11 ms·

Deep learning can’t be trusted, brain modelling pioneer says

113 points by emrah 5y ago

amelius 5y ago
He formulated his theory in the 70s, and it didn't catch on. How likely is it that he is right about this?
- Den-vr 5y ago
  Many excellent things were formulated in the 70s and didn't catch on because supporting technologies weren't practical yet. It was an excellent time to be a fresh PhD
  - jjtheblunt 5y ago
    it was also an excellent time to be a really little kid, with so much to marvel at (which is really to your point!)
- UncleOxidant 5y ago
  The roots of deep learning are from around the same era, if not a bit earlier - it's just that we didn't have the computing power to do much with it so DL kind of languished until ~15 years ago with the advent of capable GPUs. Is ART important? Can it do what DL does now? Hard to say. A lot of money and effort has been poured into DL to get it this far. If ART had gotten the attention/investment instead would it be where DL is now?
mgaunard 5y ago
While Deep got all the hype, it seems that a lot of people realized that other forms of machine learning are better for most applications. I don't know about this ART thing though, never heard of it.
- space_fountain 5y ago
  Have they though? I think I've run into realizations that deep learning is too expensive or not yet scalable enough to be useful in some applications, but like other commenters on this thread. It can feel like a lot of the criticisms are grounded in some sort of feeling about what true intelligence should look like and an idea that number crunching can't possible be it
  - Retric 5y ago
    It depends on what you mean by machine learning, but in practice stuff like video game enemy AI generally avoids deep learning for many reasons behind cost or scale. Time, tuning, complexity, and consistency for example are major issues in the real world. Sure the SCII AI eventually became very strong but it’s also somewhat immersion breaking to see all the odd stuff it ends up doing. And that’s with a well tuned game, in something under active development you end up with a host of other issues.
    - space_fountain 5y ago
      Those are good points. Worth noting that video game AIs also often cheat. The goal for a video game ai is less to play the game well then to convince the player that they played the game well, but I think there definitely is something about believability or maybe human likeness that weirdly sometimes deep systems get very badly wrong
      - Jensson 5y ago
        Applying deep learning to computer game AI is just really hard. Not even a data center worth of compute and a big team of world leading scientists could create an AI able to beat the best players at games like starcraft or dota. It isn't reasonable to spend many times more more money making an AI for a game than you spent making the entire game, and the AI can't even run on any normal computer so you can't even use it at the end.
        space_fountain 5y ago
shadowgovt 5y ago
I can't speak for ART one way or the other as a technology, but this is a good time to remember that there's no particular reason to believe that a good model for training a machine learning system will map analogously to how human brains actually function. For all we know, there are multiple ways to converge to a trainable input recognition system. And if ART is bringing comprehensibility to the table, it may look even less like what actual brains do than modern deep learning algorithms; nothing about what I know regarding how brains operate suggests to me that they are comprehensible as anything other than "a pattern that works."
version_five 5y ago
Deep learning is very good at certain problems, much better (in some sense, particularly the ability to generalize with less fine tuning and feature engineering) than methods that came before it. It had undeniably opened up tons of new research applications, but has of course fallen short of the hype around it. I'm still very bullish on the post-hype potential of deep learning for solving real problems, it just requires a much clearer understanding of what it does and doesn't do, and adapting solutions to work within it's constraints. All that to say, it's easy to find flaws with it, but it has been game changing. I'm naturally skeptical about someone who has just published a book on something hes been working on for 50 years that he believes is "better" than the current (admittedly fad) technology. I'd like to see some real examples about the specific kinds of problems his tech solves, and how it solves them better than other methods. If he's saying it's better than deep learning on things deep learning is good at (e.g. can it beat a neural network model at classifying imagenet?) then I call BS absent a compelling demo. If it's better at some other thing, let's hear what that is.
- jostmey 5y ago
  > absent a compelling demo I think the progress was the result of competitions that kept everyone blindfolded to test datasets. No one could exaggerate the performance of their methods, allowing deep learning to emerge as a winning solution. I think deep learning has fallen short of the hype because researchers are exaggerating their results, and what is needed to propel progress is a renewed focus (with funding) for competitions.
- code_biologist 5y ago
  Exactly. If it's really better than deep learning, he can publish a library implementing it. Plenty of us are hungry for high performance, stable, explainable ML and the place for ART's approach would quickly be recognized.
- kumarvvr 5y ago
  Off Topic : I am interested in learning DL, not superficially (like most of online courses), but a more theoretical foundation, one that enables me to think and envisage new architectures, new techniques or combinations. Can you help me with any self study stuff that will enable me to learn as such.
ffhhj 5y ago
Is it possible to reverse the text to summary application in OpenAI?
bcatanzaro 5y ago
Ten+ years into the DL revolution and we are still getting shallow hit piece articles like this from IEEE. Look, everyone will jump on the ART bandwagon if it ever actually beats deep learning. Machine Learning is a very empirical field! But all the people throwing rocks at Deep Learning have failed to propose anything that actually works better.
- bArray 5y ago
  > Ten+ years into the DL revolution and we are still getting shallow hit piece articles like this from IEEE. I think the latest IEEE articles have been a little intellectually lightweight, perhaps we're just simply not their audience. Still, in the style of IEEE papers, I would like to see references for the claims made. > But all the people throwing rocks at Deep Learning have failed to propose anything that actually works better I was thinking something similar. DL for sure has problems, but I am yet to see something better. ART doesn't just need to be better, it needs to be better in _at least_ one of: accuracy, training time, recall time, amount of training data, quality of training data.
TheDudeMan 5y ago
I have not heard of the guy or ART. Extremely high reviews on Amazon: https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/product-reviews/0190070552/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/p...
- cowpig 5y ago
  Unfortunately Amazon reviews, at least in my experience, mean very little any more. There are just too many marketing firms that specialize in messing with them. Anecdotal example: a few months ago I bought an electric shaver that was very inexpensive and had thousands of 5-star reviews. It failed two months later, and when I went to leave the negative review, I found that the product no longer existed, but another identical-looking product with a different name from a different(?) company was the best-reviewed cheap electric shaver.
- rmbyrro 5y ago
  The curve doesn't look legit. Percentages for each star level. I have the impression Amazon could predict potentially manipulated reviews with reasonable accuracy by analyzing the curve. Edit: not claiming or implying anything about the author, book or the reviews. Just pointing the curve doesn't look legit to me.
savant_penguin 5y ago
"Stephen Grossberg explains why his ART model is better" Please don't explain me why your model is better. _show me_ your model is better with results. I see a lot of: "this model is explainable and has no forgetting modes". But I cannot care about an explainable model that does not work. There are many classic explainable models, how do they compare to art in real life applications? "The problem with today’s AI, he says, is that it tries to imitate the results of brain processing instead of probing the mechanisms that give rise to the results." Today's AI works really well for many applications (computer vision has leaped in quality). But of course I did not expect perfection from ImageNet trained models. I also did not expect gpt3 to be agi. But are they good (especially when compared with what we had not long ago)? Hell yeah Also, the advancements in protein folding are superb. I understand the author is not dismissing the current advancement. But if you claim your model is better you ought to compare it with the state of the art
natch 5y ago
Github repo? Hard to assess vague claims without seeing something concrete.
- masswerk 5y ago
  There's literally a link to a repository of papers in the article: http://techlab.bu.edu/resources/articles/C5.html http://techlab.bu.edu/resources/articles/C5.html
  - PeterisP 5y ago
    The latest paper there is from 2009 which is before the popularity rise of deep learning, before the creation of key methods of modern deep learning, and before the creation of most key datasets on which we would nowadays evaluate and compare the advantages and drawbacks of different methods. Literally nothing in that repository makes an argument that this method is better than deep learning in any aspect whatsoever, they don't even attempt such a comparison.
    - masswerk 5y ago
      At least, there can be found some on the theory and methods. Regarding a direct comparison, it may be difficult to do such a comparison by just running some code on data sets, which the one group of algorithms has been heavily optimized for with excessive funding, while the other has been not. Even more so, as such a review would have a difficult time to asses any of the critical features, like chances of memory loss. Comparing technologies isn't as easy as running them side by side.
  - natch 5y ago
    Nice reddit-esque attempt to use “literally” in an irrelevant context, but PDFs are not code.
    - masswerk 5y ago
      I thought, "GitHub or it didn't happen" was a bit reddit-esque regarding a strand of research engaging since the 1970s.
kleiba 5y ago
"Numbers talk, bullshit walks." (L. Torvalds) Let's see the performance of ART on some benchmark tasks, then we can talk.
- YeGoblynQueenne 5y ago
  I appreciate that you're saying something that you think is only common sense, but I suspect that is most likely because 99% of the machine learning papers you've seen do nothing else but claim a new SOTA result on some set of benchmarks. Yet just because that's what (almost) everyone is doing doesn't mean it's good research. Quite the contrary, that's the reason why machine learning research has become a quagmire and progress has stalled. Here's Geoff Hinton on benchmarks (again; I cite that all the time): GH: One big challenge the community faces is that if you want to get a paper published in machine learning now it's got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don't think that's encouraging people to think about radically new ideas. Now if you send in a paper that has a radically new idea, there's no chance in hell it will get accepted, because it's going to get some junior reviewer who doesn't understand it. Or it’s going to get a senior reviewer who's trying to review too many papers and doesn't understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that's really bad. What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That's I think the main downside of the fact that we've got this inversion now, where you've got a few senior guys and a gazillion young guys. https://www.wired.com/story/googles-ai-guru-computers-think-more-like-brains/ https://www.wired.com/story/googles-ai-guru-computers-think-... And here's a recent paper noting yet another way machine learning benchmarks are borked: https://arxiv.org/abs/2003.08907 https://arxiv.org/abs/2003.08907 P.S. And, please leave that aggressive attitude out of research discussions ("bullshit walks"?). Science is not the constant comparison of dick measurements. That's perhaps the point of sports, but anything that stifles debate and inhibits the development of new ideas is anti-science.
  - 5y ago
dathinab 5y ago
Can't be trusted => can't be fully trusted != is useless Also nothing is perfect. And even severely imperfect things can be reasonable good solution/improve a situation. So don't base a curt ruling on it. Be very careful about using it when it can put lives at risk (e.g. self driving). EDIT:And make the people selling the system responsible for it's failure!! You can't expect a person you tell to not touch the wheel for hours to then be able to react asap if something goes wrong, that's not how humans work. Hence putting the responsibility on the human which happens to sit behind the wheel is completely unreasonable.END EDIT Anyway my main problem with DL is that its hard to evaluate how trust-able it is if applied to a very diverse real world situations (i.e. self driving). You can evaluate it on data sets but data sets are extremely unlikely to get anywhere close to representing a diverse reality, and while each edge case not represented is very rare due to the diversity of reality there is a endless number of them so sooner or later they will be hit. And as they are edge case not represented by your data or evaluation sets you can't relay reason well what will happen if they are it. Furthermore due to the nature of data collection it's not rare for independent data sets to unexpectedly share subtle biases. A good example is a Tesla car getting confused because in front of it was a truck which had traffic lights loaded on the back. A extremely rare/unusual situation, which happened anyway. So you have to layer different systems, preferable also some fallback emergency systems not dependent on DL.
plutonorm 5y ago
"Guy with skin in the game rubbishes the competition"
ineedasername 5y ago
I'd like to see some working applications w/ datasets that could be played around with and tweaked, apart from just the papers he's published. (And even then the linked page from the article is largely broken, searching or browsing by author returns an error for me and I'm not paging through 26 pages of listing to find one of his papers) Without that, this sounds interesting but isn't something I can really evaluate beyond "researcher claims his research is better than status quo"
ineedasername 5y ago
putting lives at risk That's true of traditional models as well. The difference is that when there's a screw up it might be a little easier to determine the cause. That is certain useful, but if there are, on average, fewer screw ups with DL then the net benefit would seem to favor DL. What I'd really like is to have direct access to a working ART model and dataset for it... I wasn't able to find one after a bit if searching though. (I am however on mobile at the moment which makes that a bit more cumbersome)
Animats 5y ago
This is a legit researcher, but the article is mostly a "why mine is better" without justification. There must be something better to read on this. The performance problem with deep learning is well known. Great results up to 90%+ of the time, totally bogus a few percent of the time. Great for ad targeting. Automatic driving, not so much. If somebody got past that, it's important.
cwhy 5y ago
Deep learning based systems are at least trustable as working animals I think... They share a lot of similarities. You can not know how it thinks, but you can train them, observe them, and then trust them to a certain level.
bArray 5y ago
Can anybody point to high-level resources as to how ART works? The Wikipedia article is light on details [1], the 'types' described look basic and preliminary. [1] https://en.wikipedia.org/wiki/Adaptive_resonance_theory https://en.wikipedia.org/wiki/Adaptive_resonance_theory
deleted 5y ago
[deleted]
ChaitanyaSai 5y ago
Disclaimer: I have a PhD from the department Stephen Grossberg founded. Gail Carpenter, the co-creator of ART, was my advisor. I am also co-authoring a neuroscience non-fiction book that features Steve's work, along with other stuff. (Just so you know I am a reasonably biased observer). Most of you have probably not heard of Stephen Grossberg. He is in my opinion one of the greatest neuroscientists of this generation. Maybe the greatest theoretical neuroscientist ever. Neuroscience is swimming (barely) in a ton of data, but very little in the way of a unifying framework. Steve's body of work over 60 years provides that. ART is only one part of it. ART has some way to go before proving itself as undeniably useful as deep-learning on the application side of things. But it is very compelling as a model for how our (mammalian) brains might be operating. One fundamental idea, which is worth considering in applications is how almost all of deep-learning is founded on the idea of an "error" (A mis-match between expectation and the prediction) and how this flows through the system. Backprop is obviously the most popularly known method for this flow. ART is fundamentally different in that it works with the idea of a match rather than a mis-match. This foundational even philosophical difference is interesting because if allows for learning to happen all the time even while perceiving is simultaneously happening. It also allows for learning to be unsupervised. This philosophical departure point is actually very interesting: If you rely entirely on the idea of an error and throw out the rest, can you even hope to build a conscious system? Steve btw also has what I think is the most elegant and mechanistically precise explanation of consciousness, which builds on ART. Even if you are skeptical of the current promise of ART as a replacement for deep-learning, his explanation of how ART-like systems enable consciousness (and different conscious percepts) are alone worth the price of admission. I’ve written more about Stephen Grossberg and his book here: https://saigaddam.medium.com/the-greatest-neuroscientist-youve-never-heard-of-17c61b654a3e https://saigaddam.medium.com/the-greatest-neuroscientist-you...
- Isinlor 5y ago
  It's weird how many unrecognized geniuses there are in the neuroscience all claiming to have explained the brain and people just need to recognize their genius. All sounds very similar to Jeff Hawkins.
  - 5y ago

113 points by emrah 5y ago

amelius 5y ago

He formulated his theory in the 70s, and it didn't catch on. How likely is it that he is right about this?

Den-vr 5y ago
Many excellent things were formulated in the 70s and didn't catch on because supporting technologies weren't practical yet. It was an excellent time to be a fresh PhD
- jjtheblunt 5y ago
  it was also an excellent time to be a really little kid, with so much to marvel at (which is really to your point!)
UncleOxidant 5y ago
The roots of deep learning are from around the same era, if not a bit earlier - it's just that we didn't have the computing power to do much with it so DL kind of languished until ~15 years ago with the advent of capable GPUs. Is ART important? Can it do what DL does now? Hard to say. A lot of money and effort has been poured into DL to get it this far. If ART had gotten the attention/investment instead would it be where DL is now?

mgaunard 5y ago

While Deep got all the hype, it seems that a lot of people realized that other forms of machine learning are better for most applications. I don't know about this ART thing though, never heard of it.

space_fountain 5y ago
Have they though? I think I've run into realizations that deep learning is too expensive or not yet scalable enough to be useful in some applications, but like other commenters on this thread. It can feel like a lot of the criticisms are grounded in some sort of feeling about what true intelligence should look like and an idea that number crunching can't possible be it
- Retric 5y ago
  It depends on what you mean by machine learning, but in practice stuff like video game enemy AI generally avoids deep learning for many reasons behind cost or scale. Time, tuning, complexity, and consistency for example are major issues in the real world. Sure the SCII AI eventually became very strong but it’s also somewhat immersion breaking to see all the odd stuff it ends up doing. And that’s with a well tuned game, in something under active development you end up with a host of other issues.
  - space_fountain 5y ago
    Those are good points. Worth noting that video game AIs also often cheat. The goal for a video game ai is less to play the game well then to convince the player that they played the game well, but I think there definitely is something about believability or maybe human likeness that weirdly sometimes deep systems get very badly wrong
    - Jensson 5y ago
      Applying deep learning to computer game AI is just really hard. Not even a data center worth of compute and a big team of world leading scientists could create an AI able to beat the best players at games like starcraft or dota. It isn't reasonable to spend many times more more money making an AI for a game than you spent making the entire game, and the AI can't even run on any normal computer so you can't even use it at the end.
      - space_fountain 5y ago

shadowgovt 5y ago

I can't speak for ART one way or the other as a technology, but this is a good time to remember that there's no particular reason to believe that a good model for training a machine learning system will map analogously to how human brains actually function. For all we know, there are multiple ways to converge to a trainable input recognition system. And if ART is bringing comprehensibility to the table, it may look even less like what actual brains do than modern deep learning algorithms; nothing about what I know regarding how brains operate suggests to me that they are comprehensible as anything other than "a pattern that works."

version_five 5y ago

Deep learning is very good at certain problems, much better (in some sense, particularly the ability to generalize with less fine tuning and feature engineering) than methods that came before it. It had undeniably opened up tons of new research applications, but has of course fallen short of the hype around it. I'm still very bullish on the post-hype potential of deep learning for solving real problems, it just requires a much clearer understanding of what it does and doesn't do, and adapting solutions to work within it's constraints. All that to say, it's easy to find flaws with it, but it has been game changing. I'm naturally skeptical about someone who has just published a book on something hes been working on for 50 years that he believes is "better" than the current (admittedly fad) technology. I'd like to see some real examples about the specific kinds of problems his tech solves, and how it solves them better than other methods. If he's saying it's better than deep learning on things deep learning is good at (e.g. can it beat a neural network model at classifying imagenet?) then I call BS absent a compelling demo. If it's better at some other thing, let's hear what that is.

jostmey 5y ago
> absent a compelling demo I think the progress was the result of competitions that kept everyone blindfolded to test datasets. No one could exaggerate the performance of their methods, allowing deep learning to emerge as a winning solution. I think deep learning has fallen short of the hype because researchers are exaggerating their results, and what is needed to propel progress is a renewed focus (with funding) for competitions.
code_biologist 5y ago
Exactly. If it's really better than deep learning, he can publish a library implementing it. Plenty of us are hungry for high performance, stable, explainable ML and the place for ART's approach would quickly be recognized.
kumarvvr 5y ago
Off Topic : I am interested in learning DL, not superficially (like most of online courses), but a more theoretical foundation, one that enables me to think and envisage new architectures, new techniques or combinations. Can you help me with any self study stuff that will enable me to learn as such.

ffhhj 5y ago

Is it possible to reverse the text to summary application in OpenAI?

bcatanzaro 5y ago

Ten+ years into the DL revolution and we are still getting shallow hit piece articles like this from IEEE. Look, everyone will jump on the ART bandwagon if it ever actually beats deep learning. Machine Learning is a very empirical field! But all the people throwing rocks at Deep Learning have failed to propose anything that actually works better.

bArray 5y ago
> Ten+ years into the DL revolution and we are still getting shallow hit piece articles like this from IEEE. I think the latest IEEE articles have been a little intellectually lightweight, perhaps we're just simply not their audience. Still, in the style of IEEE papers, I would like to see references for the claims made. > But all the people throwing rocks at Deep Learning have failed to propose anything that actually works better I was thinking something similar. DL for sure has problems, but I am yet to see something better. ART doesn't just need to be better, it needs to be better in _at least_ one of: accuracy, training time, recall time, amount of training data, quality of training data.

TheDudeMan 5y ago

I have not heard of the guy or ART. Extremely high reviews on Amazon: https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/product-reviews/0190070552/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/p...

cowpig 5y ago
Unfortunately Amazon reviews, at least in my experience, mean very little any more. There are just too many marketing firms that specialize in messing with them. Anecdotal example: a few months ago I bought an electric shaver that was very inexpensive and had thousands of 5-star reviews. It failed two months later, and when I went to leave the negative review, I found that the product no longer existed, but another identical-looking product with a different name from a different(?) company was the best-reviewed cheap electric shaver.
rmbyrro 5y ago
The curve doesn't look legit. Percentages for each star level. I have the impression Amazon could predict potentially manipulated reviews with reasonable accuracy by analyzing the curve. Edit: not claiming or implying anything about the author, book or the reviews. Just pointing the curve doesn't look legit to me.

savant_penguin 5y ago

"Stephen Grossberg explains why his ART model is better" Please don't explain me why your model is better. _show me_ your model is better with results. I see a lot of: "this model is explainable and has no forgetting modes". But I cannot care about an explainable model that does not work. There are many classic explainable models, how do they compare to art in real life applications? "The problem with today’s AI, he says, is that it tries to imitate the results of brain processing instead of probing the mechanisms that give rise to the results." Today's AI works really well for many applications (computer vision has leaped in quality). But of course I did not expect perfection from ImageNet trained models. I also did not expect gpt3 to be agi. But are they good (especially when compared with what we had not long ago)? Hell yeah Also, the advancements in protein folding are superb. I understand the author is not dismissing the current advancement. But if you claim your model is better you ought to compare it with the state of the art

natch 5y ago

Github repo? Hard to assess vague claims without seeing something concrete.

masswerk 5y ago
There's literally a link to a repository of papers in the article: http://techlab.bu.edu/resources/articles/C5.html http://techlab.bu.edu/resources/articles/C5.html
- PeterisP 5y ago
  The latest paper there is from 2009 which is before the popularity rise of deep learning, before the creation of key methods of modern deep learning, and before the creation of most key datasets on which we would nowadays evaluate and compare the advantages and drawbacks of different methods. Literally nothing in that repository makes an argument that this method is better than deep learning in any aspect whatsoever, they don't even attempt such a comparison.
  - masswerk 5y ago
    At least, there can be found some on the theory and methods. Regarding a direct comparison, it may be difficult to do such a comparison by just running some code on data sets, which the one group of algorithms has been heavily optimized for with excessive funding, while the other has been not. Even more so, as such a review would have a difficult time to asses any of the critical features, like chances of memory loss. Comparing technologies isn't as easy as running them side by side.
- natch 5y ago
  Nice reddit-esque attempt to use “literally” in an irrelevant context, but PDFs are not code.
  - masswerk 5y ago
    I thought, "GitHub or it didn't happen" was a bit reddit-esque regarding a strand of research engaging since the 1970s.

kleiba 5y ago

"Numbers talk, bullshit walks." (L. Torvalds) Let's see the performance of ART on some benchmark tasks, then we can talk.

YeGoblynQueenne 5y ago
I appreciate that you're saying something that you think is only common sense, but I suspect that is most likely because 99% of the machine learning papers you've seen do nothing else but claim a new SOTA result on some set of benchmarks. Yet just because that's what (almost) everyone is doing doesn't mean it's good research. Quite the contrary, that's the reason why machine learning research has become a quagmire and progress has stalled. Here's Geoff Hinton on benchmarks (again; I cite that all the time): GH: One big challenge the community faces is that if you want to get a paper published in machine learning now it's got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don't think that's encouraging people to think about radically new ideas. Now if you send in a paper that has a radically new idea, there's no chance in hell it will get accepted, because it's going to get some junior reviewer who doesn't understand it. Or it’s going to get a senior reviewer who's trying to review too many papers and doesn't understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that's really bad. What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That's I think the main downside of the fact that we've got this inversion now, where you've got a few senior guys and a gazillion young guys. https://www.wired.com/story/googles-ai-guru-computers-think-more-like-brains/ https://www.wired.com/story/googles-ai-guru-computers-think-... And here's a recent paper noting yet another way machine learning benchmarks are borked: https://arxiv.org/abs/2003.08907 https://arxiv.org/abs/2003.08907 P.S. And, please leave that aggressive attitude out of research discussions ("bullshit walks"?). Science is not the constant comparison of dick measurements. That's perhaps the point of sports, but anything that stifles debate and inhibits the development of new ideas is anti-science.
- 5y ago

dathinab 5y ago

Can't be trusted => can't be fully trusted != is useless Also nothing is perfect. And even severely imperfect things can be reasonable good solution/improve a situation. So don't base a curt ruling on it. Be very careful about using it when it can put lives at risk (e.g. self driving). EDIT:And make the people selling the system responsible for it's failure!! You can't expect a person you tell to not touch the wheel for hours to then be able to react asap if something goes wrong, that's not how humans work. Hence putting the responsibility on the human which happens to sit behind the wheel is completely unreasonable.END EDIT Anyway my main problem with DL is that its hard to evaluate how trust-able it is if applied to a very diverse real world situations (i.e. self driving). You can evaluate it on data sets but data sets are extremely unlikely to get anywhere close to representing a diverse reality, and while each edge case not represented is very rare due to the diversity of reality there is a endless number of them so sooner or later they will be hit. And as they are edge case not represented by your data or evaluation sets you can't relay reason well what will happen if they are it. Furthermore due to the nature of data collection it's not rare for independent data sets to unexpectedly share subtle biases. A good example is a Tesla car getting confused because in front of it was a truck which had traffic lights loaded on the back. A extremely rare/unusual situation, which happened anyway. So you have to layer different systems, preferable also some fallback emergency systems not dependent on DL.

plutonorm 5y ago

"Guy with skin in the game rubbishes the competition"

ineedasername 5y ago

I'd like to see some working applications w/ datasets that could be played around with and tweaked, apart from just the papers he's published. (And even then the linked page from the article is largely broken, searching or browsing by author returns an error for me and I'm not paging through 26 pages of listing to find one of his papers) Without that, this sounds interesting but isn't something I can really evaluate beyond "researcher claims his research is better than status quo"

ineedasername 5y ago

putting lives at risk That's true of traditional models as well. The difference is that when there's a screw up it might be a little easier to determine the cause. That is certain useful, but if there are, on average, fewer screw ups with DL then the net benefit would seem to favor DL. What I'd really like is to have direct access to a working ART model and dataset for it... I wasn't able to find one after a bit if searching though. (I am however on mobile at the moment which makes that a bit more cumbersome)

Animats 5y ago

This is a legit researcher, but the article is mostly a "why mine is better" without justification. There must be something better to read on this. The performance problem with deep learning is well known. Great results up to 90%+ of the time, totally bogus a few percent of the time. Great for ad targeting. Automatic driving, not so much. If somebody got past that, it's important.

cwhy 5y ago

Deep learning based systems are at least trustable as working animals I think... They share a lot of similarities. You can not know how it thinks, but you can train them, observe them, and then trust them to a certain level.

bArray 5y ago

Can anybody point to high-level resources as to how ART works? The Wikipedia article is light on details [1], the 'types' described look basic and preliminary. [1] https://en.wikipedia.org/wiki/Adaptive_resonance_theory https://en.wikipedia.org/wiki/Adaptive_resonance_theory

deleted 5y ago

[deleted]

ChaitanyaSai 5y ago

Disclaimer: I have a PhD from the department Stephen Grossberg founded. Gail Carpenter, the co-creator of ART, was my advisor. I am also co-authoring a neuroscience non-fiction book that features Steve's work, along with other stuff. (Just so you know I am a reasonably biased observer). Most of you have probably not heard of Stephen Grossberg. He is in my opinion one of the greatest neuroscientists of this generation. Maybe the greatest theoretical neuroscientist ever. Neuroscience is swimming (barely) in a ton of data, but very little in the way of a unifying framework. Steve's body of work over 60 years provides that. ART is only one part of it. ART has some way to go before proving itself as undeniably useful as deep-learning on the application side of things. But it is very compelling as a model for how our (mammalian) brains might be operating. One fundamental idea, which is worth considering in applications is how almost all of deep-learning is founded on the idea of an "error" (A mis-match between expectation and the prediction) and how this flows through the system. Backprop is obviously the most popularly known method for this flow. ART is fundamentally different in that it works with the idea of a match rather than a mis-match. This foundational even philosophical difference is interesting because if allows for learning to happen all the time even while perceiving is simultaneously happening. It also allows for learning to be unsupervised. This philosophical departure point is actually very interesting: If you rely entirely on the idea of an error and throw out the rest, can you even hope to build a conscious system? Steve btw also has what I think is the most elegant and mechanistically precise explanation of consciousness, which builds on ART. Even if you are skeptical of the current promise of ART as a replacement for deep-learning, his explanation of how ART-like systems enable consciousness (and different conscious percepts) are alone worth the price of admission. I’ve written more about Stephen Grossberg and his book here: https://saigaddam.medium.com/the-greatest-neuroscientist-youve-never-heard-of-17c61b654a3e https://saigaddam.medium.com/the-greatest-neuroscientist-you...

Isinlor 5y ago
It's weird how many unrecognized geniuses there are in the neuroscience all claiming to have explained the brain and people just need to recognize their genius. All sounds very similar to Jeff Hawkins.
- 5y ago