[HN Gopher] The Principles of Deep Learning Theory
___________________________________________________________________
The Principles of Deep Learning Theory
Author : Anon84
Score : 167 points
Date : 2022-04-16 11:41 UTC (11 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| charleshmartin wrote:
| What can they predict with the theory ?
| mark_l_watson wrote:
| First, thanks to the publisher and authors for making this freely
| available!
|
| I retired recently after using neural networks since the 1980s. I
| still spend at least 10 hours a week keeping up with DL, RL, etc.
| it seems like the roof has blown off the field recently, progress
| increases exponentially. I like material that makes me think of
| NNs with different intuitions. I am working on a CC licensed book
| consisting my experiments, in Jupiter notebook/Colab form -
| expect me to be shamelessly plugging that in a few months.
|
| In the book, I especially loved this quote:
|
| "You can hide a lot in a large-N matrix." - Steve Shenker - John
| McGreevy
| maleldil wrote:
| How do you keep up with the deep learning community? There
| seems to be a deluge of new papers, and most of them aren't
| worth our time. What do you do?
| mark_l_watson wrote:
| I follow about a dozen DL people on Twitter, and use their
| recommendations in tweets. It is a deluge, and it is
| impossible to keep up, really.
| montenegrohugo wrote:
| I'm always looking for new people to follow, could you
| write up the accounts you find most
| useful/novel/interesting?
| [deleted]
| hikingsimulator wrote:
| It's really neat to find people on HN who've been working on
| those structures for such a long time. If you can indulge me,
| what is a lesser known or obscure book on neural networks or an
| adjacent topic that you think would deserve to be read?
| mark_l_watson wrote:
| Perhaps some of the really old texts by Kahonen, Carver Mead,
| etc.?
|
| For more modern material, there are a few new good books on
| Transformers. Transformers are interesting because they were
| designed for efficiency: layers the same size, encoding both
| data and time sequencing information in each sample (so
| recurrent NNs aren't required), etc.
| pfd1986 wrote:
| Mind sharing some titles?
| moyix wrote:
| I have been enjoying Natural Language Processing with
| Transformers [1]. It's largely focused on the Huggingface
| library, but Chapter 3 has a very nice walkthrough that
| builds up the encoder portion of an encoder-decoder
| Transformer from "scratch" (it still uses some primitives
| found in PyTorch like nn.Embedding). The decoder portion
| is covered in less depth and they instead refer folks to
| Karpathy's awesome minGPT [2], which implements a
| decoder-only (GPT-style) Transformer in ~300 lines of
| nicely-commented Python+PyTorch code.
|
| For a higher-level conceptual view of how Transformers
| work, you can check out the now-classic "Illustrated
| Transformer" series [3] and this programmer-oriented
| explanation (with code in Rust) from someone at Anthropic
| [4].
|
| [1] https://www.oreilly.com/library/view/natural-
| language-proces...
|
| [2] https://github.com/karpathy/minGPT
|
| [3] https://jalammar.github.io/illustrated-transformer/
|
| [4] https://blog.nelhage.com/post/transformers-for-
| software-engi...
| jointpdf wrote:
| Not OP, but I think this is a great (and obscure?) book on
| neural networks: https://page.mi.fu-berlin.de/rojas/neural/
|
| Published in 1996, but the content is all foundational.
| throwaway693 wrote:
| I hear this phrase everywhere "Every layer is a change in
| representation of the previous layer". Is this mathematically
| proven or is this an assumption looking at the image
| classification models layer?
| karpierz wrote:
| That seems true by definition, as each layer is a functional
| transformation of the previous layer.
|
| I think want they want to imply is "Each layer is a
| semantically meaningful representation, changed from the
| previous layer" which I don't think is a verifiable claim (as
| what is semantically meaningful isn't an objective claim)
| euphetar wrote:
| Well since the next layer outputs are a linear transformation
| plus some nonliearity of the previous function, it's a fact
| that it's a change in representation. But I guess the true
| question is broader: "Is it proven that the next layer is
| preparing a feature representation for the next one?".
|
| I don't know if it is mathematically proven, but you can easily
| see it yourself when making an image classifier with one hidden
| layer. It has some really good evidence for an assumption, at
| this point I would call it at least empirical evidence.
| MathYouF wrote:
| Since this is hacker news I'm sure there's at least a few people
| who will read this that are the ML research engineers who write
| papers on their architectures which achieve SoTA results on
| different benchmarks, or establish new datasets, or publish new
| tooling like different loss functions/optimizers (which usually
| are published alongside some new SoTA results achieved by
| integrating them into a current SoTA architecture).
|
| I've wondered from that crowd (the productive crowd) if they get
| any value from papers like these, if reading them ever gives any
| insight. I don't seem to ever glean very much from them, whereas
| just reading the formerly mentioned papers and thinking for
| myself and experimenting seems to give me the intuition I
| personally need.
|
| I'm starting to feel there's a whole secondary field within ML of
| people who publish 50+ page math-heavy theory only that yields no
| actual results nor value in achieving those results, but maybe
| I'm wrong. I'll ask around at the conferences this year. I'd be
| willing to read papers like these if it turned out it'd help me
| but as time is limited, I feel I'm likely better off reading more
| papers that show results and their architectures (which usually
| have a bit of light speculating/intuiting mixed in as well) than
| reading this.
|
| Also, no offence meant to the writers of the paper who might read
| this. If you have any thoughts on this line of inquiry as well
| I'd be interested to hear them. Maybe there's a lot of practical
| value I'm missing out on by not taking the time to read and
| understand papers like these.
| aaraujo002 wrote:
| IMHO these books are not used for research. In order to
| contribute to research you need to be at the cutting edge of
| the existing contributions which are the most recent papers. On
| the other hand, these kind of books are useful because there
| are a huge aggregation of past research and it is based on
| these kinds of ressources that professors make their courses
| and that undergrads and graduates students learn.
| axg11 wrote:
| Direct value: no.
|
| Indirect value: there are many facets to every field of
| maths/science/philosophy and each facet makes progress at a
| different pace. This type of paper doesn't have any impact on
| my day-to-day thinking but I don't doubt that this approach
| will eventually lead to better methods and understanding of
| deep learning. Unfortunately that understanding could take 50
| years for all we know.
|
| In the meantime, progress in applications of deep learning are
| outpacing progress in theory by several orders of magnitude.
| The original GAN paper by Ian Goodfellow was published in 2014.
| Just 8 years later we have ML models that can generating
| convincing images from text prompts, arguably beyond what 99.9%
| of humans are capable of. Theory has no chance when engineering
| is moving so rapidly.
| goodtraveler wrote:
| And yet none of these systems can do anything beyond
| generating statistical patterns adapted to our visual sense.
| It's certainly a worthwhile achievement but I have yet to see
| anyone make a convincing case that there is a way to scale
| these systems to anything resembling intelligence. My current
| test for whether these systems are showing us how
| intelligence actually works in the human brain is a
| convincing proof of Cauchy's integral formula and I'm certain
| this benchmark will remain unsolved for the foreseeable
| future.
| aesch wrote:
| Why would your benchmark for intelligence in the human
| brain be something that less than 1% of humans are capable
| of achieving? To me that type mathematical proof is an
| example of a very specific type of intelligence rather than
| general intelligence.
| goodtraveler wrote:
| Because if an algorithm is capable of setting up the
| conceptual machinery for making sense of and explaining
| Cauchy's integral formula then that algorithm is
| revealing something about the structure of the human
| brain and as such it would be a good benchmark for
| understanding how human intelligence actually works (not
| just in the 1% of people). Moreover, mathematics is a
| fantastic proving ground for testing algorithms that
| purport to explain symbolic intelligence because if
| something can not work in a mathematical setting/context
| then there is no hope it will ever work in the real world
| since real world intelligence is much more than symbol
| shuffling. Cauchy's integral formula is a reasonable
| proxy for proving understanding of symbolic systems and
| not just juggling their statistical
| properties/associations.
|
| If you think that Cauchy's integral formula is too
| complicated then there are probably simpler problems that
| would also serve as reasonable proxies of symbolic
| understanding, e.g. elementary group theory and linear
| algebra.
| MathYouF wrote:
| Could you give a simple example of proof that real world
| since real world intelligence is much more than symbol
| shuffling? I'm a bit unfamiliar with the idea.
| goodtraveler wrote:
| I don't appreciate the sarcasm. If you're confused then
| say so, save the snark for someone else. If you're asking
| a sincere question then you can go outside and watch a
| few animals navigate the world. Squirrels are a good one,
| as are crows and ravens. Or as the kids say these days,
| you should touch some real grass instead of virtual ones.
| MathYouF wrote:
| It was a genuine question, I've not heard an intuitive or
| simple explanation of the concept. Your assumed combative
| tone is very reminiscent of others I've heard bring up
| the concept though (like Gary Marcus). I still haven't
| been able to hear a good and simple explanation though
| from your cohort despite my genuine curiosity.
| inciampati wrote:
| These syntheses can give powerful grounding to the ideas you
| get intuitively across dozens or hundreds of applications
| driven papers. They are useful.
| scribu wrote:
| The way I see it, the utility of theory is raising the
| baseline. It guides you away from dead ends, so you don't waste
| time on approaches that could never work.
|
| It's necessarily high-level, so you still need to learn about
| specific approaches to get practical things done.
| MathYouF wrote:
| I think I'd need an author of one of those (SoTA) papers to
| walk me through how reading part of a 50+ page theory paper
| helped them achieve any marginal improvement on their path
| towards productive contributions for me to really understand
| or believe it. If I could have even a single example
| explained to me I think I'd get it.
| mellavora wrote:
| Maybe not reading it, but did you consider that writing
| that paper helped them clarify their thinking on the
| subject?
|
| And based on that clarity, they were able to then go on and
| make immediately productive contributions?
| MathYouF wrote:
| I'm a bit lost on who the 'they' are and what the 'paper'
| is in this case: The authors of this paper writing this
| paper, or the authors of SoTA papers writing SoTA papers.
|
| A third option you may have meant which makes sense to me
| in context is the authors of this paper going on to write
| SoTA papers. I actually looked through the published
| works of the first two authors and didn't find any
| practical work, all similar very long form math notation
| heavy theoretic papers, which supports what I initially
| worried about that there's basically two diverging
| branches of ML papers, one of which I'm skeptical about
| the practical value of. I have actually not seen any
| examples of first authors on theory papers going on to
| publish SoTA results on applications, seeing such a thing
| I would also consider pretty convincing evidence of the
| utility of these papers and the symbiotic unification of
| the two groups.
|
| I wonder if there's been similar concerns raised
| historically, like at the advent of electricity, between
| practical engineers focused on creating groundbreaking
| applications versus people still focused on theory, and
| in retrospect what contributions continued to be gained
| by the theoretical work afterwards in those cases.
| nabla9 wrote:
| Yes.
|
| When you pick up a SoTA algorithm and try to apply it into a
| particular problem, ... it does not work or does not work well
| enough.
|
| You must modify or fix it. Combine it with other approaches. Be
| able to know what algorithms to pick. Without knowing how the
| thing works, what the internal dynamics is, what are the
| bottlenecks and limitations you get nowhere. Trying to fix
| problems by blindly tweaking parameters is not going to work.
|
| You don't have to be able to analyze and do research on
| internals by yourself, but you must understand the papers and
| be able to get the idea. Just like engineer doing signal
| processing must know lot about Fourier transform, wavelets,
| Laplace transforms even if they forget the details and forget
| some equations.
| criticaltinker wrote:
| _> we develop the notion of representation group flow (RG flow)
| to characterize the propagation of signals through the network.
| By tuning networks to criticality, we give a practical solution
| to the exploding and vanishing gradient problem _
|
| I am surprised and a bit disappointed this paper does not mention
| mean field theory or dynamical isometry at all.
|
| Mean field theory applies methods from physics - namely random
| matrix and free probability theory - to derive an exact
| analytical solution for information flow through a neural
| network.
|
| It turns out that simply initializing the weights of a plain CNN
| using a delta-orthogonal kernel allows all frequency components
| (Fourier modes) to propagate through the network with minimal
| attenuation. Specifically, networks train well when their input-
| output Jacobians exhibit dynamical isometry, namely the property
| that the entire distribution of singular values is close to 1.
| This technique effectively solves the exploding/vanishing
| gradient problem.
|
| The impact is shocking: the time to train a NN to a given
| accuracy becomes _independent_ of network depth. No tricks like
| batch normalization, dropout, or anything else are needed. This
| insight has been proven for a wide range of architectures from
| plain FFNs to CNNs, RNNs, and even transformers.
|
| I highly recommended reading the papers "How to Train a 10,000
| Layer Neural Network" [1], and "ReZero is All You Need: Fast
| Convergence at Large Depth" [2].
|
| [1] https://arxiv.org/abs/1806.05393
|
| [2]
| https://proceedings.mlr.press/v161/bachlechner21a/bachlechne...
| quantum_mcts wrote:
| Curious how this work relies a lot on the mathematical language
| of Quantum Field Theory. Wick theorem. Criticality. RG flows. As
| someone with background in Physics I had a permanent deja-vu
| reading this.
| mjburgess wrote:
| The level of analysis deep learning systems get is considerably
| out-of-proportion, and speaks to a superstitious view of how ML
| works. Namely, that it uncovers latent representational structure
| not present in the data.
|
| (Incidentally, its clear this book doesnt make this mistake,
| already identifying that NN is basically kNN).
|
| Rather ML algorithms are just rememberings of data coupled with
| various degrees of compression, with kNN having zero compression
| -- just a straight weights=data; and NNs having fairly
| significant levels, where weights=ensemble(compressions(data)).
|
| We should therefore regard the ML step as incredibly trivial, it
| is just a clever process of averaging over the data given to it.
| The whole "magic" of ML, such as it is, is *only* in the data.
| And this is where the word "data" hamstrings our ability to see
| properly.
|
| Everything *isnt* data; and "data" isnt some source of
| information. The world exists, and "data" is just what we call
| _any_ measurement of _any_ part of it by _any_ means. "Data" is
| only *relevant* to the probelm we're trying to solve if we do
| incredible amounts of experimental work to carve-the-world along
| its joints, ie., to have the right concepts; and incredible
| amounts of work to measure along its joints, ie., to have the
| right units. *And then* to eliminate all the coincidences and
| irrelevances. *And then* to provide that to a machine, which at
| this point, does basically nothing but automate our effort.
|
| Almost all data it is possible to collect is useless, indeed, an
| infinite amounts is useless. The magic of ML is a sleight-of-hand
| trick -- we dont really need to know how its averaging of our
| data does anything useful -- it almost never does.
|
| Rather, it is our "experimental design" which produces the
| usefulness of the system. ML algorithms are just interpolations
| and averages through data _prepared_ to produce useful averages
| by (literally millenia) of human ingenuity.
|
| It takes actual intelligence to do this because the world isnt
| data, and almost any measurement one cares to make (with eyes,
| even) produces endless ambiguities and coincidences that you have
| to "be in the world" to resolve; and resolution is a dynamic
| process which you "have to be here for".
| nkozyra wrote:
| It sounds like the unique attribute you're describing in humans
| is nuance and contextual understanding, but we developed those
| as a way of doing something a neutral network does inherently
| by means of error adjustment.
|
| I don't think there's ever an expectation that the data is
| "truth" but that we have to contextualize it and adjust it.
| Humans adapted to those through different mechanisms than NN
| but the goal is still fundamentally the same: to predict
| something with as much accuracy as possible despite the
| infinite noise of the data that is the world.
| mjburgess wrote:
| Alas the goal isnt the same, and here "predict" is the
| culprit. What ML systems do is offer statistical estimates,
| which is one very narrow meaning of "predict".
|
| When I "predict" what will happen when a glass falls, I'm not
| giving you any statistical estimates. I'm running a mental
| simulation of the world in _its relevant parts_ and using
| this to _predict_ what will happen.
|
| The heart of basically almost all knowledge of the world is
| actually counterfactual (ie., requiring simulation), it's
| always "if this happens (in the simulation), this will happen
| (in the simulation)".
|
| To acquire this ability to predict one has to understand,
| ie., to have the right concepts, reason with them and so on.
| When a person "has the concept `7`" they can reason thus: "6
| eggs would be fewer", "7 is an odd number", "7 is a whole
| quantity", "with 7 less of 10, i'd have 3" etc.
|
| Ie., `7` is available to play a fundamental role in
| simulating scenarios and _reasoning_ with them.
|
| When a NN produces a mapping from {Images} -> {Labels} such
| that the squiggle `7` produces the label `"7"` it doesnt do
| so via prediction in the meaningful sense of the world.
| Rather it has found a means of averaging sqiggles, and
| compares one squiggle to its averages and reports what the
| historical similar squiggles were labelled.
|
| This is a game which is only useful because _we_ , who can
| reason with `7` _profoundly_ about the world (etc.), have
| produced datasets which express our knowldge of the world in
| ways that dont require it. Ie., we present to the machine
| something whose average is useful.
|
| The machine is not trying to, nor does it, nor can it,
| predict. It has no knoweldge at all. It is just a
| correspondance table we have provided.
| daveed wrote:
| I hear what you're saying, but I would not state so
| confidently what prediction "is" and what concepts "are",
| in the human model.
| mjburgess wrote:
| I think this conversation gets away from itself very
| quickly if you run with generic terms: data, prediction,
| and so on.
|
| People think that NNs/ML/etc. works, and now the relevant
| questions are philosophical: if it works, _how_.
|
| But it doesnt work. In very silly trivial ways. It's
| _only_ taking averages over historical data. And all it
| can do is that.
|
| As soon as you are alive to what thinking _about_ things
| requires (ie., a live embeddedness into an environment,
| imagining scenarios, etc.) it 's trivial to expose the
| magic show. It isnt philosophical, it's quite literally
| just broken and doesnt work.
|
| I cannot ask GPT, "do you like what i'm wearing?", or
| "what would it take to change your mind on drug
| legalisation?" -- and so on. Indeed, I can't ask it, "if
| I have seven eggs, should I make a cake with 6 and keep
| the 7th -- or should I just use 7?".
|
| I can't ask it anything which actually requires it to
| reason with concepts, because it doesnt have any; and I
| cant ask it _about_ anything around now because it is
| just summarising what text has already been produced.
|
| The whole AI hype industry has a vested interest in the
| philosophy here, ask its premised on everythign working.
| Almost nothing works. The idea that GPT presents any
| philosophical challenges is nearly absurd; about as many
| as a shredder at a library.
| mark_l_watson wrote:
| I like your comments, even though I fall into the DL
| fanboy camp.
|
| I hope that you read Gary Marcus's and Ernest Davis' book
| "Rebooting AI" - I think it would resonate with you, and
| it made me get a lot more interested in so-call "hybrid-
| AI" systems.
| PaulDavisThe1st wrote:
| Again, I'm broadly in agreement with you, but:
|
| > I can't ask it anything which actually requires it to
| reason with concepts, because it doesnt have any; and I
| cant ask it about anything around now because it is just
| summarising what text has already been produced.
|
| I think you might have too strong of a belief that your
| subjective experience of reasoning is indicative of the
| actual machinery. I think it would be crazy to reject
| this view entirely, but it also seems possible to me that
| the whole experience of "reasoning with concepts" is not
| actually what we do at all, but merely what we experience
| when we do something else. Essentially, some variant on
| Dennett's intentional stance but applied to ourselves.
| mjburgess wrote:
| I'm always really happy to get to a technical description
| -- because I think these philosophical replies make it
| seem like we're talking about a working system. And there
| isnt one.
|
| So what i'm interested in is machine systems for which we
| have (1) no prior specification of the environment; and
| which in the end (2) anticipate permutations in the
| environment which arent specified.
|
| intelligence is the solution to this, for (1) form
| concepts which carve out the environment using your body;
| and (2) use these concepts to engage in counter-factual
| reasoning which simulate possible change.
|
| I think a _fatal_ issue for AI is that these arent even
| in the class of problems being addressed. Talking about
| intentionality here gives the field vastly more credit
| than it deserves. We arent even at the level of basic
| concept formation.
| PaulDavisThe1st wrote:
| > So what i'm interested in is machine systems for which
| we have (1) no prior specification of the environment;
| and which in the end (2) anticipate permutations in the
| environment which arent specified.
|
| But there are no biological systems that do this either
| (other than to the extent that by virtue of their
| embodiement, they may necessarily come more of a builtin
| "understanding" of the (physical) environment)).
| mjburgess wrote:
| Yes, I dont think intelligence is cognitive -- it's the
| _content_ of cognition. Where does that come from?
| Essentially our bio-organic structure as distributed
| across the body, and esp. motor cortext.
|
| In otherwords, the AI problem is unsolvable by any
| system. It's the wrong problem. Cognition, as a formal
| structure, isnt intelligent.
| webmaven wrote:
| _> Yes, I dont think intelligence is cognitive -- it 's
| the content of cognition. Where does that come from?
| Essentially our bio-organic structure as distributed
| across the body, and esp. motor cortext._
|
| All right, so where does that (the organic structure
| including the motor cortex that creates cognition) come
| from?
| rndphs wrote:
| >When a person "has the concept `7`" they can reason thus:
| "6 eggs would be fewer", "7 is an odd number", "7 is a
| whole quantity", "with 7 less of 10, i'd have 3" etc.
|
| I just input this into GPT-3. Its responses are in italics,
| this is first try no rewriting or retrying anything:
|
| This is a test of understanding of the concept of "the
| number 7".
|
| Question: Is 7 odd? Answer: _Yes, 7 is an odd number._
|
| Question: Is 6 eggs fewer than 7 eggs? Answer: _Yes, 6 eggs
| is fewer than 7 eggs._
|
| Question: With 7 less of 10, what would I have? Answer: _I
| would have 3._
|
| Question: Is 7 a whole quantity? Answer: _Yes, 7 is a whole
| quantity._
|
| This is mostly a joke because I think I understand where
| you are coming from (and that you are hypothesising that
| gpt3's responses are an elaborate trick of sorts). But I
| don't believe AI has to take the same route as human
| intelligence, and I don't think we really understand what a
| concept is or how it behaves from a signal/data
| perspective, but I think that may be inconsequential for
| creating general AI.
|
| Also people are can be really stupid sometimes and also
| have failures, and the concepts that people hold can be
| incorrect or flawed etc. So it may be useful also to
| compare human failures with AI failures, rather than just
| AI failures with human successes.
| dotnet00 wrote:
| I think NN=kNN is reductive to the point of near uselessness as
| a statement. You can treat human object recognition as a sort
| of kNN as well in that we 'detect' a large number of features,
| the grouping of which ultimately allows us to recognize
| something. But of course that doesn't actually tell us much
| about how we recognize things, since that explanation just
| changes the question to "how do we detect useful features?".
|
| Similarly, I think most ML people recognize that NN classifiers
| are just learning to look for groups of features, yet that
| doesn't really count as an analysis because what we want to
| understand is why it arrives at the features it does.
|
| >The magic of ML is a sleight-of-hand trick -- we dont really
| need to know how its averaging of our data does anything useful
| -- it almost never does.
|
| I strongly disagree, we do need to know how the 'averaging'
| does what it does so we can improve our models further. A lot
| of big steps in NN architectures have come in part from
| applying theories and insights regarding the properties of the
| training process, so a deeper understanding of the process may
| allow us to make better models, ones that have better accuracy
| as well as ones that train faster with less data.
|
| >Rather, it is our "experimental design" which produces the
| usefulness of the system. ML algorithms are just interpolations
| and averages through data prepared to produce useful averages
| by (literally millenia) of human ingenuity.
|
| >It takes actual intelligence to do this because the world isnt
| data, and almost any measurement one cares to make (with eyes,
| even) produces endless ambiguities and coincidences that you
| have to "be in the world" to resolve; and resolution is a
| dynamic process which you "have to be here for".
|
| I think most of the industry agrees that current ML models are
| sort of like being able to make the icing but not the cake (i.e
| AGI), so I'm not really sure what you're trying to say. Of
| course the models need a general intelligence to filter general
| data down to a specific problem and set things up for the model
| to learn?
| mjhirn wrote:
| I have been working and thinking about data and ML models for
| many years and this is probably the most concise and lucid
| summary of a post NN philosophy I have heard so far. It also
| resonates with me. Thanks for sharing.
| youhavetosayit wrote:
| << almost any measurement one cares to make (with eyes, even)
| produces endless ambiguities and coincidences that you have to
| "be in the world" to resolve; and resolution is a dynamic
| process which you "have to be here for". >>
|
| Sounds like a magic view of the mind to me.
|
| It's suspicious that this is never said about the function of
| Liver or Kidneys, only about the function of the Brain.
|
| Maybe the brain is protecting itself, like if you ask a cow
| about steaks.
| mjburgess wrote:
| The brain doesnt do it, the body does.
|
| The way we can tell if a stick is bent, or not, in a fluid
| _is to remove the stick_.
|
| Animals arent magic, theyre just actually present in the
| world.
|
| The converse is true: if the world's structure could be
| uncovered by data analysis, then it would be something like
| The Matrix; and the whole history of science would be
| falsified. It turns out the hardwork of experiment was never
| required, one could just blindly measure anything in any way,
| and somehow, knowledge would be produced.
| lifeisstillgood wrote:
| So (trying to grok this) in order to "know" I have picked
| the box up, I can feel its weight, wave my hand in the
| empty space etc. I am "in the world" and use side channels
| to build my own reward function for my own eyesight.
| (presumably this is the first 9+ months of life.
|
| if I only had eyesight i could not build a reward function
| ?
|
| Does this mean AI could do this if they had other sensors
| like weight gauges in the servos?
| mjburgess wrote:
| Well the body has properties which enable it to formulate
| concepts which begin simply as motor techniques. It is
| unlikely that servos have the right properties, but it's
| an open question.
|
| I strongly suspect the body's ability to organize itself
| at the cellur-to-organ level over time, ie., to grow, is
| an essential component of how we develop & deploy novel
| motor techniques.
|
| It is these techniques which we use to resolve the
| ambiguity inherent within any system of measurement --
| we, of course, _learn to see_ -- but i think "learn"
| here is really a complex form of motor control which ends
| up structruing our perception and then reflexivily, we
| build on that to develop more motor techniques.
|
| We learn to see in the sense that we first learn to move,
| then we see better, and we cycle until we're building
| electron microscopes. Explicit cognition is really just a
| book-keeping/accounting system to tidy up this process.
| In modelling _only this_ , we're relying on "doing all
| the hardwork" for machines.
|
| Intelligence isnt what cognition does when it tidy's up
| your concepts, its the very having of those concepts.
| Cognition is a big, largely neurotic unself-aware
| misdirection. It's what academics think is important --
| failing to notice they have any body at all.
|
| They only got their cherished abstractions by first
| moving their meat -- and whilst that can be dropped for
| the most pure and formal of ideas (eg., arithmetic) -- it
| cannot be dropped for discovery _as such_.
| geysersam wrote:
| > one could just blindly measure anything in any way, and
| somehow, knowledge would be produced.
|
| Is this not what astronomers do? Sure they also have inputs
| from experiments made on Earth, but the actual
| investigation of "the stars" is reliant solely on data
| analysis.
|
| What is _the structure of the world_ if not the
| correlations in some measurement data?
| XuMiao wrote:
| From the physics point of view, an intelligence is to keep the
| universe running in the perfect order, no waste of energy to
| increase the temperature. All accurate predictions align our
| current states to the future. If we look deep enough, we always
| find information coming from the past and the future. The only
| trouble is that our computation power is bounded, and computing
| itself generates heat waste.
|
| Human always feel that we have free will to design. It's likely
| a mistake in our intelligence unless the universe is infinitely
| dimensional where we can process the energy flow in any way we
| want.
| jah242 wrote:
| There might be some truth in what you say for very large image
| and language models that use supervised learning.
|
| It is really hard to see how this 'it's just a lot of good
| data' view applies to deep reinforcement learning where the
| model learns multi step policies from raw input data (e.g a
| camera on a robot) with only a rough high level reward function
| to guide it.
|
| If therefore (as seems to be the case) you can abstract the
| information humans need to provide to the model/learning system
| to ever high levels of reward function (and thereby vastly
| reduce the information provided by humans) then it seems very
| hard to argue that the model (and the training process) isn't
| doing to some degree what you describe as:
|
| 'incredible amounts of experimental work to carve-the-world
| along its joints, ie., to have the right concepts; and
| incredible amounts of work to measure along its joints, ie., to
| have the right units. _And then_ to eliminate all the
| coincidences and irrelevances. '
|
| For example, imagine a robot learning from scratch to pick
| objects up based on raw pixel data with only a scalar reward
| function - where in this process is the human preparing the
| data so the model only has to average?
| mjburgess wrote:
| > For example, imagine a robot learning from scratch to pick
| objects up based on raw pixel data with only a scalar reward
| function - where in this process is the human preparing the
| data so the model only has to average?
|
| Great -- so do you have an example of such a system?
|
| I'd be inclined, initially, to deny that it exists. If your
| reward function _expresses_ a reward for the goal of
| "picking up objects with (pixel-space) properties etc.",
| you're cheating. In this case, the reward function serves the
| role of the data: ie., prepared by us to work. Indeed, a
| function _is just_ a dataset -- and the reward function here
| is being sampled by the system.
|
| You'd need to show me a system whose reward function /
| dataset didnt "contain the solution", in the manner of
| animals who respond to the world without already having all
| the information about it.
|
| The relevant capacity a system needs to have, in both cases,
| is being able to take a profoundly ambiguous environment and
| produce a dataset/reward-fn which "carves along its joints".
| Ie., which effectively eliminates that ambiguity.
|
| When such ambiguity & coincidence is eliminated, there's
| basically nothing left to do -- it's that _basic nothing_
| which we task machines with doing. Ie. running
| `mean(sample(unambigious relevant well-carved data))`.
|
| You'll note its the *properties* of the data which express
| intelligence & learning.
| xfs wrote:
| Plenty of RL systems learn to play video games just fine
| without fine-tuned rewards, but I see this line of thought
| isn't actually what you're getting at.
|
| I would assume serious ML people would not be overly
| ambitious and overstep their claims beyond empirical
| realms. You were saying ML "uncovers latent
| representational structure not present in the data", but I
| would guess the claim, if that is what you're going
| against, is merely that the latent structures exist, and no
| Truth is really "uncovered" by ML per se, in the
| Heideggerian sense.
|
| I agree ML hasn't really produced an Understanding of the
| world. The carving along the joints is in other words a
| symbolic abstraction of the world that is a radical
| simplification, for which only Reason is capable of, and ML
| hasn't shown to be capable of Reason. As an aside, I also
| would not assume the ambiguity you refer to can be fully
| eliminated even by human intelligence, just see how
| languages are fully of ambiguity, or even quantum
| mechanics.
|
| But again, when philosophical critiques are launched
| against ML, the usual story is ML advocates would retreat
| to the success of ML in the empirical realms. I'm reminded
| of the Norvig vs Chomsky debate by this.
| mistrial9 wrote:
| > to play video games
|
| If I am following here, a key part of this argument is
| that models only represent things "in bounds" of the
| model, and that unsupervised, iterative approaches are
| especially susceptible to this. Video games are
| enormously constrained, artifical model environments, and
| therefore by definition are completely discoverable.
|
| Meanwhile, human cognition and the real actual world,
| have vast and subtle detail, and also are not completely
| knowable at any level minus some physics or similar. Tons
| of possible data sets are not necessarily discoverable or
| constrained, yet humans can investigate and draw
| conclusions, sometimes in very non-obvious ways.
|
| Falling back to pure philosophy, personally I am heavily
| on the side of the human, and in the wake of Kurt Godel,
| believe that plenty of formal systems are never complete,
| nor can they be shown to be complete or incomplete.
| mjburgess wrote:
| I think this debate has historically suffered from being
| conducted purely philosophically. Hediegger, Dreyfus
| (Ponty et al.) needed a bit more science and mathematics
| to see through the show.
|
| All we need to do to make the Heideggerian point is ask
| the RL researcher what his reward function is. Have him
| right it out, and note, that its a disjunction of
| properties which _already_ carve the environment of the
| robot.
|
| In otherwords, the failure of AI is far less of a mystery
| than philosophy alone seems to imply. Its a failure in a
| very very simple sense if one just asks the right
| technical questions.
|
| For RL, all we need ask is, "what will the machine do
| when it encounters an object outside of your pregiven
| disjunction?"
|
| The answer, of course, is fall over.
|
| Hardly what we fear when the wolf learns our movements,
| or what we love when a person shows us how to play a
| piano for the first time. The very thing we want, and we
| are told we have, isnt there... and it's not "not there"
| philosophically... its not there in the actual journal
| paper.
| xfs wrote:
| The Heideggerian point is a start, but I don't think it's
| enough to just point out a failure like this. This
| allegation is something like "The answer is already
| encoded in the question" like of trick, similar to one
| played in Foucault's episteme, where science itself is
| always-already a social construction without which it is
| impossible to happen.
|
| The trick is challenging on first sight but it won't go
| very far, because it just tells us what ML lacks but
| doesn't tell us what ML can have and how to go there. We
| need a new kind of Turing test that actually reflects the
| power of human intellect.
| mjburgess wrote:
| I suspect even thinking there's a "test" has it wrong.
|
| Yes, there's an experimental test -- as in, testing to
| see if salt is salt. But I dont think there's a _formal_
| test... as soon as you specify it, you 've eliminated the
| need for intelligence. Intelligence is in that process of
| specification.
|
| In otherwords, we should be able to ask the machine "what
| do you think of woody allen's films?" and rather than
| just taking _any answer_.. we need an empirical test to
| see if the machine has actually understood the question.
| Not a formal test.
|
| There is no doubt a sequence of replies which will
| convince a person that the machine has understood the
| question: just record them, and play them back.
|
| We're not interested in the replies. We're interested in
| whether the machine is actually thinking about the world.
| Is it evaluating the films? What are its views? What if I
| show it a bad film and say it wasnt by woody allen? What
| then?
|
| There's something _wrong_ in seeing this as a formal,
| rather than experimental, process. For any given machine
| we will need specific hypothesis tests as to its
| "intelligence", and we will need to treat it like any
| other empirical system.
| xfs wrote:
| OK, maybe "Turing test" was a bad hint because too often
| its extension turns into a philosophical rabbit hole of
| defining intelligence.
|
| I want to get back to your initial statement about
| uncovering and structures, which I think is still
| grounded in the empirical realm. I think a less ambitious
| new test could be about the "uncovering" between analog
| data and the structures. To be real uncovering, the
| structures must be symbolic, not just transformed analog
| representation, and the symbolic structures must be
| useful, e.g. provide radical reduction of computational
| complexity compared to equivalent computation with analog
| data.
|
| The point is to test if the machine can make the right
| abstraction (real uncovering) and also connect the
| abstraction with the data, not just games with words.
| mannykannot wrote:
| > For RL, all we need ask is, "what will the machine do
| when it encounters an object outside of your pregiven
| disjunction?"
|
| > The answer, of course, is fall over.
|
| There is no reason to think humans are qualitatively
| different in this regard, it is just that it does not
| happen very often. One case where it does is that humans
| pilots, no matter how competent, are incapable of flying
| without external visual references or instrument proxies
| for them.
| sdenton4 wrote:
| AlphaGo comes to mind...
| jah242 wrote:
| This would be one example from Deepmind using raw pixel
| input to stack objects. This has a relatively detailed
| reward function (but is also a very complicated task) -
| https://arxiv.org/abs/2110.06192
|
| There are other examples from OpenAI a while back using
| even just sparse rewards (i.e binary 1, 0 for success or
| failure over the whole task) - but these weren't pixel
| input if I remember correctly -
| https://openai.com/blog/ingredients-for-robotics-research/
|
| I m afraid if you think providing any reward function is
| cheating then we have fundamentally different views of what
| AI/ML even means/involves. It appears humans and likely all
| animals have largely pre-programmed reward functions
| developed over billions of years of evolution (pain is bad,
| food is good, etc.). These reward functions are ultimately
| what underpin what we are trying to do, what outcomes are
| good/bad, to what degree we 'want' to explore vs exploit.
| The idea that human and animal 'intelligence' is born as a
| blank slate with nothing to guide it and no reward function
| to maximise doesn't seem to bear any resemblance to
| reality.
|
| The only difference between a reward function that tells a
| robot 'you need to stack these objects but I m not going to
| tell you where in 3D space the objects are or where they
| need to go to stacked or the shapes/forces involved' and an
| animal that is born with a reward function that says 'you
| need to find food and shelter but I m not going to tell you
| how to collect the food or where to find shelter' is the
| level of abstraction. Fundamentally they appear the same.
|
| You are pulling a sleight of hand when you suggest 'in the
| manner of animals who respond to the world without already
| having all the information about it' - there is a vast
| difference between an abstract reward function (which
| humans and animals also have) and 'having all the
| information about [the world]'.
| moyix wrote:
| I realize it's a much more cleanly defined domain, but how
| does this interpretation explain MuZero?
| axg11 wrote:
| NN == kNN is a really interesting thought, but I think it's the
| wrong framing.
|
| All learning is bound by kNN, including, I would argue, human
| intelligence. When we compare and contrast things, be it words,
| objects, or images, the very best we can do is to compare those
| "units" to all other examples that we have ever observed.
|
| There is no such thing as generalising beyond kNN. The real
| magic in generalising is not how many nearest neighbours you
| are able to compare against, the magic is in _which_ qualities
| you use to determine the kNN. Another way to state this is:
| intelligence is in the distance metric/embedding/compression,
| not the ability to process all examples.
|
| When we say that humans are able to generalise, what we really
| mean is that we are able to compare and contrast specific
| subsets of qualities for a given object against all other
| things objects we have knowledge for. The way we choose and
| weight those specific qualities is what results in our
| "intelligence".
|
| So when we say that NN == kNN, I think this is actually a
| really impressive achievement for neural nets. Neural networks
| are able to produce kNN level results with orders of magnitude
| lower compute. Humans too, are able to do this, and if we're
| being very thorough, we sometimes fall back to kNN-like
| processes
| mjburgess wrote:
| Intelligence is in the properties of the data supplied to the
| kNN. Not in its distance function.
|
| Yes, kNN is the bound if you formulate learning as: there
| exists an f_true: Pixels->Animals; we have an infinite
| sampling of (Pixels, Animals); kNN performs at 100%.
| Therefore kNN is the bound.
|
| Whereas I dont think this has much to do with _learning_.
| Learning for me is: the function probably doesnt exist, we
| dont know what its domain would even be, and we have no idea
| how to sample that space. You know: the history of human
| civilization, but also: the history of a day of any animal 's
| life.
|
| We encounter problems daily whose functions can only be
| modelled by partial input and output domains, and our job is
| to find ways of resolving the inherent ambiguities in those
| situations. Ie., to find what parts of the world are
| relevant, to find ways of measuring it, and hence to find
| more data _until_ the ambiguity resolves itself.
|
| kNN being a perfect algorithm is a catastrophe for this whole
| formulation of AI. All it means is that the whole field of
| machine learning _has as its limit_ what you can do if you
| already know the answer. You can see from this that it isnt
| _learning_ at all.
|
| The situation is exactly backwards: ML assumes relevant well-
| carved unambiguous data. The whole problem of learning is
| precisely to _produce_ it!
| vladf wrote:
| Looks like someone (GP) beat me to the punch with my
| sibling comment
| (https://news.ycombinator.com/item?id=31053031)!
|
| > we have infinite sampling
|
| We don't. You're taking the learning out of the process
| you're discussing by using an infinite sample. Of course,
| then, the machine learning part becomes trivial, and it's
| just an optimization problem for minimal training error!
|
| If we have all the data, like infinite data, then we have
| the classification for every one of the (finite) number of
| possible images already. At that point, why stop at kNN?
| Use a hashmap!
| dotnet00 wrote:
| >kNN being a perfect algorithm is a catastrophe for this
| whole formulation of AI. All it means is that the whole
| field of machine learning has as its limit what you can do
| if you already know the answer. You can see from this that
| it isnt learning at all.
|
| Doesn't the existence of unsupervised training and models
| that solve previously unsolved problems somewhat invalidate
| the NN=kNN analogy then? The protein folding problem didn't
| have an already known overall answer for instance. The
| model clearly 'learned' representational information that
| we are not aware of in order to be so much more effective
| at solving the problem than we are.
|
| Or similarly with the Starcraft or Dota AI, while some
| strategies learned were similar to humans, others were
| entirely novel. I think that's getting pretty close to
| 'learning' as you've defined it.
| mjburgess wrote:
| Animal learning is unsupervised, sure, but with no domain
| goal and no relevant dataset.
| dotnet00 wrote:
| I'd argue that 'survive and reproduce' is not all too
| different as a goal from 'maximize this score' when it
| comes to problems as open ended as playing MOBAs. There
| isn't much of a dataset and there are endless numbers of
| ways to go about solving the problem.
| mjburgess wrote:
| Sure, animal goals are self-regulative and defined in
| terms of their capacities alone.
|
| If you can build a system like this, with no presumed
| domain distribution internally, then you're a step
| towards intelligence.
| mannykannot wrote:
| I think you are probably right about needing to be "in
| the world" (if by that, you mean being able to interact
| with it and see how its state, and therefore the data you
| have about it, changes), but I feel you are being too
| hasty in ruling out any situation where humans have had
| any role in preparing the data.
|
| In the evolution of natural intelligence, physics
| provides the domain distribution and Darwin has explained
| what the reward function is. It does not follow that no
| learning can occur in an environment where either or both
| of the domain and reward function are defined by humans.
|
| As far as I can tell, this was the explicit goal of
| AlphaGo Zero. Even if we were to accept your position
| that the data it was started with "contained the
| solution", then the fact it does rather better than
| humans at finding that solution is, by itself,
| significant. (In this view, one might characterize the
| evolution of natural intelligence as finding, within the
| biosphere domain, a rather successful solution to the
| survival problem.)
|
| It would seem to me to be begging the question to say
| that the success of AlphaGo Zero is not learning because
| the environment was human-specified (At the same time, as
| Go is a highly-constrained environment, there is no
| reason to suppose that its successes indicate we are
| anywhere near AGI.)
| mjburgess wrote:
| I dont mind people preparing data to build intelligent
| machines -- I mind what data they are preparing, and
| subsequently, what their claims about these machines are.
|
| If you can build a hand to grasp objects, and train its
| substructure to grasp this-way-and-that -- fine. So long
| as it can, in the end, also train itself... as we all do
| when we type.
|
| Nature provides _something_ to bootstrap learning, but it
| isnt "data" in the sense in which ML requires data. It
| isnt relevant, quantified, premeasured. The "data" nature
| provides is in our biochemistry .. how we react to our
| environment, etc.
|
| If AI research can produce an intelligence which is able
| to formulate the very terms of problems it wishes to
| solve, great. I dont see "summarising the solution" as a
| strategy which is event in the ballpack.
| mannykannot wrote:
| To be more specific, do you regard the data AlphaGo Zero
| was initialized with as being relevant, quantified, and
| premeasured to the point that no learning occurred
| between then and its defeat of expert players? I don't
| think the data they had as they learned to become experts
| was any less relevant, quantified, and premeasured - and
| quite possibly more so, if they read about tactics and
| strategy in the game.
|
| While I agree that nature's data is less relevant,
| quantified, and premeasured than what current ML feeds
| on, I don't see that as establishing that there is a
| relevant qualitative distinction that renders this divide
| unbridgeable in principle. Every organism that senses its
| environment is processing data.
|
| With regard to formulating the very terms of problems it
| wishes to solve, I have no difficulty in seeing that this
| has not been achieved yet, and personally, I don't expect
| it any time soon. At the same time, you seem to be very
| close to saying that no artificial system could do this
| because its goals are always, in some sense, those of its
| creators. To be clear, I would regard such a position as
| mostly avoiding the issues.
| randcraw wrote:
| > Intelligence is in the properties of the data supplied to
| the kNN. Not in its distance function.
|
| If this were true, then all people who observe the same
| training samples would learn equally well and predict
| equally well. But we don't. Some of us invent a better loss
| function than others or reject irrelevant data better. The
| representation formed by the data is surely derived, not
| fixed in its raw form. Forming a better representation of
| the data is the art of learning better. I'm convinced
| learning and remembering what you learn is more than kNN;
| in humans it involves data compression and
| recontextualization as you improve your loss function or
| tie your old representation to newly learned data -- old
| raw data and representations that heretofore you had not
| believed were relevant to that objective (or lesson).
| just-ok wrote:
| > _all people who observe the same training samples would
| learn equally well and predict equally well. But we
| don't._
|
| How do you know that we don't? It's an impossible
| experiment to conduct, right? Since same training samples
| == every stimuli experienced since birth, and even then
| you have the problem of possibly-different "initial
| conditions" i.e. development of the brain in the womb.
| Maybe if we did replicate that, we _would_ learn &
| predict equally well.
| s_Hogg wrote:
| I like what you're saying, but doesn't this just reduce to
| "garbage in, garbage out"?
| euphetar wrote:
| As an ML Research Scientist, I have never heard this
| interpretation. It's a very interesting thought that NN == kNN.
| It puts some of my lingering intuitions in clear wording. Thank
| you for this.
|
| I think you are close to truth. This would explain why even the
| largest language models can't generalize beyound the training
| set.
|
| At the same time, I disagree that analysis is out of
| proportion. It might be some clever averaging, but it does
| useful and interesting things. Take a look at Google: some
| clever averaging can get you a long way. It would be great to
| understand how it works and how can we go beyond it.
|
| I do believe we need a paradigm shift, but it does not come out
| of nothing.
| VirusNewbie wrote:
| > This would explain why even the largest language models
| can't generalize beyound the training set.
|
| Why do you say this, they do. I can teach gpt about new
| objects I have made up and it's physical properties and it
| can understand them. That seems a clear example of
| generalizing beyond training set, or do you not think so?
| axg11 wrote:
| What do you mean by: large language models cannot generalize
| beyond the training set?
|
| That's one of the main reasons they're so impressive, they
| _are_ able to do this, to a limited extent.
| euphetar wrote:
| If that was true, a model trained on one commonsense Q&A
| dataset would be able to answer questions from another
| commonsense Q&A dataset without finetuning. But they can't.
| It's the same for every task you can find, but especially
| evident on commonsense reasoning benchmarks. At least last
| time I actively researched the question, I haven't found a
| single task where NNs definitely generalize.
|
| When researchers dig in, they find that the neural network
| is learning wrong things. Word matching between answer and
| question, learning to model the annotator who asked a lot
| of questions because his Q&A answers are predictable, and
| stuff like this.
|
| There was a great review of the problem, but I can't find
| it, so I will have to link to this article [1], which gives
| an overview of issues with current NLP models.
|
| [1] https://thegradient.pub/frontiers-of-generalization-in-
| natur...
| geysersam wrote:
| The fact that NN does not generalize in a particular
| (arguably very challenging) task trained on a particular
| data set etc. does not mean NN never generalizes.
|
| Don't know exactly what is meant by "generalization" in
| this context. I'd argue that it's very unclear what the
| difference between "interpolation" and "extrapolation"
| even is in a high dimensional and sparsely sampled space.
|
| Would a NN trained to recognize "fur" trained on
| different kinds of dog fur also activate when it
| encounters wolf, cat or even bear fur? This seems quite
| plausible, and seems like a kind of generalization.
| mjburgess wrote:
| I am myself trying to get closer to a clear formulation of
| this problem, which is why I'm writing here. Here's what I
| have so far, ML systems (eg., NN) remember
| averages (, compressions) of historical data. They are
| useful, wrt the problem, iif (1) the problem's target
| function exists; (2) the data is relevant, unambiguous, well-
| carved; and if (3) these properties will hold regardless of
| likely permutations to the problem's framing.
| Systems are given data with these properties by significant
| amounts of experimental design, work, and effort by people.
| Absent these properties, data is useless.
| Producing data with these properties requires intelligence,
| and no machine systems exist which can do it.
|
| My issue with research into ML _on the whole_ , is that it
| *assumes* these properties _and then_ explains how the
| systems work. I understand why this is interesting from a
| formal perspective... but it fails to note that this
| situation is almost never how ML is used.
|
| There is no function from Image->Animal, ie., biologists
| arent just idiots who _could have_ just looked at some pixel
| patterns. Pixel patterns are radically ambigious wrt to
| `Animal`, and so even an infinite sampling of (Image, Animal)
| is not enough for ML.
|
| ... so what on earth are ML systems doing?
|
| This is a bigger research question: _to characterise how ML
| performs_ when this assumed setup _fails_. And you know, that
| research _almost_ doesnt exist. This is an industry led by
| partisans to its success.
|
| What do you think would happen if research actually talked
| about the dynamics of ML systems performance when (1) the
| target doesnt exist; (2) data isnt relevant & unambigious;
| (3) the problem framing will permute most times its
| deployed....
|
| Suddenly we'd have an explaination of why 2016 wasnt the year
| self-driving cars were delivered. And indeed, likewise, of
| why even 2036 wont be.
| euphetar wrote:
| One good lens I know is that a neural network is just good
| at approximating stuff. Trained properly, you can have it
| approximate a distribution. A conditional distribution like
| p(animal_species=dog | image=what I am seeing)
| (discriminative model, e.g. classifier), or even a joint
| one p(animal,image) (generative model,
| Autoencoder/GAN/VAE/Diffusion).
|
| There is also an information theoretic lens about
| compression, which is probably very close to what you are
| thinking about, but I haven't studied it yet.
|
| Regarding Image -> Animal. An image of an animal is a
| projection of the animal onto a 2D plane, plus lots of
| noise. So there is some dependance between an image of an
| animal and the animal. Biologists can get a lot of
| information looking at a photo of an animal. In some sense
| they are always looking at two images from their eyes.
|
| But the problem you are talking about is indeed serious,
| and far from solved. You can't understand the real world
| from 2D images with the current approaches. Ideally we want
| neural networks to build a 3D (or even 4D, with time?)
| model of reality. Instead we find them trying to guess
| labels based on patterns. May favourite example is the
| tiger-dog [1]. Still, there is evidence NNs are doing some
| clever things [2]. My guess is that the problem is that we
| just haven't found a way to formulate the task for the
| solution we want. In the current formulation it's easiest
| for the model to minimize the loss by sticking to patterns,
| so why do something else?
|
| There is a lot of research on more applied ML that asks the
| questions you are asking. It's just that this paper is
| another attempt at a theoretical explanation.
|
| I agree on the self-driving cars. We can't have truly self-
| driving cars until a model can generalize, which none can't
| at the moment. The core question is: if we had a "dumb"
| model that does clever averaging and successfully covers
| 99% cases, such that the car is dumb in special cases, but
| smarter than most human drivers in usual cases, would it
| justify deploying the cars? If this was the case, dumb ML
| might be enough for self-driving. It's definitely enough
| for self-driving in walled garden conditions, so there is
| some evidence that with enough data we can brute force our
| way to a tolerable solution.
|
| [1] https://www.dropbox.com/s/ucvflwwrm8idnp6/photo_2022-03
| -29_2... [2] https://distill.pub/2020/circuits/
| mjburgess wrote:
| Yes, we do require models parameterised by both space and
| time -- but really, we require implementations of these
| models -- the implementation i'm thinking of is called _a
| body_.
|
| Why? Well, consider the best sort of such models:
| physics. What is "the mass of the sun"? What is "the
| sun"? There is nothing in all of physics which says
| anything exists, nor what its boundaries are. Least of
| all what "the sun" is.
|
| Physics, all of science, is counter-factual: _if_
| something exists, _then_.
|
| You're never going to get to "what a table is" just by
| table(x, t) -- because there is something in the
| background which asserts "tables exist" and that is the
| concernful actions of animals which care to partition
| reality this way.
|
| Reality, in the end, measured in _every possible way_ is
| still ambiguous. It still leaves open how one actually
| refers to any of it. Where one places a boundary. What
| _the unit_ is going to be, in our descriptions.
|
| There is no way around starting from the other direction:
| not with facts already provided; but with no facts at
| all. You have to build a system which cares, that then
| induces a partitioning, that can then change its caring;
| and so on.
|
| This is an extremely partical concern. A car cannot drive
| itself, in the relevant sense, if it doesnt care about
| anything; and more severely, if it doesnt care like we
| do.
|
| The car isnt going to be able to modify its concepts in
| response to being challenged -- by other people, by the
| environment, etc. because it has no reason to. There is
| nothing which is important to it. And hence, when
| confronted by the need to adapt, the car will kill
| people.
| djoldman wrote:
| My hot take is that this correct in spirit.
|
| However, it just moves the conceptual line between data/feature
| engineering and modeling so as to make the data/feature
| engineering encompass nearly all of the process.
|
| By this definition of ML, one might just say that clever ML is
| a really clever way to engineer useful features, after which we
| "merely" average them.
|
| It feels like semantics.
| mjburgess wrote:
| From a certain POV the structure of an animal's body, as for
| example, it learns to ride a bike _can_ be given this kind of
| formulation.
|
| Namely, that the muscle tissue and the motor cortext express
| a latent representation of _riding_ which is _formed_ under
| the animal 's self-regulative goals of "stability" and
| "getting to some location" etc.
|
| From this POV, one can model any aspect of reality with this
| sort of language. I used to be annoyed by this, but less so
| now. Annoyed because it's pretty hopelessly useless describe
| any complex physical system this way. It only makes sense if
| your relevant "feature spaces" are discrete and
| computationally tractable... i dont think our muscles are
| good candidates. (Nor likewise, is almost anything).
|
| Nevertheless, you are right that "ML" on a domain whose
| dimensions are infinite, and in which slices through those
| dimensions are infinite, and whose models are chaotic -- then
| _ML_ with an infinity of time to run would produce an animal.
| And indeed, an intelligent system.
|
| One has to remember here that no one is doing this. What
| theyre doing is taking a highly regulated non-chatoic,
| tractable, measured and largely unambigious slice through
| these spaces --- sampling them --- and taking an average.
|
| Who gave you the knife to take this slice? Was it your
| cognition, was it the world?
|
| No, neither, it was your body -- largely; and its ability to
| regulate your imagination; and to guide action.
|
| Formulating reality as-if its patterns can be determined
| statistically leads to absurd levels of non-computability.
| Every possible partitioning of the world, in every possible
| arrangment, moving in every possible way -- what discrete
| model of this is there?
|
| There isn't one. And this isnt semantic. The challenge of
| building an intelligent system is a (bio-)engineering one: we
| require a material which grows intelligently, whose internal
| chaos is useful. Not a dumb little calculator which comes in
| to average our efforts after the fact.
| sdenton4 wrote:
| "Namely, that it uncovers latent representational structure not
| present in the data."
|
| Who thinks this? You're talking to straw-men. The latent
| structure is absolutely present in the data, but a series of
| transforms makes the structure more readily available. (Where
| else would it come from? Forest spirits whispering to the
| model?)
|
| The 'availability' is made evident by training a linear
| regression for classification on the learned representation
| (keeping the base network fixed). This works decently-well for
| a good representation, and not at all for raw pixels. The point
| is that you can use 'simple' algorithms - including kNN - on
| this transformed view of the inputs; it's not magical at all.
|
| FWIW, I find the classification problem space to be riddled
| with conceptual holes, while other application areas are much
| less problematic. The use of neural networks for compression
| makes a lot of sense, and much of representation learning can
| be thought of as augmentation-invariant compression for some
| family of augmentations. Some of the recent big advances are
| really about getting away from the problems of classification.
|
| Meanwhile, reinforcement learning, diffusion learning,
| recurrent networks, and GANs all bring different kinds of
| 'interactivity' into the learning problem, and resolve your
| complaint about ML algorithms working on 'just data.' The
| iterative processes in these models provide space to push
| against the data with decisions, and react accordingly.
|
| I find your complaints to be lacking in curiosity or even
| knowledge about what people are actually doing in the field...
| Great results keep piling up and ML systems are used by
| billions of people every day, while you continue to call no-
| true-scotsman...
| mjburgess wrote:
| Representations in the animal sense can be observed as that
| which is in common across inferential contexts inn which
| animals are confronted by the need to use them:
|
| My model of "pen" can be observed in action when: I write
| with a pen; I'm asked to pass a pen; when I'm asked "what
| color pen do you like to write with?" and so on.
|
| What is this structure "pen"? Is it a latent pattern within
| my experience? No! It is a means of regulating my body,
| imagination, emotion, etc. "The pen is mightier than the
| sword!" is something I can agree, or disagree with; I have a
| feeling about it. And in inferential contexts which require
| me to feel, I feel.
|
| This is what animals acquire when they understand the world.
| This is what everyone believes ML is uncovering, whether they
| realise it or not. We just dont have these ML-like templates,
| so we don't realise how thin and brittle they are.
|
| When ML is run on text, is detects character patterns which
| form "character templates" that other text can be compared
| to. I cannot ask, sincerely, what the machine thinks about
| whether "the pen is mightier than the sword" -- because it
| has no attitude towards that proposition.
|
| The character structure of the phrase "the pen is mightier
| than the sword" relative to a body of text, has nothing to do
| with any concepts -- that structure has nothing to do with
| the inferential contexts in which those concepts will become
| useful.
|
| Rather, this thin character-space text structure can be
| repeated to people in the form of an illusion: we the dumb
| apes who have the concepts are fooled into thinking that some
| machine assembling of these words had something to say.
|
| This is a very cheap trick, and fails pretty trivially. The
| relevant structure to be learnt, the representation, has
| nothing to do with sequences of characters.
|
| Though the industry likes to talk about "latent structure" in
| this data, it isnt latent. It is right there on the surface,
| indeed: it just is that surface.
|
| When the machine is challenged to do anyything other than
| report this average surface structure, it fails. ML systems
| dont _work_. They improve ROI on the margins, but automating
| basically broken inferential rules _at scale_ -- it is the
| automation here, and speed that is useful. Not the actual
| quality of inference.
| sdenton4 wrote:
| "They improve ROI on the margins, but automating basically
| broken inferential rules at scale -- it is the automation
| here, and speed that is useful. Not the actual quality of
| inference."
|
| This is trivially incorrect, and again shows a lack of
| curiosity. A couple obvious counterexamples are AlphaGo
| (and descendants) and AlphaFold, both of which have quality
| of inference far out stripping previous efforts. In
| AlphaFold's case the results are pushing forward a whole
| field of science. Plenty of systems at this point in
| history outperform human experts, and not just on speed
| metrics.
|
| '"The pen is mightier than the sword!" is something I can
| agree, or disagree with; I have a feeling about it. And in
| inferential contexts which require me to feel, I feel.'
|
| What are these feelings? Are they necessary for learning?
| Will airplanes never fly until they learn to flap their
| wings like birds?
|
| 'we the dumb apes who have the concepts are fooled into
| thinking that some machine assembling of these words had
| something to say.'
|
| Still arguing with straw men here...
|
| 'The relevant structure to be learnt, the representation,
| has nothing to do with sequences of characters.'
|
| Of course it does. For BERT, the output is a conditional
| probability of words based on surrounding context.
| goodtraveler wrote:
| OP makes great points that are not being addressed here.
| If you believe that animal intelligence is reducible to
| abstract symbol shuffling then it should be possible to
| extend the existing approaches to actual robotic
| automatons but it is clear that these models are unable
| to deal with the dynamic structure of the world and there
| is no reasonable avenue to actually make that happen. So
| either intelligence is reducible to abstract symbol
| shuffling or there is a semantic fib happening here that
| substitutes "abstract symbol shuffling" for
| "intelligence". The fact that we imbue these abstract
| symbols with meaning is where the intelligence comes
| from, i.e. people attribute intelligent behavior to these
| systems because we have learned to attribute intelligent
| behavior to the production of abstract symbols.
|
| It's clear to me that current mathematics is insufficient
| for creating intelligence (let alone general
| intelligence). If you know of anyone that makes a
| coherent case for the mathematical basis of intelligence
| then I would like to see those references.
| sdenton4 wrote:
| The OP makes overly broad arguments about AI that get
| details the details wrong and have obvious
| counterexamples in current ML techniques and results.
|
| On the ML side, there's been steady progress with an
| accumulation of very visible wins, accompanied by nearly-
| invisible adoption of techniques in day-to-day usage.
| (eg, keyboard text completion and text to speech
| systems.)
|
| Much of this progress has been in specific subdomains, as
| combining domains requires massively more effort and
| investment. However, it's starting to happen; CLIP
| methods create joint embedding spaces for multiple
| modalities, and have led to the zero-shot learning we see
| exhibited in DALL-E. This is stuff that simply wasn't
| possible five years ago, building on sub-domain tools
| which have greatly increased in their quality such as
| BERT.
|
| These joint embedding spaces will get better, encompass
| more modalities, and fuel further results. For example,
| as joint text-video embeddings become more powerful,
| we'll have embeddings which jointly encode text and
| physics.
|
| As a result, I would be extremely hesitant to write off
| robotics applications. There's no big-headlines
| breakthrough on ML robotics /right now/, but we also
| didn't have a clear road to zero-shot image generation
| from text inputs a couple years ago. Notice that many of
| the existing 'big' results were obviously impossible
| until they very much were not.
|
| Finally, I don't believe that we have a good enough
| definition of 'intelligence' to say with any certainty
| whether current mathematics is sufficient or not. Don't
| underestimate the potential for simple components to
| produce complex behaviors, though. But also, don't expect
| Pinnochio; intelligence may be broader than the human
| implementation.
| goodtraveler wrote:
| OpenAI shuttered their robotics division because they did
| not see any viable path to commercial applications so
| they pivoted to generating pixel art. Similarly, DeepMind
| has not been making any claims of achieving AGI because
| they're smart enough to realize that statistical modeling
| of physical systems is a very small subset of what counts
| as intelligence.
|
| I'm not dismissive of the progress in the field. What I
| find confusing is why so many people are convinced that
| what we are seeing with these abstract symbol shuffling
| systems is intelligence. All it does is confuse the
| average person about what these tools are capable of
| because at the moment they are only capable of amplifying
| biases in existing data sets. No statistical model can
| escape this trap and at the moment we essentially have
| automated bias amplifiers that are being sold as some
| kind of revolution in designing intelligent systems.
| vegesm wrote:
| I don't really get the first part of your comment. You say an
| NN is "just" compression + kNN and does no representational
| learning. But finding a compression (a transformation in other
| words) that makes kNN feasible on the data is exactly what
| people mean when they say it finds a hidden representation. It
| is a highly non-trivial task: e.g. simple distance in pixel
| space between images would get you useless results.
| mjburgess wrote:
| People have the notion that a latent representation in the
| animal sense, ie., a concept -- is the same thing as your
| "representation" in the NN sense.
|
| That's not the case. You're right that if I find a predictive
| compressions of faces, say F1...n then they arent literal
| "rememberings". And they seem to be able to participate in a
| decision process (eg., classification) which doesnt seem to
| target pixel patterns.
|
| However I think this is kind of an illusion. What `F1..n`
| are, are ambiguous pixel-space projections of the abstraction
| which isnt present in this projection. When I have the
| concept "this type of face" I can reason with it _beyond_
| similarity in pixel-space.
|
| When we form representations we arent restricted to reasoning
| with them in only one space (eg., how faces look as pixels).
| We (perhaps superstitiously) impart to machine
| "representations" an actual depth which they lack.
|
| They are templates derived from the spaces they live in, eg.,
| pixel-space; and have only the properties that space affords
| (eg., pixel-geometry). Reasoning beyond that space, and those
| properties, doesnt work. People think it does. This is the
| illusion.
|
| Templates derived from this data, that we provide, function
| like actual representations because we simplify the world for
| the machine -- and prepare its environment so that its pixel-
| space templates are good enough.
| PaulDavisThe1st wrote:
| I've been with you so far, but ..
|
| > When I have the concept "this type of face" I can reason
| with it beyond similarity in pixel-space.
|
| I think there are at least two possible things that might
| going on here:
|
| 1. we're "trained" on non-pixel data (to use the same
| framing) and so it seems obvious that we would reason with
| a concept like "this type of face" in a non-pixel space.
|
| 2. the experience of "reasoning with it" is an illusion,
| and is merely the subjective experience that we have when
| our brains do stuff with whatever their underlying
| representation is. That is, we may have no real knowledge
| of what space our own "face model" is built on, what it
| represents, what properties it considers.
| mjburgess wrote:
| So, another option, is that our concepts arent really
| _about_ their targets.
|
| For example, my "pen" is actually a bundle of largely
| motor techniquess (also: emotional regulation technquies
| etc.) which is _about_ how I coordinate _myself_.
|
| In a sense all my concepts are _about_ me. We might say
| that in congition we abstract these "primal concepts"
| into properties which are then _about_ their targets,
| sure. This is why cognition is a bit of a charlatan.
|
| In being able to regulate my own imagination, motor
| skills, emotions etc. with these _techniques_ , I can
| actually discover new things about the "abstract
| concepts" that they imply.
|
| Since my "pen" isnt really anything like "a pen" nor even
| my experiences of pens... it is rather, "a way of me
| moving everything within me" -- I can simulate these
| movements and discover new things about their implied
| abstractions. Indeed, I can do this in the world: I can
| explore actual pens by moving my body differently.
|
| This is one of the _big_ issues with the AI paradigm as
| it has always existed: researchers are still talking
| about environments _as if_ they had properties which were
| already there. Everything is formualted as-if the problem
| were solved. The world is just some bundle of facts
| (data, propositions, etc.) and representations are just
| subsets of these.
|
| This misses, you know, the world. The thing that hits
| you. And it misses, entirely, what happens when it hits
| you.
| PaulDavisThe1st wrote:
| Sounds like a re-visit of a lot of the ideas in "situated
| action", popular in some AI circles in the 90s. That also
| included concepts about how we reduce cognitive load by
| storing information (and procedure) in our built
| environments.
|
| I'm not, though, that I agree about the pen example. I
| mean, you're completely correct in your description of
| all these different elements of your "pen". But to the
| extent that a concept of/about something is really never
| anything like the thing, it's not a particularly
| important aspect of concepts. I think that what's
| important is that all of these embodied, semi-reflective
| elements of your "pen" concept sit _in parallel_ with
| some abstract concepts about pens. Pens do have
| properties that are already there, but in many contexts
| as you note, these are less important than the ones
| embodied by you.
| mjburgess wrote:
| I suspect we'll find that these abstractions are no where
| within us. We dont have ML-like templates of stuff.
|
| Whenever we need to reason about the properties of pens,
| our coordinative bodily structure generates these
| abstractions for cognition to operate on. They're
| ephemeral, and live only when cognising an issue.
|
| To model cognition then, is to model only the symptom of
| intelligence; not the actual process itself.
| pizza wrote:
| Maybe we're talking about different mathematical formalisms..
|
| When you say latent representational structure not present in
| the data, am I correct in thinking you mean:
| argmin theta: Loss = dist(data, model(data))
|
| where model(data) = layerL( ... layer3 (
| layer2 ... layer1(data) ))
|
| is actually obscuring some conditioning factor
| human_understanding(observed data)
|
| which actually goes into all the layers (by their choice of
| design, initialization), distance functions, optimization
| steps, data cleaning, splits, etc.?
|
| And furthermore, also that P(model simply
| memorizes observations + human provided inductive cues | model
| finds structure) >> P(model does
| something beyond memorization of observations + human cues |
| model finds structure)
|
| ? What would be your test to determine this? For instance, how
| would you measure the information content (under some
| pragmatic-enough encoding that could be realizable and built
| today) of the memorization + human cues, vs memorization +
| human cues + the extra bit that the model concludes? Not a
| gotcha question, curious about your answer.
|
| Though, I have to say something about the first bit. Suppose
| you were comparing the safety and probability of catastrophic
| failure of some of the earliest buildings made in human
| civilization compared to something made in 2022. And suppose
| you conditioned that probability on the sum total of human
| knowledge at the time. The same laws of physics apply, but
| different amounts of knowledge went into producing each
| artifact.
|
| Can we say that the latent structure (human theory of
| engineering) in the modern building (model) reveals more about
| the ground truth of the world (laws of physical reality) than
| is inside of the observations data set (does it collapse or
| not) than the old model? I think we can. And I don't feel that
| embarrassed to say that deep learning is just a part of the
| wider landscape of human experience.
|
| You may be interested in this article that proposes Rip van
| Winkle's Razor: if you apply a state of the art method to an
| old dataset, for a fair comparison, you need to measure and
| take into account the increase in tooling that has become
| available since the time the benchmark was first released:
| http://www.offconvex.org/2021/04/07/ripvanwinkle/
| mjburgess wrote:
| Yes, I think that's largely right.
|
| What we require is a "bridge" from any particular feature
| space to the target. A compression of a feature space doesn't
| produce this bridge. When we look at a photo, its ink-space
| arragement can be bridged to its depicted situation via our
| ability to brigde.
|
| We see in the ink, a world. We do this via concepts which
| bridge. We get those by being in the world.
|
| We can "roughly accept" limited forms of inference which
| simply operate within a feature space, ie., we can be fooled
| by photos "made on photoshop" which appear to depict a
| situation which never really existed.
|
| A machine operates in "inkspace" and we rig it only ever to
| produce "human-bridgable" arragements of ink. The machine
| itself doesnt draw a situation -- as with an artist painting
| a room. A machine isnt in the room.
|
| It is, _as-if_ it were in the room, and as if it had painted
| it. This illusion is created _by us_ , we rig the system to
| average historical paintings of rooms, and only ever show the
| apes-at-home a good picture. They think, looking at it, that
| the machine has painted something.
|
| One formulation of this, I suppose, is to add "|
| HumanUnderstanding(Y-as-labelled, Y-the-actual-target)" to
| everything
| vladf wrote:
| This comment is making a "slight of hand" itself: how do we
| define the metric for kNN? That will directly impact kNN
| performance.
|
| Using Euclidean norm over pixels on imagenet will not get you
| anywhere.
|
| By recasting classification into a kNN lens you're basically
| (in the large data regime) recasting the problem to kernel
| learning under some underspecified RBF kernel with an unknown
| "proximity" metric instead of Euclidean.
|
| In grad school, someone in my lab actually tried scaling kernel
| methods directly to imagenet too. I don't think that ended up
| working naively (an interesting neural variant worked for
| CIFAR10, though [1]).
|
| If all NNs are doing is efficiently learning an appropriate kNN
| metric (or, more idiomatically and generally put, learning
| kernel parameters for some implicit neural kernel), that's
| still really powerful and all we've done is just renamed
| "learning representations."
|
| [1]: https://arxiv.org/abs/2003.02237
| canjobear wrote:
| If neural networks do compression, then they are implicitly
| providing structure not present in the data. Given a dataset
| there are always many different ways to compress it, each
| corresponding to some implicit distribution over unseen
| datapoints. Most of these distributions don't correspond to
| real-world generalizations. Yet neural networks do often--not
| always--choose models for compression that correspond to real
| generalizations. This is nontrivial.
| clircle wrote:
| "We have more data than ever, more good data than ever, a lower
| proportion of data that are good, a lack of strategic thinking
| about what data are needed to answer questions of interest,
| sub-optimal analysis of data, and an occasional tendency to do
| research that should not be done."
| zmgsabst wrote:
| This is self-righteous nonsense.
|
| DNNs end up encoding parts equivalent to type theories in their
| operations -- extracting an "internal language" (in the TT/CAT)
| sense which models the data. In particular, convolutions are a
| form of extracting/deducing types from your input. A full DNN
| deduces a model of the data in which it can type the incoming
| data and then reason about relations on those types to decide
| the challenge.
|
| What is it that you think you're doing, besides forming a model
| from data in which you reason based on an internal language,
| which describes the data you've seen?
|
| Your post comes across as ignorant of eg, the overlap between
| TT and DNNs, while simultaneously full of woo about how humans
| operate.
| mjburgess wrote:
| Sure, but all this is only within one space. The "internal
| language" is a formalism _of that space_ , and not beyond.
|
| What I think we're doing is, you know, forming concepts which
| are not constrained to spaces. I dont even really think our
| concepts, which are mostly just bundles-of-techniques, live
| within the space of the target object, nor its measurment
| space (ie., that of experience).
|
| Eg., my "pen" includes being able to grasp a pen (indeed, the
| relevant motor skills are prior-to and necessary-for the
| abstract concept formation).
|
| What you're talking about is a pixel-space projection of
| "pen" being able to function like the (animal, body-first)
| concept "pen". It doesnt.
|
| Rather "pen" in pixel space is, sure, an "internal langauge"
| of a sampling of AllPixelPatterns concerned with pixel-space
| projections of "pen". This i'd call a "template", and I dont
| think it has almost anything to do with
| concepts/representations/etc.
|
| What we are doing when we acquire motor techniques which
| produce "ways of sensing and moving" that eventually could
| become reified as the abstract "Pixel Pen" is really _nothing
| at all_ like sampling PixelPenSpace and deriving a tempalte.
|
| To see the difference, consider that our concept allows us to
| resolve ambiguities -- eg., if I think something might be a
| pen, I can go beyond one space of measurement (eg., sight) --
| eg., move the pen, write with it etc. -- and thus return to
| the target space "Is Pen?" with sufficient confidence.
|
| Once such "mere templates" exist, everything we do, isnt
| required. Indeed, a calculator can take over at that point;
| put it in a cupboard forever to repeat whatever thin
| inferences pixel templates admit.
| zmgsabst wrote:
| > Sure, but all this is only within one space. The
| "internal language" is a formalism of that space, and not
| beyond.
|
| This is incorrect:
|
| You can swap encoders and final fully connected sections,
| then mildly retrain the network to substantially save on
| training effort -- "transfer learning".
|
| Further, you can compose networks, eg image recognition on
| top of something extracting a structural map.
|
| This implies that the structures they're "learning"
| generalize to different contexts -- with a bit of
| retraining. In much the way your knowledge of programming
| transfers between languages (swapping encoders) and tasks
| (swapping final connected network).
|
| Your point seems deeply based on this incorrect
| understanding of DNNs.
| mjburgess wrote:
| Sure, as set up by the way we have constructed the
| dataset. "Green" isn't near "Leaves" because the world
| placed them there.
|
| All these properties follow from properties of the data
| which we have structured to produce these solutions.
|
| We arrange the target domain of one dataset up, so that
| the target domain of another is aligned --- and in doing
| so, solve the only problem which requires intelligence.
|
| Yes, the character-space structure of Y labelled stuff
| aligns with the pixel space structure of Y labelled
| stuff. And feature space templates can likewise be
| associated.
|
| This is just more of the same. King +Woman isn't Queen
| --- it isn't anything. This isn't reasoning. And likewise
| PicOfKing+PicOfW isnt PicOfQueen.
|
| This is schizophrenic pseudoscience --- it's actually the
| mind of a schizophrenic who obtains sequential thoughts
| just by non-semantic associations.
|
| The latent structure being learnt in both cases is just
| the coincidences we rig in the Y domain --- Inhave no
| doubt they 'transfer', we wrote them in
| zmgsabst wrote:
| We don't write them in though.
|
| You can show a DNN a large sample of photos of animals,
| retain the lower sections of the network, and then show
| it a large sample of landscapes -- and it will be able to
| recognize those faster precisely because a well-trained
| network picked up on the latent structure of shapes, and
| can reason about shapes in its internal language for
| describing pictures. It's acquiring genuine semantic
| information -- and learning about new experiences
| relative to that previous acquired understanding.
|
| We didn't create that similarity of form in nature -- and
| DNNs discover that in much the way we do: finding
| recurring, abstract patterns to utilize in our internal
| language.
| mjburgess wrote:
| We labelled them -- their pixel space geometric structure
| has no inherent relevance to their character space
| structure --- and the only inferential contexts this will
| work in is where we rig the deployment to only require
| these sorts of coincidences.
|
| Animals acquire robust techniques which are not brittle
| to these sorts of thin contexts.
|
| We do so by not templating pregiven data -- but by
| coordinating our bodies in the world, and rearranging it,
| so as to determine how to regulate ourselves in response
| to it.
|
| Only a teen reading cosmo would think King-Man=Woman. The
| reason we can even interpret this thin formalism is
| because of our robust understanding of the concepts that
| these character symbols name.
|
| With these concepts we don't cross domains, we regulate
| our own imagination, and so on, so as to create
| representations which are thick across an infinity of
| domains.
|
| Talking in terms of feature domains has it backwards --
| it assumes they're available for measurement. We don't
| transfer thin templates across domains. Our
| representations aren't within a feature space --- they're
| techniques for self-regulation.
|
| With these we can construct templates in an infinite
| number of different feature spaces -- ie., project by
| imagination from our techniques
| mistrial9 wrote:
| the downvotes on this one show some blind alligiance to
| word2vec it seems to me; I tend towards questioning the
| playing field, not clamping to a (large) set of useful
| vactors to be the new "hammer looks for nails"
| c0mptonFP wrote:
| Your thinking is human hubris in full force.
|
| Humans ability of conceptual modeling is entirely
| constrained by their perception. Humans can't conceptualize
| a tesseract. All of their conceptualizations are in some
| form related to their perception.
|
| When you're thinking of a pen, are you thinking of the
| atoms, molecule compositions and all the quantum effects
| going on? No. Your mind is, just like ML, working from a
| template constrained by your perception (visual, haptic,
| auditory).
|
| Humans are more advanced than ML, but they are nothing
| special.
| mjburgess wrote:
| Perception in animals changes the structure of their
| bodies as they are coordinating with their environment,
| such that they acquire motor techniques and hence new
| ways of structuring their perceptions.
|
| Perceiving the world isnt a passive activity in which
| facts strike your eyes. The world doesnt have these
| facts, there is no data, and nothing from which to simply
| "average to a template".
|
| When light strikes your eye, there is no "keyboard" in
| it. Nothing in it from which derive even a template of a
| keyboard.
|
| There are only templates in datasets _we_ prepare -- and
| we dont prepare them by actually "encountering datasets
| in reality". Rather, we arrange reality and measure it
| so-as-to-be-templateable.
|
| What animals have is the ability, with effort, to engage
| in this dynamic -- to arrange the world to make it
| knowable. It is this process of arrangement which
| requires intelligence; not "taking the average after it
| has happened".
|
| There is nothing in the world to be "percieved" in the ML
| sense, as in: ready for analysis. That's an illusion we
| construct: we make "perceiable data".
|
| It is our bodies, and our concerns, which make the world
| itself perceivable. The world itself is infinitely dense
| with infinities stacked on infinities. There isnt "data"
| to be templated.
|
| And this isnt theorertical: no ML system will ever work.
| All that works is our data preparation.
| xfs wrote:
| Human can't visualize a tesseract, but human can
| conceptualize the Idea of a tesseract in the symbolic
| space, by math, physics, or in other words, by Reason.
|
| The Symbolic is a radically simplification of all the
| complexities impossible to be fully sensible. Even though
| the simplification is always particular, contingent, and
| full of ambiguity (human languages) and often inaccuracy
| (Newton's laws vs relativity), without the
| simplification, without Reason, ML systems are probably
| like animals, eventually succumbing to the full force of
| the complexity of reality.
| YorkshireSeason wrote:
| Could you elaborate on the connection between Deep Learning
| and type theory, resp. topos theory that you allude to? It
| sounds fascinating. At the some time, I'm slighly surprised
| that I am not aware of results in this direction.
| zmgsabst wrote:
| I think it mostly exists at the level of folk lore right
| now, with preliminary bridges. I've spoken some with people
| at, eg HoTT 2019 or Topos Institute speakers, but I think
| the state of publishing is that the other direction is
| being worked out fully -- since it solves problems in the
| math community like accelerating proof engines via GPUs.
| (Maybe, I hope.)
|
| There's three approaches to seeing the connection:
|
| 1. If the manifold hypothesis is true, then the semantic
| information between the original manifold and reduced
| manifold will be in the topology of that map -- and your TT
| is a relabeling of the synthetic topology description.
|
| 2. For the convolution comment, take a TT statement, view
| it as a CuTT structure, and draw the construction diagram
| of that "shape". Project that into a matrix encoding and...
| convolutions are right-side type division! Eg, finding a
| submatrix that matches "Line(...)" in your overall matrix.
| But it's right-sided division since you can only remove the
| "innermost" factor in a product that way. (There's also a
| left-handed, but it's less relevant here.)
|
| 3. The paper on ML via hyper plane splits to create data
| coverings also suggests that what we really want to know is
| the topological relationships between kinds of data. This
| is furthered by, eg, "animal" in word2vec ideally being the
| centroid of the open ball covering animals, as they're
| embedded.
| mjburgess wrote:
| The manifold hypothesis is true when the measurement
| bases aren't orthogonal, and there's lots of them eg.,
| when we measure 3D objects by their relative position to
| 100 objects in a room.
|
| That isnt a bridge principle between spaces which measure
| different properties.
|
| The low-D structure of any high-D dataset is only a
| "washing out" of spare measurements of the same
| properties. These arent different "property-spaces", ie.,
| the low-D structure isnt a world one has bridged to via
| statistics (or grad desc, etc.).
|
| The properties the formal structure of text captures
| aren't the properties of objects.. they're something like
| the social-vocab-coincidental usage of language users in
| how they "orient their words" around topics. If we change
| our usage, models which find this structure, become
| invalidated.
|
| We language-users routinely change this structure, and
| robustly against misunderstanding, because we bridge to
| the relevant property spaces by being in the world.
| VirusNewbie wrote:
| >because we bridge to the relevant property spaces by
| being in the world.
|
| Isn't there an implicit conclusion here that the extra
| dimensions of 'world being' would actually make the
| manifold hypothesis true, it's just missing data in the
| training model? Not to say it's trivial to fix, that
| missing data might be derived laws of physics, smell,
| shared world models of a society etc. but none the less I
| fail to see how _conceptually_ any of that couldn 't be
| reduced to data.
| zwaps wrote:
| In statistics, there's a joint distribution of x and f(x) to be
| learned. In this piece, there's a distribution over trained
| models that naturally depends on the initialized parameters. It's
| interesting when data is considered non-random, and the
| probabilistic variation arises from weight initialization.
|
| While this serves to characterize the training process, the
| authors see the goal of the training procedure to converge to a
| target function f(x), with known values for x. This is of course
| not the substantive goal of the DL/ML researcher or practitioner
| (where the target function is unknown, and the goal is to learn
| the joint distribution and hopefully not fit the latent f(x)
| function exactly).
|
| On the one hand, uncovering how a NN would behave with different
| lengths and widths for a fixed set of data seems an important
| step, and although I haven't read far enough to see any
| interesting results yet, I am sure the authors have much in
| store.
|
| On the other hand, I feel like most questions of practical
| relevance - like generalization, model choice given data,
| causality, statistical properties of the entire design process
| and so on - all require us to think about the data generating
| process, rather than only the weight initialization process.
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