[HN Gopher] The Bitter Lesson (2019)
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The Bitter Lesson (2019)
Author : radkapital
Score : 95 points
Date : 2020-07-09 15:37 UTC (7 hours ago)
(HTM) web link (incompleteideas.net)
(TXT) w3m dump (incompleteideas.net)
| kdoherty wrote:
| Potentially also of interest is Rod Brooks' response "A Better
| Lesson" (2019): https://rodneybrooks.com/a-better-lesson/
| ksdale wrote:
| I think it's plausible that many technological advances follow a
| similar. Something like the steam engine is a step-improvement,
| but many of the subsequent improvements are basically the obvious
| next step, implemented once steel is strong enough, or machining
| precise enough, or fuel is refined enough. How many times has the
| world changed qualitatively, simply in the pursuit of making
| things quantitatively bigger or faster or stronger?
|
| I can certainly see how it could be considered disappointing that
| pure intellect and creativity doesn't always win out, but I,
| personally, don't think it's bitter.
|
| I also have a pet theory that the first AGI will actually be
| 10,000 very simple algorithms/sensors/APIs duct-taped together
| running on ridiculously powerful equipment rather than any sort
| of elegant Theory of Everything, and this wild conjecture may
| make me less likely to think this a bitter lesson...
| throwaway7281 wrote:
| This reminds me of the Banko and Brill paper "Scaling to very
| very large corpora for natural language disambiguation" -
| https://dl.acm.org/doi/10.3115/1073012.1073017.
|
| It is exactly the point and it is something not a lot of
| researchers really grok. As a researcher you are so smart, why
| can't you discover whatever you are seeking? I think in this
| decade, we see a couple more scientific discoveries by brute
| force which will hopefully will make the scientific type a bit
| more humble an honest.
| KKKKkkkk1 wrote:
| Today Elon Musk announced that Tesla is going to reach level-5
| autonomy by the end of the year. Specifically
|
| _There are no fundamental challenges remaining for level-5
| autonomy. There are many small problems. And then there 's the
| challenge of solving all those small problems and then putting
| the whole system together._ [0]
|
| I feel like this year is going to be another year in which the
| proponents of brute-force AI like Elon and Sutton will learn a
| bitter lesson.
|
| [0] https://twitter.com/yicaichina/status/1281149226659901441
| typon wrote:
| Elon Musk announcing something doesn't make it true
| vlmutolo wrote:
| It's funny when you've been thinking for months about how speech
| recognition could really benefit from integrating models of the
| human vocal tract...
|
| and then you read this
| sqrt17 wrote:
| Here's a thing: incorrect assumptions that are built into a
| model are more harmful than a model that assumes too little
| structure. If you model the vocal tract and the actual exciting
| things are the transient noises that occur when we produce
| consonants, at best there's lots of work with not much to show
| and at worst you're limiting your model in a negative way.
| That's the basis for the "every time we fired a linguist,
| recognition rates improved" from 90s speech recognition.
|
| On the other end of the spectrum, data and compute ARE limited
| and for some tasks we're at a point where the model eats up all
| the humanity's written works and a couple million dollars in
| compute and further progress has to come from elsewhere because
| even large companies won't spend billions of dollars in compute
| and humanity will not suddenly write ten times more blog
| articles.
| visarga wrote:
| I think we're far from having used all the media on the
| internet to train a model. GPT-3 used about 570GB of text
| (about 50M articles). ImageNet is just 1.5M photos. It's
| still expensive to ingest the whole YouTube, Google Search
| and Google Photos in a single model.
|
| And the nice thing about these large models is that you can
| reuse them with little fine-tuning for all sorts of other
| tasks. So the industry and any hacker can benefit from these
| uber-models without having to retrain from scratch. Of
| course, if they even fit the hardware available, otherwise
| they have to make due with a slightly lower performance.
| gwern wrote:
| "Every time I fire an anatomist and hire a TPU pod, my WER
| halves."
| PeterisP wrote:
| I think that your particular example is very relevant.
|
| Of course a good speech recognition system needs to model all
| the relevant characteristics of the human vocal tract as such,
| and of the many different vocal tracts of individual humans!
|
| But this is substantially different from the notion of
| integrating a _human-made_ model of the human vocal tract.
|
| In this case the bitter lesson (which, as far as I understand,
| does apply to vocal tract modeling - I don't personally work on
| speech recognition but colleagues a few doors down do) is that
| if you start with some data about human voice and biology; you
| develop some explicit model M, and then integrate it into your
| system, then it does not work as well if you properly design a
| system that will learn speech recognition on the whole,
| _learning_ an implicit model M ' of the relevant properties of
| the vocal tract (and the distribution of these properties in
| different vocal tracts) as a byproduct of that, given
| sufficient data.
|
| A hypothesis (which does need more research to be demonstrated,
| though, but we have some empirical evidence for similar things
| in most aspects of NLP) on the reason for this is that the
| human-made model M _can 't_ be as good as the learned model
| because it's restricted by the need to be understandable by
| humans. It's simplified and regularized and limited in size so
| that it can be reasonably developed, described, analyzed and
| discussed by humans - but there's no reason to suppose that the
| ideal model that would perfectly match reality is simple enough
| for that; it may well be reducible to a parameteric function
| that simply has too many parameters to be neatly summarizable
| to a human-understandable size without simplifying in ways that
| cost accuracy.
| ruuda wrote:
| A slightly more recent post, that really opened my eyes to this
| insight (and references The Bitter Lesson) is this piece by Gwern
| on the scaling hypothesis:
| https://www.gwern.net/newsletter/2020/05#gpt-3
| mtgp1000 wrote:
| >We want AI agents that can discover like we can, not which
| contain what we have discovered. Building in our discoveries only
| makes it harder to see how the discovering process can be done.
|
| I think these lessons are less appropriate as our hardware and
| our understanding of neural networks improve. An agent which is
| able to [self] learn complex probabilistic relationships between
| inputs and outputs (i.e. heuristics) requires a minimum
| complexity/performance, both in hardware and neural network
| design, before any sort of useful[self] learning is possible.
| We've only recently crossed that threshold (5-10 years ago)
|
| >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
|
| Admittedly, I'm not quite sure of the author's point. They seem
| to indicate that there is a trade-off between spending time
| optimizing the architecture and baking in human knowledge.
|
| If that's the case, I would argue that there is an impending
| perspective shift in the field of ML, wherein "human knowledge"
| is not something to hardcode explicitly, but instead is
| implicitly delivered through a combination of appropriate data
| curation and design of neural networks which are primed to learn
| certain relationships.
|
| That's the future and we're just collectively starting down that
| path - it will take some time for the relevant human knowledge to
| accumulate.
| lambdatronics wrote:
| TL;DR: AI needs a hand up, not a handout. "We want AI agents that
| can discover like we can, not which contain what we have
| discovered." I was internally protesting all the way through the
| note, until I got to that penultimate sentence.
| rbecker wrote:
| Yeah, it takes a careful, charitable reading to not interpret
| it as "don't bother with understanding or finding new methods,
| just throw more FLOPS at it".
| francoisp wrote:
| building a model for and with domain knowledge == premature
| optimization? In the end a win on kaggle or a published paper
| seems to depend on tweaking hyperparameters based on even more
| pointed DK: data set knowledge...
|
| I wonder what would be required to build a model that explores
| the search space of compilable programs in say python that sorts
| in correct order. Applying this idea of using ML techniques to
| finding better "thinking" blocks for silicon seems promising.
| astrophysician wrote:
| I think what he's basically saying is that priors (i.e. domain
| knowledge + custom, domain-inspired models) help when you're data
| limited or when your data is very biased, but once that's not the
| case (e.g. we have an infinite supply of voice samples), model
| capacity is usually all that matters.
| maest wrote:
| For contrast, take this Hofstadter quote:
|
| > This, then, is the trillion-dollar question: Will the approach
| undergirding AI today--an approach that borrows little from the
| mind, that's grounded instead in big data and big engineering--
| get us to where we want to go? How do you make a search engine
| that understands if you don't know how you understand? Perhaps,
| as Russell and Norvig politely acknowledge in the last chapter of
| their textbook, in taking its practical turn, AI has become too
| much like the man who tries to get to the moon by climbing a
| tree: "One can report steady progress, all the way to the top of
| the tree."
|
| My take is that there is something intelectually unsatisfying
| about solving a problem by simply throwing more computational
| power at it, instead of trying to understand it better.
|
| Imagine in a parallel universe where computational power is
| extremely cheap. In this universe, people solve integrals
| exclusively by numerical integrations so there is no incentive to
| develop any of the Analysis theory we currently have. I would
| expect that to be a net negative in the long run as theories like
| Gen Relativity would be almost impossible to develop without the
| current mathematical apparatus.
| YeGoblynQueenne wrote:
| Where is this quote from, please?
|
| To play devil's advocate, I think retort to your comment about
| "intellectually satisfying" methods is "yeah, but, they work".
| And in any case, "intellectually satisfying" doesn't have a
| formal definition in computer science or AI so it can't very
| well be a goal, as such.
|
| My own concern is exactly what Russel & Norvig seem to say in
| Hofstadter's comment: by spending all our resources on clmbing
| the tallest trees to get to the moon, we're falling behind from
| our goal, of ever getting to the moon. That's even more so if
| the goal is to use AI to understand our own mind, rather than
| to beat a bunch of benchmarks.
| self wrote:
| The quote is from this article:
|
| https://www.theatlantic.com/magazine/archive/2013/11/the-
| man...
| totally_a_human wrote:
| This page seems to be down. Is there a mirror?
| aszen wrote:
| Interesting, I wonder what happens now that Moore's law is
| considered dead and we can't rely on computation power increasing
| year over year. To make further progess with general purpose
| search and learning methods we will need lots more computational
| power which may not be cheaply available. Then do we focus our
| efforts on developing more efficient learning strategies like the
| one we have in our minds ?
|
| I do agree with the part about not embedding human knowledge into
| our computer models, any knowledge worth learning about any
| domain the computer should be able learn on its own to make true
| progress in AI.
| PeterisP wrote:
| Can you elaborate why you think that Moore's law is considered
| dead? For me it seems that the general progress for the
| computing hardware in question (GPUs and specialized ASICs, not
| consumer CPUs) we're still seeing steady improvements in
| transistors/$ and flops/$ and expect it to still continue for
| some time at least.
| aszen wrote:
| Yes specialized hardware for AI are seeing steady
| improvements, I'm curious if these improvements rely on the
| particulars of the algorithms running on these machines. As
| an example several of the AI chips use lower precision
| floating point numbers than general CPUs since the algorithms
| in use for training nns don't need the higher precision.
|
| I actually wonder if having specialized AI hardware isn't the
| same problem as having specialized AI models, that is in the
| short term it will improve efficiency but in the long run
| prevent discovery of newer general learning strategies
| because they won't run faster in existing specialized
| hardware.
| abetusk wrote:
| Moore's law might be dead but the deeper law is still alive.
|
| Moore's law is technically "the number of transistors per unit
| area doubles every 24 months" [1]. The more important law is
| that the cost of transistors halves every 18-24 months.
|
| That is, Moore's law talks about how many transistors we can
| pack into a unit area. The deeper issue is _how much it costs_.
| If we can only pack in a certain amount transistors per area
| but the cost drops exponentially, we still see massive gains.
|
| There's also Wright's law that comes into play [3] that talks
| about dropping exponential costs just from institutional
| knowledge (2x in production leads to (.75-.9)x in cost).
|
| [1] https://en.wikipedia.org/wiki/Moore%27s_law
|
| [2] https://www.youtube.com/watch?v=Nb2tebYAaOA
|
| [3] https://en.wikipedia.org/wiki/Experience_curve_effects
| aszen wrote:
| Agreed the cost aspect of Moore's law may continue to remain
| true, especially with chiplets with varying fab nodes and 3d
| architectures. Wright's law will also bring down costs as
| lower nm nodes mature.
|
| But as mentioned in the comments below ai model training is
| increasing exponentially (compute required to train models
| has been doubling every 3.6 months) so it still far outstrips
| the cost savings.
| noanabeshima wrote:
| The amount of compute used in the largest AI training runs has
| been exponentially growing:
|
| https://openai.com/blog/ai-and-compute/
|
| The amount of compute required for Imagenet classification has
| been exponentially decreasing:
|
| https://openai.com/blog/ai-and-efficiency/
| aszen wrote:
| Very interesting links, thanks for sharing.
|
| So the trend isn't changing we still need bigger models to
| make progress in NLP and CV, while the algorithmic
| effeciencies are promising but they aren't giving anywhere
| near the same improvements as larger models.
|
| I'm curious how long this trend will continue and if there's
| anything promising that can reverse this trend
| PeterisP wrote:
| IMHO the main thing that determines this trend is whether
| the results are _good enough_. For the most part, there 's
| only some overlap between the people who work on better
| results and people who work on more efficient results,
| those research directions are driven by different needs and
| thus also tend to happen in different institutions.
|
| As long as our proof of concept solutions don't yet solve
| the task appropriately, as long as the solution is weak
| and/or brittle and worse than what we need for the main
| partical applications, most of the research focus - and the
| research progress - will be on models that try and give
| better results. It makes sense to disregarding the compute
| cost and other impractical inconveniences when working on
| pushing the bleeding edge, trying to make the previously
| impossible things possible
|
| However, when tasks are "solved" from the academic proof-
| of-concept perspective, then generally the practical,
| applied work on model efficiency can get huge reductions in
| computing power required. But that happens _elsewhere_.
|
| The concept of technology readiness level
| (https://en.wikipedia.org/wiki/Technology_readiness_level)
| is relevant. For the NLP and CV technologies that are in
| TRL 3 or 4, the efficiency does not really matter as long
| as it fits in whatever computing clusters you can afford;
| this is mainly an issue for the widespread adoption of some
| tech in industry by the time the same tech is in TRL 6 or
| so, and this work mostly gets done by different people in
| different organizations with different funding sources than
| the initial TRL 3 research.
| aglionby wrote:
| My background is in NLP - I suspect we'll see similar in
| language processing models as we've seen in vision models.
| Consider this[1] article ("NLP's ImageNet moment has
| arrived"), comparing AlexNet in 2012 to the first GPT model 6
| years later: we're just a few years behind.
|
| True, GPT-2 and -3, RoBERTa, T5 etc. are all increasingly
| data- and compute-hungry. That's the 'tick' your second
| article mentions.
|
| We simultaneously have people doing research in the 'tock' -
| reducing the compute needed. ICLR 2020 was full of
| alternative training schema that required less compute for
| similar performance (e.g. ELECTRA[2]). Model distillation is
| another interesting idea that reduces the amount of
| inference-time compute needed.
|
| [1] https://thegradient.pub/nlp-imagenet/
|
| [2] https://openreview.net/pdf?id=r1xMH1BtvB
| hpoe wrote:
| So I know Moore's law is "dead" (dead as in Cobol or dead as in
| Elvis?) and progress is definitely slower than it has been
| historically however we have only began to really start
| leveraging parallelization at scale from a software
| perspective, so I think we have some runway in that direction,
| and of course the looming elephant on the horizon, Quantum
| computing.
|
| Sure it is in it's infancy but assuming that the research
| continues to prove that quantum computing is viable I expect it
| to be an even bigger deal than the move from vacuum tubes to
| transistors. At that point we'll be dealing with an entirely
| different world in computing.
| nessunodoro wrote:
| it's kind of poetic that the chief bottleneck of advancement in
| the field is now the physical universe -
| annoyingnoob wrote:
| That is a wall of words, I can't even read it in that format.
| koeng wrote:
| This lesson can be applied to synthetic biology right now, though
| it is still in its infant stages.
|
| At least a few of the original synthetic biologists are a bit
| disappointed in the rise of high-throughput testing for
| everything, instead of "robust engineering". Perhaps what allows
| us to understand life isn't just more science, but more "biotech
| computation".
| auggierose wrote:
| I guess it depends on what you trying to do. I had a computer
| vision problem where I was like, hell yeah, let's machine learn
| the hell out of this. 2 months later, and the results were just
| not precise enough. It took me 2 more months, and now I am
| solving the task easily on an iPhone via Apple Metal in
| milliseconds with a hand-crafted optimisation approach ...
| jefft255 wrote:
| His advice really concerns more scientific research and its
| long-term progress, and not really immediate applications. I
| think that injecting human knowledge can lead to faster, more
| immediate progress, and he seems to believe that too. The
| "bitter lesson" is that general, data-driven approaches will
| always win out eventually.
| [deleted]
| sytse wrote:
| The article says we should focus on increasing the compute we use
| in AI instead of embedding domain specific knowledge. OpenAI
| seems to have taken this lesson to heart. They are training a
| generic model using more compute than anything else.
|
| Many researchers predict a plateau for AI because it is missing
| the domain specific knowledge but this article and the benefits
| of more compute that OpenAI is demonstrating beg to differ.
| throwaway7281 wrote:
| Model compression is an active research field and will probably
| be quite lucrative, as you will literally able to save
| millions.
| dyukqu wrote:
| Previous discussion:
| https://news.ycombinator.com/item?id=19393432
| JoeAltmaier wrote:
| Got to believe, this is like heroin. Its a win until it isn't.
| Then where will AI researchers be? No progress for 20 (50?) years
| because the temptation to not understand but to just build
| performant engineering solutions, was so strong.
|
| In fact, is the researcher supposed to be building the most
| performant solution? This article seems alarmingly misinformed.
| To understand 'artificial intelligence' isn't a race to VC money.
| visarga wrote:
| AI as a field relied mostly on 'understanding' based approaches
| for 50 years without much success. These approaches were too
| brittle and ungrounded. Why return to something that doesn't
| work?
|
| DNNs today can generate images that are hard to distinguish
| from real photos, super natural voices and surprisingly good
| text. They can beat us at all board games and most video games.
| They can write music and poetry better than the average human.
| Probably also drive better than an average human. Why worry
| about 'no progress for 50 years' at this point?
| JoeAltmaier wrote:
| Because, they can't invent a new game. Unless of course they
| were only designed to invent games, and by trial and error
| and statistical correlation to existing games, thus producing
| a generic thing that relates to everything but invents
| nothing.
|
| I'm not an idiot. I understand that we won't have general
| purpose thinking machines any time soon. But to give up
| entirely looking into that kind of thing, seems to me to be a
| mistake. To rebrand the entire field as calculating results
| to given problems and behaviors using existing mathematical
| tools, seems to do a disservice to the entire concept and
| future of artificial intelligence.
|
| Imagine if the field of mathematics were stumped for a while,
| so investigators decided to just add up things faster and
| faster, and call that Mathematics.
| visarga wrote:
| What GPT-3 and other models lack is embodiment. There are
| of course RL agents embodied in simulated environments,
| like games and robot sims, but this pales in comparison to
| our access to nature and the human society. When we will be
| able to give them a body they will naturally rediscover
| play and games.
|
| Human superiority doesn't come just from the brain, it
| comes from the environment this brain has access to - other
| humans, culture, tools, nature, and the bodily affordances
| (hands, feet, eyes, ability to assimilate organic food...).
| AI needs a body and an environment to evolve in.
| otoburb wrote:
| >> _This article seems alarmingly misinformed._
|
| I hate appeals to authority as much as anybody else on HN, but
| I'm not sure that we could say Rich Sutton[1] is "misinformed".
| He's an established expert in the field, and if we discount his
| academic credentials then at least consider he's understandably
| biased towards this line of thinking as one of the early
| pioneers of reinforcement learning techniques[2] and currently
| a research scientist at DeepMind leading their office in
| Alberta, Canada.
|
| [1] https://en.wikipedia.org/wiki/Richard_S._Sutton
|
| [2] http://incompleteideas.net/papers/sutton-88-with-
| erratum.pdf
| JoeAltmaier wrote:
| He's writing that article for a reason, to be sure. Its just
| not the one that the article says its about, I'm thinking.
| fxtentacle wrote:
| The current top contender on AI optical flow uses LESS CPU and
| LESS RAM than last year's leader. As such, I strongly disagree
| with the article.
|
| Yes, many AI fields have become better from improved
| computational power. But this additional computational power has
| unlocked architectural choices which were previously impossible
| to execute in a timely manner.
|
| So the conclusion may equally well be that a good network
| architecture results in a good result. And if you cannot use the
| right architecture due to RAM or CPU constraints, then you will
| get bad results.
|
| And while taking an old AI algorithm and re-training it with 2x
| the original parameters and 2x the data does work and does
| improve results, I would argue that that's kind of low-level
| copycat "research" and not advancing the field. Yes, there's a
| lot of people doing it, but no, it's not significantly advancing
| the field. It's tiny incremental baby steps.
|
| In the area of optical flow, this year's new top contenders
| introduce many completely novel approaches, such as new
| normalization methods, new data representations, new
| nonlinearities and a full bag of "never used before" augmentation
| methods. All of these are handcrafted elements that someone built
| by observing what "bug" needs fixing. And that easily halved the
| loss rate, compared to last year's architectures, while using
| LESS CPU and RAM. So to me, that is clear proof of a superior
| network architecture, not of additional computing power.
| cgearhart wrote:
| I have read this before and broadly agree with the point--it's no
| use trying to curate expertise into AI. But I don't think
| modeling p(y|x) or it's friend p(y, x) is the end we're looking
| for either. But, it's unreasonably effective, so we keep doing
| it. (I don't have an answer or an alternative; causality appeals
| to my intuition, but it's really clunky and has seemingly not
| paid off.)
| sgt101 wrote:
| Actually I feel like causalities time has come. The framework
| that has convinced me is just the simple approach of doing
| controlled experiments over observational data to establish
| causal links via DAGs no need for any drama!
| cgearhart wrote:
| It seems to be just shuffling around the hard part of the
| problem. Causality still depends on some unstructured
| optimization problem of generating and evaluating causal
| diagram candidates. I haven't really seen it applied where
| the set of potential causal relationships is huge.
| avmich wrote:
| > When a simpler, search-based approach with special hardware and
| software proved vastly more effective, these human-knowledge-
| based chess researchers were not good losers.
|
| It's like calling Russia a loser in Cold War. Technically the
| effect is reached; practically the side which "lost" gained
| possibly largest benefits.
| glitchc wrote:
| When it comes to games, exploitation (of tendencies, weaknesses),
| misdirection, subterfuge and yomi play a far bigger role in
| winning than actual skill. Humans are much better than computers
| at all of those. Perhaps a dubious honour, but an advantage
| nonetheless. We're only really in trouble when the machine learns
| to reliably replicate the same tactics.
| elcomet wrote:
| I think that computers managed to beat humans at poker already.
| (Online poker, which is different from physical games, where of
| course AI cannot compete)
| YeGoblynQueenne wrote:
| >> In computer chess, the methods that defeated the world
| champion, Kasparov, in 1997, were based on massive, deep search.
|
| "Massive, deep search" that started from a book of opening moves
| and the combined expert knowledge of several chess Grandmasters.
| And that was an instance of the minimax algorithm with alpha-beta
| cutoff, i.e. a search algorithm specifically designed for two-
| player, deterministic games like chess. And with a hand-crafted
| evaluation function, whose parameters were filled-in by self-
| play. But still, an evaluation function; because the minimax
| algorithm requires one and blind search alone did not, could not,
| come up with minimax, or with the concept of an evaluation
| function in a million years. Essentially, human expertise about
| what matters in the game was baked-in to Deep Blue's design from
| the very beginning and permeated every aspect of its design.
|
| Of course, ultimately, search was what allowed Deep Blue to beat
| Kasparov (31/2-21/2; Kasparov won two games and drew another).
| That, in the sense that the alpha-beta minimax algorithm itself
| is a search algorithm and it goes without saying that a longer,
| deeper, better search will inevitably eventually outperform
| whatever a human player is doing, which clearly is not search.
|
| But, rather than an irrelevant "bitter" lesson about how big
| machines can perfom more computations than a human, a really
| useful lesson -and one that we haven't yet learned, as a field-
| is why humans can do so well _without search_. It is clear to
| anyone who has played any board game that humans can 't search
| ahead more than a scant few ply, even for the simplest games. And
| yet, it took 30 years (counting from the Dartmouth workshop) for
| a computer chess player to beat an expert human player. And
| almost 60 to beat one in Go.
|
| No, no. The biggest question in the field is not one that is
| answered by "a deeper search". The biggest question is "how can
| we do that without a search"?
|
| Also see Rodney Brook's "better lesson" [2] addressing the other
| successes of big search discussed in the article.
|
| _____________
|
| [1]
| https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)#Des...
|
| [2] https://rodneybrooks.com/a-better-lesson/
| new2628 wrote:
| At least in chess, if it is not the search, then it is probably
| the evaluation function.
|
| Expert players have likely a very well-tuned evaluation
| function of how strong a board "feels". Some of it is
| explainable easily: center domination, diagonal bishop,
| connected pawn structure, rook supporting pawn from behind,
| others are more elaborate, come with experience and harder to
| verbalize.
|
| When expert players play against computers, the limitation of
| their evaluation function becomes visible. Some board may feel
| strong, but you are missing some corner case that the minmax
| search observes and exploits.
| YeGoblynQueenne wrote:
| I like to caution against taking concepts from computer
| science and AI and applying them directly to the way the
| human mind works. Unless we know that a player is applying a
| specific evaluation function (e.g. because they tell us, or
| because they vocalise their thought process etc) then even
| suggesting that "players have an evaluation function" is
| extrapolating far from what it is safe. For one thing- what
| does a "function" look like in the human mind?
|
| Whatever human minds do, computing is only a very general
| metaphor for it and it's very risky to assume we understand
| anything about our mind just because we understand our
| computers.
| burntoutfire wrote:
| > No, no. The biggest question in the field is not one that is
| answered by "a deeper search". The biggest question is "how can
| we do that without a search"?
|
| My guess is that we're doing pattern recognision, where we
| recognize taht a current game state is similar to a situation
| that we've been in before (in some previous game), and recall
| the strategy we took and the outcomes it had lead to. With
| large enough body of experience, you can to remember lots of
| past attempted strategies for every kind of game state (of
| course, within some similarity distance).
| blt wrote:
| This insight is the essence of the AlphaZero architecture.
| Whereas a pure Monte Carlo Tree Search (MCTS) starts each
| node in the search tree with a uniform distribution over
| actions, AlphaZero trains a neural network to observe the
| game state and output a distribution over actions. This
| distribution is optimized to be as similar as possible to the
| distribution obtained from running MCTS from that state in
| the past. It's very similar to the way humans play games.
| dreamcompiler wrote:
| Are we certain that well-trained human players are not doing
| search? It's possible that a search subnetwork gets "compiled
| without debugger symbols" and the owner of the brain is simply
| unaware that it's happening.
| gwern wrote:
| I'm not sure why YeGoblynQueenne thinks this is such a
| mystery. (This is not the first time I've been puzzled by
| their pessimism on HN.) There is no mystery here: AlphaZero
| shows that you can get superhuman performance by searching
| only a few ply by sufficiently good pattern recognition in a
| highly parameterized and well-trained value function, and
| MuZero makes this point even more emphatically by doing away
| with the formal search entirely in favor of an more abstract
| recurrent pondering. What more is there to say?
| YeGoblynQueenne wrote:
| >> (This is not the first time I've been puzzled by their
| pessimism on HN.)
|
| I don't understand why you keep making personal comments
| like that about me. I suspect you don't realise that they
| are unpleasant. Please let me make it clear: such personal
| comments are unpleasant. Could you please stop them? Thank
| you.
| YeGoblynQueenne wrote:
| MuZero performs a "formal search". In many more ways than
| one, for example optimisation is still a search for an
| optimal search of parameters. But I guess you mean that it
| doesn't perform a tree search? Quoting from the abstract of
| the paper on arxiv [1]:
|
| _In this work we present the MuZero algorithm which, by
| combining _a tree-based search_ with a learned model,
| achieves superhuman performance in a range of challenging
| and visually complex domains, withoutany knowledge of their
| underlying dynamics_.
|
| (My underlining)
|
| If I remember correctly, MuZero is model-free in the sense
| that it learns its own evaluation function and reward
| policy etc (also going by the abstract). But it retains
| MCTS.
|
| Indeed, it wouldn't really make sense to drop MCTS from the
| architecture of a system designed to play games. I mean, it
| would be really hard to justify discarding a component that
| is well known to work and work well, both from an
| engineering and a scientific point of view.
|
| _________________
|
| https://arxiv.org/abs/1911.08265
| YeGoblynQueenne wrote:
| >> Are we certain that well-trained human players are not
| doing search?
|
| Yes- because human players can only search a tiny portion of
| a game tree and a minimax search of the same extent is not
| even sufficient to beat a dedicated human in tic-tac-to, leta
| lone chess. That is, unless one wishes to countenance the
| possibility of an "unconscious search" which of course might
| as well be "the grace of God" or any such hand-wavy non-
| explanation.
|
| >> It's possible that a search subnetwork gets "compiled
| without debugger symbols" and the owner of the brain is
| simply unaware that it's happening.
|
| Sorry, I don't understand what you mean.
| oezi wrote:
| Why do you dismiss the unconscious search that humans do in
| Go? Having learned Go some years ago it is such an exciting
| thing to realize that with practice the painstaking process
| of consciously evaluating the myriads possibilities of
| moves gives way to just "seeing" solutions out of nothing.
| You can really feel that your brain did wire itself up to
| do analysis for you at a level that is subconscious but
| interfaces so gracefully with your conscious cognition that
| it is a real marvel.
| YeGoblynQueenne wrote:
| >> Why do you dismiss the unconscious search that humans
| do in Go?
|
| The question is why you say that humans perform an
| unconscious search when they play Go. And what kind of
| search is it, other than unconscious? Could you describe
| it, e.g. in algorithmic notation? I mean, I'm sure you
| couldn't because if you could then the problem of
| teaching a computer to play Go as well as a human would
| have been solved years and years ago. But, if you can't
| describe what you're doing, then how do you know it's a
| "search"?
|
| Note that in AI, when we talk of "search" (edit: at
| least, in the context of game-playing) we mean something
| very specific: an algorithm that examines the nodes of a
| tree and applies some criterion to label each examined
| node as a target node or not a target node. Humans are
| absolutely awful at executing such an algorithm with our
| minds for any but the most trivial of trees, at least
| compared to computers.
| mtgp1000 wrote:
| >But, rather than an irrelevant "bitter" lesson about how big
| machines can perfom more computations than a human, a really
| useful lesson -and one that we haven't yet learned, as a field-
| is why humans can do so well without search
|
| I think the answer is heuristics based on priors(e.g. board
| state), which we've demonstrated (with alphago and derivatives,
| especially alphago zero) that neural networks are readily able
| to learn.
|
| This is why I get the impression that modern neural networks
| are quickly approaching humanlike reasoning - once you figure
| out how to
|
| (1) encode (or train) heuristics and
|
| (2) encode relationships between concepts in a manner which
| preserves a sort of topology (think for example of a graph
| where nodes represent generic ideas)
|
| You're well on your way to artificial general reasoning - the
| only remaining question becomes one of hardware (compute,
| memory, and/or efficiency of architecture).
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