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I like Gary Marcus as a personality and I look out for his work. Recently he's been doing this thing where he lists examples of deep learning failures (which ar

I like Gary Marcus as a personality and I look out for his work. Recently he's been doing this thing where he lists examples of deep learning failures (which are trivially easy to find) and then proposing symbolic / causal learning as an alternative. This can be somewhat repetitive, but it can also illustrate some important ideas about and limitations of the current paradigm. I think that the best way to communicate the message that deep learning isn't enough is to use a different approach to achieve superior results. Ideally something that makes a lot of money quickly. There are only so many people that read nautil.us articles. But everyone pays attention to things that make a lot of money. I can point out many conceptual flaws in the internal combustion engine. And I can also propose other types of engines as alternatives. But people don't really start paying attention until someone makes a Tesla. If symbol manipulation can do a better job of identifying pictures of rabbits or humans holding stop signs, then let's see it.

9wzYQbTYsAIc 5y ago
If deep learning isn’t enough, then brains aren’t enough, right? Edit: No offense to Gary Marcus, first heard of him after reading this article.
- ravi-delia 5y ago
  Brains are a) far far more recurrent than any practical deep learning system b) using something other than backprop, and probably that something is much more efficient
  - 9wzYQbTYsAIc 5y ago
    Deep learning is like fish, there are a lot of types of fish. Some will eat you and some you eat. My understanding of deep learning is that it is a methodology, not a technique.
    - ravi-delia 5y ago
      Deep learning is all the very many models that use layers of neural networks. You can tweak it a lot, but there comes a point where you're not working with neural networks anymore. The human brain has reached that point.
      - 9wzYQbTYsAIc 5y ago
        > there comes a point where you're not working with neural networks anymore. The human brain has reached that point No offense, just startled, but that’s the most self-contradictory thing I’ve ever heard. The human brain is by definition a neural network…
        ravi-delia 5y ago
        "Neural Network", tragically, is a term that refers to a specific kind of model inspired by a sort of 70s understanding of human neurons. Deep learning refers to those models. The human brain is made of neurons, and by God do they network, but the term has been co-opted so it is not a neural network. A particular region or set of neurons that do one thing might be called a neural circuit, and the whole thing can be called the connectome (though that's sort of debatable).
        9wzYQbTYsAIc 5y ago
        Maybe it’s time for “academic justice” to reclaim the term.
        ravi-delia 5y ago
        I'd love nothing more, believe me. But even if neural networks were taken to unambiguously refer to the neurons of animals, deep learning would still refer to the computational model, whatever its new name would be.
        9wzYQbTYsAIc 5y ago
- tasty_freeze 5y ago
  Nope. I don't think anyone thinks the brain is an N layer deep network. There are many networks, and they cross-couple inputs and outputs from various subnetworks into others. There are also hormonal regulatory systems with feedback mechanisms that interact with these networks both is inputs and outputs. The brain is hugely more complicated and messy than anything that, say, GPT-3 is doing.
  - 9wzYQbTYsAIc 5y ago
    So Parallel Distributed Processing, it is? Apparently deep learning has been beat-to-death as a word, but the way deep learning was explained to me, it is an approach not a specification.
    - kortilla 5y ago
      Yeah, if you dilute the meaning down to “feedback loop”, then deep learning and the brain work the same way.
      - 9wzYQbTYsAIc 5y ago
        I’m not sure that it is correct to limit deep learning to just a feedback loop, but cybernetics (if the thermostat variety, not the terminator variety) might be the right word.
  - hajile 5y ago
    I'd also add that the subconscious parts of the brain seem to function quite a bit differently from the conscious parts. It's hard to reason about something like "why does my eye see how it does" when those parts aren't consciously accessible.
- visarga 5y ago
  > If deep learning isn’t enough, then brains aren’t enough, right? No, not right. Humans need other humans to be smart, we need culture, technology and science. We're not that smart - just 700 years ago we were dying of the plague without even knowing the germ theory of disease, even with our lives on the line we couldn't solve it. We're only INCREMENTALLY smart over our current cultural level.
  - 9wzYQbTYsAIc 5y ago
    How do you explain non-human intelligence and tool usage, where there is no known culture?
    - visarga 5y ago
      They can still individually learn tool usage by imitation, random exploration and reinforcement. But without culture they can't build on it.
    - krageon 5y ago
      Human supremacist world-view. Sadly incredibly common, once you drill down.
      - visarga 5y ago
        It's culture-supremacist actually. Information needs to copy itself into the future, that's the essence of both life (genes) and culture (memes).
        9wzYQbTYsAIc 5y ago
        Culture supremacy is still just bio-supremacy. Memes don’t exist except through genes. Arguing that memes exist outside of genes is like arguing that the Platonic solids are real.
    - maze-le 5y ago
      Actually, tool usage in primates is a form of culture (protoculture). Those groups that have mastered it can be better of than those that didn't -- giving them an evolutionary advantage. The "tools" can be suprisingly complex, see for example: https://web.archive.org/web/20110608070200/http://www.uic.edu/classes/osci/osci590/8_1%20Chimpanzee%20Medicine%20Chest.htm https://web.archive.org/web/20110608070200/http://www.uic.ed...
ta_tunestub 5y ago
Amen!
wanderingmind 5y ago
Likewise, it is easy to criticize coal, natural gas and nuclear for their shortcomings. But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. Talk is easy, action is hard
- xiphias2 5y ago
  The problem with coal is that there’s an invisible CO2 quota that if we as a species overstep it will kill more people than help. If it was me, I would try to get rid all taxes, and tax imports and local goods based on average world CO2 output compared to the country that I’m in. As people don’t generally think long term, this policy is unpopular, so it won’t get implemented.
  - catlikesshrimp 5y ago
    <<But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. Talk is easy, action is hard>>
    - krageon 5y ago
      The fact of the matter is that it's not hard to do at all, it's just expensive, unpopular or only practical with sustained public support. Because they are human problems, talking about technical solutions is entirely the wrong approach.
- asdfasgasdgasdg 5y ago
  > But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. We are actually doing that, though. Across the globe, the fraction of energy generated by non-renewables has been falling consistently for more than a decade. We went from being less than 5% solar + wind a decade ago to more than 10% now, and the trend is accelerating. By 2026 we're expected to be nearing 20% of all power generation renewable. Things aren't going fast, but infrastructure doesn't go fast, and no proponent of renewable energy expected it to. I think this is in no way comparable to the symbolic learning thing where there is no sign of relative progress.
mardifoufs 5y ago
>deep learning isn't enough is to use a different approach to achieve superior results. Proponents of symbolic learning should start by just achieving results that are even remotely close go those achieved with deep learning. Nevermind outperforming it. Because the reality is, for NLP and anything related to CV, deep learning has consistently wildly outperformed every other approach. Thousands of ai researchers have tried to make "old school" AI work for those problems with only very limited success. Now my background is in CV (not in the field anymore, but was until 2019) so I'm not sure about how well other methods stack up against DL for other use cases. But to me symbolic approaches for most of what DL excels at just seem like a completely unfeasible (and overcomplicated) pipedream. I don't disagree with the premise that current research has kind of stalled compared to the early-mid 2010s but that mostly means we need to figure out new ways to do ML and DL, not because DL has failed as a concept . & keep in mind that the fact we can even say that things have "stalled" is because we got spoiled by the huge performance leaps that DL made possible in the first place. The symbolic AI crowd have always had a "2 more weeks" narrative where they promise that they will figure out a given problem very soon if only x condition was true. The problem is that they have almost always terribly underdelivered, and that was true even when 99% of the research and funding was focused on symbolic or hard AI. Subsymbolic approaches could also be argued to have yielded somewhat underwhelming results (vs the hype) but the other methods are in a league of their own.
- pishpash 5y ago
  It's mainly the discomfort of the people who were left behind by DL speaking, not much else to see. We should absolutely let the data speak for itself.
  - mardifoufs 5y ago
    Yeah you are right, & it's a bit just dogmatic at this point. I guess there's also a clash between purists that have an idea on how an ideal AI should be vs the others who are a lot more results focused. Nobody that I know in the DL community wouldn't be excited to switch to another method if it meant achieving actual measurable improvements. So if the data was conclusive, the alternatives would be used. No one cares about the purity of the method or whatever, because the messiness is inherent to DL. Also, the current AI community is very very focused on performance and state of the art results (that can be a problem in some cases but not w.r.t to method agnosticism) rather than on any big theories on how human intelligence ought to be translated mathematically (or not)
canjobear 5y ago
> I like Gary Marcus as a personality and I look out for his work. That's funny, my interest in reading this article went to zero the moment I saw he wrote it.
fmvab 5y ago
I think this is unfair. Tens (hundreds?) of billions of dollars have gone into deep learning and, as I understand it, most effort consists of scaling up this golden goose and presenting its "new" successes in the best light possible. Every time throwing scale at the problem produces an impressive result, billions more dollars flow in, thousands of more people are nudged into the field. We got the first orders of magnitude of scaling "free" from GPUs, but the rest are going to come at considerable cost. Whether or not there is a better alternative, if deep learning is in fact as over-hyped as the author claims, this could be a tremendous waste of money and intellect that could be spent on literally anything else, not just machine learning (maybe they could put those resources into crypto instead /s). That alone is enough reason to want intellectually honest skeptical takes, whether or not the author has a better idea. In addition, within AI, it makes it much harder for people for people to do anything else. If there is a contraction in the field it will likely cause another giant AI winter. Somebody should start thinking of a use for all that compute. The internal combustion engine did not require as much of a drain on resources before it produced results. Is deep learning actually making significant amounts of money, funding and valuations excluded?
- sdenton4 5y ago
  What you're missing is that the gigantic initial success models have consistently gotten much much lighter after the hype cycle moves on from the initial release. In my own domain, WaveNet - speech synthesis vastly outperforming previous methods - went from fantastically expensive to being able to run on cheap phones. You can run bird song id on your phone in real time in the Merlin app. There's a proliferation of light neutral networks for lots of specific use cases, and it's already happening all around you.
  - fmvab 5y ago
    Thank you — that is definitely encouraging. It doesn’t sound like a killer app yet but it certainly seems to establish deep learning’s place at least as part of a modern software stack.
skywal_l 5y ago
One example of this from a while ago: https://www.newyorker.com/tech/annals-of-technology/the-pastry-ai-that-learned-to-fight-cancer https://www.newyorker.com/tech/annals-of-technology/the-past... TLDR: A guy in Japan who worked on a solution to identify different types of pastries ended up creating a computer vision framework used in many domains. All this without deep learning. The article delves into the challenge that deep-learning brought to his business.
- Asooka 5y ago
  I really wish they would open source some of the system or at least write papers outlining the algorithms used. Also I wonder if we can have a neural net that generates these classical approaches given a class of objects to identify. Or maybe once you have trained a neural net to work on recognising basic features, you could transform (compile?) it to an algorithm that can be debugged and expanded.
TomSwirly 5y ago
This argument, "You can't point out any limitations unless you yourself can do better", is not logical. --- The issue is that in order to be intelligent for any useful meaning of the word, a program has to at least appear to do symbolic manipulation and to change its state as a result. If I tell some intelligent program, "I was born in London", I don't really care if it has actual symbols for me and for London, but I do expect to somehow "remember" this and "reason" about it. Later if someone asks the program, "Was Tom born in England?", I would expect it to answer, "Yes", and if asked "Why?", answer, "Tom said he was born in London and London is in England" - like a bright five-year-old would answer. --- Current AI programs do nothing like this, and there doesn't seem to be a path to this through machine learning and other systems for extracting statistical data from large corpuses. The idea that symbols will simply "emerge" from huge, static statistical engines seems like wishing for magic, not a research program. As a result, some simple tasks are impossibly difficult for machine learning systems. For example, support that I have come to the mistaken conclusion over many years that Arnold Schwarzenegger is German, because of the "corpus" of information I have seen. But if I meet him, and he mentions that he's Austrian, I will right away update my database without question, even though I met have read 100 things that made me believe he was German. This is impossible with any of the statistical systems we have. The only way to update them is to re-run them with more information. And it's the quantity of information that counts. The idea that Arnold's word on his own life might be much more important that 100 other sources cannot really be represented in general. This is an issue that needs to be resolved before we get to something we can call actually intelligent - it needs to be at least as smart as a 5-year-old person in being easily able to learn or correct facts one at a time, like "Arnold is Austrian".

9wzYQbTYsAIc 5y ago

If deep learning isn’t enough, then brains aren’t enough, right? Edit: No offense to Gary Marcus, first heard of him after reading this article.

ravi-delia 5y ago
Brains are a) far far more recurrent than any practical deep learning system b) using something other than backprop, and probably that something is much more efficient
- 9wzYQbTYsAIc 5y ago
  Deep learning is like fish, there are a lot of types of fish. Some will eat you and some you eat. My understanding of deep learning is that it is a methodology, not a technique.
  - ravi-delia 5y ago
    Deep learning is all the very many models that use layers of neural networks. You can tweak it a lot, but there comes a point where you're not working with neural networks anymore. The human brain has reached that point.
    - 9wzYQbTYsAIc 5y ago
      > there comes a point where you're not working with neural networks anymore. The human brain has reached that point No offense, just startled, but that’s the most self-contradictory thing I’ve ever heard. The human brain is by definition a neural network…
      - ravi-delia 5y ago
        "Neural Network", tragically, is a term that refers to a specific kind of model inspired by a sort of 70s understanding of human neurons. Deep learning refers to those models. The human brain is made of neurons, and by God do they network, but the term has been co-opted so it is not a neural network. A particular region or set of neurons that do one thing might be called a neural circuit, and the whole thing can be called the connectome (though that's sort of debatable).
        9wzYQbTYsAIc 5y ago
        Maybe it’s time for “academic justice” to reclaim the term.
        ravi-delia 5y ago
        I'd love nothing more, believe me. But even if neural networks were taken to unambiguously refer to the neurons of animals, deep learning would still refer to the computational model, whatever its new name would be.
        9wzYQbTYsAIc 5y ago
tasty_freeze 5y ago
Nope. I don't think anyone thinks the brain is an N layer deep network. There are many networks, and they cross-couple inputs and outputs from various subnetworks into others. There are also hormonal regulatory systems with feedback mechanisms that interact with these networks both is inputs and outputs. The brain is hugely more complicated and messy than anything that, say, GPT-3 is doing.
- 9wzYQbTYsAIc 5y ago
  So Parallel Distributed Processing, it is? Apparently deep learning has been beat-to-death as a word, but the way deep learning was explained to me, it is an approach not a specification.
  - kortilla 5y ago
    Yeah, if you dilute the meaning down to “feedback loop”, then deep learning and the brain work the same way.
    - 9wzYQbTYsAIc 5y ago
      I’m not sure that it is correct to limit deep learning to just a feedback loop, but cybernetics (if the thermostat variety, not the terminator variety) might be the right word.
- hajile 5y ago
  I'd also add that the subconscious parts of the brain seem to function quite a bit differently from the conscious parts. It's hard to reason about something like "why does my eye see how it does" when those parts aren't consciously accessible.
visarga 5y ago
> If deep learning isn’t enough, then brains aren’t enough, right? No, not right. Humans need other humans to be smart, we need culture, technology and science. We're not that smart - just 700 years ago we were dying of the plague without even knowing the germ theory of disease, even with our lives on the line we couldn't solve it. We're only INCREMENTALLY smart over our current cultural level.
- 9wzYQbTYsAIc 5y ago
  How do you explain non-human intelligence and tool usage, where there is no known culture?
  - visarga 5y ago
    They can still individually learn tool usage by imitation, random exploration and reinforcement. But without culture they can't build on it.
  - krageon 5y ago
    Human supremacist world-view. Sadly incredibly common, once you drill down.
    - visarga 5y ago
      It's culture-supremacist actually. Information needs to copy itself into the future, that's the essence of both life (genes) and culture (memes).
      - 9wzYQbTYsAIc 5y ago
        Culture supremacy is still just bio-supremacy. Memes don’t exist except through genes. Arguing that memes exist outside of genes is like arguing that the Platonic solids are real.
  - maze-le 5y ago
    Actually, tool usage in primates is a form of culture (protoculture). Those groups that have mastered it can be better of than those that didn't -- giving them an evolutionary advantage. The "tools" can be suprisingly complex, see for example: https://web.archive.org/web/20110608070200/http://www.uic.edu/classes/osci/osci590/8_1%20Chimpanzee%20Medicine%20Chest.htm https://web.archive.org/web/20110608070200/http://www.uic.ed...

ta_tunestub 5y ago

Amen!

wanderingmind 5y ago

Likewise, it is easy to criticize coal, natural gas and nuclear for their shortcomings. But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. Talk is easy, action is hard

xiphias2 5y ago
The problem with coal is that there’s an invisible CO2 quota that if we as a species overstep it will kill more people than help. If it was me, I would try to get rid all taxes, and tax imports and local goods based on average world CO2 output compared to the country that I’m in. As people don’t generally think long term, this policy is unpopular, so it won’t get implemented.
- catlikesshrimp 5y ago
  <<But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. Talk is easy, action is hard>>
  - krageon 5y ago
    The fact of the matter is that it's not hard to do at all, it's just expensive, unpopular or only practical with sustained public support. Because they are human problems, talking about technical solutions is entirely the wrong approach.
asdfasgasdgasdg 5y ago
> But, its hard to create an alternative electricity generation/storage that can support constant base load and can ramp up in minutes to deal with additional load. We are actually doing that, though. Across the globe, the fraction of energy generated by non-renewables has been falling consistently for more than a decade. We went from being less than 5% solar + wind a decade ago to more than 10% now, and the trend is accelerating. By 2026 we're expected to be nearing 20% of all power generation renewable. Things aren't going fast, but infrastructure doesn't go fast, and no proponent of renewable energy expected it to. I think this is in no way comparable to the symbolic learning thing where there is no sign of relative progress.

mardifoufs 5y ago

>deep learning isn't enough is to use a different approach to achieve superior results. Proponents of symbolic learning should start by just achieving results that are even remotely close go those achieved with deep learning. Nevermind outperforming it. Because the reality is, for NLP and anything related to CV, deep learning has consistently wildly outperformed every other approach. Thousands of ai researchers have tried to make "old school" AI work for those problems with only very limited success. Now my background is in CV (not in the field anymore, but was until 2019) so I'm not sure about how well other methods stack up against DL for other use cases. But to me symbolic approaches for most of what DL excels at just seem like a completely unfeasible (and overcomplicated) pipedream. I don't disagree with the premise that current research has kind of stalled compared to the early-mid 2010s but that mostly means we need to figure out new ways to do ML and DL, not because DL has failed as a concept . & keep in mind that the fact we can even say that things have "stalled" is because we got spoiled by the huge performance leaps that DL made possible in the first place. The symbolic AI crowd have always had a "2 more weeks" narrative where they promise that they will figure out a given problem very soon if only x condition was true. The problem is that they have almost always terribly underdelivered, and that was true even when 99% of the research and funding was focused on symbolic or hard AI. Subsymbolic approaches could also be argued to have yielded somewhat underwhelming results (vs the hype) but the other methods are in a league of their own.

pishpash 5y ago
It's mainly the discomfort of the people who were left behind by DL speaking, not much else to see. We should absolutely let the data speak for itself.
- mardifoufs 5y ago
  Yeah you are right, & it's a bit just dogmatic at this point. I guess there's also a clash between purists that have an idea on how an ideal AI should be vs the others who are a lot more results focused. Nobody that I know in the DL community wouldn't be excited to switch to another method if it meant achieving actual measurable improvements. So if the data was conclusive, the alternatives would be used. No one cares about the purity of the method or whatever, because the messiness is inherent to DL. Also, the current AI community is very very focused on performance and state of the art results (that can be a problem in some cases but not w.r.t to method agnosticism) rather than on any big theories on how human intelligence ought to be translated mathematically (or not)

canjobear 5y ago

> I like Gary Marcus as a personality and I look out for his work. That's funny, my interest in reading this article went to zero the moment I saw he wrote it.

fmvab 5y ago

I think this is unfair. Tens (hundreds?) of billions of dollars have gone into deep learning and, as I understand it, most effort consists of scaling up this golden goose and presenting its "new" successes in the best light possible. Every time throwing scale at the problem produces an impressive result, billions more dollars flow in, thousands of more people are nudged into the field. We got the first orders of magnitude of scaling "free" from GPUs, but the rest are going to come at considerable cost. Whether or not there is a better alternative, if deep learning is in fact as over-hyped as the author claims, this could be a tremendous waste of money and intellect that could be spent on literally anything else, not just machine learning (maybe they could put those resources into crypto instead /s). That alone is enough reason to want intellectually honest skeptical takes, whether or not the author has a better idea. In addition, within AI, it makes it much harder for people for people to do anything else. If there is a contraction in the field it will likely cause another giant AI winter. Somebody should start thinking of a use for all that compute. The internal combustion engine did not require as much of a drain on resources before it produced results. Is deep learning actually making significant amounts of money, funding and valuations excluded?

sdenton4 5y ago
What you're missing is that the gigantic initial success models have consistently gotten much much lighter after the hype cycle moves on from the initial release. In my own domain, WaveNet - speech synthesis vastly outperforming previous methods - went from fantastically expensive to being able to run on cheap phones. You can run bird song id on your phone in real time in the Merlin app. There's a proliferation of light neutral networks for lots of specific use cases, and it's already happening all around you.
- fmvab 5y ago
  Thank you — that is definitely encouraging. It doesn’t sound like a killer app yet but it certainly seems to establish deep learning’s place at least as part of a modern software stack.

skywal_l 5y ago

One example of this from a while ago: https://www.newyorker.com/tech/annals-of-technology/the-pastry-ai-that-learned-to-fight-cancer https://www.newyorker.com/tech/annals-of-technology/the-past... TLDR: A guy in Japan who worked on a solution to identify different types of pastries ended up creating a computer vision framework used in many domains. All this without deep learning. The article delves into the challenge that deep-learning brought to his business.

Asooka 5y ago
I really wish they would open source some of the system or at least write papers outlining the algorithms used. Also I wonder if we can have a neural net that generates these classical approaches given a class of objects to identify. Or maybe once you have trained a neural net to work on recognising basic features, you could transform (compile?) it to an algorithm that can be debugged and expanded.

TomSwirly 5y ago

This argument, "You can't point out any limitations unless you yourself can do better", is not logical. --- The issue is that in order to be intelligent for any useful meaning of the word, a program has to at least appear to do symbolic manipulation and to change its state as a result. If I tell some intelligent program, "I was born in London", I don't really care if it has actual symbols for me and for London, but I do expect to somehow "remember" this and "reason" about it. Later if someone asks the program, "Was Tom born in England?", I would expect it to answer, "Yes", and if asked "Why?", answer, "Tom said he was born in London and London is in England" - like a bright five-year-old would answer. --- Current AI programs do nothing like this, and there doesn't seem to be a path to this through machine learning and other systems for extracting statistical data from large corpuses. The idea that symbols will simply "emerge" from huge, static statistical engines seems like wishing for magic, not a research program. As a result, some simple tasks are impossibly difficult for machine learning systems. For example, support that I have come to the mistaken conclusion over many years that Arnold Schwarzenegger is German, because of the "corpus" of information I have seen. But if I meet him, and he mentions that he's Austrian, I will right away update my database without question, even though I met have read 100 things that made me believe he was German. This is impossible with any of the statistical systems we have. The only way to update them is to re-run them with more information. And it's the quantity of information that counts. The idea that Arnold's word on his own life might be much more important that 100 other sources cannot really be represented in general. This is an issue that needs to be resolved before we get to something we can call actually intelligent - it needs to be at least as smart as a 5-year-old person in being easily able to learn or correct facts one at a time, like "Arnold is Austrian".