Comment by squidmaster
9 hours ago
> Before injecting this cocktail into your bloodstream, would you want to know that there is at least one human cancer researcher who understands the mechanism behind this cure.
Tao is out of his lane; lots of medicines don't have understood mechanisms. We don't even have the mechanisms behind general anesthesia nailed down; do you want to forgo it when the docs cut you open to remove your tumors?
AI has been turning computer science into biology for the past decade or so. By which I mean that things like neural networks need to be investigated empirically, constructing methodologies and instruments that more closely resemble how fields like biology and medicine have to probe the very complex and messy reality that is beyond our current capacity to fully express in symbolic precision.
Now math gets to deal with that same reckoning. They were already well on their way there with previous Lean proofs, but this has pushed things beyond that horizon and I'm not sure some of the mathematicians are ready for it.
Science has always had an empirical component separate from its theoretical one. For a long time in human history, science was mostly empirical. The periodic table is a great example of mostly empirical observation organized into a pattern. I think the science of the 20th century was the "triumph of theory" so many of us have forgotten what a more empirically driven STEM world is like.
Agreed, I keep thinking to myself that there's a huge mathematical question right in front of us today that is in exactly this vein - all the various nuances of why LLM's work so well is a mathematical question. As far as I know, it's not really understood beyond "we do this basic thing (that makes sense) to predict that a noun is followed by a verb, and then we scale it up a bazillion fold and it can contribute to mathematics research".
As a comparison, classical computing has been scaled up a bazillion fold too, and can do things which are absolutely miraculous, but every layer of abstraction is discretely understandable.
While studying neural networks empirically like biology is one possible approach, there's no reason for that to be the blessed approach other than a combination of inertia and current lack of understanding. It's been only 15 years since AlexNet. Scientists struggled for centuries to model atoms before developing quantum mechanics and later QFT as an accurate quantitative framework.
Also, while biological systems simply exist in nature, artificial neural networks are ultimately mathematical objects with various properties that have yet to be uncovered.
I really like this way of putting it
On the other side, I’ve never gone to college, yet I was able to take an extremely important and complicated equation and decompose it into its constituent parts, animating each with data visualizations and animated tables of values. [0] This was several frontier model iterations ago.
The LLM model was able to break down and express math in such a simple way that I could understand and follow the training of the LLM model itself!
Are the math and computation accurate? I don’t know, and likely there are significant errors. Nonetheless, if I had a little more time -- not infinite time -- I would be able to prove whether they are or not.
Likely this is the path forward for understanding the mechanisms of medicine, and since most humans learn by doing and interacting with an environment, interacting like this will become how we use AI for learning in the near future.
[0] https://adamsohn.com/grpo/
I mean no disrespect, but it's a basic minimization problem with regularization. Most engineering students will learn this in first year of Masters, if not in Bachelor level. To you it might seem important and complicated, but to me, one look at the objective function was enough. That's what happens when you truly "understand" something.
Indeed, aren't medical trials based on its effects, rather than how it works?
Would you prefer to take the medicine that is proven to work or the one that is quite interesting for academic reasons behind its understood mechanisms but doesn't actually work?
Right, the better argument would've been to say that we should wait until the drug undergoes some early trials, rather than just "some expert looking at it".
Lots of problems in mathematics didn’t have solutions just a month ago.
Some mechanism of particular substrates being effective on a condition (commonly when a medicine is repurposed) not being fully understood is not the same as just guessing with a medical compound. AI boosters keep coming out with this line but it's basically wordplay to conflate the clinical/biomedical version of "not fully understood" with the LLM industry version of "not fully understood"
I feel as though Tao is getting alot of public attention that he hasnt had since his childhood, which is why he is releasing all of these blog posts
I feel like a lot of this stuff is rationalizing emotions and self-interest, but that's very unnecessarily rude.
This has to be some kind of projection, right? Tao was getting LOTS of attention before the AI saga.
Was he? He was a child prodigy, which got him a lot of coverage in wider media, but then grew up and really became known in math circles. I'm sure he made lots of contributions to pure math and all and has won multiple academia-focused awards, but he didn't really make news outside of the math/academic area. Suddenly, with this AI math thing in the past year or two, he is starting to get more non-academic focused attention.
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That sounds like something someone never got the attention they wanted would say.