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Why Robot Brains Need Symbols

88 points by zwieback 8y ago

317070 8y ago
No, they don't. Now, feel free to _show_ me that they do, and I will gladly accept to have been wrong. But this argument that because of some very theoretical view on the problem the current engineering solutions should be abandoned, without actually providing good engineering alternatives, is weird. I have a bit of a "not even wrong" feeling on the symbolist side of the debate. Deep neural networks have serious flaws, but they can at least be pointed out due to them already solving engineering problems. It is easy to say a field should move to your direction, but they will probably only do it when provided with tangible evidence. AI has moved into the realm of application, and that means the scientific bar for purely theoretical ideas has been raised compared to 20 years ago. And right now, the tangible evidence for symbolism is simply not cutting it.
- scotty79 8y ago
  I think they need symbols but they just need to be taught symbols. If you kept child stationary and show it just static photos one at a time you wouldn't be surprised that it can't tell bus lying on the side in the snow from a snow plow. What neural networks need is the same thing little children need, motion, rotation, things stacking hitting and occluding each other ... and possibly even interaction. We haven't taught NN to see properly yet and guy wants to hardwire math into them because he thinks that's the only option. Humans learn symbols late and only with deliberate effort.
  - Asooka 8y ago
    On the other hand humans have built-in special processing stages already. One that comes to mind is that the neurons in your eyes in effect run a sharpening filter on the incoming light before the data ever enters your brain. I don't think it's a stretch to propose that we should implement certain features as special-purpose processing stages that the neural network can use, with the goal of having a more efficient system. The holy grail would be a neural network that has programming capabilities and can write bare metal programs for itself.
    - scotty79 8y ago
      > On the other hand humans have built-in special processing stages already. Yes. But there were cases where human could perform function elsewhere that was usually done in special site when thst site got damaged.
- stult 8y ago
  Or to frame your argument in terms of science rather than engineering, it's a falsifiable hypothesis. The author should go try to test it and then let us know if it works.
  - bsaul 8y ago
    i’d say the bus picture classification is a pretty telling example that something’s missing in the amount of knowledge deep learning is able to extract from training data sets. Anybody who understand what a school bus is wouldn’t mistake it for a snow plow in the other pictures.
- vanderZwan 8y ago
  > And right now, the tangible evidence for symbolism is simply not cutting it. In AI/ML perhaps. In the biological and psychological sciences the importance of symbols to cognitive functioning is pretty well established. Just look at how important the mastery of language is for complicated thought. One of the main differences between linguistic thought and other forms of thinking is all about symbol manipulation. (This says nothing about the quality of this article of course)
  - FakeComments 8y ago
    It’s also pretty well known in math and CS/SE communities. Heck, the mathematical research into the power of language, and in particular the ability to embed models of reasoning into equations to allow you to formalize your meta-reasoning, is what led to the birth of electric computers. So I find it shallow skepticism to suggest there’s not evidence that it would be useful to AI, because literally the most cursory glance would show mounds of it. The real challenge is and always has been how to combine or embed symbolic reasoning capabilities efficiently within a fuzzier approximator, allowing for “getting close” in a fuzzy way then fine tuning it using more advanced rules. That mimics more closely how we think, where we broadly intuit a few potential answers, then use higher order thinking to sift through them.
    - vanderZwan 8y ago
      > The real challenge is and always has been how to combine or embed symbolic reasoning capabilities efficiently within a fuzzier approximator, allowing for “getting close” in a fuzzy way then fine tuning it using more advanced rules. Well, maybe the problem is that we spent so much time basing models on the visual part of the brain that we overlooked the rest: https://www.quantamagazine.org/new-ai-strategy-mimics-how-brains-learn-to-smell-20180918/ https://www.quantamagazine.org/new-ai-strategy-mimics-how-br...
- chimprich 8y ago
  > But this argument that because of some very theoretical view on the problem the current engineering solutions should be abandoned That's not the author's argument at all. Even the subtitle of the article says "We’ll need both deep learning and symbol manipulation to build AI.".
  - posterboy 8y ago
    A compromise between the two would be to identify powerful abstructions over nodes and nets that are equivalent to symbol manipulation circuits. Self assembly and self modification are certainly not strange to symbolic logic, but this doesn't work so well for most programmers. So we need more geniuses and they need to tailor neural nets to help them build symbolic logic (if only as intermediate debugging output).
- drb91 8y ago
  Getting tasks done has never been a ringing endorsement for the general applicability of the underlying algorithm. Meanwhile, the entirety of human communication is built on symbols. If computers can’t deal with them, how useful can computers ultimately be? Roombas can only improve so much.
- giardini 8y ago
  If you're willing to accept a (possible) inexplicable intelligence that cannot explain its decisions or reasoning processes then you'd be fine w/o symbolism. In fact there may be an example for you (perhaps in your pocket): The next time you have a decision to make, phrase it as a Heads/Tails question and flip a coin. Over its time of use, the coin will be right until it is wrong, putting you in exactly the same situation as you would be with an inexplicable intelligence. So you see, intelligence is not that far away! But explicable predictive intelligence requires reasoned prediction and can answer "Why that prediction?" when asked and can learn if a prediction fails. The coin cannot but will be adequate, at least until it fails sufficiently to induce you to abandon it.
mrcoder111 8y ago
A symbol is just a frozen weight in a neural network.
Esai 8y ago
AI development has hit a plateau in terms of performance and funding saturation. All investment is now expected to deliver some semblance of market performance before the great Margin Call of 2019 hits, which means scaling out AI to absurd domains and deploying vast armies of surplus humans to either code or emulate profitable-enough behavior to pass the risk on to the next investor round. In the end, the Mechanical Turk has gone worldwide with no brakes.
- buboard 8y ago
  What is the evidence for a plateau? The corporate temperaments are irrelevant to the actual progress of the field. If anything , ai systems have not even started being deployed , and the field is even missing a proper theoretical framework. this should indicate that it's still very early stage
  - Esai 8y ago
    There's a long history of cycles in this field. https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter The plateau comes from the problems with vanishing gradients. https://en.wikipedia.org/wiki/Vanishing_gradient_problem https://en.wikipedia.org/wiki/Vanishing_gradient_problem
ajuc 8y ago
This reads like 19-th century engineer arguing we need articulated wings for heavier-than-air flight because our current methods doesn't work and birds have them :)
opless 8y ago
He's right about LeCun. Yann really does not like Deep Learning being criticized. Please someone who knows much more than me about deep learning tell me how a deep learning ai can explain how it came to an answer. It can't. By definition. You can say that it's a equation with a lot of terms, but that isn't really showing your working out. You need a symbolic system to show that working out. This isn't to say that deep learning is worthless, just not worth the amount of column inches it's getting at the moment.
- ben_w 8y ago
  > Please someone who knows much more than me about deep learning tell me how a deep learning ai can explain how it came to an answer. > It can't. By definition. What definition are you using? From the my understanding, if you can explain your reasoning, then a deep learning system can in principle learn how to explain its reasoning. If it couldn’t, you couldn’t either.
  - opless 8y ago
    We're not deep learning systems. So reasoning by analogy there isn't correct. (Aside: There have been experiments that show that many explanation for [everyday] actions are, in fact made up after the decision to execute an action. Which gives some insight to the whole nature of free will - but that's another discussion entirely. However, indeed we're able to show our workings for higher level workings, like mathematical proofs. ) Deep learning systems, are examples of supervised learning. Once trained, they cannot adapt to new input. You have to retrain to accommodate new features that you want to capture. Deep learning systems, are in essence a very long equation that hides all meaning over the entire network - good luck in finding out why it came to a decision. You might get lucky and see a definitive triggering path through the network, but that's an exception to the rule, as far as I understand. (I'm willing to be proven wrong here.) Symbolic systems, can trivially be made to trace the code execution paths they made for any action they might take. (also - hi Ben! I recognise your blog!)
    - FakeComments 8y ago
laichzeit0 8y ago
I really don't get Gary Marcus' fundamental problem. He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? Surely no one is being held academically ransom to Deep Learning. If Ps (and Qs) is a better approach then do the research and publish the results. Instead he just seems to whine about it on Twitter all day and attack people who are actually "doing the research and publishing the results."
- deleted 8y ago
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- Eridrus 8y ago
  Gary isn't a technologist, but he wants to set the stage with critique so that when/if a fusion of the ideas does better he can claim to have been important to that, regardless of the fact that the idea is obvious, and the implementation is what is hard
  - deleted 8y ago
    [deleted]
- YeGoblynQueenne 8y ago
  >> He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? It's a slightly garbled (and so not immediately recognisable, perhaps) pun on the verse "All that we're saying/ is give peace a chance" from John Lennon's "Give Peace a Chance". So it's not addressed to anyone- it's just his attempt at injecting a bit of levity in the debate. EDIT: >> If Ps (and Qs) is a better approach then do the research and publish the results. Well, people have done that, yes. For example, the Evans and Grefenstette paper Marcus' article cites towards the end has shown in a very clear manner the power of combining symbolic with sub-symbolic approaches, as has the work of, off the top of my head, Artur D'avila Garcez (neuro-symbolic computation), Luciano Serafini (a differentiable logic), and many others. And yet, my intuition at least is that most people who have heard about Deep Learning, haven't heard about that work.
  - 8y ago
dana321 8y ago
Not this again.
polkapolka 8y ago
Symbolic AI is machine programming. Connectionist AI is machine learning. Machine programming simply does not scale. It is also not biologically plausible: it is not as if God put symbols into our brain, these formed, are prefaced by, and were learned from neural activity. Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results. Deep learning is the most promising direction AI went into since a long time. Finding flaws in DL does not point back to a programmer crafting handwritten rules to correct it. It merely points to more DL research needed to have machines correct this themselves. Preferrably differentiable.
- hopler 8y ago
  Why do you think human-style learning can scale? Human brains are terrible at basic arithmetic like raising 3 to the 6th power.
  - polkapolka 8y ago
    Statistical learning with connectionist architectures is driving current AI at scale. To me, this paradigm is also the most promising: learn from data bottom-up, not from experts top-down.
- giardini 8y ago
  polkapolka says>"Machine programming simply does not scale....Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results." 1. We can scale "machine programming" by making computers faster and more complex, 2. The spam problem can be solved with Bayesian methods (which I consider to be part of "machine programming"): a connectionist solution is not necessary. Certainly connectionist deep learning is exciting and should be investigated as far as possible but it is only one of many tools. We should avoid being the man with a hammer who thinks that every problem looks like a nail.
  - polkapolka 8y ago
    Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere. Bayesian models underperform to DL by a wide margin (though it is a step up from handwritten rules: if DEAR FRIEND then Spam score++.
afpx 8y ago
This is something that’s been a challenge to my team for a long time. For us, machine learning has produced very good inferences, but it doesn’t create models of how the universe works. For example, if we input a lot of raw weather data into a statistical model, we can predict how it may affect the power output of a solar array. But, I can’t get the machine to ‘understand’ that clouds decrease solar availability.
cs702 8y ago
Actually, deep learning models do use symbols. We call these symbols "embeddings" or "representations" in a vector or tensor space... but these symbols may not have any known or readily interpretable human meaning. People who are uneducated in basic vector (and tensor) math may have difficulty understanding how this is possible, but it's a fact. For example, the ELMo embedding of a particular word in a sentence is a symbol that represents the complex syntax, semantics, and polysemy of that word given its location and use in the text.[a] Similarly, a trained WaveNet model represents a sequence of text as a (fairly large) tensor embedding that encodes the information necessary for generating the corresponding speech; this embedding is a symbol too.[b] As a final example, the next-to-last layer's embedding of an image processed through a trained Detectron model contains representations of every identified object in the image; those representations are symbols too.[c] Moreover, the kind of automatic differentiation that is necessary for training deep learning models with backprop reduces into manipulation of symbols in a tree, viewed through the right lens. Witness, for example, the recent work done by Mike Innes et al for the Julia language.[d] We could very well be in the early stages of adoption of "deep differentiable programming" for a growing number of cognitive tasks that might not be solvable via human manipulation of symbols. The challenge for deep learning going forward, as Bengio says and Marcus quotes, is in figuring out how to "extend it to do things like reasoning, learning, causality, and exploring the world."[e] As far as I can tell, everyone in the field already agrees on these goals; many researchers are thinking about and wondering how it might be possible to pursue them. Does Marcus understand this? His complaining about the "lack of symbols" is... misguided, I think. [a] https://allennlp.org/elmo https://allennlp.org/elmo [b] https://deepmind.com/blog/wavenet-generative-model-raw-audio/ https://deepmind.com/blog/wavenet-generative-model-raw-audio... [c] https://research.fb.com/downloads/detectron/ https://research.fb.com/downloads/detectron/ [d] https://julialang.org/blog/2018/12/ml-language-compiler https://julialang.org/blog/2018/12/ml-language-compiler [e] https://twitter.com/GaryMarcus/status/1065280340669816832 https://twitter.com/GaryMarcus/status/1065280340669816832
mindgam3 8y ago
Previous discussion: https://news.ycombinator.com/item?id=18639359 https://news.ycombinator.com/item?id=18639359
d--b 8y ago
My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. When I think "the house is red", I know what it means very well, but I'm also able to doubt or modulate my understanding of the symbols. These conversations would be hard to put in symbols: - This house is red - No! It's crimson! - But crimson is red! Or - The house is red - No! it's green! - Nah, it's red, you're colorblind Brain logic is MUCH fuzzier than symbolic logic. Symbols exist, sure, but they're part of a bigger logical soup. And I believe that no low level logical circuit exists in the brain (which also explains why humans are so slow at logic, calculation and so on). Maybe symbolic logic could be a intermediate step for some applications. Or maybe humans could be much smarter if they had access to some symbolic logic processing unit. But as far as research is involved, I think there is way more to gain if we managed to have symbolic logic emerge from deep neural nets. In fact, you could argue that Alpha Go definitely developed some kind of symbolic logic, especially in the end-game, where there's little intuition and way more calculation.
- YeGoblynQueenne 8y ago
  >> My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. Who said anything about hardcoding anything? Marcus is advocating for the use of gradient descent to learn symbols- he even cites the DeepMind paper on θILP, a differentiable Inductive Logic Programming system that learns symbolic rules with deep learning.
  - d--b 8y ago
    From the article > Their solution? A hybrid model that vastly outperformed what a purely deep net would have done, incorporating both back-propagation and (continuous versions) of the primitives of symbol manipulation, including both explicit variables and operations over variables This is what I called "hardcoding some kind of symbolic logic". Is it not?
    - deleted 8y ago
mindgam3 8y ago
Robot brains need symbols because the rhetoric around deep learning is getting out control. In the video analysis below of the latest AlphaZero vs Stockfish paper, AlphaZero is described as "DeepMind's general-purpose artificial intelligence system". Let's give credit where it's due. AlphaZero is an impressive algorithm with results in 3 different types of perfect-information games: chess, go, shogi. But to describe it as "general purpose AI" is simply absurd. Yes, this isn't an official Deep Mind video, but this is the kind of rhetoric they are putting it out there. I'm open to the possibility that deep learning might one day solve some of its core problems (like the elementary schoolbus-snowplow errors described in the source article) and turn into a general purpose AI. But we aren't there yet, and we're not even close. https://www.youtube.com/watch?time_continue=13&v=2-wFUdvKTVQ https://www.youtube.com/watch?time_continue=13&v=2-wFUdvKTVQ
- soVeryTired 8y ago
  I think there's some ambiguity there regarding what is general-purpose: if you want to be charitable, you might interpret the description as saying it's a general-purpose system rather than a general-purpose AI. Who knows what they really meant though...
  - mindgam3 8y ago
    My point is, calling a system that learned how to play 3 types of board games "general purpose" in any sense is a bit of a stretch. At best this is a "general purpose games-playing" system.

88 points by zwieback 8y ago

No, they don't. Now, feel free to _show_ me that they do, and I will gladly accept to have been wrong. But this argument that because of some very theoretical view on the problem the current engineering solutions should be abandoned, without actually providing good engineering alternatives, is weird. I have a bit of a "not even wrong" feeling on the symbolist side of the debate. Deep neural networks have serious flaws, but they can at least be pointed out due to them already solving engineering problems. It is easy to say a field should move to your direction, but they will probably only do it when provided with tangible evidence. AI has moved into the realm of application, and that means the scientific bar for purely theoretical ideas has been raised compared to 20 years ago. And right now, the tangible evidence for symbolism is simply not cutting it.

scotty79 8y ago
I think they need symbols but they just need to be taught symbols. If you kept child stationary and show it just static photos one at a time you wouldn't be surprised that it can't tell bus lying on the side in the snow from a snow plow. What neural networks need is the same thing little children need, motion, rotation, things stacking hitting and occluding each other ... and possibly even interaction. We haven't taught NN to see properly yet and guy wants to hardwire math into them because he thinks that's the only option. Humans learn symbols late and only with deliberate effort.
- Asooka 8y ago
  On the other hand humans have built-in special processing stages already. One that comes to mind is that the neurons in your eyes in effect run a sharpening filter on the incoming light before the data ever enters your brain. I don't think it's a stretch to propose that we should implement certain features as special-purpose processing stages that the neural network can use, with the goal of having a more efficient system. The holy grail would be a neural network that has programming capabilities and can write bare metal programs for itself.
  - scotty79 8y ago
    > On the other hand humans have built-in special processing stages already. Yes. But there were cases where human could perform function elsewhere that was usually done in special site when thst site got damaged.
stult 8y ago
Or to frame your argument in terms of science rather than engineering, it's a falsifiable hypothesis. The author should go try to test it and then let us know if it works.
- bsaul 8y ago
  i’d say the bus picture classification is a pretty telling example that something’s missing in the amount of knowledge deep learning is able to extract from training data sets. Anybody who understand what a school bus is wouldn’t mistake it for a snow plow in the other pictures.
vanderZwan 8y ago
> And right now, the tangible evidence for symbolism is simply not cutting it. In AI/ML perhaps. In the biological and psychological sciences the importance of symbols to cognitive functioning is pretty well established. Just look at how important the mastery of language is for complicated thought. One of the main differences between linguistic thought and other forms of thinking is all about symbol manipulation. (This says nothing about the quality of this article of course)
- FakeComments 8y ago
  It’s also pretty well known in math and CS/SE communities. Heck, the mathematical research into the power of language, and in particular the ability to embed models of reasoning into equations to allow you to formalize your meta-reasoning, is what led to the birth of electric computers. So I find it shallow skepticism to suggest there’s not evidence that it would be useful to AI, because literally the most cursory glance would show mounds of it. The real challenge is and always has been how to combine or embed symbolic reasoning capabilities efficiently within a fuzzier approximator, allowing for “getting close” in a fuzzy way then fine tuning it using more advanced rules. That mimics more closely how we think, where we broadly intuit a few potential answers, then use higher order thinking to sift through them.
  - vanderZwan 8y ago
    > The real challenge is and always has been how to combine or embed symbolic reasoning capabilities efficiently within a fuzzier approximator, allowing for “getting close” in a fuzzy way then fine tuning it using more advanced rules. Well, maybe the problem is that we spent so much time basing models on the visual part of the brain that we overlooked the rest: https://www.quantamagazine.org/new-ai-strategy-mimics-how-brains-learn-to-smell-20180918/ https://www.quantamagazine.org/new-ai-strategy-mimics-how-br...
chimprich 8y ago
> But this argument that because of some very theoretical view on the problem the current engineering solutions should be abandoned That's not the author's argument at all. Even the subtitle of the article says "We’ll need both deep learning and symbol manipulation to build AI.".
- posterboy 8y ago
  A compromise between the two would be to identify powerful abstructions over nodes and nets that are equivalent to symbol manipulation circuits. Self assembly and self modification are certainly not strange to symbolic logic, but this doesn't work so well for most programmers. So we need more geniuses and they need to tailor neural nets to help them build symbolic logic (if only as intermediate debugging output).
drb91 8y ago
Getting tasks done has never been a ringing endorsement for the general applicability of the underlying algorithm. Meanwhile, the entirety of human communication is built on symbols. If computers can’t deal with them, how useful can computers ultimately be? Roombas can only improve so much.
giardini 8y ago
If you're willing to accept a (possible) inexplicable intelligence that cannot explain its decisions or reasoning processes then you'd be fine w/o symbolism. In fact there may be an example for you (perhaps in your pocket): The next time you have a decision to make, phrase it as a Heads/Tails question and flip a coin. Over its time of use, the coin will be right until it is wrong, putting you in exactly the same situation as you would be with an inexplicable intelligence. So you see, intelligence is not that far away! But explicable predictive intelligence requires reasoned prediction and can answer "Why that prediction?" when asked and can learn if a prediction fails. The coin cannot but will be adequate, at least until it fails sufficiently to induce you to abandon it.

mrcoder111 8y ago

A symbol is just a frozen weight in a neural network.

Esai 8y ago

AI development has hit a plateau in terms of performance and funding saturation. All investment is now expected to deliver some semblance of market performance before the great Margin Call of 2019 hits, which means scaling out AI to absurd domains and deploying vast armies of surplus humans to either code or emulate profitable-enough behavior to pass the risk on to the next investor round. In the end, the Mechanical Turk has gone worldwide with no brakes.

buboard 8y ago
What is the evidence for a plateau? The corporate temperaments are irrelevant to the actual progress of the field. If anything , ai systems have not even started being deployed , and the field is even missing a proper theoretical framework. this should indicate that it's still very early stage
- Esai 8y ago
  There's a long history of cycles in this field. https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter The plateau comes from the problems with vanishing gradients. https://en.wikipedia.org/wiki/Vanishing_gradient_problem https://en.wikipedia.org/wiki/Vanishing_gradient_problem

ajuc 8y ago

This reads like 19-th century engineer arguing we need articulated wings for heavier-than-air flight because our current methods doesn't work and birds have them :)

opless 8y ago

He's right about LeCun. Yann really does not like Deep Learning being criticized. Please someone who knows much more than me about deep learning tell me how a deep learning ai can explain how it came to an answer. It can't. By definition. You can say that it's a equation with a lot of terms, but that isn't really showing your working out. You need a symbolic system to show that working out. This isn't to say that deep learning is worthless, just not worth the amount of column inches it's getting at the moment.

ben_w 8y ago
> Please someone who knows much more than me about deep learning tell me how a deep learning ai can explain how it came to an answer. > It can't. By definition. What definition are you using? From the my understanding, if you can explain your reasoning, then a deep learning system can in principle learn how to explain its reasoning. If it couldn’t, you couldn’t either.
- opless 8y ago
  We're not deep learning systems. So reasoning by analogy there isn't correct. (Aside: There have been experiments that show that many explanation for [everyday] actions are, in fact made up after the decision to execute an action. Which gives some insight to the whole nature of free will - but that's another discussion entirely. However, indeed we're able to show our workings for higher level workings, like mathematical proofs. ) Deep learning systems, are examples of supervised learning. Once trained, they cannot adapt to new input. You have to retrain to accommodate new features that you want to capture. Deep learning systems, are in essence a very long equation that hides all meaning over the entire network - good luck in finding out why it came to a decision. You might get lucky and see a definitive triggering path through the network, but that's an exception to the rule, as far as I understand. (I'm willing to be proven wrong here.) Symbolic systems, can trivially be made to trace the code execution paths they made for any action they might take. (also - hi Ben! I recognise your blog!)
  - FakeComments 8y ago

laichzeit0 8y ago

I really don't get Gary Marcus' fundamental problem. He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? Surely no one is being held academically ransom to Deep Learning. If Ps (and Qs) is a better approach then do the research and publish the results. Instead he just seems to whine about it on Twitter all day and attack people who are actually "doing the research and publishing the results."

deleted 8y ago
[deleted]
Eridrus 8y ago
Gary isn't a technologist, but he wants to set the stage with critique so that when/if a fusion of the ideas does better he can claim to have been important to that, regardless of the fact that the idea is obvious, and the implementation is what is hard
- deleted 8y ago
  [deleted]
YeGoblynQueenne 8y ago
>> He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? It's a slightly garbled (and so not immediately recognisable, perhaps) pun on the verse "All that we're saying/ is give peace a chance" from John Lennon's "Give Peace a Chance". So it's not addressed to anyone- it's just his attempt at injecting a bit of levity in the debate. EDIT: >> If Ps (and Qs) is a better approach then do the research and publish the results. Well, people have done that, yes. For example, the Evans and Grefenstette paper Marcus' article cites towards the end has shown in a very clear manner the power of combining symbolic with sub-symbolic approaches, as has the work of, off the top of my head, Artur D'avila Garcez (neuro-symbolic computation), Luciano Serafini (a differentiable logic), and many others. And yet, my intuition at least is that most people who have heard about Deep Learning, haven't heard about that work.
- 8y ago

dana321 8y ago

Not this again.

polkapolka 8y ago

Symbolic AI is machine programming. Connectionist AI is machine learning. Machine programming simply does not scale. It is also not biologically plausible: it is not as if God put symbols into our brain, these formed, are prefaced by, and were learned from neural activity. Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results. Deep learning is the most promising direction AI went into since a long time. Finding flaws in DL does not point back to a programmer crafting handwritten rules to correct it. It merely points to more DL research needed to have machines correct this themselves. Preferrably differentiable.

hopler 8y ago
Why do you think human-style learning can scale? Human brains are terrible at basic arithmetic like raising 3 to the 6th power.
- polkapolka 8y ago
  Statistical learning with connectionist architectures is driving current AI at scale. To me, this paradigm is also the most promising: learn from data bottom-up, not from experts top-down.
giardini 8y ago
polkapolka says>"Machine programming simply does not scale....Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results." 1. We can scale "machine programming" by making computers faster and more complex, 2. The spam problem can be solved with Bayesian methods (which I consider to be part of "machine programming"): a connectionist solution is not necessary. Certainly connectionist deep learning is exciting and should be investigated as far as possible but it is only one of many tools. We should avoid being the man with a hammer who thinks that every problem looks like a nail.
- polkapolka 8y ago
  Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere. Bayesian models underperform to DL by a wide margin (though it is a step up from handwritten rules: if DEAR FRIEND then Spam score++.

afpx 8y ago

This is something that’s been a challenge to my team for a long time. For us, machine learning has produced very good inferences, but it doesn’t create models of how the universe works. For example, if we input a lot of raw weather data into a statistical model, we can predict how it may affect the power output of a solar array. But, I can’t get the machine to ‘understand’ that clouds decrease solar availability.

cs702 8y ago

Actually, deep learning models do use symbols. We call these symbols "embeddings" or "representations" in a vector or tensor space... but these symbols may not have any known or readily interpretable human meaning. People who are uneducated in basic vector (and tensor) math may have difficulty understanding how this is possible, but it's a fact. For example, the ELMo embedding of a particular word in a sentence is a symbol that represents the complex syntax, semantics, and polysemy of that word given its location and use in the text.[a] Similarly, a trained WaveNet model represents a sequence of text as a (fairly large) tensor embedding that encodes the information necessary for generating the corresponding speech; this embedding is a symbol too.[b] As a final example, the next-to-last layer's embedding of an image processed through a trained Detectron model contains representations of every identified object in the image; those representations are symbols too.[c] Moreover, the kind of automatic differentiation that is necessary for training deep learning models with backprop reduces into manipulation of symbols in a tree, viewed through the right lens. Witness, for example, the recent work done by Mike Innes et al for the Julia language.[d] We could very well be in the early stages of adoption of "deep differentiable programming" for a growing number of cognitive tasks that might not be solvable via human manipulation of symbols. The challenge for deep learning going forward, as Bengio says and Marcus quotes, is in figuring out how to "extend it to do things like reasoning, learning, causality, and exploring the world."[e] As far as I can tell, everyone in the field already agrees on these goals; many researchers are thinking about and wondering how it might be possible to pursue them. Does Marcus understand this? His complaining about the "lack of symbols" is... misguided, I think. [a] https://allennlp.org/elmo https://allennlp.org/elmo [b] https://deepmind.com/blog/wavenet-generative-model-raw-audio/ https://deepmind.com/blog/wavenet-generative-model-raw-audio... [c] https://research.fb.com/downloads/detectron/ https://research.fb.com/downloads/detectron/ [d] https://julialang.org/blog/2018/12/ml-language-compiler https://julialang.org/blog/2018/12/ml-language-compiler [e] https://twitter.com/GaryMarcus/status/1065280340669816832 https://twitter.com/GaryMarcus/status/1065280340669816832

mindgam3 8y ago

Previous discussion: https://news.ycombinator.com/item?id=18639359 https://news.ycombinator.com/item?id=18639359

d--b 8y ago

My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. When I think "the house is red", I know what it means very well, but I'm also able to doubt or modulate my understanding of the symbols. These conversations would be hard to put in symbols: - This house is red - No! It's crimson! - But crimson is red! Or - The house is red - No! it's green! - Nah, it's red, you're colorblind Brain logic is MUCH fuzzier than symbolic logic. Symbols exist, sure, but they're part of a bigger logical soup. And I believe that no low level logical circuit exists in the brain (which also explains why humans are so slow at logic, calculation and so on). Maybe symbolic logic could be a intermediate step for some applications. Or maybe humans could be much smarter if they had access to some symbolic logic processing unit. But as far as research is involved, I think there is way more to gain if we managed to have symbolic logic emerge from deep neural nets. In fact, you could argue that Alpha Go definitely developed some kind of symbolic logic, especially in the end-game, where there's little intuition and way more calculation.

YeGoblynQueenne 8y ago
>> My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. Who said anything about hardcoding anything? Marcus is advocating for the use of gradient descent to learn symbols- he even cites the DeepMind paper on θILP, a differentiable Inductive Logic Programming system that learns symbolic rules with deep learning.
- d--b 8y ago
  From the article > Their solution? A hybrid model that vastly outperformed what a purely deep net would have done, incorporating both back-propagation and (continuous versions) of the primitives of symbol manipulation, including both explicit variables and operations over variables This is what I called "hardcoding some kind of symbolic logic". Is it not?
  - deleted 8y ago

mindgam3 8y ago

Robot brains need symbols because the rhetoric around deep learning is getting out control. In the video analysis below of the latest AlphaZero vs Stockfish paper, AlphaZero is described as "DeepMind's general-purpose artificial intelligence system". Let's give credit where it's due. AlphaZero is an impressive algorithm with results in 3 different types of perfect-information games: chess, go, shogi. But to describe it as "general purpose AI" is simply absurd. Yes, this isn't an official Deep Mind video, but this is the kind of rhetoric they are putting it out there. I'm open to the possibility that deep learning might one day solve some of its core problems (like the elementary schoolbus-snowplow errors described in the source article) and turn into a general purpose AI. But we aren't there yet, and we're not even close. https://www.youtube.com/watch?time_continue=13&v=2-wFUdvKTVQ https://www.youtube.com/watch?time_continue=13&v=2-wFUdvKTVQ

soVeryTired 8y ago
I think there's some ambiguity there regarding what is general-purpose: if you want to be charitable, you might interpret the description as saying it's a general-purpose system rather than a general-purpose AI. Who knows what they really meant though...
- mindgam3 8y ago
  My point is, calling a system that learned how to play 3 types of board games "general purpose" in any sense is a bit of a stretch. At best this is a "general purpose games-playing" system.