Yan laon the head of meta's AI division 
just did a 3our interview with Lex 
fredman in which he covered robots AGI 
and so much more I went through the 
whole interview I grabbed the bits that 
I thought were most interesting and I 
encourage you to watch the whole video 
I'll drop a link to that in the 
description below in this first clip Yan 
talks about how autor regressive llms 
basically the large language models that 
we all know today like open AI chaty PT 
won't get us to AGI Yan is a huge 
proponent of Open Source but he doesn't 
think our current technology will allow 
us to achieve AGI let's watch for a 
number of reasons the first is that 
there is a number of characteristics of 
intelligent behavior for example the 
capacity to understand the world 
understand the physical world the 
ability to remember and retrieve things 
persistent memory the ability to reason 
and the ability to plan those are four 
essential characteristic of intelligent 
systems or entities humans animals LMS 
can do none of those or they can only do 
them in a very primitive way they don't 
really understand the physical world 
they don't really have persistent memory 
they can't really reason and they 
certainly can't plan if you expect the 
system to become intelligent just you 
know without having the possibility of 
doing those things uh you're making a 
mistake and then next following up on 
the premise that our current 
Technologies won't get us to AGI Yan 
talks about the amount of data necessary 
to train large language models as 
compared to what humans use to you know 
essentially train our own minds and he 
says that the amount of data used to 
train large language models is actually 
not that much compared to what humans 
need and this is where we start to get 
into synthetic data and I've been 
preparing my thoughts about synthetic 
data quite a bit because the fact of the 
matter is I don't think humans are going 
to Pro I don't think humans are going to 
produce enough data to actually reach 
AGI using large language models even if 
our current architecture could allow us 
to get there so synthetic data meaning 
meaning data that has been created by 
other artificial intelligence is one of 
those necessary ingredients to reaching 
AG let's take a look at this video those 
llms are trained on enormous amounts of 
text basically the entirety of all 
publicly available texts on the internet 
right that's typically on the order of 
10 to the 13 tokens each token is 
typically two byit so that's two 10 to 
the 13 bytes as training data it would 
take you or me 170,000 years to just 
read through this at 8 hours a day uh so 
it seems like an enormous amount of 
knowledge right that those systems can 
accumulate but then you realize it's 
really not that much data if you you 
talk to developmental psychologist and 
they tell you a four-year-old has been 
awake for 16,000 hours in his or life 
and the amount of information that has 
reached the visual cortex of that child 
in four years is about 10 to the 15 
bytes and you can compute this by 
estimating that the optical nerve carry 
about 20 meab megabytes per second 
roughly and so so 10 to the 15 bytes for 
a 4-year-old versus 2 * 10 13 bytes for 
170,000 years worth of reading what it 
tells you is that through sensory input 
we see a lot more information than we 
than we do through language and that 
despite our intuition most of what we 
learn and most of our knowledge is 
through our observation and interaction 
with the real world not through language 
everything that we learn in the first 
few years of life and certainly 
everything that animals learn has 
nothing to do with language and then 
again continuing with the large language 
models are not going to allow us to 
reach AGI he basically says that just 
using language is not enough to model 
the world to have a world model even 
though language is highly compressed he 
describes how when people are talking 
and thinking about things they're using 
much more than just language we have a 
world model inside our head and we're 
conceptualizing ideas and perceptions 
before we even convert those into 
language it's very interesting that 
think about but because of this he says 
we're not going to reach AGI with 
language alone we need some other kind 
of technology to help us augment the 
language part let's look at that video 
it's a big debate among uh philosophers 
and also cognitive scientists like 
whether intelligence needs to be 
grounded in reality uh I'm clearly in 
the camp that uh yes uh intelligence 
cannot appear without some grounding in 
uh some reality it doesn't need to be 
you know physical reality could be 
simulated but um but the environment is 
just much richer than what you can 
express in language language is a very 
approximate representation of our 
percepts and our mental models right I 
mean there there's a lot of tasks that 
we accomplish where we manipulate uh a 
mental model of uh of the situation at 
hand and that has nothing to do with 
language and next kind of proving the 
point that large language models in 
their current form aren't going to reach 
AGI Yan goes on to describe why large 
language models are really good at some 
things like creative writing like coding 
passing the bar exam and other things 
like that but really bad at other things 
like self-driving and the ability to 
Simply pick up a cup and for some reason 
large language models are really good at 
some things and really bad at others so 
let's take a look you know we have llms 
that can pass pass the bar exam so they 
must be smart but then they can't learn 
to drive in 20 hours like any 
17-year-old they can't learn to clear 
out the dinner table and fill up the 
dishwasher like any 10-year-old can 
learn in one shot why is that like you 
know what what are we missing what what 
type of learning or or reasoning 
architecture or whatever are we missing 
that basically prevent us from from you 
know having level five sing Cars and 
domestic robots and then finally kind of 
closing the loop on AGI and what's 
possible with our current large language 
model architecture he goes on to talk 
about how kind of world model reasoning 
is Out Of Reach for large language 
models as they are today he continues to 
explain that when humans are talking 
about things and conceptualizing things 
in the real world we're doing so much 
more than just speaking in our minds so 
he's really doubling down on this point 
which I find fascinating let's take a 
look at this clip we're not going to be 
able to do this with the type of llms 
that we are uh working with today and 
there's a number of reasons for this but 
uh the main reason is the way llm llms 
are trained is that you you take a piece 
of text remove some of the words in that 
text you mess them you replace by 
replace by blank markers and you train a 
gnetic neural net to predict the words 
that are missing and if you build this 
neural net in a particular way so that 
it can only look at words that are to 
the left of the one is trying to predict 
then what you have is a system that 
basically is trying to predict the next 
word in a text right so then you can 
feed it a text a prompt and you can ask 
it to predict the next word it can never 
predict the next word exactly and so 
what it's going to do is produce a 
probability distribution over all the 
possible words in your dictionary in 
fact it doesn't predict words it 
predicts tokens that are kind of subw 
units and so it's easy to handle the 
uncertainty in the prediction there 
because there's only a finite number of 
possible words in the dictionary and you 
can just compute a distribution over 
them then what you what the system does 
is that it it picks a word from that 
distribution of course there's a higher 
chance of picking words that have a 
higher probability within that 
distribution so you sample from that 
distribution to actually produce a word 
and then you shift that word into the 
input and so that allows the system not 
to predict the second word and once you 
do this you shift it into the input Etc 
that's called Auto regressive prediction 
and which is why those llms should be 
called Auto regressive llms there is a 
difference between this kind of process 
and a process by which before producing 
a word when you talk when you and I talk 
you and I are bilinguals we think about 
what we're going to say and it's 
relatively independent of the language 
in which we're going to say it when we 
we talk about like I don't know let's 
say mathematical concept or something 
the kind of thinking that we're doing 
and the answer that we're planning to 
produce is not linked to whether we're 
going to see it in French or Russian or 
English and next we continue our 
discussion about language as a world 
model now Yan is in one camp that 
believes language alone cannot be a 
world model and I want to know what you 
think do you agree or disagree when you 
see something like Sora which uses 
Transformers which uses current 
architecture that is found in large 
language models it is pretty darn good 
at simulating the world but it's not 
perfect and if it's ever going to get 
there is still a question and he 
continues to hammer the point that large 
language models are not enough to reach 
a world model but this actually reminds 
me of a couple things there's the movie 
arrival and if you haven't seen it it's 
a fantastic science fiction movie but 
the gist is aliens come down from space 
and they speak a different language and 
they have an expert linguist try to 
figure out what that language is and 
over time they figure it out and the 
language itself is what the aliens are 
there to give Humanity as kind of the 
great gift and all of a sudden because 
this expert langu now understands this 
new language she starts to gain all of 
this power and so yeah it's super 
interesting to think about that language 
actually shapes perception and that is 
something I believe in if we don't have 
a word for something then most likely we 
can't describe it and maybe we can't 
even perceive it as it really is so I 
believe language is incredibly important 
but I'll probably defer to Yan thinking 
that language alone is not enough to 
have a world model you're saying your 
thinking is same in French as it is in 
English well it it depends what kind of 
thinking right if it's just uh if it's 
like producing puns I get much better in 
French than English about that no but so 
is there an abstract representation of 
puns like is your humor an abstract like 
when you tweet and your tweets are 
sometimes a little bit spicy what's is 
there an abstract representation in your 
brain of a tweet before maps on to 
English there is an asct representation 
of uh Imagining the reaction of a reader 
to that uh text or you start with 
laughter and then figure out how to make 
that happen or figure out like a 
reaction you want to cause and and then 
figure out how to say it right so that 
it causes that reaction but that's like 
really close to language but think about 
like a ma mathematical concept or um you 
know imagining you know something you 
want to build out of wood or something 
like this right the kind of thinking 
you're doing has absolutely nothing to 
do with language really like it's not 
like you have necessarily like an 
internal monologue in any particular 
language you you're you know imagining 
mental models of of the thing right I 
mean if I if I ask you to like imagine 
what this uh water bottle will look like 
if I rotate it 90° that has nothing to 
do with language so clearly there is you 
know a more abstract level of 
representation in which we we do most of 
our thinking and we plan what we're 
going to say if the output is is you 
know uttered words as opposed to an 
output being uh you know muscle actions 
we we plan our answer before we produce 
it and LMS don't do that they just 
produce one word after the other 
instinctively if you want and in this 
next clip Yan talks about if we're able 
to have a world model based on 
prediction alone and again the way that 
large language models work is that it's 
essentially just predicting the next 
token in a sentence the next word in a 
sentence and he actually says yes just 
based on prediction alone that part of 
the technology we will be able to 
achieve AGI we will be able to achieve a 
world model but just based on language 
prediction alone he doesn't think so and 
this starts to get into simulation 
Theory so do you need to understand 
every single atom do you need to predict 
how every single atom is going to move 
to truly create a world model it turns 
out maybe not and this is actually 
something a Twitter user had replied to 
me which I had never really thought 
about with simulation Theory I always 
always thought that you had to predict 
every single atom but that's not 
necessarily true you only have to 
predict enough that the perceiver the 
person who is viewing this reality is 
convinced that it looks real so just 
enough but not everything so let's take 
a look at this clip so can you build 
this first of all by prediction and the 
answer is probably yes can you build it 
by predicting words and the answer is 
most probably no because language is 
very poor in terms of weak or low 
bandwidth if you want there's just not 
enough information there so building 
World models means observing the world 
and understanding why the world is 
evolving the way the way it is and then 
uh the the extra component of a world 
model is something that can predict how 
the world is going to evolve as a 
consequence of an action you might take 
right so World model really is here is 
my idea of the state of the world at 
time T here is an action I might take 
what is the predicted state of the world 
at time t plus one now that state of the 
world doesn't does not need to represent 
everything about the world it just needs 
to represent enough that's relevant for 
this planning of of the action but not 
necessarily all the details now here is 
the problem you're not going to be able 
to do this with generative models so a 
generative model has trained on video 
and we've tried to do this for 10 years 
you take a video show a system a piece 
of video and then ask it to predict the 
reminder of the video basically predict 
what's going to happen either one friend 
at a time or a group of friends at a 
time but yeah uh a large video model if 
you want the idea of of doing this has 
been floating around for a long time and 
at at Fair colleagues and I have been 
trying to do this for about 10 years and 
you can't you can't really do the same 
trick as with llms because you know llms 
as I said you can't predict exactly 
which word is going to follow a sequence 
of words you can predict a distribution 
of words now if you go to video what you 
would have to do is predict a 
distribution over all possible frames in 
a video and we don't really know how to 
do that properly we do not know how to 
represent distribution over High 
dimensional continuous spaces in ways 
that are useful and that's that there 
lies the main issue and the reason we 
can do this is because the world is 
incredibly more complicated and richer 
in terms of information than than text 
text is discrete video is high 
dimensional and continuous a lot of 
details in this okay in this next clip 
he breaks down why video prediction 
basically frame by frame prediction or 
even an entire video prediction is 
really hard to do and this interview 
came out after Sora so as you're 
listening to this think about what Sora 
was able to achieve and compare it to 
what Yan is saying here so if I take a 
video of this room and the video is you 
know a camera panning around there is no 
way I can predict everything that's 
going to be in the room as I pan around 
the system cannot predict what's going 
to be in the room as the camera is 
panning maybe it's going to predict this 
is this is a room where there's a light 
and there is a wall and things like that 
I can't predict what the painting on the 
wall looks like or what the texture of 
the couch looks like certainly not the 
texture of the carpet so there's no way 
I can predict all those details One Way 
possibly to handle this which we've been 
working for a long time is to have a 
model that has what's called a latent 
variable and the latent variable is fed 
to an Neal net and it's supposed to 
represent all the information about the 
world that you don't perceive yet that 
you need to augment the the system for 
the prediction to do a good job at 
predicting pixels including the fine 
texture of the of the carpet and the and 
the couch and and the painting on the 
wall that has been a complete failure 
and we've tried lots of things we tried 
uh just straight neural Nets we tried 
Gans we tried Vees all kinds of 
regularized Auto encoders we tried many 
things and in this next clip he goes 
into the details about why video 
prediction doesn't work with our current 
architecture our current technology and 
of course Yan recently released jeppa 
which is what he believes is going to be 
able to augment large language models 
and Transformers to be able to allow to 
actually have a world model with video 
so let's see what he says about that so 
the reason this doesn't work first of 
all I have to tell you exactly what 
doesn't work because there is something 
else that does work uh so the thing that 
does not work is training a system to 
learn representations of images by 
training it to reconstruct a good image 
from a corrupted version of it that's 
what doesn't work and we have a whole 
slew of techniques for this uh that are 
you know variant of dining Auto incoder 
something called Mae developed by some 
of my colleagues at Fair Max Doo encoder 
so it's basically like the you know llms 
or or or or things like this where you 
train a system by corrupting text except 
you corrupt images you remove Patches 
from it and you train gantic neet to 
reconstruct the features you get are not 
good and you know they're not good 
because if you now train the same 
architecture but you train it supervis 
with label data with text textual 
descriptions of images you do get good 
representations and the performance on 
recognition task is much better than if 
you do this self-supervised pre trining 
the architecture of the encoder is good 
okay but the fact that you train the 
system to reconstruct images does not 
lead it to produce to learn good generic 
features of images when you train a 
self-supervised way self supervised by 
reconstruction and now let's watch what 
he has to say about jepa and again jepa 
was just released by meta under Yan's 
leadership with his entire team and it's 
a very interesting project it gives the 
ability to augment large language models 
with the ability to predict video and 
I'm still wrapping my mind around it so 
I'm going to let him explain it the 
alternative is joint embedding so now 
instead of training a system to encode 
the image and then training it to 
reconstruct the the full image from a 
corrupted version you take the full 
image you take the corrupted or 
transformed version you run them both 
through encoders which in general are 
identical but not necessarily and then 
you you train a predictor on top of 
those encoders to predict the 
representation of the full input from 
the representation of the corrupted one 
so joint embedding because you're you're 
taking the the full input and the 
corrupted version run them both through 
encoders you get a joint and then you're 
you're saying can I predict the 
representation of the full one from the 
representation of the corrupted one and 
I call this a JEA so that means joint 
Ting predictive architecture because J 
embedding and there is this predictor 
that predicts the representation of the 
good guy from from the bad guy and the 
big question is how do you train 
something like this and until 5 years 
ago or six years ago we didn't have 
particularly good answers and now he's 
going to talk about about what is the 
difference between large language models 
and jeppa and why them working together 
might be the solution to reach a gii and 
a world model first of all uh what's the 
difference with generative architectures 
like llms so llms or Vision systems that 
are trained by reconstruction generate 
the inputs right they generate the 
original input that is non-corrupted 
non-transformed so you have to predict 
all the pixels and there is a huge 
amount of resources spent in the system 
to actually predict all those pixels all 
the details 
uh in a JEA you're not trying to predict 
all the pixels you're only trying to 
predict an abstract representation of of 
the inputs and that's much easier in 
many ways so what the Jad system when 
it's being trained is trying to do is 
extract as much information as possible 
from the input but yet only extract 
information that is relatively easily 
predictable so there's a lot of things 
in the world that we cannot predict like 
for example if you have a cell driving 
car driving down the street there may be 
uh trees around the around the road and 
it could be a windy day so the the 
leaves on the tree are kind of moving in 
kind of semi chaotic random ways that 
you can't predict and you don't care so 
what you want is your anoder to 
basically eliminate all those details it 
will tell you there's moving leaves but 
it's not going to keep the details of 
exactly what's going on and so when you 
do the prediction in representation 
space you're not going to have to 
predict every single Pixel of a relief 
and that not only is a lot simpler but 
also it allows the system to essentially 
learn an abstract representation of of 
the world where you know what can be 
model then predicted is preserved and 
the rest is viewed as noise and 
eliminated by the encoder so it kind of 
lifts the level of abstraction of the 
representation if you think about this 
this is something we do absolutely all 
the time whenever we describe a 
phenomenon we describe it at a 
particular level of abstraction and we 
don't always describe every natural 
phenomenon in terms of quantum field 
Theory right that would be impossible 
right so we have multiple levels of 
abstraction to describe what happens in 
the world starting from Quantum field 
Theory to like atomic theory and ules 
you know in chemistry you know all the 
way up to you know kind of concrete 
objects in the real world and things 
like that so the we we can't model 
everything at the lowest level that's 
what the idea of JEA is really on is 
really about learn abstract 
representation in a self-supervised uh 
Manner and you know you can do it 
hierarchically as well so that I think 
is an essential component of an 
intelligent system and in language we 
can get away without doing this because 
language is already to some level 
abstract and already has eliminated a 
lot of information that is not 
predictable so we can get away without 
doing the J Bing without you know 
lifting the abstraction level and by 
directly two words now in this next clip 
Yan talks about hierarchical planning 
and what that essentially means is there 
is a ton of planning that needs to 
happen for basic actions that we take 
every single day so to describe 
hierarchical planning let's use an 
example let's say I want to drive to the 
store so first I have to get up and walk 
to my car so how do I do that well first 
I have to actually stand up well how do 
I do that well my brain has to tell my 
legs to use certain muscles to push me 
up and then how do I do that and it's 
this recursive algorithm that is 
required and every single step from 
something as basic as just walking to 
the car is actually thousands or really 
just infinite steps if you really think 
about it and so how do you model that 
how do you actually model that with 
current architecture and so that's what 
he starts to talk about here let's watch 
so hierarchical planning is absolutely 
necessary if you want to plan complex 
actions uh if I want to go from let's 
say from New York to Paris this the 
example I use all the time and I'm 
sitting uh in my office at NYU my 
objective that I need to minimize is my 
distance to Paris at a high level a very 
as track representation of my uh my 
location I will have to decompose this 
into two sub goals first one is go to 
the airport second one is catch a plane 
to Paris okay so my sub goal is now 
going to the airport my objective 
function is my distance to the airport 
how do I go to the airport where I have 
to go in the street and H a taxi which 
you can do in New York okay now I have 
another sub goal go down on the street 
uh well that means uh going to the 
elevator going down the elevator walk 
out the street how do I go to the 
elevator I have to uh stand up from my 
chair open the door of my office go to 
the elevator push push the button how do 
I get up from my chair like you know you 
can imagine going down all the way down 
to basically what amounts to millisecond 
by millisecond muscle control obviously 
you're not going to plan your entire 
trip from New York to Paris in terms of 
millisecond by millisecond muscle 
control first that would be incredibly 
expensive but it will also be completely 
impossible because you don't know all 
the conditions of what's going to happen 
how long it's going to take to catch a 
taxi or to go to the airport with 
traffic I mean you you would have to 
know exactly the condition of everything 
to be able to do this planning and you 
don't have the information you you have 
to do this hierarchical planning so that 
you can start acting and then sort of 
replanning as you go nobody really knows 
how to do this in AI nobody knows how to 
train a system to learn the appro 
multiple levels of representation so 
that hierarchical planning works and now 
Lex is going to press Yan about scaling 
because what we've seen with the ability 
to scale large language models is truly 
amazing we've gone from GPT 1 2 3 now 
we're at four almost at five and the 
increase in parameters the increase in 
GPU processing power has Unleashed 
incredible capabilities from these large 
language models and now you have Sora 
and Sora proved if you increase compute 
you can get much better results so Lex 
really tries to press Leon about hey if 
we keep scaling up can't we reach super 
intelligence with our current 
Technologies and he also asked what Alan 
Turing would think about current Ai and 
if you're not familiar with Alan Turing 
he created the Turing test amongst many 
other things and the Turing test is 
simply a test to see if artificial 
intelligence is truly intelligent and 
all it really needs to do is convince a 
human that it is human and with our 
current technology that's very possible 
especially if you just do it over text 
but even with voice it's possible and so 
Yan says something really funny about it 
I'm not going to spoil that joke take a 
look does it make sense to you that 
they're able to form enough of a 
representation in the world to be damn 
convincing essentially passing the 
original touring test with flying colors 
well we're fooled by their fluency right 
we just assume that if a system is 
fluent manipulating language then it has 
all the characteristics of human 
intelligence but that impression is 
false we're really fooled by it what do 
you think Alan tan would say it without 
understanding anything just hanging out 
with it an on Turing would decide that a 
turning test is a really bad test okay 
this is what the AI Community has 
decided many years ago that the train 
test was a really bad test of 
intelligence next Yan discusses 
hallucinations why do they happen why 
are they such a big limitation for 
current large language models and it is 
a huge limitation it's probably probably 
one of the biggest limitations to it 
being widely adopted in the corporate 
setting there are a lot of techniques to 
prevent hallucinations but let's find 
out why they actually occur so because 
of the auto regressive prediction every 
time at an produces a token or word 
there is some level of probability for 
that word to take you out of the set of 
reasonable answers and if you assume 
which is a very strong assumption that 
the probability of such error is that 
those errors are independent across a 
sequence of tokens being produced M what 
that means is that every time you 
produce a token the probability that you 
rest you you stay within the the set of 
correct answer decreases and it 
decreases exponentially if there's a 
nonzero probability of making a mistake 
which there appears to be then there's 
going to be a kind of drift yeah and 
that drift is exponential it's like 
errors accumulate so so the probability 
that an answer will be nonsensical 
increases exponentially with the number 
of tokens all right this next clip is 
very interesting Yan talks about why he 
doesn't like reinforcement learning or 
at least why reinforcement learning is 
very good but only for a very narrow use 
case let's watch why do you still hate 
uh reinforcement learning I don't hate 
reinforcement learning and I think I 
think it should not be abandoned 
completely but I think it its use should 
be minimized because it's incredibly 
inefficient in terms of samples and so 
the the proper way to train a system is 
to First have it learn good 
representations of the world and World 
models from Mostly observation maybe a 
little bit of interactions now there's 
two things you can use if you've learned 
a world model you can use the world 
model to plan a sequence of actions to 
arrive at a particular objective you 
don't need ourl unless the way you 
measure whether you succeed might be 
inexact your idea of whether you're 
going to fall from your bike might be 
wrong or whether the person you're 
fighting with MMA is going to do 
something and do something else so 
there's two ways you can be wrong either 
your your objective function does not 
reflect the actual objective function 
you want to optimize or your world model 
is inaccurate the prediction you were 
making about what was going to happen in 
the world is inaccurate if you want to 
adjust your world model while you are 
operating the world or your objective 
function that is basically in the realm 
of RL this is what RL deal deals with to 
some extent right so adjust your world 
model and the way to adjust your world 
model even in advance is to explore 
parts of the space for your world model 
where you know that your world model is 
inaccurate that's called curiosity 
basically or play when you play you kind 
of explore part of the stay space that 
you know you don't want to do in for 
real because it might be dangerous but 
but you can adjust your world model 
without killing yourself basically so 
that's what you want to use RL for when 
when when it comes time to learning a 
particular task you already have all the 
good representations you already have 
your world model but you want you need 
to adjust it for the situation at hand 
that's when you use RL now to the part 
that I'm truly excited about Yan talks 
about why open source is so important he 
references Gemini 1.5s wokeness he 
references llm biases and why opsource 
models are the answer to all of it and I 
tend to agree with much of what he says 
so let's take a look a lot of people 
have been very critical of the recently 
released Google's Gemini 1.5 for 
essentially in my words I could say 
super woke woke in the uh negative 
connotation of that word uh there is 
some almost hilariously absurd things 
that it does like it modifies history 
generating images of a u black George 
Washington or perhaps more seriously 
something that you commented on Twitter 
which is refusing to comment on or 
generate images of uh or even 
descriptions of tianan square or the the 
Tank Man one of the most sort of 
legendary protest images in history and 
of course these images are highly 
censored by the Chinese government and 
therefore everybody started asking 
questions of what is the process of 
Designing these llms what is what is 
what is the role of censorship in these 
so you uh commented on Twitter saying 
that open source is the answer can you 
explain I I actually made a comment on 
just about every Social Network I can 
and I've I I've uh I've made that point 
multiple times in in various forums um 
here's my my point of view on this 
people can complain that AI systems are 
biased and they generally are biased by 
the distribution of the training data 
that they've been trained on that 
reflects biases in society and that is 
potentially offensive to some people or 
potentially not and and some techniques 
to debias then become offensive to some 
people because of you know historical 
incorrectness and things like that and 
so you can ask the question you can ask 
two questions the first question is is 
it possible to produce an AI system that 
is not biased and the answer is 
absolutely not and it's not because of 
technological challenges although they 
are technological challenges to that 
it's because bias is in the eye of the 
beholder different ideas about what 
constitutes bias you know for a lot of 
uh a lot of things I mean there are 
facts that are you know indisputable but 
there are a lot of opinions or or things 
that can be expressed in different ways 
and so you cannot have an unbiased 
system that's just an impossibility and 
so what's the what's the answer to this 
and the the answer is the same answer 
that we found in Liberal democracy about 
the press the press needs to be free and 
uh diverse we have free speech for a 
good reason is because we don't want all 
of our information to come from a unique 
Source cuz that's opposite to the whole 
idea of democracy and progress of ideas 
and even science right in in science 
people have to argue for different 
opinions and science makes progress when 
people disagree and they come up with an 
answer and you know a consensus forms 
right and it's true in all democracies 
around the world so there is a a future 
is already happening where every single 
one of our interaction with the digital 
world will be mediated by AI systems 
we're going to have smart glasses you 
can already buy them from MAA the ran 
MAA where you know you can talk to them 
and they are connected with an llm and 
you you can get answers on any question 
you have or you can be looking at a 
monument and there is a camera in the in 
the system that in in the glasses you 
can ask it like can what can you tell me 
about this uh building or this Monument 
you can be looking at a menu in a 
foreign language and it thing will 
translate it for you or can do realtime 
translation if we speak different 
languages so a lot of our interaction 
with the digital world are going to be 
mediated by those systems in the near 
future you know increasingly the search 
engines that we're going to use are not 
going to be search engines they're going 
to be dialog systems we just ask a 
question and it will answer and then 
point you to perhaps appropriate 
reference for it but here is the thing 
we cannot afford those systems to come 
from a handful of companies on the west 
coast of the US because those systems 
will constitute the repository of all 
human knowledge and we cannot have that 
be controlled by a small number of 
people right it has to be diverse for 
the same reason the Press has to be 
diverse so how do we get a diverse set 
of AI assistance um it's very expensive 
and difficult to train a based model 
right a based llm at the moment you know 
in the future might be something 
different but at the moment that's an 
llm so only a few companies can do this 
properly if some of those top systems 
are open source anybody can use them 
anybody can fine-tune them if we put in 
place some systems that allows any group 
of people whether they are individual 
citizens groups of citizens government 
organizations NOS companies whatever to 
take those open- Source systems AI 
systems and fine-tune them for their own 
purpose on their own data then we're 
going to have a very large diversity of 
different AI systems that are 
specialized for all of those things 
right so I tell you I talked to the 
French government quite a bit and the 
French government will not accept that 
the digital diet of all their citizen be 
controlled by three companies on the 
west coast of the US that's just not 
acceptable it's a danger to democracy 
regardless of how well-intentioned those 
companies are and it's also a danger to 
local culture to values to language I 
was talking with the founder of infosis 
in India he's funding a project to 
fine-tune Lama 2 the open source model 
produced by by meta so that Lama 2 
speaks all 22 official languages in 
India I mean you can't have any of this 
unless you have open source platforms so 
with open source platforms you can have 
ai systems that are not only diverse in 
terms of political opinions or things of 
that type but in terms of language 
culture value systems political opinions 
technical abilities in various domains 
and you can have an industry an 
ecosystem of companies that fine-tune 
those open source systems for vertical 
applications in Industry right you you 
have I don't know a publisher has 
thousands of books and they want to 
build a system that allows a customer to 
just just ask a question about any the 
content of any of their books you need 
to train on their proprietary data you 
have a company we have one within meta 
is called Metate and it's basically an 
llm that can answer any question about 
internal stuff about about the company 
very useful a lot of companies want this 
right a lot of companies want this not 
just for their employees but also for 
their customers to take care of their 
customers so the only way you're going 
to have an AI industry the only way 
you're going to have ai systems that are 
not uniquely biased is if you have open 
source platforms on top of which any 
group can build specialized systems the 
direction of of inevitable direction of 
history is that the vast majority of AI 
systems will be built on top of Open 
Source platforms and on the topic of 
Open Source Lex asks him okay if it's 
open source how do you actually run a 
business based on open source what are 
the economics of Open Source and this 
has been proven over the years we have a 
lot of very successful open- Source 
projects that have made a lot of money 
many of the standards that we use on the 
internet today many of the standards 
that we use with databases with code 
architecture everything a lot of that is 
open source and there are a lot of 
benefits with open source and a lot of 
companies have made a lot of money with 
open source just look at Android for 
Google just as an example off the top of 
my head so let's see what Yan says about 
the economics of meta's open- source 
contributions you have several business 
models right the business model that uh 
meta is built around is you offer a 
service and the the financing of that 
service is uh either through ads or 
through business customers so for 
example if you have an llm that uh you 
know can help a mom and pop pizza place 
by you know talking to the customers 
through WhatsApp and so the customers 
can order a pizza and the system will 
just you know ask them like what topping 
do you want or websites blah blah blah 
the business will pay for that and 
otherwise you know if it's a system that 
is on the more kind of classical 
Services it can be uh ad supported or 
you know there's several models but the 
point is if you have a big enough 
potential customer base and you need to 
build that system anyway for them it 
doesn't hurt you to actually distribute 
it an open source and then Lex continues 
to press him about the open source 
economics why doesn't another company 
just come along take that open source 
project and build competition and that 
probably will happen and that's probably 
good but at the end of the day meta is 
still meta they have an entire 
multi-billion human user base to sell 
their products and services to so let's 
see what he says the the bet is is more 
we have we already have a huge user base 
and customer base right so it's going to 
be useful to them whatever we offer them 
is going to be useful and there is a way 
to derive revenue from this and it 
doesn't hurt that you know we provide 
that system or the base the base model 
right the foundation model in open 
source for others to build applications 
on top of it too if those applications 
turn out to be useful for our customers 
we can just buy it from them it could be 
that they will improve the platform in 
fact we see this already I mean there is 
you know literally millions of downloads 
of uh Lama 2 and thousands of people who 
have you know provided ideas about how 
to make it better so you know this this 
clearly accelerates progress to make the 
system available to a wide community of 
people and there's literally thousands 
of businesses who are building 
applications with it our ability to 
meta's ability to derive revenue from 
this technology is not impaired by the 
distribution of it of based models in 
open source now we continue on the whole 
biases front many employees at tech 
companies tend to lean left and that is 
a point that Lex makes and I tend to 
agree with and so aren't those biases 
going to be built into large language 
models and again Yan points to open 
source being the answer I don't think 
the issue has to do with the political 
leaning of the people designing those 
systems it has to do with the 
acceptability or political leanings of 
the their customer base audience 
right so a big company cannot afford to 
offend too many people so they're going 
to make sure that whatever product they 
put out is safe whatever that means and 
it's very possible to overdo it and it's 
also very possible to it's impossible to 
do it properly for everyone you're not 
going to satisfy everyone so that's what 
I said before you cannot have a system 
that is unbiased is perceived as 
unbiased by everyone it's going to be 
you know you push it in one way one set 
of people are going to see it as biased 
and then you push it the other way and 
another set of people is going to see it 
as biased and then in addition to this 
there's the issue of if you push the 
system perhaps a little too far in One 
Direction it's going to be non-factual 
right you're going to have you know 
black Nazi soldiers in yes we should we 
should mention image generation of of uh 
black Nazi soldiers which is not 
factually accurate right and can be 
offensive for some people as well right 
so you know it's going to be impossible 
to kind of produce systems that are 
unbiased for everyone so the only 
solution that I see is is diversity and 
diversity in the full meaning of that 
word diversity of in every possible way 
and continuing on the open source front 
now they're going to talk about Free 
Speech guard rails on AI censorship 
biases and everything I love this 
conversation and hearing Yan frame open 
source in this light makes a ton of 
sense to me let's watch I mean there are 
some limits to what you know the same 
way there are limits to free speech 
there has to be some limit to the kind 
of stuff that those systems might be 
authorized to to produce you know some 
guardrail so I mean that's one thing 
I've been interested in which is in the 
type of architecture that we were 
discussing before where the output of 
the system is the result of an inference 
to satisfy an objective that objective 
can include guard rails and we can put 
guard rails in open source systems I 
mean if we eventually have systems that 
are built with this blueprint we can put 
guard rails in those systems that 
guarantee that there is sort of a 
minimum set of guard rails that make the 
system non- dangerous and non-toxic Etc 
you know basic things that everybody 
would agree on and then you know the the 
fine tuning that people will add or the 
additional guardwell that people will 
add will kind of cater to their 
Community whatever it is next if you 
wanted a little preview of when llama 3 
is coming and what it's going to be 
about this is the clip for you let's 
watch there's going to be like various 
versions of of llama that are you know 
improvements of previous Lamas bigger 
better multimodal things like that and 
then in future generation systems that 
are capable of planning that really 
understand how the world Works maybe are 
trained from video so they have some 
World model maybe you know capable of 
the type of reasoning and planning I was 
talking about earlier like how long is 
that going to take like when is the 
research that is doing going in that 
direction going to sort of feed into the 
product line if you want of Lama I don't 
know I can't tell you and there is you 
know a few breakthroughs that we have to 
basically go through before we can get 
there you'll be able to monitor our 
progress because we publish our research 
right so last week we published the Via 
work which is sort of a first step 
towards Training Systems from video and 
then the next step is going to be World 
models based on kind of this type of 
idea training training from video there 
similar work at a Deb also and taking 
place people and also at UC Berkeley on 
world models video a lot of people are 
working on this I think a lot of good 
ideas are coming are appearing my bet is 
that those systems are going to be Jeet 
likee they're not going to be gen 
generative models and uh we'll see what 
the future will tell there's really good 
work at a gentleman called danar Hafner 
who is not Deep Mind who who's worked on 
kind of models of this type that learn 
representations and then use them for 
planning or learning tasks by 
reinforcement learning now going back a 
few topics they're going to talk about 
the power and efficiency in the human 
mind to process all of this data and 
what we get from it compared to large 
language models and it's really not even 
a comparison large language models 
require a ton of data a ton of 
processing power a ton of energy to 
train and to use versus a human brain 
which is incredibly efficient we're 
still far in terms of computer power 
from you know what we would need to 
match the compute power of the human 
brain you know this may occur in the 
next couple decades but but we're still 
some ways away and certainly in terms of 
power efficiency we're really far so a 
lot of progress to make in uh in in in 
Hardware right now a lot of progress is 
is is not I mean there a bit coming from 
Silicon technology but a lot of it 
coming from architectural Innovation and 
quite a bit coming from more efficient 
ways of you know implementing the 
architectures that have become popular 
basically combination of Transformers 
and comets so you know there's still 
some ways to go until we're going to 
saturate we're going to have to come up 
with like new new principles new 
fabrication technology new uh basic 
components perhaps you know based on 
sort of different principles and those 
classical digital semas and next they're 
going to start talking about AGI when is 
it coming and Yan has made this point 
over and over again a lot of people 
think AGI is just going to be some 
switch that is flipped one day and it's 
just going to be this inflection point 
that AGI just has this quote unquote 
hard takeoff but he doesn't think that's 
the case he thinks it's going to be 
Progressive let's see what he has to say 
so first of all it's not going to be an 
event right the idea somehow which you 
know is popularized by science fiction 
and Hollywood that you know somehow 
somebody is going to discover the secret 
the secret to AI or human level AI or 
Ami whatever you want to call it and 
then you know turn on a machine and then 
we have a GI that's just not going to 
happen it's not going to be an event 
it's going to be gradual progress are we 
going to have systems that can learn 
from video how the world works and learn 
good World presentation yeah before we 
get them to the scale and performance 
that we observe in humans it's going to 
take quite a while are we going to get 
systems that can have large amount of 
associative memory so they can they can 
remember stuff yeah but same it's not 
going to happen tomorrow I mean there is 
some basic techniques that need to be 
developed we have a lot of them but like 
you know to get this to work together 
with full system is another story how we 
going to have systems that can reason 
and plan perhaps along the lines of 
objective driven AI architectures that I 
I described before yeah but like before 
we get this to work you know properly 
it's going to take a while and before we 
get all those things to work together 
and then on top of this have systems 
that can learn like hierarchical 
planning hierarchical representation 
systems that can be configured for a lot 
of different situation at hands the way 
the human brain can all of this is going 
to take you know at least a decade and 
probably much more because there are a 
lot of problems that we're not seeing 
right now that we have not encountered 
and so we don't know if there is a easy 
solution within this framework work you 
know it's it's not just around the 
corner I mean I've I've been hearing 
people for the last 12 15 years claiming 
that you know edgi is just around the 
corner and being systematically wrong 
and I knew they were wrong when they 
were saying it I call their and 
next he talks about AI 
to see the wide spectrum of people's 
beliefs about AGI and AI Doom okay so AI 
doomers imagine all kinds of catastrophe 
scenarios of how AI could Escape or 
control and basically kill us all and 
that relies on a whole bunch of 
assumptions that are mostly false so the 
first assumption is that the emergence 
of super intelligence is going to be an 
event that at some point we're going to 
have we're going to figure out the 
secret and we'll turn on a machine that 
is super intelligent and because we' 
never done it before it's going take 
over the world and kill us all that is 
false it's not going to be an event 
we're going to have systems that are 
like as smart as a cat has all the have 
all the characteristics of you know 
human level intelligence but their level 
of intelligence would be like a cat or a 
pirrot maybe or something and then we're 
going to work our way up to kind of make 
those things more intelligent and as we 
make them more intelligent we're also 
going to put some guard rails in them 
and learn how to kind of put some guard 
rails so they behave properly and we're 
not going to do this with just one it's 
not going to be one effort it's going to 
be lots of different people doing this 
and some of them are going to succeed at 
making intelligent systems that are uh 
controllable and safe and have the right 
guard rails and if some other goes Rogue 
then we can use the the good ones to go 
against the Rogue ones uh so it's going 
to be my you know smart AI police 
against your Rogue AI it's not going to 
be like you know we're going to be 
exposed to like a single Rogue AI that's 
going to kill us all that's just not not 
happening now there is another fallacy 
which is the fact that because the 
system is intelligent it necessarily 
wants to take over and there is several 
arguments that make people scar of this 
which I I think are completely false as 
well so one of them is you know in 
nature it seems to be that the more 
intelligent species other one that end 
up dominating the other distinguishing 
the others sometimes by Design sometimes 
just by mistake and so you know there is 
sort of uh Thinking by which you say 
well if AI systems are more intelligent 
than us surely they're going to 
eliminate us if not by Design simply 
because they don't care about us and 
that's just Preposterous for for a 
number of reason 
um first reason is they're not going to 
be a species they're not going to be a 
species that competes with us they're 
not going to have the desire to dominate 
because the desire to dominate is 
something that has to be hardwired into 
an intelligent system it is hardwired in 
humans it is hardwired in baboons in 
chimpanzees in wolves not in orang Tong 
the species in which this desire to 
dominate or submit or or attain status 
in other ways is is specific to social 
species non-social species like our Tong 
don't have it right and they are as 
smart as we are almost and then next 
they tackle the comparison between AI 
AGI and nuclear systems and there's been 
a lot of comparisons between nuclear and 
Ai and I don't necessarily agree that's 
an Apples to Apples comparison and Yan 
tends to agree and he explains why but 
not only that he explains why AI is 
essentially going to be our filter to 
the rest of the internet and to other AI 
models an AI model is not going to be 
able to directly communicate with us 
we're not even going to see it he 
actually brings brings up the analogy of 
a spam filter for email we get a ton of 
spam and most of which we never see and 
we never need to see so it's interesting 
to think about but it's also a little 
worrying to think that an AI is going to 
be my filter to all of my information 
diet so that AI system designed by 
Vladimir Putin or whatever or his uh 
minions you know is going to be uh 
talking to trying to talk to every 
American to uh convince them to vote for 
you know whoever whoever is Puttin or 
ride people up against each other 
they're not going to be talking to you 
they're going to be talking to your AI 
assistant which is going to be as smart 
as their that because as I said in the 
future every single one of your 
interaction with the digital world will 
be mediated by your AI assistant so the 
first thing you're going to ask is is 
this a scam like is this thing like tell 
me TR like it's not even going to be 
able to get to you because it's only 
going to talk to your a assistant and 
your a assistant is not not even going 
to it's going to be like a filter you're 
not even seeing the email the spam email 
right it's automatically put in a folder 
that you never see it's going to be the 
same thing and next he talks about 
robots and I've been making a lot of 
videos about robots lately so let's see 
what Yan has to say about robots like 
the next decade I think is going to be 
really interesting in robots like the 
the emergence of the robotics industry 
has been in the waiting for 10 20 years 
without really emerging other than for 
like you know kind of pre-program 
behavior and stuff like that and the 
main issue is again the Maric Paradox 
like you know how do we get those system 
to understand how the world works and 
and kind of you know plan actions and so 
we can do it for really specialized 
tasks the way Bon Dynamics goes about it 
is you know basically with a lot of um 
handcrafted dynamical models and careful 
planning uh in advance which is very 
classical robotics with a lot of 
innovation a little bit of perception 
but it's still not like they can't build 
a domestic robot we're still some 
distance away from completely autonomous 
level five driving and we're certainly 
very far away from having uh you know 
level five autonomous driving by A 
system that can train Itself by driving 
20 hours like any 17-year-old a lot of 
the people working on robotic Hardware 
at the moment are are betting or banking 
on the fact that AI is going to make 
sufficient progress towards that and 
last Lex asks a good question what gives 
you hope about Humanity over the next 
few decades and Yan gives some good 
answers there I really love hearing this 
I love ending it on of a positive note 
we can make Humanity Smarter with AI I 
mean AI basically will amplify human 
intelligence it's as if every one of us 
will have a staff of smart AI assistants 
they might be smarter than us they'll do 
our bidding perhaps execute uh tasks in 
ways that are much better than we could 
do ourselves because they'll be smarter 
than us and so it's like everyone would 
be the the boss of a staff of super 
smart virtual people so we shouldn't 
feel threatened by by this any more than 
we should feel threatened by being the 
manager of a group of people some of 
whom are more intelligent than us I 
certainly have a lot of experience with 
this that's actually a wonderful thing 
so uh having machines that are smarter 
than us that assist us in our all of our 
tasks our daily lives whether it's 
professional or personal I think would 
be absolutely wonderful thing because 
intelligence is the most is the 
commodity that is most in demand that's 
really what I mean all the mistakes are 
it makes is because of lack of 
intelligence really or lack of knowledge 
which is you know related making people 
smarter which just can only be better I 
mean for the same reason that you know 
public education is a good thing and 
books are a good thing and the internet 
is also a good thing intrinsically and 
even social networks are a good thing if 
you run them properly it's difficult but 
you know you can helps the communication 
of information and knowledge and the 
transmission of knowledge so AI is going 
to make Humanity smarter so that's it I 
encourage you to check out the video in 
full it is very long hopefully this cut 
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