No, because there’s one problem with AI researchers, which is aspirational naming and aspirational stuff. So machine learning, that’s aspirational naming.
But machine learning is real.
But the naming is aspirational. The machine is not necessarily there.
So just because there’s curriculum learning in AI, it’s a field. But let me give you a paper from my former manager, Samy Bengio, who is a superstar in machine learning, not as famous as his older brother Yoshua. I ask him, “Aren’t you tired of your whole life being, like, debunking the whole reasoning thing every single paper you write?”
He quit after I got fired from Google, and he’s now head of machine learning research at Apple. If you look at almost every single paper that they have, it’s showing how if you change the benchmarks on reasoning slightly, the whole thing breaks down. It’s not reasoning.
Got it.
Let me give you another example. Just because your models, the stochastic patterns that you trained to print out certain tokens, you call them chain-of-thought reasoning. You didn’t know that they were thinking, you don’t know it’s a chain, you just know that these are tokens that are being printed out, but you call them chain-of-thought reasoning. Now you’re saying that they’re reasoning already.
One of the biggest crises that we have right now is actually sound scientific research. So if you look at my work, if I ever have access to the data, the code, the training data, and the evaluation data, which none of these companies give you those things—you don’t even know if they ingested that benchmark during training or not.
If you ingest a benchmark during training, it’s like studying to the test. It’s like me coming to an exam knowing what the answers to those 10 questions are already, studying that, and writing it down, right? Every time I have had access to these things, I have shown how their claims are not correct.