I appeared on camera for an interview not

so long ago. And I was really surprised by how many of you Fellow Scholars said that

you would like to see more. So first of all, thank you so much to all

of you for the kind words. Second, I thought let's try this and hope that you

will enjoy it. Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.

Look, it only took 1,000 episodes. Now, I have an amazing paper for you because scientists

at DeepMind did something pretty insane. Our question today is can an AI invent something that

is fundamentally new and pushes humanity forward? Well, they said that their new AI agent can

actually do research and even write research papers. Most of the core content anyway.

Is that insane? Well…it’s not. A lot of other people have tried it and the only insane thing

about it was how many poor papers they wrote.

But it turns out… there is levels to this game. You see, I visited the research group that is

behind this work last year. I flew to Mountain View into this crazy lab, and a grumpy

guard didn’t even want to let me in first. Crazy town. So I was very surprised that they

are guarding these secrets and they take them very seriously. What is even more surprising

is that now they give some of those secrets away to all of us for free. Now that

is insane! More on that in a moment. So I talked to these scientists, this was the

research group of Quoc Le. They are brilliant. They wrote an AI that was able to do a gold

medal worthy performance on the mathematical olympiad. This is serious business. Then they

released this technique, anyone who is made out of money bags and pays for the Gemini Advanced

can use it, it is called Deep Think. And now, this AI is even better than that. They call

it Aletheia. Now that, once again is insane.

Okay, so what does it do? Well, it

promises that it does research. It solves novel problems. This is something

that could push humanity forward. Now that is so much harder than the mathematical

olympiad. Why is that? Well, in these contests, you have a not that huge piece of core

knowledge you are supposed to have, and every problem can be guaranteed to

be solved by those small set of tools. Every problem is nice, shiny, and polished. Tough, but polished. You know what is not

polished at all? Real life problems. With these open problems, we don’t even know

if they are solvable at all. Maybe they are impossible, or maybe possible, but not with our

current tools. That’s the point: no one knows. When this technique is given a problem,

the generator starts working on it, creates a candidate solution, and now here is

one of the important parts of the paper. The

verifier. This takes a look, and says, okay bro

this is junk. Start again. This is essentially a filter. You know, that’s actually good life

advice. Sometimes it’s good to have a filter, so you don’t just shoot those hot takes out

there into the ether. Now every now and then, the solution looks pretty good, and could

maybe pass with a few modifications. Then, it gets polished for another round of reviews,

and so it goes. Sounds simple…maybe even trivial right? So what is so scientific about

this? Why doesn’t every system do that? Well, that’s easier said than done. In fact,

it is almost impossible to pull off. Why? One, when the AI is doing something

fundamentally new, unfortunately, hallucinations still happen. Yup.

It just makes stuff up. Fake papers, fictitious authors, you name

it. All kinds of junk comes out.

Two, when you want to compute

1+1 or other simple things, you have tons of training data about it out

there. You can verify that easily. But if you want to do frontier research? There is

no training data on what we don't even know yet. Of course there isn’t! You are trying

to invent things no one understands yet. These two factors make it extremely

difficult to get an AI to do something fundamentally new and useful. So how did

they pull it off? With three key steps. First, Alethia does not use this formal rigid math

language to check its own proofs. It uses natural English language. That is notoriously hard,

because when the AI checks its own writing, it just blindly agrees with it.

We humans do that too! Now here, the researchers found a way to separate the

thinking part from the answer part. So the

messy train of thought is hidden from the

verifier, it cannot trick itself into just blindly agreeing with itself. Brilliant. Our

brains would need something like that too. Then, two they let the computer think

longer. That’s not new. However, they added some optimizations to this, so

much so that the model they have now is just as smart as the one from 6 months ago.

But hold on to your papers Fellow Scholars, because yes, same smarts, but it uses a 100

times less compute. What! Crazy. They trained a much stronger base model which made it

more efficient at reasoning. So this one, even without internet access, beats the

mathematical olympiad gold AI easily. About 65% was improved to 95%. Wow. It went from

a bit better than a coinfip to destroying

the tasks made for some of the best human minds.

All this in just a few months. I am out of words. Now three, they gave the AI the

ability to search for stuff. We are talking about Google after all.

Once again, that is easy. However, getting the AI to read and combine techniques

from dozens and dozens of cutting-edge research papers without losing its mind. Now that is

hard. You saw it earlier, this really happens! They heavily trained this AI to be

able to use these tools and research works that are out there. That was what

finally stopped it from making up junk. Okay, so how good is it? First I saw that

it solved a few of these Erdős problems. It autonomously found the answer to 4 open math

puzzles left behind by a legendary Hungarian mathematician. Is that insane? I asked

a mathematician friend. He told me yeah, that’s pretty good, but there are

so many of these problems out there,

and not a ton of people work on them.

In other words, they are fairly easy, they were just ignored by experts for

years. So not nearly as good as I thought. But then, it stepped up its game and

wrote the core contents of a research paper. On something new. Note that the final

paper is written up by a human scientist. They had one paper on calculating constants

in arithmetic geometry. And then it helped human scientists write 4 other papers, like

finding new limits for interacting particles. So how good are these research works? Well, they are submitted for peer review

and that’s going to take quite a while. So, in the meantime, they had a

bunch of math experts look at it, many of them independent scientists. They

checked it for correctness and novelty, and it checks out man. I think for the first

time ever, an AI created core parts of a research work that is new, it has impact, it is

useful. That is…wow. What a time to be alive!

So I told you there is levels to this

game. So where are we now? Level 0 is negligible novelty work, it can do

that. Level 1 is somewhat novel work, it can do that too. But now, it can help a

person create publishable-level research. That is incredible. But wait, it can also do

that autonomously. An absolute game changer. Levels 3 and 4, those are groundbreaking

works, these are out of reach, but I ask you Fellow Scholars, given the pace

of progress, for how long? For 6 more months? And I think that is something that

needs to be talked about more. Research helping the people

live a better life. Love it. And thank you so much to all of you Fellow

Scholars for watching us over the years. We can only exist because of you Fellow

Scholars. I really hope that you enjoyed this. It allows me to talk about papers

where there is not a lot of visual content, and I really wanted to share this with you. Let

me know in the comments if we should do more.