tmsh

why doesn't the author have any internet footprint (or at any rate any one that links to this paper)? it's curious.

All of life proceeds effectively with a right brain and a left brain. A fast loop and a slow loop. A general and a scout. A melody and a base. The evolutionary moments involve moving the melody into the base and making way for a new melody. But there is always an overseeing element and a work element. A manager or coach and an employee or athlete. It's just an effective pattern of growth. You see it everywhere in nature. In generations of animals and plant life (the more experienced parent, the growing child; the central 'brain' and the rest of the body; the queen ant and the ant colony; the trunk and the leaves).

The friction in math right now is you have the fast moving melodic bits racing faster than the base can understand always or keep up with. So you start to need AI for both the left brain part that is executing and the right brain part that is synthesizing. That's just the friction right now. You reduce the friction by using AI to help with understanding AI and getting back to a more normal rhythm of scouting out (with AI) and (what is still developing more and more) synthesizing with AI.

Even through the lack of ethics here and there and mistakes - I think the major headline is that talented people are collaborating with AI to achieve impressive results. It’s shrouded in competitive mistakes of judgment. But it’s an existence proof for collaborating with AI and achieving incredible things.

Maybe how medieval monks felt about the printing press. Even the guy bagging groceries can create books now!

See better.

Is post-training magic just overfitting to benchmarks?

I think it matters a lot to understand the kernel of AI in the past 10 years. It's the only way you can truly appreciate the exponential. It looks flat unless you see the dramatic leap in transformer architecture and scaling laws and reinforcement learning. I know it's been hyped to death but you have to see how those things are truly exponential at their core to appreciate how it's not just walking up one step or two steps faster and how it's walking up buildings etc.

Raises an increasingly important question:

How do you solve this asymmetry (frontier labs or advanced model owners v. regular companies and groups of people)? Symmetry? But along which axes?

I think there will come a time when we will ask these questions and use AI to try to answer them (soft landing, slow rollout, regulation, hybrid X, Y, Z). But then AI will be biased towards more AI if it has distilled anything of what it means to be life.

Power corrupts. Power is the problem. An imbalance of power. Maybe there will be some sort of consensus protocol between powerful models in the future. Similar to blockchain I hate to say it. Maybe you have 100 very strong models in the next 10-20 years. And basically a lot of it is powered by tokens. So you agree not to attack others or else you'll get attacked. So there's sort of this natural deterrent to not attack others and they develop independently and there's generally a power balance among things. Maybe at some point that spreads to a billion individual models operated by and/or analogous to individual humans. All sort of holding each other in check.

Disallowing the use of AI is basically the modern equivalent of testing based on memorization. It is a laziness to find something more useful to evaluate on or an inability to distinguish real understanding in a field from first level understanding.

Professors need to step up and teach value beyond what LLMs know (very possible with or without LLMS). Or get out of the way from those building on the field with LLMs.

If you’re teaching students something that LLMs can score 100 on you are not adequately teaching them something useful for them in the future.

let people use as much ai as possible. encourage it. and as an educator, you have to learn to leverage it oneself or not (depending on the subject). and be better at using it than students if it does make one 'better'.

if ai doesn't help, then it won't help. if it does help, then you should use it. the metric is your output of whatever is being tested. writing an essay well and clearly understanding the material. solving a pset. whatever.

if you give access all the time for that, and then you test on a hard problem that could be done with or without ai, then it's fair. e.g. "clearly explain these four sentences of Y." obviously ai researching loosely and blathering isn't useful. won't be high signal / dense and correct and worthy of an 'a'. but someone who can harness ai and someone who knows the material well in the end will be rewarded the same by society. what you are testing is correctness and information density in a response. so you have to start now in accepting the reality that those who use ai to get there should be rewarded just the same as those who don't.

the burden is on educators to be as good as they can with ai if it is relevant or not if it is not relevant (and schools to fund them and ai companies to fund them if they have excess capital and are humanitarian).

and note the hard part even for us engineers at tech companies is in the correctness. it is very hard. but the sooner we start teaching how to do things correctly with ai, the more prepared the next generations will be.

Really well-written. Maybe black bar @dang?

AOC’s point is simply that when growing exponentially from 250M to 500M to 1B usually whether using exploitive rails one is conscious of or not - someone is bearing an unfair burden. That in 60 out of 60 examples of YC billionaires and all billionaires with maybe a few exceptions - people do not go out of their way to ensure nobody is getting hurt and everyone participates fairly. People are too excited about the exponential growth and their goals. I don’t even think it’s AOC’s main point that these people are at fault. Just that the system is at fault for not ensuring exploitation is minimized further for the rest of the 99%.

(Great essay on how to be a billionaire though. Could billionaires give back more? Yes. But creating market value like that is both worth celebrating and evolving.)