All right, get ready.
Because today, we're tackling some serious math.
Oh yeah, it's gonna be a fun one.
You wanted to know if AI can solve
really hard math problems.
Yeah.
And to answer that,
we're doing a deep dive into frontier math.
Okay.
A brand new benchmark designed to test AI's limits.
So like, how good is AI really at doing math?
Yeah, think of it like the SATs for AI,
but way, way harder.
Like way harder.
So what's really interesting here is that
we're not just talking about like your typical
high school algebra.
No, no, no, this is the big leagues.
Frontier math is all about pushing AI
beyond its comfort zone with problems
that even experienced mathematicians find challenging.
Yeah, stuff that even makes like seasoned math pros
sweat a little.
So it's kind of like giving a calculator
a PhD level exam.
That's a great way to put it.
Is that a good analogy?
It really is.
Okay.
Because the thing is, current AI benchmarks,
they're getting kind of easy.
Okay.
AI is hitting near perfect scores on tests like GSMAK
or math, which means we need a new challenge, right?
We need to push the limits.
Yeah, yeah.
So like AI's aced all the beginner levels
and now we need the boss fight.
Right, the boss fight, yeah.
Exactly.
Okay, that makes sense.
And plus there's this whole thing about contamination.
Contamination.
Yeah, it means like AI might have seen similar problems
when it was being trained, right?
So it's not really solving them from scratch.
Frontier math,
they try to avoid this by using all new problems.
Yeah.
Stuff that AI has never encountered before.
So it's like making sure the AI isn't just like
spitting out memorized answers.
You got it.
It's got to think deeply and creatively.
Just like a human mathematician would.
Okay, so how did they come up with these
brain melting math problems?
Yeah.
Did they like walk a bunch of mathematicians
in a room with no pizza until they came up
with like truly evil problems?
It wasn't quite that dramatic.
But they did assemble this team of over 60 mathematicians.
And this ranged from like grad students
all the way up to some of the biggest names in math,
you know?
People who've won something called the Fields Medal.
The Fields Medal, is that like the Nobel Prize of math?
You could say that, yeah.
It's one of the highest honors a mathematician can receive.
So they've got like the best of the best.
Absolutely.
Coming up with these problems.
And having that range of expertise,
that was important, right?
Because it made sure that frontier math covers
like a huge spectrum of math.
We're talking number theory,
combinatorics, algebraic geometry, you name it.
Wow, okay, so we've got these insanely hard problems
created by like the smartest mathematicians in the world.
But can you give us a taste?
Like what do these problems actually look like?
Absolutely, let's look at some examples from the paper.
One problem, which is rated as high difficulty,
involves figuring out how often a certain pattern
shows up with prime numbers.
Oh, okay.
It sounds simple, but it actually connects
to some of the biggest unsolved mysteries in math.
Like something called Arton's conjecture.
Wait, so even humans haven't figured these problems out?
That's right.
And that's part of what makes frontier math so cool, right?
Yeah.
It's not just about testing AI.
It's about pushing the boundaries of like
what we know as humans.
That's pretty awesome.
It is.
Okay, so that's next level.
What about another example?
All right, another one.
This is rated high medium difficulty.
Okay.
It asks AI to design a specific type
of polynomial equation.
Okay.
And the solution, you have to visualize these like
twisting and looping paths in a really abstract way.
Whoa.
It's not the kind of math you'd find in a textbook.
No, so it's not just about crunching numbers.
No.
It's about like real creative thinking.
Exactly.
And there's more.
Okay.
Another problem uses something called piatic numbers.
Piatic numbers, huh?
Which is a totally different way of thinking about numbers
than what we're used to.
It's like, uh, whole new world of math.
Okay, my brain is starting to hurt a little bit.
But this is cool.
So the big question is, how did AI actually do on these
frontier math challenges?
Well, let's just say it wasn't exactly a stellar performance.
Oh no, did they completely crash and burn?
None of the AI models they tested could solve more than
2% of the problems.
Wow.
It was a real wake-up call.
It shows that AI still has a long way to go before it can
match, you know, real human mathematicians at this kind
of hardcore math.
Yeah, wow, that's a reality check.
Yeah, it wasn't quite the AI takeover some people
were maybe predicting.
So a total wipeout.
But I'm curious, which AI models were even brave enough
to step into the ring on this one?
Oh, they tested some of the heavy hitters like GPT-4,
Claude, couple different versions of open AI's,
01 model, Grock 2, even Google DeepMind's Gemini.
Whoa, so the all-star team of AI?
Yeah, these are like the most advanced AI systems we've got.
And they still got like completely stumped.
Pretty much, yeah, frontier math was a whole other ball game.
Okay, so no AI came close to solving like a decent chunk
of the problems.
But I'm curious, were there any like interesting patterns
in how they approached it?
Yeah, actually there were some interesting differences.
Some AI like 01 Preview and Gemini,
they kind of just jumped right into answering
without a whole lot of like exploration or experimenting.
Yeah, they were like skimming the textbook
and thinking they were ready for the exam
without really understanding the material.
Yeah, they seem to rely a lot
on pattern recognition, quick calculations,
but maybe not so much on that deep conceptual understanding.
Okay, that makes sense.
What about the others?
Did any of them take a different approach?
Yeah, what about the other AI models?
Some models like Grock 2 seem to be more methodical.
They ran more code experiments.
Interesting.
Tried different approaches like reflecting on the results
before they gave a final answer.
It's more like the student who's carefully working
through the practice problems.
You got it.
Double checking their work.
Exactly.
Okay, so that's reflected in how much they actually used
like computationally.
It is, yeah.
Interesting, like is one approach better than the other?
That's a great question.
And honestly, the research doesn't say for sure.
It just points out this difference in strategy.
Like this is something to pay attention to.
Yeah.
It's something to keep researching.
It makes me wonder though,
is it like how we're training these AIs,
are we accidentally rewarding speed
over that deeper thinking?
That's a really good point.
Like is that built into how we're making them?
It leads to an even bigger question, right?
Like how do we evaluate AI in the first place?
Oh, interesting.
Because as these systems get more complex,
maybe just looking at did they get the right answer,
that's not enough anymore.
Right, right.
We gotta look at the process, the reasoning, the strategy.
So it's not just like did they get an A on the test?
It's like how are they learning?
How are they thinking?
Exactly.
Are they learning to think like really deeply
and creatively like a mathematician?
Or are they just getting really good
at finding shortcuts and mimicking patterns
without truly understanding?
Right, without the deeper knowledge.
And those are big questions, right?
And frontier math, it really forces us to think about that.
So it's not just a test for AI.
No.
It's a test for us,
for how we're even approaching this whole thing.
I'd say so, yeah,
it's a challenge for the whole AI field.
You know, we've been talking about the AI struggles,
but I'm kinda curious,
did the mathematicians who designed these problems,
did they find them hard too?
Oh, absolutely.
They interviewed several fields medalists.
Oh, remember, those are the top mathematicians in the world.
Yeah, the best of the best.
And even a top coach
for the International Mathematical Olympiad.
Wow, okay, so this is-
Like the Olympics of math competitions
for high school students.
Next level.
Yeah.
What did they think about this?
They all agreed the problems were really hard,
even for them.
Okay, good, I feel a little better
about my own math skills now.
Huh, that's reassuring.
It wasn't just the concepts,
it was the creative thinking, the problem solving.
Like that was the tricky part.
They did point out that the way frontier math is set up,
you know, giving numerical answers
instead of formal proofs,
that's not really typical for math research.
But it still needs that deep understanding,
that ability to connect different ideas.
So it's still real math,
it's just a different way of showing it.
Yeah, exactly.
So then they asked these mathematicians,
when do you think AI will be able to crack frontier math?
Okay.
And their estimates were,
well, let's just say they weren't holding their breath.
It's a years, decades.
Yeah, that seemed to be the consensus.
Wow, that's a long time.
It is, but here's where things get interesting.
Okay.
They were much more optimistic
about human AI collaboration.
Oh, tell me more about that, what did they picture?
They were thinking, you know, AI could be a really powerful
research assistant.
Helping mathematicians explore ideas,
test things out, verify calculations.
But like a souped up calculator.
Yeah.
But that can also like brainstorm with you.
Exactly.
And they thought that kind of team humans
and AI working together,
they could make progress on frontier math way sooner
Oh, interesting.
Than AI could on its own.
So we don't have to wait for AI
to become some kind of math genius.
Right.
Before it can actually be useful,
we can start working with it now.
Exactly.
That's cool.
It is, but for this to actually work,
we need the right tools, the right way for them
to talk to each other.
Yeah.
AI needs to understand human instructions.
And humans need to be able to guide the AI's thinking.
Like learning to speak each other's language.
I like that, yeah, building a bridge
between those two minds.
Yeah.
And that's a challenge in itself.
It is.
But I like the idea of like teamwork.
Yeah.
Like powerful stuff.
So I'm sold on this team idea.
Yeah.
But what are some of the like practical hurdles
we got it clear to actually make this happen?
Well, for one thing, AI models right now,
they're still pretty limited.
Okay.
When it comes to this really hardcore math,
even with human guidance.
Right.
It might be tough for them to make a lot of progress.
So it's like having a brilliant,
but inexperienced assistant.
Exactly.
Tons of potential, but still needs a lot of training.
And that training, like we talked about before,
it's hard because there's not enough data.
Right.
In these really specific areas of math.
So we need more data, is that what you're saying?
We do.
And we also need better ways to create synthetic data.
Okay.
That actually captures how complex
these math problems are.
Okay.
So data is one thing,
but what about the computing power, right?
Yeah.
We mentioned earlier,
it could take days on Google servers to solve one problem.
It's not really practical if you're trying to do research.
You're right.
We need more efficient algorithms,
better hardware that can handle
these really intense calculations
without breaking the bank.
Right.
Without taking forever.
Exactly.
So it's a lot of different challenges all at the same time.
It is, but the good news is
there's a ton of research happening in all these areas.
Okay. That's good.
There's some hope then.
Yeah, I think so.
It seems like frontier math has been
like a shakeup for the MCU world.
It has.
It's forcing everyone to rethink how they're doing things.
It's a wake-up call and a call to action.
It reminds us that true AI intelligence,
we're not there yet,
but it shows us the way forward.
How to get to that future where humans and AI
are working together to solve these amazing math problems.
Cool. This has been a really interesting journey.
We started with a pretty simple question.
Can AI solve really hard math?
Yeah.
And we've ended up exploring so much more.
We have.
We have a lot of sense of AI, human AI teams.
What even is AI intelligence?
Yeah.
It's been a lot.
And we need more data, more computing power.
It's a big challenge.
Yeah, it really is.
But what's amazing to me is the sense of possibility.
Yeah.
AI is still so,
we're just starting to understand what it can do.
Right.
Who knows what crazy discoveries are waiting for us
as AI gets smarter.
Maybe AI will not only solve the problems in frontier math,
but also help us discover new math concepts
that we haven't even thought of yet.
I wouldn't bet against it.
That would be amazing.
It would be a sign that AI isn't just copying us.
Right.
It's coming up with its own understanding of math.
Okay, well, sadly, that's all the time we have
for today's deep dive.
Oh!
Thanks for coming along with us
on this journey into AI and math.
It's been fun.
It really has.
Until next time, keep those minds curious
and keep on diving deep.
Welcome back for the final part of our deep dive
into frontier math.
It's been a wild ride.
It has.
We started off realizing that AI isn't quite ready
to take over the math world,
but then we saw the potential of these human AI teams
pushing the limits of what we know.
And we also talked about,
what even is AI intelligence?
Yeah.
It's not just about getting the right answer anymore.
No, it's about the process, the reasoning,
the creativity behind it all.
So as we wrap things up, I'm curious.
Yeah.
What do you think are the bigger implications of all this?
It's a good question.
What does frontier math tell us about AI as a whole
and where it's going?
Well, I think it's both a reality check
and a roadmap, you know what I mean?
Like on the one hand, it shows us the limits
of what AI can do right now,
especially with that abstract reasoning,
creative problem solving, which is important for math,
but also for so many other fields too.
So it's telling us that we haven't reached
that like true artificial general intelligence yet.
Right, that AI that can think like a human
across all these different areas.
Okay, that makes sense.
But on the other hand, frontier math gives us clues
about where AI needs to get better.
Okay, I like that.
By seeing how AI struggled with these problems,
researchers can figure out the weak spots, right?
And focus on making new algorithms,
new ways of training these systems.
So it's like a diagnostic tool for the whole field of AI.
Okay, and this isn't just about math problems then, right?
No, it's bigger than that.
Because the skills we're talking about,
abstract reasoning, logic, creativity,
those are important for everything.
Absolutely, if AI can master that stuff,
it can change so many things.
So the lessons from frontier math could ripple out
and affect all kinds of AI research.
I think so, yeah.
From science and engineering to maybe even art.
It's pushing the boundaries of what AI can do.
And it's making everyone aim higher.
I love that.
It's not just about making AI good at math,
it's about using math to create a whole new kind of AI.
That's so cool.
One that's more powerful, more flexible, more like us.
Well, this deep dive has been mind blowing.
I've enjoyed it.
We went from a simple question,
can AI solve hard math?
Yeah.
To talking about the future of AI.
It's all connected.
It really is.
It's been awesome sharing this journey with you
and with everyone listening.
It has.
I hope it's made everyone a little more curious
about this stuff.
Me too.
And I think that's a perfect note to end on.
Yeah.
Thanks for joining us on this exploration of AI and math.
It's been a pleasure.
It really has.
Until next time, keep those minds questioning,
keep exploring, and keep diving deep
into this amazing world of knowledge.
Couldn't have said it better myself.