Richard Sutton, professor of computing science at the University of Alberta, posted a 26-word summary of his Bitter Lesson principle advocating scalable search and learning over human knowledge for AI progress
Replies noted current models depend on human-generated data and prompts.
@agarwl_ discarding humand data (be it in LLM or in non-trivial "classic" RL env) is just complete nonsense. Even if Rich says it. But I'm not sure he's actually saying this here.
Guess he should use more than 26 words, but then it wouldn't sound Ilya-style mysterious anymore :)
Rishabh Agarwal@agarwl_Perplexed by this take: Sure, let's not mainly do supervise learning on human knowledge, but it makes sense to build off it instead of the *let's do it from scratch*. People cite AlphaGo vs AlphaGo Zero as a quintessential example of how using human-generating data is suboptimal but it was *imitating* it that was suboptimal. What if we learned from that data assuming it was suboptimal in the first place (so not supervised learning but RL like mindset of using that data)
@RichardSSutton I think it is worth studying human knowledge, particularly understanding the structure of its abstractions, as they can provide guidance about the kinds of things humans learn and machines do not (yet). I wouldn't call that "distraction".
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
#101Richard Sutton@RICHARDSSUTTONThe bitter lesson in 26 words:
Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
#128Yisong Yue@YISONGYUEAlso abstractions & knowledge curation.
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
i wonder whether this needs an update.
current methods, such as they are, leverage massive amounts of human knowledge as their primary fuel. they would be lost without it.
and they even build some knowledge into their system prompts.
and lately they build knowledge into their harnesses, usually by over 50 tools that have been carefully crafted with human knowledge.
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
#196Rishabh Agarwal@AGARWL_Perplexed by this take: Sure, let's not mainly do supervise learning on human knowledge, but it makes sense to build off it instead of the *let's do it from scratch*.
People cite AlphaGo vs AlphaGo Zero as a quintessential example of how using human-generating data is suboptimal but it was *imitating* it that was suboptimal.
What if we learned from that data assuming it was suboptimal in the first place (so not supervised learning but RL like mindset of using that data)
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
@RichardSSutton The bitter lesson also applies to how you work, not just what you build. Don't let human capacity be your bottleneck. Instead focus on methods and tools for creating impact that leverage computation.
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
The bitter lesson also applies to how you work, not just what you build. Don't let human capacity be your bottleneck. Instead focus on methods and tools for creating impact that leverage computation.
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
#437Xiao Ma@INFOXIAObitter and sad
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
#488kache@YACINEMTBGOD GAVE US THE UNIVERSE, THE ORACLE. WE MUST MINE IT
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
@jiaxinwen22 I agree with you on continued technical progress on AI basically not needing human research taste at all. I think this frees us to work on things where the target is totally unclear, e.g. interpretability
Jiaxin Wen@jiaxinwen22I might be one of the few people who is most bearish on human research taste and bullish on automated research: - "AIs can only do hyperparameter search" is mainly a skill issue with bad automated research setups. - human taste is overrated, e.g. frontier labs / neolabs are doing pretty simlar things. - human taste might win in a low-compute world, but not a high-compute world we're entering.
@jiaxinwen22 Although if you look into how frontier labs are acquiring data, I think it feels way more human taste driven than one would expect. But yeah algos etc. overrated
Aryaman Arora@aryaman2020@jiaxinwen22 I agree with you on continued technical progress on AI basically not needing human research taste at all. I think this frees us to work on things where the target is totally unclear, e.g. interpretability
#949Zhiting Hu@ZHITINGHUNatural language is human-created representation of the world.
Is the ultimate form of the bitter lesson to bypass natural language entirely and learn a new representation from the world itself?
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
@jiaxinwen22 how do you explain point 3 given that debate is empirically hard, LLM judge systematically fails to make reliable more subjective judgements (which is basically what taste is), etc? why doesn't this just result in the slop problems we see now when people try and scale SWE TTC?
Jiaxin Wen@jiaxinwen22I might be one of the few people who is most bearish on human research taste and bullish on automated research: - "AIs can only do hyperparameter search" is mainly a skill issue with bad automated research setups. - human taste is overrated, e.g. frontier labs / neolabs are doing pretty simlar things. - human taste might win in a low-compute world, but not a high-compute world we're entering.
@jiaxinwen22 median human taste is not great. the tails are pretty good, but for the purposes of "will AI replace a profession" the media is not hard to beat.
Jiaxin Wen@jiaxinwen22I might be one of the few people who is most bearish on human research taste and bullish on automated research: - "AIs can only do hyperparameter search" is mainly a skill issue with bad automated research setups. - human taste is overrated, e.g. frontier labs / neolabs are doing pretty simlar things. - human taste might win in a low-compute world, but not a high-compute world we're entering.
#1460Jiaxin Wen@JIAXINWEN22However, I hope humans keep doing our own research, with *strong* tastes and priors. Not all research is about outcomes. Sometimes you just want to solve/understand a problem in a way that feels like yours.
Jiaxin Wen@jiaxinwen22I might be one of the few people who is most bearish on human research taste and bullish on automated research: - "AIs can only do hyperparameter search" is mainly a skill issue with bad automated research setups. - human taste is overrated, e.g. frontier labs / neolabs are doing pretty simlar things. - human taste might win in a low-compute world, but not a high-compute world we're entering.
#1460Jiaxin Wen@JIAXINWEN22I might be one of the few people who is most bearish on human research taste and bullish on automated research: - "AIs can only do hyperparameter search" is mainly a skill issue with bad automated research setups. - human taste is overrated, e.g. frontier labs / neolabs are doing pretty simlar things. - human taste might win in a low-compute world, but not a high-compute world we're entering.
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
@menhguin If the job of top human experts is just to write a few high-level directions and ask AIs to solve, I’d still call it automated research.
Minh Nhat Nguyen @ AIE SG 🇸🇬@menhguin@jiaxinwen22 median human taste is not great. the tails are pretty good, but for the purposes of "will AI replace a profession" the media is not hard to beat.
i agree that human taste contributes a lot to frontier lab data quality. But I'd not be surprised that automated research proposes a very alien way to rewrite/score/filter data that outpeform humans. The way LMs absorb data is inherently very alien. so notions on difficulty, quality, diversity would be quite different from a human perspective vs. from an AI perspective
Aryaman Arora@aryaman2020@jiaxinwen22 Although if you look into how frontier labs are acquiring data, I think it feels way more human taste driven than one would expect. But yeah algos etc. overrated
#1627Aviv Tamar@AVIVTAMAR1Probably the best position paper in ML, now fits in a tweet
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
@agarwl_ the key issue seems to be “we’re operating inside of a bad box, here’s the good box” people like to agree on the second part, but few internalize why “human knowledge” is so attractive and why “how humans learn” is another humanization
Rishabh Agarwal@agarwl_Perplexed by this take: Sure, let's not mainly do supervise learning on human knowledge, but it makes sense to build off it instead of the *let's do it from scratch*. People cite AlphaGo vs AlphaGo Zero as a quintessential example of how using human-generating data is suboptimal but it was *imitating* it that was suboptimal. What if we learned from that data assuming it was suboptimal in the first place (so not supervised learning but RL like mindset of using that data)
@RichardSSutton The center of mass of AI history is like six month ago, so I'd say it was mostly about LLMs and learning from humans
Richard Sutton@RichardSSuttonThe bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
