Is AI Weird Enough to Actually Make Scientific Discoveries?
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Today on the AI Daily Brief, will AI actually lead to scientific breakthroughs or not? The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. ♪

Hello, friends. Welcome back to another long reads episode of the AI Daily Brief. This week, we have something really interesting. Hugging Face co-founder Thomas Wolff just wrote a blog post this week challenging a very prominent essay by Anthropic CEO Dario Amodei.

Dario had written this essay, which we read here as well, called Machines of Loving Grace, in which he talked about what he thought AI was going to do positively in the next century. One of the big areas was scientific breakthrough, and Thomas Wolff, it turns out, doesn't agree. The piece that he wrote was called The Einstein AI Model. And so first, what we're going to do, as we always do, is we're going to turn it over to the 11 Labs version of me to listen to this piece, and then we will come back and talk about it a little bit more.

I shared a controversial take the other day at an event, and I decided to write it down in a longer format. I'm afraid AI won't give us a compressed 21st century. The compressed 21st century comes from Dario's Machine of Loving Grace, and if you haven't read it, you probably should. It's a noteworthy essay.

In a nutshell, the paper claims that over a year or two, we'll have a country of Einstein sitting in a data center. And it will result in a compressed 21st century during which all the scientific discoveries of the 21st century will happen in the span of only 5 to 10 years. I read this essay twice. The first time I was totally amazed. AI will change everything in science in five years, I thought.

A few days later I came back to it, and while rereading I realized that much of it seemed like wishful thinking. What we'll actually get, in my opinion, is a country of yes-men on servers, if we just continue on current trends. But let me explain the difference with a small part of my personal story.

I've always been a straight-A student. Coming from a small village, I joined the top French engineering school before getting accepted to MIT for PhD. School was always quite easy for me. I could just get where the professor was going, where the exam's creators were taking us, and could predict the test questions beforehand. That's why, when I eventually became a researcher, more specifically a PhD student, I was completely shocked to discover that I was a pretty average, underwhelming, mediocre researcher.

While many colleagues around me had interesting ideas, I was constantly hitting a wall. If something was not written in a book, I could not invent it unless it was a rather useless variation of a known theory. More annoyingly, I found it very hard to challenge the status quo, to question what I had learned. I was no Einstein, I was just very good at school. Or maybe even, I was no Einstein in part, because I was good at school.

History is filled with geniuses struggling during their studies. Edison was called "addled" by his teacher. Barbara McClintock got criticized for "weird thinking" before winning a Nobel Prize. Einstein failed his first attempt at the ETH Zurich entrance exam. And the list goes on. The main mistake people usually make is thinking Newton or Einstein were just scaled-up good students, that a genius comes to life when you linearly extrapolate a top 10% student.

This perspective misses the most crucial aspect of science: the skill to ask the right questions and to challenge even what one has learned. A real science breakthrough is Copernicus proposing, against all the knowledge of his days, in ML terms we would say, despite all his training dataset, that the Earth may orbit the Sun rather than the other way around.

To create an Einstein in a data center, we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask. One that writes, what if everyone is wrong about this, when all textbooks, experts, and common knowledge suggest otherwise?

Just consider the crazy paradigm shift of special relativity and the guts it took to formulate a first axiom like, "Let's assume the speed of light is constant in all frames of reference," defying the common sense of these days and even of today.

Or take CRISPR, generally considered to be an adaptive bacterial immune system since the 80s until 25 years after its discovery, Jennifer Doudna and Emmanuelle Charpentier proposed to use it for something much broader and general: gene editing, leading to a Nobel Prize. This type of realization: "We've known XX does YY for years, but what if we've been wrong about it all along? Or what if we could apply it to the entirely different concept instead?"

is an example of outside-of-knowledge thinking, or paradigm shift, which is essentially making the progress of science. Such paradigm shifts happen rarely, maybe one to two times a year, and are usually awarded Nobel Prizes once everybody has taken stock of the impact. However rare they are, I agree with Dario in saying that they take the lion's share in defining scientific progress over a given century, while the rest is mostly noise.

Now let's consider what we're currently using to benchmark recent AI model intelligence improvement. Some of the most recent AI tests are for instance the grandiosely named "Humanity's Last Exam" or "Frontier Math." They consist of very difficult questions, usually written by PhDs, but with clear, closed-end answers. These are exactly the kinds of exams where I excelled in my field. These benchmarks test if AI models can find the right answers to a set of questions we already know the answer to.

However, real scientific breakthroughs will come not from answering known questions, but from asking challenging new questions and questioning common conceptions and previous ideas. Remember Douglas Adams' Hitchhiker's Guide? The answer is apparently 42, but nobody knows the right question. That's research in a nutshell.

In my opinion, this is one of the reasons LLMs, while they already have all of humanity's knowledge and memory, haven't generated any new knowledge by connecting previously unrelated facts. They're mostly doing "manifold filling" at the moment, filling in the interpolation gaps between what humans already know, somehow treating knowledge as an intangible fabric of reality. We're currently building very obedient students, not revolutionaries.

This is perfect for today's main goal in the field of creating great assistants and overly compliant helpers. But until we find a way to incentivize them to question their knowledge and propose ideas that potentially go against past training data, they won't give us scientific revolutions yet.

If we want scientific breakthroughs, we should probably explore how we're currently measuring the performance of AI models and move to a measure of knowledge and reasoning able to test if scientific AI models can for instance: 1. Challenge their own training data knowledge 2. Take bold counterfactual approaches 3. Make general proposals based on tiny hints

Ask non-obvious questions that lead to new research paths. We don't need an A-plus student who can answer every question with general knowledge. We need a B student who sees and questions what everyone else missed.

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I absolutely love this piece. I actually had a weirdly proximate experience to this, if you'll indulge me for a minute. When I was in high school, I did a thing called Academic Decathlon. It's a national competition in the United States. At the time that I was doing it, there were something like 25,000 kids around the country, and it was very competitive.

A version of it was later featured in a Spider-Man movie, but that's neither here nor there. Basically, this thing was a 10-event academic competition that kids would study all year. And when I say study, I mean 5, 6, 10 hours a day. To put a fine point on this, I would literally skip school to study. I would go to school, but instead of going to my classes, I would go to the coach's office and just sit there and study all day.

For two years in a row, I was top five in the country, and I had a chance to learn a lot about the other kids who were also at the top of the list. The one thing they all shared was an insane willingness to work hard, but most of them, as I would later find out, tracking their time through college and then their careers, were very inside-the-box thinkers. They came from schools that had good programs, that knew what to do to turn out champions, and so they put in the work and got out the result.

I had always sort of thought that those people would go on to be very successful. And I guess by the qualifications of following a very specific career path, getting advanced degrees, and getting a consistent and well-paying job, they were. But none of them were disruptors. None of them were entrepreneurs. None of them were builders. And obviously, if you've heard of the stories of entrepreneurs, the most famous ones, the ones we hold up as societal examples, tended not to be those types of people.

They tended to be iconoclastic. Very often they were bad in traditional schools. They had a restlessness, a curiosity, a set of qualities that drove them to yearn for more and to be willing to play outside the rules of the system to get it.

Now, what I am not doing here is drawing any sort of value judgment on which of these is a better way to live. Lord knows, as someone who can't escape my entrepreneurial bent, a lot of points in my life would have been a lot easier if I had been one of those other types of kids. But I do think it's relevant for this conversation as we assume this straight line between the LLMs of today, which are basically like the best academic decathlon students you could have ever possibly imagined.

having read all the things, studied all the things, and who now can remember all the things and tell you all the things, but who aren't creating anything for themselves. Now, my question to Thomas would be, how hard would it be to take that base that we have now and get the LLM to quote-unquote think in different ways? In other words, does it require just a different prompt, or is it really about a totally different architecture that's necessary?

Given how much we point to scientific achievement and scientific advancement as the universally agreed upon upside of AI, I actually think that these questions are worth pondering and worth really digging into. Now, perhaps the big labs are, and have already come to some conclusions about how this is going to work.

Perhaps, for example, it's wrong to think about the independent iconoclastic genius as the model for LLMs when actually the way that scientific discovery is going to happen is a thousand different agents powered by all sorts of different LLMs smashing ideas against one another, running war game scenario testing, and seeing what comes up. Still, I'm really glad that Thomas wrote this post. I think it's very good food for thought, and I'm excited to see what people actually go do with it.

For now, though, that is going to do it for today's AI Daily Brief. Appreciate you listening, as always. And until next time, peace.