Welcome back everyone for another deep dive.
You guys have been digging through a ton of stuff
on OpenAI's O3 model.
Definitely making waves.
Yeah, and we are here to help you synthesize it all
and break it down.
So- This is absolutely-
What seems to have everyone, especially buzzing,
is O3's performance on the ARC AGI competition.
Kind of an intelligence test for AI.
Yeah, it's kind of like a benchmark,
you know, for how general an AI is.
Like, you know, people always talk about
can AI beat us at chess or can it write code or whatever.
You know, like those are very specific tasks.
This is really about can it learn and adapt
to new problems that it's never seen before.
So when we're talking about ARC AGI,
are we talking about, you know,
robots doing Rubik's cubes or something like that?
Yeah, no, no, no.
So it's all about visual puzzles.
So imagine like you've got these grids with colored squares
and the AI has to kind of figure out like,
what's the pattern, what's the rule,
and complete the puzzle.
But the really interesting thing is that these puzzles
are designed to be solvable using only
what they call core knowledge priors,
which is like the basic stuff that humans
just kind of get about the world.
Like object permanence or something like that.
Exactly.
So it's like, you know, things about like objects
and basic geometry and stuff like that.
So no specialized knowledge needed.
And the idea is to make it a fair comparison
between human and artificial intelligence.
Makes sense.
And so yeah, for years, you know,
AI has struggled to make much headway on these puzzles.
Like previous ARC AGI competitions,
they saw progress kind of incremental
going from like 0% to about 33%.
Over four years, it seemed like
just scaling up existing AI models,
like, you know, just making them bigger wasn't enough.
Right.
And then 03 comes along
and suddenly it's a completely different game.
Exactly.
That's what has everybody so excited.
So what did open AI achieve with this new model?
So they achieved a score of 75.7% success rate
on the ARC AGI pub semi-private evaluation set,
which is a mouthful.
But basically it's a massive leap forward.
Huge.
Compared to the slow progress we've seen before.
It's like, you know, finding that missing piece
of the puzzle.
Something that unlocks this whole new level of capability.
75.7%, like that is a huge jump.
It almost sounds too good to be true.
How are they even testing 03?
I mean, yeah.
So they used two different configurations,
a high efficiency setup using only six samples.
And then a low efficiency setup requiring 2024 samples.
So the high efficiency one shows that 03
can actually learn pretty quickly with minimal data.
Wow.
Whereas the low efficiency one kind of reveals
its potential for even higher accuracy
when given more information.
So I'm guessing more samples, more computing power,
more cost.
Yeah, you got the cost per task range
from like 17 to 20 bucks.
Compared to about five bucks for a human.
But you know, you gotta remember,
computational costs keep dropping.
AI efficiency keeps rising.
So this isn't really a permanent limitation.
But more sign of where we're headed.
Okay, so it sounds like 03's success
isn't just about throwing more computing power at the problem.
Right, it's not just bigger or trained on more data.
It represents like a fundamentally different approach
to AI architecture.
It's about designing AI systems that can learn and adapt
in a way that's more analogous to how human cognition works.
Okay, so let's dive into the technical stuff for a sec.
We all know about LLMs, these large language models,
the brains behind chatbots and text generators and all that.
But it seems like 03 is doing something kind of different.
Yeah, traditional LLMs, they're great
at recognizing patterns and applying what they've learned.
But throw them a curve ball, like a truly new task,
and they often stumble.
03 on the other hand, works more like a massive library
of what they call vector programs.
Think of these as abstract representations
of concepts and procedures that 03 can access and execute.
So it's not just memorizing a bunch of solutions.
Exactly, the key innovation is that it can generate
and execute its own programs at test time.
Hmm, interesting.
So it's like it's thinking on its feet,
it's working through the problem,
coming up with new solutions.
Learning to learn.
It is, that's the really exciting part here.
It's not just applying what it knows,
but actually generating new knowledge
to solve novel problems.
So does that mean 03 is thinking like a human?
Well, that is the million dollar question, isn't it?
To understand how it achieves this,
we need to look at how it combines deep learning
with a powerful search strategy.
Okay.
Like it has this giant toolbox of programs,
and it knows which tool to use for the job,
even if it's never seen that particular job before.
Wow, so it's learning to learn,
this feels like a huge step forward in AI,
but is it really thinking, have we achieved AGI with 03?
That's what we're here to discuss.
I think that's a question that has been debated for decades,
and there's no easy answer.
03's performance on ARC AGI is remarkable,
but AGI is about more than just solving puzzles.
Yeah, I was gonna say, just because an AI can solve
complex puzzles, it doesn't mean it's as intelligent
as a human in every way.
Precisely.
Humans excel at so many things,
language, creativity, social interaction,
understanding emotions.
03 might be a whiz at visual puzzles,
but it still struggles with tasks
that humans find relatively easy.
Like saying someone's a master chess player
doesn't automatically make them a genius at everything.
Right, exactly.
They might be brilliant in that domain,
but struggle with other things.
So what is the secret sauce
behind 03's puzzle solving prowess?
The article mentioned this thing called
Deep Learning Guided Program Search.
Yeah, that's the heart of it.
Instead of just applying pre-learned patterns,
03 actively searches for the best solution
by generating and testing its own programs.
So it's coming up with its own solutions.
Yeah, imagine it like having a giant toolbox,
knowing which tool to use,
even for a task you've never seen before.
Wow, so it's not just memorizing.
It's not just memorization,
it's really about this ability to reason and problem solve.
That sounds like a massive search problem.
How does it do that efficiently?
Well, one way is through what they call
chains of thought or co-teas.
Instead of just spitting out an answer,
03 generates a series of steps, like a thought process.
Interesting.
To arrive at a solution,
it's like the AI is showing its work,
letting us peek into its decision-making.
Oh, so it's not just randomly trying things.
Exactly, it's actually reasoning through the problem.
Okay.
And there's some evidence that it might also be
using something similar to Monte Carlo Tree Search,
which is a technique commonly used in game-playing AI,
like AlphaGo and stuff.
It's a way to efficiently explore
a vast number of possibilities
by focusing on the most promising options.
Gotcha.
And dealing with a massive number of potential solutions,
you need a smart way to narrow down the search.
Exactly.
So it sounds like 03 combines this deep learning
with a really powerful search strategy
to achieve this breakthrough.
That's a great way to put it.
And while we're still unraveling the intricacies
of how 03 works, its success on ARC-AGI is undeniable.
It's a significant step forward
in the pursuit of artificial general intelligence.
But are there any skeptics out there,
people who aren't convinced
that this is really a breakthrough?
Well, there are always skeptics and that's healthy.
We've seen these AI hype cycles come and go,
but 03 is different.
It's not just a faster, bigger version
of what we had before.
It represents a fundamental shift
in how we approach AI development.
It's not just about more data or more computing power,
but about new ideas, new algorithms,
new ways of thinking about AI.
Precisely.
And that's what makes it so exciting.
It's not the finish line, but it's a signpost.
Pointing towards the future where AI can truly learn,
adapt and solve problems in ways we can only imagine today.
So back to the big question,
have we achieved AGI with 03?
Is this the artificial general intelligence
we've been waiting for?
It's a question that's been debated for decades
and there's no easy answer.
03's performance on ARC-AGI is remarkable,
but AGI is about more than just solving puzzles.
It's about having the same breadth and depth
of intelligence as a human.
So things like language creativity,
social intelligence, emotional understanding,
all that kind of stuff that 03
hasn't necessarily mastered yet.
Exactly.
While 03 can solve problems that stump traditional AI,
it's still fall short in areas
that humans find effortless, for example.
It struggles with common sense reasoning
and understanding social nuances.
It's like that old saying,
if it walks like a duck and quacks like a duck,
it must be a duck.
Just because an AI can solve these complex puzzles
doesn't mean it has the full range
of human-like intelligence.
So what's the next step in this AGI journey?
Where do we go from here?
Well, the creators of ARC-AGI
are already developing RSAGI2.
Oh, wow.
A more challenging version of the competition
designed to push the boundaries of AI even further.
Early testing suggests that RSAGI2
will be a significant hurdle for 03 and other AI systems.
So they're raising the bar,
making it even harder for AI to succeed.
So what will that tell us
about the state of artificial general intelligence
if an AI manages to crack ARC-AGI2?
Does that mean we've finally reached AGI?
It would certainly be a major accomplishment
showing that we're making significant progress towards AGI.
But it's important to remember that ARC-AGI,
even in its more challenging form,
is still just one benchmark.
It's like saying, okay, this AI aced the SATs.
So it must be ready for college.
Exactly.
You all know that academic success
is just one piece of the puzzle.
Exactly.
And that comes to being a well-rounded individual.
So what else needs to be done to really assess AGI?
We need a diverse set of benchmarks
that test a wide range of cognitive abilities.
We need to go beyond puzzle solving and challenge AI
to understand language, navigate social situations,
even exhibit creativity.
It's like we need a comprehensive IQ test for AI,
one that measures not just problem solving,
but also the ability to learn, adapt,
and interact with the world in a truly human-like way.
It sounds like ARC-AGI II is gonna be the real test
for these AI systems like O3.
What are some of the specific challenges
that it's expected to pose?
Well, one key area is what they call
compositional generalization,
which is the ability to kind of take pieces of knowledge
and combine them in new ways to solve unfamiliar problems.
Humans do this effortlessly,
but it's a major hurdle for a lot of AI systems.
So if you had a box of Legos,
a human can look at those bricks
and see countless ways to build something new.
Exactly.
But an AI might struggle to go beyond
the instructions in the manual or whatever.
That's a perfect analogy.
ARC-AGI II is designed to test this compositional ability
by presenting puzzles that require the AI
to combine familiar concepts in these unfamiliar ways.
It sounds like a real brain teaser,
even for an advanced AI like O3.
What else makes ARC-AGI II more challenging?
Well, the puzzles themselves
are expected to be much more intricate,
requiring more steps and more abstract reasoning.
Oh, wow.
To solve.
It's not just about making the puzzles harder,
but more conceptually challenging.
Demanding a deeper level of understanding
and problem solving.
It's like we're really testing the limits
of our current understanding of intelligence.
Absolutely.
Both human and artificial.
And that's what makes this whole field so fascinating.
Yeah, by pushing the boundaries of AI,
we're also deepening our understanding
of our own cognitive abilities.
So let's imagine for a moment that O3,
or some future AI, manages to crack ARC-AGI II.
What would that tell us
about the state of artificial general intelligence?
It would certainly be a major accomplishment.
Yeah.
A strong indication that we're on the right track,
but even ARC-AGI II is just one benchmark.
Right.
It's like saying, okay, this AI aced the SATs.
Right.
So it must be ready for college.
Exactly.
And you and I both know that academic success
is just one piece of the puzzle.
Exactly.
When it comes to doing a well-rounded individual.
Right.
AGI is about more than just performing well on a test.
Right.
It's about having that same breadth and depth
of intelligence we see in humans.
So even if an AI can solve these complex puzzles
and even outperform humans on certain tasks,
doesn't necessarily mean that it's achieved
true human-like intelligence.
Exactly.
That's why we need a variety of benchmarks
that test a wide range of cognitive abilities.
We need to challenge AI to understand language,
navigate social situations, even be creative.
It's like we need to create a comprehensive IQ test.
Yeah.
For AI measuring, not just problem solving,
but learning adaptation and interaction with the world
in a truly human-like way.
That's a great way to put it.
And as we develop more sophisticated benchmarks,
we'll get a clearer picture of what it truly means
to create artificial general intelligence.
Well, it seems like we've covered a lot of ground today
from the intricacies of the ARC-AGI competition
to the impressive capabilities of OpenAI's O3 model.
We're living in a time of extraordinary advancements
in AI, and the pursuit of artificial general intelligence
is more captivating than ever.
Absolutely.
And the most exciting part is that this field is constantly
evolving.
What seems groundbreaking today might be commonplace tomorrow
as researchers continue to push the boundaries of what's
possible.
As we wrap up this deep dive, one question lingers.
If O3 can learn to solve complex visual puzzles,
what other domains might be revolutionized
by this type of adaptive AI?
How might it impact feels like scientific discovery
creative arts?
Or even everyday problem solving?
It's a thrilling time to follow these AI developments,
and we encourage you to stay curious.
Who knows what breakthroughs await us just around the corner?