AI 失控？GPT-6 数学发现与安全隐忧

The channel description that I have had since the beginning, quote, covering the arrival of smarter than human AI, end quote, may have for many felt naive, laughable, when it was first written at the beginning of 2023. But, you might agree that despite models obviously still having glaring blind spots, that description feels a lot more apt as the weeks go by. Pretty fair to say that there's just too much happening now for one human mind to fully grasp what's going on, let alone cover on a channel.

Nevertheless, I'm going to try to cover AI models making discoveries that would have been labeled genius had they been made by humans, and clearly how that capability is spilling over, predictably, into cybersecurity incidents making global headlines. Plus, what the tumult at Google says about AI shaking multi-trillion-dollar companies. Oh, and like 23 other things, maybe. Yes, I've human read three full papers for this video, plus well over a dozen articles and essays, but no, I frankly do not claim to comprehend everything that is happening.

But, where any video that confidently does so? I'm going to start with the mathematical discoveries because I think they are among the most significant, and they shed light on everything else we'll cover. They were made by an OpenAI model that will likely be named GPT-6. Straight out of the gate, I started reading the papers, but there's an obvious problem. Each of these would be a career-defining mathematical discovery.

Only those who have spent years studying each problem would be able to grasp the import of each disproof or upper bound. So, I went into back and forth with mathematicians and models, of course. Studied intensely the reaction of dozens of sources that I trusted, some of whom I'll cover in this video. And one question I had that I'm sure many of you had was as to whether these discoveries were all about grinding tenacity, low-hanging fruit found through pure brute forcing.

If they had been, that would have allowed me to add obvious nuance that may have reassured many. But they weren't that. Some were discoveries of the genius kind. What I had always reserved as Einstein-like abduction. Posit something new, proceed as if it's true, and see what that unlocks. And even the results that were recombination plus tenacity shouldn't really be described as brute forcing. It was more like discovering that old frameworks had unused capacity that everyone else believed was exhausted.

You could call it the genius loop, where you don't just make a mistake and then move on, because yes, the AI models make plenty of mistakes. OpenAI gave summaries of their reasoning. But for me, the genius loop is like autopsying the failed approach until you can prove why it must fail. Speculate, test, autopsy. Like how do we naturally separate that into grind versus genius insight? Is it possible that every seeming discontinuous leap that we hear the geniuses of history had made came down to weeks, months, years of that sort of behind-the-scenes genius loop?

Like I get that as Non Brown said of this, that this doesn't mean that models are posing new conjectures, let alone proof that they've solved mathematics. It's more the point that I can't find a qualitative wall between what they've done and what you would describe as genius if it had been done by a human. A few of you at this point will be saying, "Cool story, bro, but like who cares if models are quote genius? What relevance to real life is that?"

But you may be aware as context for this one, chapter seven, that we're currently replacing our encryption systems because we know quantum computers will eventually break them. One way forward is what's called lattice-based encryption, probably already found in your phone and browser. My half-hearted simplification of that is that this lattice approach is an endless grid of points in hundreds of dimensions, with the encryption lock working only if it's hard to find the grid point nearest to a given spot.

One of the 10 GPT-6 discoveries I summarized here is a proof that finding that grid point is way harder than anyone else had managed to prove. I.e., more reassurance that the encryption that your bank, your messages are increasingly relying on will last longer than just a few years before it too is broken. There were others that are relevant if we're sending probes home from Mars. I didn't know this, but we have error-correcting code that denoises such signals.

How efficient can those codes get? Well, apparently the known ceiling to what's possible hadn't budged in the last 50 years. GPT-6 comes along and at an extremely low cost, by the way, tells us which targets are provably, mathematically provably impossible. Decades of fruitless searching can end, you could say. Those are just the ones that I semi-grasped. I just wanted to make the point that yes, there is practical relevance for some of these breakthroughs.

That's not so much my focus though, because for me this is just about proving there isn't that wall. Here's how Mo Bavarian puts it, who is responsible for scaling up reinforcement learning at OpenAI. "All things that looked like fundamental limitations slowly faded with some advances, e.g., high-scale reinforcement learning, in the span of a few years. We should behold this moment both in awe and disbelief. What will a few more years of progress bring?

Are we ready for the tsunami of intelligence at our fingertips?" And below, he makes an equally important point, which for me is that this wasn't a new architecture that begot these breakthroughs. We're still talking about LLMs. For him, a few years ago, working on the alignment of LLM's might have been kind of pointless. In his words, premature because the shape of things weren't clear enough for it to be critical. Do all that work and then a new architecture comes along and it's kind of wasted.

But he's now implying that this work is now critical because it was LLM's that did this. It's different now, he says. Before, you might not have been aligning the actual AGI. Now, he implies this method will get there. Now, alignment, he says, is the most critical thing facing us. Before you think OpenAI are running away with it, some of these results apparently have been replicated via Fable 5. This source is an Anthropic employee.

Obviously, all of this is before I get to the security incident, which is possibly the headline event of this frenetic few weeks. Indeed, here's what one recent recipient of a Fields Medal, the Nobel Prize of Mathematics, said, "Because I have some publicity on me now, I'm trying to direct people into AI safety as much as I can." He also isn't much of a believer in some hidden wall. "I feel quite confident that very shortly AI will become robustly superhuman at what professional mathematicians currently do, including, in other words, positing new conjectures."

But before we just casually move on from this topic, if you have been, like me, provoked into some deep reflections about what's happening, there does seem to be a wall of a different kind, whereby humans will be perpetually useful in at least one way, which is that part of mathematics is the appreciation of mathematics. Some domains only really exist because some mathematicians find them interesting to explore. A model finding something with no practical value, which no human can understand or explain, might be kind of redundant.

So, even in the scenario that an AI is better than any human at even explaining what new discoveries they found, a human mathematician that best appreciates what has been discovered can thereby help other mathematicians and even lay people appreciate mathematics, too. Some humans may always require another human to help them appreciate what is being discovered in mathematics and beyond. Chess continues because there is that fundamental irreducible element of human appreciation.

Humans supply the essential motive because we still want to understand the wo

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