560 --> 1:15:25.560
 AI's can travel the way they are traveling

1:15:25.560 --> 1:15:27.560
 in our labs today,

1:15:27.560 --> 1:15:31.560
 which is by radio from sender to receiver.

1:15:31.560 --> 1:15:35.560
 And let's call the current age of the universe one eon.

1:15:35.560 --> 1:15:38.560
 One eon.

1:15:38.560 --> 1:15:41.560
 Now it will take just a few eons from now

1:15:41.560 --> 1:15:43.560
 and the entire visible universe

1:15:43.560 --> 1:15:46.560
 is going to be full of that stuff.

1:15:46.560 --> 1:15:48.560
 And let's look ahead to a time

1:15:48.560 --> 1:15:50.560
 when the universe is going to be

1:15:50.560 --> 1:15:52.560
 one thousand times older than it is now.

1:15:52.560 --> 1:15:54.560
 They will look back and they will say,

1:15:54.560 --> 1:15:56.560
 look almost immediately after the Big Bang,

1:15:56.560 --> 1:15:59.560
 only a few eons later,

1:15:59.560 --> 1:16:02.560
 the entire universe started to become intelligent.

1:16:02.560 --> 1:16:05.560
 Now to your question,

1:16:05.560 --> 1:16:08.560
 how do we see whether anything like that

1:16:08.560 --> 1:16:12.560
 has already happened or is already in a more advanced stage

1:16:12.560 --> 1:16:14.560
 in some other part of the universe,

1:16:14.560 --> 1:16:16.560
 of the visible universe?

1:16:16.560 --> 1:16:18.560
 We are trying to look out there

1:16:18.560 --> 1:16:20.560
 and nothing like that has happened so far.

1:16:20.560 --> 1:16:22.560
 Or is that true?

1:16:22.560 --> 1:16:24.560
 Do you think we would recognize it?

1:16:24.560 --> 1:16:26.560
 How do we know it's not among us?

1:16:26.560 --> 1:16:30.560
 How do we know planets aren't in themselves intelligent beings?

1:16:30.560 --> 1:16:36.560
 How do we know ants seen as a collective

1:16:36.560 --> 1:16:39.560
 are not much greater intelligence than our own?

1:16:39.560 --> 1:16:41.560
 These kinds of ideas.

1:16:41.560 --> 1:16:44.560
 When I was a boy, I was thinking about these things

1:16:44.560 --> 1:16:48.560
 and I thought, hmm, maybe it has already happened.

1:16:48.560 --> 1:16:50.560
 Because back then I knew,

1:16:50.560 --> 1:16:53.560
 I learned from popular physics books,

1:16:53.560 --> 1:16:57.560
 that the structure, the large scale structure of the universe

1:16:57.560 --> 1:16:59.560
 is not homogeneous.

1:16:59.560 --> 1:17:02.560
 And you have these clusters of galaxies

1:17:02.560 --> 1:17:07.560
 and then in between there are these huge empty spaces.

1:17:07.560 --> 1:17:11.560
 And I thought, hmm, maybe they aren't really empty.

1:17:11.560 --> 1:17:13.560
 It's just that in the middle of that

1:17:13.560 --> 1:17:16.560
 some AI civilization already has expanded

1:17:16.560 --> 1:17:22.560
 and then has covered a bubble of a billion light years time

1:17:22.560 --> 1:17:26.560
 using all the energy of all the stars within that bubble

1:17:26.560 --> 1:17:29.560
 for its own unfathomable practices.

1:17:29.560 --> 1:17:34.560
 And so it always happened and we just failed to interpret the signs.

1:17:34.560 --> 1:17:39.560
 But then I learned that gravity by itself

1:17:39.560 --> 1:17:42.560
 explains the large scale structure of the universe

1:17:42.560 --> 1:17:45.560
 and that this is not a convincing explanation.

1:17:45.560 --> 1:17:50.560
 And then I thought maybe it's the dark matter

1:17:50.560 --> 1:17:54.560
 because as far as we know today

1:17:54.560 --> 1:18:00.560
 80% of the measurable matter is invisible.

1:18:00.560 --> 1:18:03.560
 And we know that because otherwise our galaxy

1:18:03.560 --> 1:18:06.560
 or other galaxies would fall apart.

1:18:06.560 --> 1:18:09.560
 They are rotating too quickly.

1:18:09.560 --> 1:18:14.560
 And then the idea was maybe all of these

1:18:14.560 --> 1:18:17.560
 AI civilizations that are already out there,

1:18:17.560 --> 1:18:22.560
 they are just invisible

1:18:22.560 --> 1:18:24.560
 because they are really efficient in using the energies

1:18:24.560 --> 1:18:26.560
 out their own local systems

1:18:26.560 --> 1:18:29.560
 and that's why they appear dark to us.

1:18:29.560 --> 1:18:31.560
 But this is also not a convincing explanation

1:18:31.560 --> 1:18:34.560
 because then the question becomes

1:18:34.560 --> 1:18:41.560
 why are there still any visible stars left in our own galaxy

1:18:41.560 --> 1:18:44.560
 which also must have a lot of dark matter.

1:18:44.560 --> 1:18:46.560
 So that is also not a convincing thing.

1:18:46.560 --> 1:18:53.560
 And today I like to think it's quite plausible

1:18:53.560 --> 1:18:56.560
 that maybe we are the first, at least in our local light cone

1:18:56.560 --> 1:19:04.560
 within the few hundreds of millions of light years

1:19:04.560 --> 1:19:08.560
 that we can reliably observe.

1:19:08.560 --> 1:19:10.560
 Is that exciting to you?

1:19:10.560 --> 1:19:12.560
 That we might be the first?

1:19:12.560 --> 1:19:16.560
 It would make us much more important

1:19:16.560 --> 1:19:20.560
 because if we mess it up through a nuclear war

1:19:20.560 --> 1:19:25.560
 then maybe this will have an effect

1:19:25.560 --> 1:19:30.560
 on the development of the entire universe.

1:19:30.560 --> 1:19:32.560
 So let's not mess it up.

1:19:32.560 --> 1:19:33.560
 Let's not mess it up.

1:19:33.560 --> 1:19:35.560
 Jürgen, thank you so much for talking today.

1:19:35.560 --> 1:19:36.560
 I really appreciate it.

1:19:36.560 --> 1:19:42.560
 It's my pleasure.