Advice . . .
Tips . . .
Tricks . . .
Motivational quotes . . .
Pleasant-sounding
Meaningless garbage.
It is all about a Specific Human's
Specific Life.
@ElCaminoDeJesus El ego nos impide conectarnos con nuestro Yo Soy, el querer interminable no nos deja Ser. Transmutemos el querer en amor por lo que fue/es/será, sin dudar sobre el camino de Dios y sacudiéndonos los pies si el enemigo se presenta.
AIs aren’t minds and robots aren’t bodies.
They’re better at some things - usually the things we hate to do.
They’re worse at other things - usually the things we like to do.
Automation more than competition.
Humanoid robots at scale will feel like Alien technology:
→ Self-replicating factories of robots
→ Factories, job sites, hospitals, warehouses - automated
→ A personalized robot for every person
Physical work will be optional
"Move 37" is the word-of-day - it's when an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising, and secretly brilliant even to expert humans. It is a magical, just slightly unnerving, emergent phenomenon only achievable by large-scale reinforcement learning. You can't get there by expert imitation. It's when AlphaGo played move 37 in Game 2 against Lee Sedol, a weird move that was estimated to only have 1 in 10,000 chance to be played by a human, but one that was creative and brilliant in retrospect, leading to a win in that game.
We've seen Move 37 in a closed, game-like environment like Go, but with the latest crop of "thinking" LLM models (e.g. OpenAI-o1, DeepSeek-R1, Gemini 2.0 Flash Thinking), we are seeing the first very early glimmers of things like it in open world domains. The models discover, in the process of trying to solve many diverse math/code/etc. problems, strategies that resemble the internal monologue of humans, which are very hard (/impossible) to directly program into the models. I call these "cognitive strategies" - things like approaching a problem from different angles, trying out different ideas, finding analogies, backtracking, re-examining, etc. Weird as it sounds, it's plausible that LLMs can discover better ways of thinking, of solving problems, of connecting ideas across disciplines, and do so in a way we will find surprising, puzzling, but creative and brilliant in retrospect. It could get plenty weirder too - it's plausible (even likely, if it's done well) that the optimization invents its own language that is inscrutable to us, but that is more efficient or effective at problem solving. The weirdness of reinforcement learning is in principle unbounded.
I don't think we've seen equivalents of Move 37 yet. I don't know what it will look like. I think we're still quite early and that there is a lot of work ahead, both engineering and research. But the technology feels on track to find them.
https://t.co/JCxTdKpuzv
There is untold suffering in the lives of human beings.
The Truth is their only salvation.
Though this is a fact, it is also a fact that they will Never move toward The Truth.
For they will spend their lives
Lost in the hypnotic spell
Of pursuing prescriptive methodologies.
Can we teach LMs to internalize chain-of-thought (CoT) reasoning steps? We found a simple method: start with an LM trained with CoT, gradually remove CoT steps and finetune, forcing the LM to internalize reasoning.
Paper: https://t.co/FfuZYlWd3m
Done w/ @YejinChoinka@pmphlt 1/5