[HN Gopher] Does the Bitter Lesson Have Limits?
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Does the Bitter Lesson Have Limits?
Author : dbreunig
Score : 175 points
Date : 2025-08-01 20:21 UTC (1 days ago)
(HTM) web link (www.dbreunig.com)
(TXT) w3m dump (www.dbreunig.com)
| o11c wrote:
| This is about AI, despite the title being ambiguous.
| andy99 wrote:
| Is there more than one bitter lesson?
| terminalshort wrote:
| I've learned many
| bigstrat2003 wrote:
| I don't know, but considering I had never heard of "the
| bitter lesson" before reading this article I agree the title
| is not especially clear. Not everyone lives and breathes AI
| and has heard of stuff like that, after all.
| thrawa8387336 wrote:
| If we're going to be pedantic:
|
| This is about AI, the title is ambiguous.
|
| Despite was used unambiguously wrong.
| o11c wrote:
| "Despite" is absolutely correct when you realize that
| cheating in the title is a way to make people look at
| articles when they would rather ignore AI in favor of
| actually-useful/interesting subjects.
| myhf wrote:
| The original "Bitter Lesson" essay is about machine learning,
| but the linked article appears to be trying to apply it to
| LLMs.
| criemen wrote:
| All links render as blue strike-through line in Firefox
| (underline in Chrome), hurting legibility :(
| thwg wrote:
| interesting. i see underlines in firefox. but the width of the
| line is 2px in chrome, 1px in firefox.
| penneyd wrote:
| Fine over here using Firefox
| Cerium wrote:
| I'm getting the same effect, seems to be the css property
| "text-underline-position: under;"
| grubbypaw wrote:
| I was not at all a fan of "The Bitter Lesson versus The Garbage
| Can", but this misses the same thing that it missed.
|
| The Bitter Lesson is from the perspective of how to spend your
| entire career. It is correct over the course of a very long time,
| and bakes in Moore's Law.
|
| The Bitter Lesson is true because general methods capture these
| assumed hardware gains that specific methods may not. It was
| never meant for contrasting methods at a specific moment in time.
| At a specific moment in time you're just describing Explore vs
| Exploit.
| schmidtleonard wrote:
| Right, and if you spot a job that needs doing and can be done
| by a specialized model, waving your hands about general purpose
| scale-leveraging models eventually overtaking specialized
| models has not historically been a winning approach.
|
| Except in the last year or two, which is why people are citing
| it a lot :)
| hinkley wrote:
| Probably because this is how bubbles happen.
| anp wrote:
| I think there might be interesting time scales in between "now"
| and "my entire career" to which the bitter lesson may or may
| not apply. As an outsider to ML I have questions about the
| longevity of any given "context engineering" approach in light
| of the bitter lesson.
| energy123 wrote:
| The bitter lesson becomes more true over time, because
| inductive bias becomes less useful over time. Case in point:
| PCA/hand engineering -> CNN -> ViT.
| Animats wrote:
| The question is when price/performance hits financial limits.
| That point may be close, if not already passed.
|
| Interestingly, this hasn't happened for wafer fabs. A modern
| wafer fab costs US$1bn to US$3bn, and there is talk of US$20bn
| wafer fabs. Around the year 2000, those would have been un-
| financeable. It was expected that fab cost was going to be a
| constraint on feature size. That didn't happen.
|
| For years, it was thought that the ASML approach to extreme UV
| was going to cost too much. It's a horrible hack, shooting off
| droplets of tin to be vaporized by lasers just to generate soft
| X-rays. Industry people were hoping for small synchrotrons or
| X-ray lasers or E-beam machines or something sane. But none of
| those worked out. Progress went on by making a fundamentally
| awful process work commercially, at insane cost.
| schmidtleonard wrote:
| Fundamentally awful but spiritually delightful.
| azeirah wrote:
| Sometimes awful is the best we have, we don't have anything
| that performs at a similar level to EUV machines by ASML but
| are much simpler or tenable than what we have right now, right?
|
| Perhaps we will find something better in the future, but for
| now awful is the best we got for the cutting edge.
|
| Also, when is cutting edge not the worst it's ever been?
| xyzzy123 wrote:
| It's hard to value $20bn in 2025 vs say $2bn for cutting edge
| fab in 2000. Personally I don't think CPI captures the relative
| value of _investments_ well.
|
| If you compare it as % of gdp, or relative to m2 or % of S&P
| 500, or even size of electronics industry it's maybe 2x-4x or
| something. Which is still an increase and still a lot but
| doesn't seem as crazy to me.
|
| Another lens on it: the most valuable car company in 2000 was
| $50bn and in 2025 it is $1000bn, which I think says more about
| dollars than cars.
| thrawa8387336 wrote:
| This brings about a good point:
|
| How much of the recent bitter lesson peddling is done by compute
| salesmen?
|
| How much of it is done by people who can buy a lot of compute?
|
| Deepseek was scandalous for a reason.
| fdav wrote:
| A better lesson: https://rodneybrooks.com/a-better-lesson/
| ripe wrote:
| Rodney Brooks's analysis, as usual, is spot-on.
|
| Tldr; ML using neural networks is not really replacing human
| knowledge with computation; human work goes into encoding data,
| building datasets, and designing hyperparameters.
| benlivengood wrote:
| I think it's a little early (even in these AI times) to call HRM
| a counterexample of the bitter lesson.
|
| I think it's quite a bit more likely for HRM to scale
| embarrassingly far and outstrip the tons of RLHF and distillation
| that's been invested in for transformers, more of a bitter lesson
| 2.0 than anything else.
| turnsout wrote:
| I didn't know about HRM until this article, but it looks
| incredibly impressive, even in its current state. A 27M
| parameter model that can be trained to perform difficult
| domain-specific tasks with better results than flagship
| reasoning models? That is compelling.
| PaulHoule wrote:
| One odd thing is that progress in SAT/SMT solvers has been almost
| as good as progress in neural networks from the 1970s to the
| present. There was a time I was really interested in production
| rules and expert system shells and systems in the early 1980s
| often didn't even use RETE and didn't have hash indexes so of
| course a rule base of 10,000 looked unmanageable, by 2015 you
| could have a million rules in Drools and it worked just fine.
| bmc7505 wrote:
| The difference is that SAT/SMT solvers have primarily relied on
| single-threaded algorithmic improvements [1] and unlike neural
| networks, we have not [yet] discovered a uniformly effective
| strategy for leveraging additional computation to accelerate
| wall-clock runtime. [2]
|
| [1]: https://arxiv.org/pdf/2008.02215
|
| [2]: https://news.ycombinator.com/item?id=36081350
| PaulHoule wrote:
| RETE family algorithms did turn out to be somewhat
| parallelizable, enough to get a real speed-up on ordinary
| multicore CPUs. There was an idea in the 1980s that symbolic
| AI would be massively parallelizable that turned out to be a
| disappointment.
|
| https://en.wikipedia.org/wiki/Fifth_Generation_Computer_Syst.
| ..
| bmc7505 wrote:
| You could argue that since automatic differentiation and
| symbolic differentiation are equivalent, [1] symbolic AI
| did succeed by becoming massively parallelizable, we just
| needed to scale up the data and hardware in kind.
|
| [1]: https://arxiv.org/pdf/1904.02990
| yorwba wrote:
| > [2]
|
| In the comments, zero_k posted a link to the SAT
| competition's parallel track. The 2025 results page is here:
| https://satcompetition.github.io/2025/results.html Parallel
| solvers consistently score lower (take less time) than
| single-threaded solvers, and solve more instances within the
| time limit. Probably the speedup is nowhere near proportional
| to the amount of parallelism, but if you just want to get
| results a little bit faster, throwing more cores at the
| problem does seem like it generally works.
| bmc7505 wrote:
| > The solvers participating in this track will be executed
| with a wall-clock time limit of 1000 seconds. Each solver
| will be run an a single AWS machine of the type
| m6i.16xlarge, which has 64 virtual cores and 256GB of
| memory.
|
| For comparison, an H100 has 14,592 CUDA cores, with GPU
| clusters measured in the exaflops. The scaling exponents
| are clearly favorable for LLM training and inference, but
| whether the same algorithms used for parallel SAT would
| benefit from compute scaling is unclear. I maintain that
| either (1) SAT researchers have not yet learned the bitter
| lesson, or (2) it is not applicable across all of AI as
| Sutton claims.
| jamesblonde wrote:
| I see elements of the bitter lesson in arguments about context
| window size and RAG. The argument is about retrieval being the
| equivalent of compute/search. Just improve them, to hell with all
| else.
|
| However, retrieval is not just google search. Primary key lookups
| in my db are also retrieval. As are vector index queries or BM25
| free text search queries. It's not a general purpose area like
| compute/search. In summary, i don't think that RAG is dead.
| Context engineering is just like feature engineering - transform
| the swamp of data into a structured signal that is easy for in-
| context learning to learn.
|
| The corollory of all this is it's not just about scaling up
| agents - giving them more LLMs and more data via MCP. The bitter
| lesson doesn't apply to agents yet.
| sorenjan wrote:
| The last time I was reminded of the bitter lesson was when I read
| about Guidance & Control Networks, after seeing them used in an
| autonomous drone that beat the best human FPV pilots [0].
| Basically it's using a small MLP (Multi Layer Perceptron) on the
| order of 200 parameters, and using the drone's state as input and
| controlling the motors directly with the output. We have all
| kinds of fancy control theory like MCP (Model Predictive
| Control), but it turns out that the best solution might be to
| train a relatively tiny NN using a mix of simulation and
| collected sensor data instead. It's not better because of huge
| computation resources, it's actually more computationally
| efficient than some classic alternatives, but it is more general.
|
| [0] https://www.tudelft.nl/en/2025/lr/autonomous-drone-from-
| tu-d...
|
| https://www.nature.com/articles/s41586-023-06419-4
|
| https://arxiv.org/abs/2305.13078
|
| https://arxiv.org/abs/2305.02705
| logicchains wrote:
| >It's not better because of huge computation resources, it's
| actually more computationally efficient than some classic
| alternatives
|
| It's similar with options pricing. The most sophisticated
| models like multivariate stochastic volatility are
| computationally expensive to approximate with classical
| approaches (and have no closed form solution), so just training
| a small NN on the output of a vast number of simulations of the
| underlying processes ends up producing a more efficient model
| than traditional approaches. Same with stuff like trinomial
| trees.
| William_BB wrote:
| Interesting. Are these models the SOTA in the options trading
| industry (e.g. MM) nowadays?
| cactusfrog wrote:
| This is really interesting. I think force fields in molecular
| dynamics have underwent a similar NN revolution. You train
| your NN on the output of expensive calculations to replace
| the expensive function with a cheap one. Could you train a
| small language model with a big one?
| lossolo wrote:
| > Could you train a small language model with a big one?
|
| Yes, it's called distillation.
| jwjohnson314 wrote:
| Can you elaborate on this? I am curious what processes are
| being simulated to feed the network.
| bsoles wrote:
| But I have also seen people trying to use deep networks to
| identify rotating machinery faults, like bearings, from raw
| accelerometer data collected a high frequencies like 40 kHz.
| Whereas the spectrum of the data from running FFT on the signal
| contains fault information much more obviously and clearly.
|
| Throwing a deep network on a problem without some physical
| insight into the problem has also its disadvantages it seems.
| jvanderbot wrote:
| Yeah, we're shouting into the wind here. I have had people
| tell me directly that my ideas from old school state
| estimation were irrelevant in the era of deep learning. They
| may produce (in this case) worse results, but the long game
| I'm assured is superior.
|
| The specific scenario was estimating the orientation of a
| stationary semi trailer. An objectively measurable number and
| it was consistently off by 30 deg, yet I was the jerk for
| suggesting we move from end to end DL to trad Bar Shalom
| techniques.
|
| That scene isn't for me anymore.
| AnonymousPlanet wrote:
| > That scene isn't for me anymore.
|
| They will learn. At least when the competition beats their
| solution with a hybrid approach they can't begin to
| understand.
| physicsguy wrote:
| I'm working on this sort of thing right now in a SaaS product
| that previously didn't have support for vibration data. One
| competitor is all ML-d up to the hilt but customers don't
| like the black box and keep finding it gives false positives
| with no explanation. I think one problem is those buying not
| understanding problem; they just want to plug in a sensor and
| insights to happen, but without information about the machine
| that's never going to be able to provide useful insights
| ssivark wrote:
| > an autonomous drone that beat the best human FPV pilots
|
| Doesn't any such claim come with huge caveats -- pre specified
| track/course, no random objects flying between, etc...? ie.
| train & test distributions are ensured same by ensuring test
| time can never be more complicated than training data.
|
| Also presumably better sensing than raw visual input.
| bsoles wrote:
| There are also extremely misleading research articles out there
| promising good results with deep networks in the area of
| anomaly detection, without adequate comparison with more
| classical techniques.
|
| This well-known critical paper shows examples of AI
| articles/techniques applied to popular datasets with good-
| looking results. But, it also demonstrates that, literally, a
| single line of MATLAB code can outperform some of these
| techniques: https://arxiv.org/pdf/2009.13807
| chrisweekly wrote:
| > "MCP (Model Predictive Control)"
|
| ^ that's MPC. (MCP = Model Context Protocol)
| mentalgear wrote:
| The Neuro-Symbolic approach is what the article describes,
| without actually naming it.
| jgalt212 wrote:
| Perhaps, but I see it more as an endorsement of careful feature
| selection. Subject matter experts can do this, and once done,
| you can get a away with a much smaller model and better price /
| performance.
| pu_pe wrote:
| I'm not so sure Stockfish is a good example. The fact it can run
| on an Iphone is due to Moore's law, which follows the same
| pattern. And Deepmind briefly taking its throne was a very good
| example of the Bitter Lesson.
| mtlmtlmtlmtl wrote:
| Stockfish being so strong is not merely a result of scaling of
| computation with search and learning. Basic alpha-beta search
| doesn't really scale all that well with compute. The number of
| nodes visited grows exponentionally with the number of plies
| you look ahead. Additionally alpha-beta search is not
| embarassingly parallel. The reason Stockfish is so strong is
| that it includes pretty much every heuristic improvement to
| alpha-beta that's been thought of in the history of computer
| chess, somehow combining all of them while avoiding bugs and
| performance regressions. Many of these heuristics are based on
| chess knowledge. As well as a lot of very clever optimisation
| of data structures(transposition tables, bitboards) to
| facilitate parallel search and shave off every bit of overhead.
|
| Stockfish is a culmination of a lot of computer science
| research, chess knowledge and clever, meticulous design.
| plumthreads wrote:
| While what you mention is true, I'm not sure how it
| undermines the bitter lesson. Optimizing the use of hardware
| (which is what NNUE essentially does) is one way of
| "increasing compute." Also, NNUE was not a chess specific
| technique, it was originally developed for Shogi.
| aydyn wrote:
| Does anyone else see the big flaw with the chess engine analogy?
|
| When AlphaZero came along it blew stockfish out of the water.
|
| Stockfish is a top engine now because besides that initial proof
| of concept _there 's no money to be made by throwing compute at
| Chess_.
| Mistletoe wrote:
| I'm wondering how prophetic this is for LLMs. My hunch is a
| lot.
| itkovian_ wrote:
| I don't think people understand the point sutton was making; he's
| saying that general, simple systems that get better with scale
| tend to outperform hand engineered systems that don't. It's a
| kind of subtle point that's implicitly saying hand engineering
| inhibits scale because it inhibits generality. He is not saying
| anything about the rate, doesn't claim llms/gd are the best
| system, in fact I'd guess he thinks there's likely an even more
| general approach that would be better. It's comparing two classes
| of approaches not commenting on the merits of particular systems.
| eldenring wrote:
| Yep this article is self centered and perfectly represents the
| type of ego Sutton was referencing. Maybe in a year or two
| general methods will improve the author's workflow
| significantly once again (eg. better models) and they would
| still add a bit of human logic on top and claim victory.
| visarga wrote:
| The point about training data stands. We usually only think
| of scaling compute, but we need to scale data as well, maybe
| even faster than compute. But we exhausted the source of high
| quality organic text, and it doesn't grow exponentially fast.
|
| I think at the moment the best source of data is the chat
| log, with 1B users and over 1T daily tokens over all LLMs.
| These chat logs are at the intersection of human interests
| and LLM execution errors, they are on-policy for the model,
| right what they need to improve the next iteration.
| joe_the_user wrote:
| It occurs to me that the bitter lesson is so often repeated
| because it involves a slippery slope or moot-and-castle
| argument. IE, the meaning people assign to the bitter lesson
| ranges between all the following:
|
| _General-purpose-algorithms-that-scale will beat algorithms
| that aren 't those_
|
| _The most simple general purpose, scaling algorithm will win,
| at least over time_
|
| _Neural networks will win_
|
| _LLMs will reach AGI with just more resources_
| arrowsmith wrote:
| motte and bailey*
| xpe wrote:
| > I don't think people understand the point sutton was making;
| he's saying that general, simple systems that get better with
| scale tend to outperform hand engineered systems that don't
|
| This is your reading of Sutton. When I read his original post,
| I don't extract this level of nuance. The very fact that he
| calls it a "lesson" rather than something else, such as a
| "tendency", suggests Sutton may not hold the idea lightly*. In
| other words, it might have become more than a testable theory;
| it might have become a narrative.
|
| * Holding an idea lightly is usually good thing in my book.
| Very few ideas are foundational.
| throw1289312 wrote:
| This article focuses on the learning aspect of The Bitter Lesson.
| But The Bitter Lesson is about both search _and_ learning.
|
| This article cites Leela, the chess program, as an example of the
| Bitter Lesson, as it learns chess using a general method. The
| article then goes on to cite Stockfish as a counterexample,
| because it uses human-written heuristics to perform search.
| However, as you add compute to Stockfish's search, or spend time
| optimizing compute-expenditure-per-position, Stockfish gets
| better. Stockfish isn't a counterexample, search is still a part
| of The Bitter Lesson!
| jgalt212 wrote:
| Stockfish is neither. It's a hybrid approach.
| beepbooptheory wrote:
| I should know better than to speak anything too enthusiastically
| about the humanities or feminism on this particular forum, but I
| just want to say the connection here to Donna Haraway was a
| surprise and delight. Any one open to that world at all would
| behoove themselves to check her out. "The Cyborg Manifesto" is
| the one everyone knows, but I recently finished "Living with the
| Trouble" and can't recommend it enough!
| frozenseven wrote:
| >Donna Haraway
|
| >Any one open to that world...
|
| The "world" in question being a brand of Marxism that's super-
| explicitly anti-human. No, I'm not kidding or exaggerating.
| benreesman wrote:
| When The Bitter Lesson essay came out it was a bunch of important
| things: addressing an audience of serious practitioners,
| contrarian and challenging entrenched dogma, written without any
| serious reputational or (especially) financial stake in the
| outcome. It needed saying and it was important.
|
| But its become a lazy crutch for a bunch of people who meet
| _none_ of those criteria and perverted into a statement more
| along the lines of "LLMs trained on NVIDIA cards by one of a
| handful of US companies are guaranteed to outperform every other
| approach from here to the Singularity".
|
| Nope. Not at all guaranteed, and at the moment? Not even looking
| likely.
|
| It will have other stuff in it. Maybe that's prediction in
| representation space like JEPA, maybe its MCTS like Alpha*, maybe
| its some totally new thing.
|
| And maybe it happens in Hangzhou.
| stego-tech wrote:
| Not familiar with the cited essay (added to reading list for the
| weekend), but the post does make some generally good points on
| generalization (it me) vs specialization, and the benefits of an
| optimized and scalable generalist approach vs a niche,
| specialized approach, specifically with regards to current LLMs
| (and to a lesser degree, ML as a whole).
|
| Where I furrow my brow is the casual mixing of philosophical
| conjecture with technical observations or statements. Mixing the
| two all too often feels like a crutch around defending either
| singular perspective in an argument by stating the other half of
| the argument defends the first half. I know I'm not articulating
| my point well here, but it just comes off as a little...
| _insincere_ , I guess? I'm sure someone here will find the
| appropriate words to communicate my point better, if I'm being
| understood.
|
| One nitpick on the philosophical side of things I'd point out is
| that a lot of the resistance to AI replacing human labor is less
| to do with the self-styled importance of humanity, and more the
| bleak future of a species where a handful of Capitalists will
| destroy civilization for the remainder to benefit themselves.
| _That_ is what sticks in our collective craw, and a large reason
| for the pushback against AI - and nobody in a position of power
| is taking that threat remotely seriously, largely _because_ the
| owners of AI have a vested interest in preventing that from being
| addressed (since it would inevitably curb the very power they 're
| investing in building for themselves).
| kazinator wrote:
| > The bitter lesson is dependent on high-quality data.
|
| Arguably, so is the alternative: explicitly embedding knowledge!
|
| Nothing is immune to GIGO.
| strangescript wrote:
| The problem with the Bitter Lesson is that it doesn't clearly
| define what is a computational "hack" and what is a genuine
| architecturally breakthrough. We would be no where without
| transformers for example.
| titanomachy wrote:
| > This views organizations as chaotic "garbage cans" where
| problems, solutions, and decision-makers are dumped in together,
| and decisions often happen when these elements collide randomly,
| rather than through a fully rational process
|
| Only tangentially related, but this has to be one of the worst
| metaphors I've ever heard. Garbage cans are not typically hotbeds
| of chaotic activity, unless a raccoon gets in or something.
| quantum_state wrote:
| It would not be surprising if a bitter lesson 2.0 comes about as
| a bitter lesson to the bitter lesson.
| bravesoul2 wrote:
| Well hardware has limits so I guess so. Humans evolved with
| faculties not a massive organic generic compute engine.
| worik wrote:
| 43% of American workers have used AI at work, they are mostly
| doing it in informal ways, solving their own work problems.
| Scaling AI across the enterprise is hard
|
| A lot of firms starting into this business are "betting the farm"
| on "scaling AI across the enterprise"
|
| In my experience LLMs are incredibly useful from a simple text
| interface (I only work with text, mainly computer code). I am
| still reeling from how disruptive they are, in that context.
|
| But IMO there is not a lot of money to be made for start ups in
| that context (I expect there is not enough to justify the high
| valuations of outfits like Open AI). There should be a name for
| the curse - revolutionary technology that makes many people
| vastly more productive, but there is no real way to capture that
| value. Unless "Scaling AI across the enterprise" can succeed.
|
| I have my doubts. I am sure there will be niches, and in a decade
| or so, with hindsight, it will be clear what they are. But there
| is no reliable way to tell now
|
| The "Bitter Lesson" seems like a distraction to me. The
| fundamental problem is related: this technology is generally
| useful, much more than it is specifically useful.
|
| The "killer app" is a browser window open to
| https://chat.deepseek.com. There is not much beyond that. Not
| nothing, just not much.
|
| But so long as you have not bet your farm on "scaling AI across
| the enterprise" nor been fired by someone else who is trying, we
| should be very happy. We are in a "steam engine" moment. Nothing
| will ever be the same.
|
| And if Open AI and the like all go belly up and demote a swathe
| of billionaires to be normally rich, that is the cherry on the
| top
| softwaredoug wrote:
| In my experience, there's an opposing "bitter lesson" when trying
| to make incremental, tactical progress in user-facing AI / ML
| applications: _you're not a researcher_. Stay to tried-and-true,
| boring ML methods that have been proven at scale and then add
| human knowledge and rules to make it all work.
|
| Then, as the article mentions, some new fundamental shift
| happens, and practitioners need to jump over to a completely new
| way of working. Monkeypatching to make it all work. Rinse repeat.
| xpe wrote:
| > The biggest lesson that can be read from 70 years of AI
| research is that general methods that leverage computation are
| ultimately the most effective, and by a large margin.
|
| Be careful when anyone, even a giant in the field such as Sutton,
| posits a sweeping claim like this.
|
| My take? Sutton's "bitter lesson" is rather vague and unspecified
| (i.e. hard to pin down and test) for at least two reasons:
|
| 1. The word "ultimately" is squishy, when you think about it.
| When has enough time passed to make the assessment? At what point
| can we say e.g. "Problem X has a most effective solution"?
|
| 2. What do we mean by "most effective"? There is a lot of
| variation, including but not limited to (a) some performance
| metric; (b) data efficiency; (c) flexibility / adaptability
| across different domains; and (d) energy efficiency.
|
| I'm a big fan of Sutton's work. I've read his RL book cover-to-
| cover and got to meet him briefly. But, to me, the bitter lesson
| (as articulated in Sutton's original post) is not even wrong. It
| is sufficiently open-ended that many of us will disagree about
| what the lesson is, even before we can get to the empirical
| questions of "First, has it happened in domain D at time T?
| Second, is it 'settled' now, or might things change?"
| jgalt212 wrote:
| The Bitter Lesson assumes Moore's law is alive and well. It may
| still be alive, but not as full of vim and vigor as it once was.
| lisper wrote:
| Por que no los dos?
|
| Thirty-five years ago they gave me a Ph.D. basically for pointing
| out that the controversy du jour -- reactive vs deliberative
| control for autonomous robots -- was not a dichotomy. You could
| have the best of both worlds by combining a reactive system with
| a deliberative one. The reactive system interfaced directly to
| the hardware on one end and provided essentially a high-level API
| on the other end that provided primitives like "go that way".
| It's a little bit more complicated than that because it turns out
| you need a glue layer in the middle, but the point is: you don't
| have to choose. The Bitter Lesson is simply a corollary of Ron's
| First Law: all extreme positions are wrong. So reactive control
| by itself has limits, and deliberative control by itself has
| limits. But put the two together (and add some pretty snazzy
| image processing) and the result is Waymo.
|
| So it was no surprise to me that Stockfish, with its similar
| approach of combining deliberative search with a small NN
| computing its quality metric blows everything else out of the
| water. It has been obvious (at least to me) that this is the
| right approach for decades now.
|
| I'm actually pretty terrified of the results when the mainstream
| AI companies finally rediscover this. The capabilities of LLMs
| are already pretty impressive on their own. If they can get a
| Stockfish-level boost by combining them with a simple search
| algorithm the result may very well be the GAI that the
| rationalist community has been sounding the alarm over for the
| last 20 years.
| incognito124 wrote:
| Did you name that law by yourself?
| DonHopkins wrote:
| https://flownet.com/ron/papers/tla.pdf
|
| >In retrospect, in the story of the three-layer architecture
| there may be more to be learned about research methodology
| than about robot control architectures. For many years the
| field was bogged down in the assumption that planning was
| sufficient for generating intelligent behavior in situated
| agents. That it is not sufficient clearly does not justify
| the conclusion that planning is therefore unnecessary. A lot
| of effort has been spent defending both of these extreme
| positions. Some of this passion may be the result of a hidden
| conviction on the part of AI researchers that at the root of
| intelligence lies a single, simple, elegant mechanism. But
| if, as seems likely, there is no One True Architecture, and
| intelligence relies on a hodgepodge of techniques, then the
| three-layer architecture offers itself as a way to help
| organize the mess.
| gugagore wrote:
| What you're describing -- search + NN -- is presently under the
| term "test-time compute".
|
| The rules / dynamics / objectives of chess ( and Go ) are
| trivial to encode in a search formulation. I personally don't
| really get what that tells us about AGI.
| lisper wrote:
| No, I don't think that test-time compute is the same thing at
| all. It's a little challenging to find a definitive
| definition of TTC, but AFAICT it is just a fairly simple
| control loop around an LLM. What I'm describing is a merging
| of components with fundamentally different architectures,
| each of which is a significant engineering effort in its own
| right, to produce a whole that is greater than the sum of its
| parts. Those seem different to me, but to be fair I have not
| been keeping up with the latest tech so I could be wrong.
| gugagore wrote:
| I think search is a fairly simple control loop. Beam search
| is an example of TTC in this modern era.
|
| It is a very wide term, IME, that means anything besides
| "one-shot through the network".
|
| I think the thing about the search formulation, which is
| amenable to domains like chess and go, but not other
| domains is critical. If LLMs are coming up with effective
| search formulation for "open-ended" problems, that would be
| a big deal. Maybe this is what you're alluding to.
| alan-crowe wrote:
| I've been thinking of the issue like this: a traditional
| approach to symbolic artificial intelligence frames problem
| solving as a search of a tree. Do you go depth first, dodging
| infinite branches? Or breadth first (do you have enough
| memory?). What about iterative deepening?
|
| Sometimes the solution is in the tree, but it is too deep, and
| one runs out of time before it is found.
|
| Statistical based learning could act as a branch predictor.
| Sometimes guiding the search to go very deep in the right place
| and find the hidden solution. Sometimes guiding the search to
| go very deep in the wrong place; one runs out of time as usual.
|
| Notice the strength of hybrid approach. One isn't accepting the
| probably correct answer of the statistical part. It is only a
| guide, and if the answer is found, and the symbolic part of the
| software is correct, the answer will be reliable.
|
| I think this is already being done with maths problems. The LLM
| is writing proof attempts in Lean. But Lean is traditional
| symbolic AI. If the LLM can come up with a proof that Lean
| approves, then it really has a proof. From your comment I learn
| that Stockfish has already got something like this to work very
| well.
| user_of_the_wek wrote:
| > If AI agents can train on outputs alone, any organization that
| can define quality and provide enough examples might achieve
| similar results
|
| Great, we're safe!
| woolion wrote:
| I believe the main problem could be reframed as an improper use
| of analogies. People are pushing the analogy of "artificial
| intelligence" and "brain", etc. creating a confusion that leads
| to such "laws". What we have is a situation that is similar to
| birds and planes, they do not operate under the same principles
| at all.
|
| Looking at the original claim, we can take from birds a number of
| optimization regarding air flows that are far beyond what any
| plane can do. But, the impact that could be transfer to planes
| would be minimal compared to a boost in engine technology. Which
| is not surprising since the way both systems achieve "flight" are
| completely different.
|
| I don't believe such discourse would happen at all if it was just
| considered to be a number of techniques, of different categories
| with their own strength and weaknesses, used to tackle problems.
|
| Like all fake "laws", it is based on a general idea that is
| devoid of any time-frame prediction that would make it
| falsifiable. In "the short term" is beaten by "in the long run".
| How far is "the long run"? This is like the "mean reversion law",
| saying that prices will "eventually" go back to their equilibrium
| price; will you survive bankruptcy by the time of "eventually"?
| phkahler wrote:
| >> People are pushing the analogy of "artificial intelligence"
| and "brain", etc. creating a confusion that leads to such
| "laws". What we have is a situation that is similar to birds
| and planes, they do not operate under the same principles at
| all.
|
| But modend neural nets ARE based on biological neurons. It's
| not a perfect match by any means, but synaptic "weights" are
| very much equivalent to model weights. Model structure and size
| are bigger differences.
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