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Language: en
All right, let's jump right in. Today,
we're tackling a really fascinating
puzzle at the heart of AI research.
Something you could call the imagination
gap. We're going to explore how this
idea of world models might be the
missing piece of the puzzle. The key to
giving AI something that looks a lot
more like a real intuitive understanding
of our world. So, think about this for a
second, cuz it's a little weird. Modern
AI can do things that feel like magic,
right? It can write a poem that'll make
you tear up. It can compose music. It
can generate these unbelievably
realistic photos just from a few words.
But that same AI might fail at something
a toddler understands. It doesn't get on
a fundamental level that you can't just
shove a key into a lock sideways and
expect it to work. Why? Because it's
learned the statistical link between the
word key and the word lock. But it
doesn't understand the physics of them.
That they're solid objects with shapes
that have to fit together. That's the
imagination gap right there. And this
really gets us to the core distinction
here. Correlation versus causation.
Right now, our most powerful AIs are
absolute masters of correlation. They've
sifted through basically the entire
internet. So, they are incredible at
spotting patterns and predicting what
comes next. They know what happens. But
the real goal, the holy grail, is
causation. It's understanding why
something happens. An AI that gets
causation doesn't just know that
flipping a switch is usually followed by
a light turning on. It understands the
circuit, the flow of electricity, the
whole reason it works. And that is
exactly the problem that world models
are trying to solve. So to make that
jump from correlation to causation, the
AI needs something. You could call it an
internal universe. It needs the ability
to imagine, to run little simulations
inside its own head, just like we do.
You know, if you think about dropping a
glass, you can almost see it happen in
your mind's eye, right? You can picture
it shattering. You can imagine the sound
it'll make. That's your internal model
of the world at work. An AI needs that
same ability to predict what will happen
if it takes an action before it actually
takes it. That is the core idea of a
world model. So, here's how we're going
to break it all down today. First, we'll
nail down a definition for world models.
Then, we'll do a bit of a technical deep
dive to see how they're actually built.
After that, we'll look at some amazing
real world examples. And we'll wrap up
by looking at what the future might hold
for all this. All right, let's kick
things off with section one, defining
the world model. We're going to really
try to pin down what we mean when we
talk about giving an AI something like
common sense. Okay, so what is a world
model officially? Well, the formal
definition is an internal compressed
representation of the external world.
Now, the two really important words
there are compressed and simulate.
Compressed doesn't just mean smaller. It
means the model learns the gist of the
world, the important concepts like
gravity is a thing. Objects don't just
phase through each other. It's a
simplified sketch, not a perfect
photograph of reality. And that
simplified sketch is what allows the AI
to simulate to play out scenarios and
predict what's going to happen next
without having to risk it in the real
world. You know, sometimes the best
explanations come from the most
unexpected places. A Reddit user named
for entertain only put it perfectly.
They said, "Imagine you're planning a
trip to a shopping mall you've never
been to. You look at Google Maps. You
check out some photos. You don't know
every single detail, right? You don't
know what song will be playing or what
brand of wax they use on the floor. But
you build a mental model. You get a
sense of the layout where the food court
probably is and you use that model to
plan your trip. An AI's world model is
doing basically the exact same thing. So
you can think of a world model as having
two main jobs and they're completely
connected. The first job is
understanding. This is all about
building that internal map of how the
world works. The physics, the rules, the
cause and effect. It's the AI basically
asking why do things happen this way?
And the second job is predicting. This
is where the AI uses that map to run
simulations to figure out what to do.
It's the AI asking, "Okay, so what will
happen if I do this?" A really good
world model has to be great at both.
It's a loop. Your understanding gets
better by making predictions and your
predictions get better as your
understanding deepens. So why is this
such a huge deal for the bigger goal of
artificial general intelligence or AGI?
Well, we already talked about getting
past simple correlation to real
causality, but it's more than that. It's
about long-term planning. The AI can
think ahead by running simulations, not
just reacting to what's right in front
of it. It also makes learning way more
efficient. Think about it. A kid only
needs to see a ball drop a few times to
get the concept of gravity. An AI
without a world model might need to see
millions of examples. This also helps
with transfer learning. A robot that
learns the physics of stacking blocks
can use that same understanding to stack
plates. And it all builds towards this
foundation of intuitive physics, the
kind of effortless common sense that we
rely on every single moment. Okay, let's
get into section two. We're going to pop
the hood now and do a little technical
deep dive into how these internal
universes are actually put together. So,
the biggest technical problem is all
about focus. The world is just noisy.
There's an insane amount of information.
If an AI is playing a video game, does
it need to pay attention to the color of
the sky or the font used for the score?
Probably not. The real challenge is
teaching the AI to automatically filter
out all that junk and build a model that
only focuses on the stuff that actually
matters for making a prediction, like
the player's speed or the location of
the next platform. And that brings us to
a really clever solution from one
research paper called the parsimmonious
latent space model or PLSM. I know it's
a mouthful, but the key word is
parsimmonious. It basically just means
being frugal or stingy. In this case, it
means being stingy with complexity. The
whole point of PLSM is to force the
world model to find the absolute
simplest explanation for how the world
works. And when it does that, the
results of its actions become way more
predictable and systematic, which is
exactly what you want. The fancy term
the paper uses for this is making the
model softly state invariant. But what
that really means is that the model
learns that the result of an action is
usually the same no matter the little
details of the situation. For example,
the action move right should have the
same basic outcome whether you're
standing on a red square or a blue
square because the color is irrelevant.
The actions effect is invariant to that
feature. Now, the softly part is key
because sometimes the state does matter.
Pushing a ball on grass is totally
different from pushing it on ice. The
model has to learn what to ignore and
what to pay attention to. So, how does
it pull this off? Well, it's a pretty
elegant four-step process. First, the
model looks at the current situation and
the action it wants to take. Second, and
this is the key trick, it's not allowed
to use all the complex information about
the state. It has to create a really
simple query, like a summary of only the
most important bits. Third, it predicts
what will change based only on that
super simple query and the action. And
fourth, this is the secret sauce. It
gets penalized if that query it made was
too complicated. It's like the model has
a budget for complexity and it's forced
to learn the simplest possible question
it needs to ask to get the right answer.
And boom, here is the result. It's so
clear when you see it visually. Just
look at that top row. That's the PLSM
model. See how all those dots, which are
different states of the world, are
arranged in this beautiful, neat,
organized grid. That's the model
learning a systematic internal map of
its world. Now, compare that to the
other rows. Without this trick, it's a
warped, tangled mess. Trying to plan a
path through that would be a nightmare.
This picture shows that forcing the
model to be simple actually makes its
internal universe way more use. Now, a
tidy-l looking graph is great, but what
does it actually do? Well, the payoff is
huge. This elegant simplification
translates directly into better
performance. Because the AI has learned
a cleaner, more logical model of its
world, it gets much better at planning
and achieving its goals. And maybe even
more importantly, it can generalize what
it's learned to new situations it's
never seen before because it's learned
the underlying rules, not just memorized
one specific scenario. Let's actually
put a number on that. When they tested
this on a bunch of classic Atari games,
the Pleasant Approach boosted the score
by an average of 5.6 percentage points
over the standard model. Now, I know
5.6% 6% might not sound like a
worldchanging number, but trust me, in
this field where progress comes in tiny
little increments, that is a really
significant jump. It proves this idea
works, but the average doesn't even tell
the whole story. This is where it gets
really interesting. Look at the game up
and down. The score almost triples. It
goes from about 10,000 to nearly 30,000.
That's just a massive improvement. You
see a big jump in Kangaroo, too. And
look at Pong. The original model
actually had a negative score, meaning
it was worse than just randomly hitting
buttons. The PLSM model turns that into
a positive score. It's crystal clear. A
simpler internal world makes for a
smarter agent. Now, of course, PLSM is a
super cool approach, but it's not the
only game in town. Researchers are using
a whole toolbox of different methods.
There are variational autoenccoders or
VAEs, which are great at learning those
compressed representations. We have
diffusion models which are the engine
behind video generators like Sora and
they're amazing at creating
photorealistic future scenes. Yan Lun's
Jeepa architecture is all about
efficiency. Instead of predicting every
pixel, it just predicts important
information in an abstract way. And of
course, transformers are being used to
process long video sequences to
understand cause and effect over time.
Okay, that was our trip into the
technical weeds. For section three,
let's zoom back out and see where this
incredible technology is actually being
used in the real world today. The most
obvious application is probably
autonomous driving. A self-driving car
absolutely needs a sophisticated world
model. It has to predict what other cars
are going to do, what pedestrians might
do, and how the whole traffic situation
is going to evolve. And crucially, these
models are used to simulate millions of
miles in virtual worlds to test the AI
against those rare, super dangerous
corner cases like a ball rolling into
the street without actually putting
anyone in danger. We're even seeing a
shift now towards single end-to-end
world models like one called Uniad that
handle everything from seeing the world
to planning the route. And then there's
robotics where this is a total gamecher.
World models let a robot imagine the
result of an action before it even
moves. This is huge. There's a model
called Daydreamer that can learn to walk
almost entirely in a simulation and then
adapt to the real world in just a few
hours. Another one called Swim can learn
how to do a task just by watching
YouTube videos of people doing it. This
is how you get robots that can learn
quickly and adapt to new situations
without months of programming. But get
this, it's not just about physics in the
physical world. Researchers are using
world models to create social simulacra.
Basically, simulations of human
societies. Imagine you want to test a
new policy. You can create a virtual
town populated by AI agents powered by
LLMs who have their own memories and
reasoning. You can then watch how they
behave and interact. It's a way to model
complex social dynamics like how
information spreads before you try
things out in the real world. And this
brings us right back to models like
OpenAI's Sora. Now, there's a big debate
in the AI community about whether Sora
is a true world model. Does it really
understand cause and effect? Maybe not.
But one thing is for sure, it is an
unbelievably powerful world simulator.
It is incredible at that prediction
function we talked about. You give it a
prompt and it generates a video of a
possible future that looks and feels
real. It has an incredibly rich, even if
it's implicit, model of how our world
moves and behaves. Okay, let's head into
our final section. We've seen what world
models are, how they're built, and what
they can do. Now, let's look ahead. What
are the biggest challenges? And where is
all this going? This is one of the
biggest, most fundamental questions out
there right now. Can an AI really learn
the laws of physics, like gravity, just
by watching a ton of videos? Or is there
a limit to what you can learn just by
observing? Well, we always need to
hardcode some of those rules in. The
jury is still very much out on this, and
it leads directly to our first major
challenge. You've probably seen this in
some of the Sora videos. They look
amazing, but sometimes things are just a
little off. A glass shatters in a weird
way or something moves without a clear
cause. That's because the model has
learned visual patterns, but not the
deep causal relationships of physics.
For a cool video, that's fine. For a
self-driving car, that is not fine. You
need perfect physics for those critical
safety situations. So, a really
promising direction is to create hybrid
systems to combine these amazing
generative models with old school
explicit physics engines to get the best
of both worlds. The next huge challenge
is the classic simtoreal gap. It's one
thing for a robot to learn how to do
something in a perfect clean simulation.
It's a whole other thing to get it to
work in the messy chaotic real world.
The lighting is different. Objects have
different textures and weights. A lot
can go wrong. The really exciting future
here is creating a self-reinforcing
loop. You have a robot go out into the
real world, collect data on where its
simulation was wrong, and then use that
data to make the simulation better. The
better simulation then helps the robot
learn even faster. It's a really
powerful idea. And finally, we have the
practical and ethical hurdles. On the
practical side, these models are huge
and slow, which is a problem if you need
real-time simulation for a robot. Then
there are the ethical issues. Where does
all this training data come from? What
about privacy? What about safety? A
model that can simulate city traffic
could also be used to simulate a
terrorist attack. And of course, the
ability to generate perfectly realistic
video while that opens up a huge can of
worms with deep fakes and
misinformation. These are hard problems
we'll have to solve. You know, this
isn't a brand new idea. Its roots go all
the way back to psychology in the 1970s
with the concept of mental models. But
it really entered the modern AI
conversation in a big way in 2018 with a
landmark paper by Ha and Schmidt. By
2022, you had giants in the field like
Yan Lun arguing this was a critical path
forward for AI. And now in 2024 with
models like Sora, the idea of a world
simulator has exploded out of the lab
and into the public consciousness. So
I'll leave you with this one last
thought. We are on a path towards
creating AIs that can build their own
internal simulations of our reality. So
if we get to a point where a world model
can perfectly simulate our world, what
comes next? What kind of reality? What
kind of future will it choose to build
from there? It's a pretty profound
thought and it really speaks to both the
incredible power and the huge
responsibility that comes with building
this technology. Thanks for joining me
for this explainer.