6 ms·
It's the new underpaid employee that you're training to replace you. People need to understand that we have the technology to train models to do anything that
by m_ke 8mo ago
It's the new underpaid employee that you're training to replace you.
People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data.
If you can record a human doing anything on a computer, we'll soon have a way to automate it
- xnx 8mo agoExactly. If there's any opportunity around AI it goes to those who have big troves of custom data (Google Workspace, Office 365, Adobe, Salesforce, etc.) or consultants adding data capture/surveillance of workers (especially high paid ones like engineers, doctors, lawyers).
- cesarvarela 8mo agoLLMs have a large quantity of chess data and still can't play for shit.
- iugtmkbdfil834 8mo agoHm.. but do they need it.. at this point, we do have custom tools that beat humans. In a sense, all LLM need is a way to connect to that tool ( and the same is true is for counting and many other aspects ).
- Windchaser 8mo agoYeah, but you know that manually telling the LLM to operate other custom tools is not going to be a long-term solution. And if an LLM could design, create, and operate a separate model, and then return/translate its results to you, that would be huge, but it also seems far away. But I'm ignorant here. Can anyone with a better background of SOTA ML tell me if this is being pursued, and if so, how far away it is? (And if not, what are the arguments against it, or what other approaches might deliver similar capacities?)
- yunyu 8mo agoThis has been happening for the past year on verifiable problems (did the change you made in your codebase work end-to-end, does this mathematical expression validate, did I win this chess match, etc...). The bulk of data, RL environment, and inference spend right now is on coding agents (or broadly speaking, tool use agents that can make their own tools). Recent advances in mathematical/physics research have all been with coding agents making their own "tools" by writing programs: https://openai.com/index/new-result-theoretical-physics/ https://openai.com/index/new-result-theoretical-physics/
- dwohnitmok 8mo agoNot anymore. This benchmark is for LLM chess ability: https://github.com/lightnesscaster/Chess-LLM-Benchmark?tab=readme-ov-file https://github.com/lightnesscaster/Chess-LLM-Benchmark?tab=r.... LLMs are graded according to FIDE rules so e.g. two illegal moves in a game leads to an immediate loss. This benchmark doesn't have the latest models from the last two months, but Gemini 3 (with no tools) is already at 1750 - 1800 FIDE, which is approximately probably around 1900 - 2000 USCF (about USCF expert level). This is enough to beat almost everyone at your local chess club.
- deadbabe 8mo agoWhy do we care about this? Chess AI have long been solved problems and LLMs are just an overly brute forced approach. They will never become very efficient chess players. The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.
- rodiger 8mo agoIt's a proxy for generalized reasoning. The point isn't that LLMs are the best AI architecture for chess.
- runarberg 8mo ago> It's a proxy for generalized reasoning. And so for I am only convinced that they have only succeeded on appearing to have generalized reasoning. That is, when an LLM plays chess they are performing Searle’s Chinese room thought experiment while claiming to pass the Turing test
- deadbabe 8mo agoWhy? Beating chess is more about searching a probability space, not reasoning. Reasoning would be more like the car wash question.
- 8mo ago
- BeetleB 8mo agoAre you saying an LLM can't produce a chess engine that will easily beat you?
- menaerus 8mo agoDid you already forget about the AlphaZero?
- nicowesterdale 7mo agoI wrote a, I hope, amusing breakdown of the structural reasons why off-the-shelf Large Language Models physically cannot "see" a chess board, and continue to make illegal moves, and teleport pieces as seen in Gotham Chess' latest videos. https://www.nicowesterdale.com/blog/why-llms-cant-play-chess https://www.nicowesterdale.com/blog/why-llms-cant-play-chess
- polotics 8mo agoHow much practice have you got on software development with agentic assistance. Which rough edges, surprising failure modes, unexpected strengths and weaknesses, have you already identified? How much do you wish someone else had done your favorite SOTA LLM's RLHF?
- xyzzy123 8mo agoSure, but do you want abundance of software, or scarcity? The price of having "star trek computers" is that people who work with computers have to adapt to the changes. Seems worth it?
- worldsayshi 8mo agoMy only objection here is that technology wont save us unless we also have a voice in how it is used. I don't think personal adaptation is enough for that. We need to adapt our ways to engage with power.
- almostdeadguy 8mo agoBoth abundance and scarcity can be bad. If you can't imagine a world where abundance of software is a very bad thing, I'd suggest you have a limited imagination?
- krackers 8mo agoAbundance of services before abundance of physical resources seems like the worst of both worlds.
- lanfeust6 8mo agoAggressively expanding solar would make electrical power a solved problem, and other previously non-abatable sources of kinetic energy are innovating to use this instead of fossil fuels
- jimbokun 8mo agoIt’s not worth it because we don’t have the Star Trek culture to go with it. Given current political and business leadership across the world, we are headed to a dystopian hellscape and AI is speeding up the journey exponentially.
- Gigachad 8mo agoData clearly isn't the only issue. LLMs have been trained on orders of magnitude more data than any person has ever seen.
- badgersnake 8mo agoI think we’re past the “if only we had more training data” myth now. There are pretty obviously far more fundamental issues with LLMs than that.
- m_ke 8mo agoi've been working in this field for a very long time, i promise you, if you can collect a dataset of a task you can train a model to repeat it. the models do an amazing job interpolating and i actually think the lack of extrapolation is a feature that will allow us to have amazing tools and not as much risk of uncontrollable "AGI". look at seedance 2.0, if a transformer can fit that, it can fit anything with enough data
- agumonkey 8mo agoIt's a strange economical morbid dependency. AI companies promises incredible things but AI agents cannot produce it themselves, they need to eat you slowly first.
- gtowey 8mo agoPerfect analogy for capitalism.
- mylifeandtimes 8mo ago> the new underpaid employee that you're training to replace you. and who is also compiling a detailed log of your every action (and inaction) into a searchable data store -- which will certainly never, NEVER be used against you