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This review is a bit of a window into the past itself... after all, more time has passed since this review was written than elapsed between GEB’s publication an

by lacker 6y ago

This review is a bit of a window into the past itself... after all, more time has passed since this review was written than elapsed between GEB’s publication and this review. In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. It underestimated what behaviors could be emergent from running simple algorithms on large datasets. It is one of the most beautiful expressions of the ideas of the “classic” age of 1970’s AI, awesome to read but in the end somewhat incorrect about the future. Perhaps one day the pendulum will swing back, and we will discover that large datasets are in some ways overrated, and clever aesthetic senses of pattern are necessary for progress. On that day it will be quite interesting to reread GEB.

colordrops 6y ago
Why is there a pendulum? Aren't both necessary and two sides of the same coin? I know little of modern AI, but I've seen work where both low level raw power for NNs is combined with a symbolic organization of many NNs.
- ghaff 6y ago
  To some degree. But an enormous part of the (useful but narrow) success of AI is a specific set of ML tech, especially supervised learning. And the amount of computational horsepower and data required gives one at least some pause given what the human brain can do with apparently so much less. Which implies there are probably significant advances required in cognitive science and neurophysiology and perhaps other fields of studies we haven't even considered. We may need other ways to solve problems that have proven more elusive to brute force than it looked like 5 years or so ago (e.g. general purpose full self-driving in busy cities).
- nextos 6y ago
  Do you have any references to that work?
  - heuroci 6y ago
    I personally designed and participated in the implementation of a hybrid cognitive architecture which combined both NNs and GOFAI. We (http://www.heurolabs.com http://www.heurolabs.com) are contemplating open sourcing it, or at least publish the design documents
jdonaldson 6y ago
I might offer a slight wrinkle to this assertion. While raw power has driven an advancement, what Hofstadter is asserting is that symbolic reasoning (particularly recursion) is "unsolvable" for certain classes of problems. In other words, ML lives in a "box" that Hofstadter has defined. His work is still useful as a "lens" to understand the limits of AI, and what else it could possibly accomplish.
- PixyMisa 6y ago
  Hofstadter not only asserted that symbolic reasoning is solvable, he explained how to do it. In very broad terms, yes.
  - jdonaldson 6y ago
    Can you be more specific? Hofstadter specifically includes Cantor and Gödel as a way of showing how certain types of problems can't be solved with symbolic logic.
OskarS 6y ago
> I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. I still see this as an open question. You’re certainly correct that this kind of AI research is seriously out of vogue, but it seems to me that while “modern” brute force compute AI puts up impressive results and is a hugely useful technique, it has made exactly zero progress on anything that could be conceived of as “general intelligence”. It just doesn’t seem to me to be a thing AI researchers are even interested in any more. Like, the Twitter program that uses AI to crop images based on a dataset of a gazillion cropped images is pretty far from Turing’s thinking machines. I don’t know the way there, but it always seemed to me that the old-style AI research in the GEB style is still a rich vein we haven’t come close to mining out.
- strangegecko 6y ago
  > this kind of AI research is seriously out of vogue I would think this is a consequence of it being a very hard problem. ML gets all the industry funding and publicity because you get results that are immediately useful. You can work on General AI for decades and come up empty handed since there doesn't seem to be an incremental approach where intermediate results are useful. So it's closer to esoteric mathematics or philosophy in terms of "being en vogue". So I see this mostly as a reflection of the academic landscape in general. Funding is more focused on application and less on theoretical / fundamental research.
- joseluis 6y ago
  I very much agree. The recent outstanding advances in AI since at least the 90s seems to be in the spectrum of recognizing patterns bruteforcing through an insurmountable amount of data, arriving at black boxes that provide narrow solutions, with its inner structure remaining inescrutable to human understanding. I'm currently reading Surfaces and Essences from the same author, and it's so far been most illuminating. He very convincingly presents the thesis of the analogy being the foundation of each nd every concept and human thought. If someone could manage to apply and translate those insights into real algorithms and data structures to play with, that would be IMHO a big step towards general AI, or the very least a much more human like AI approach with its particular applications.
moonchild 6y ago
Is that conventional wisdom? Computational power is cheap, and it's certainly a tack that many are trying, but not exclusively. See, for instance, logicmoo.
taeric 6y ago
The article making the rounds a few weeks ago about rethinking general thinking feels relevant. By showing that many large neural networks are ultimately really good at memorizing the training set, in curious how much the two views you are showing are in conflict. It is the age old "rote memorization" versus "learning". In large, I suspect those do not have a clear line between them. Such that emergent behaviors are expected and can be taught.
- anaerobicover 6y ago
  Could you link the article you mention? I missed it making its rounds, but I would love to read it.
  - taeric 6y ago
    Certainly, https://news.ycombinator.com/item?id=26346226 https://news.ycombinator.com/item?id=26346226 is the post I was thinking of. Pretty sure the article is older. Do let me know if I misrepresented it. I thought it was a clever way to show that the models are not finding inherent relations in the data, by just randomly labeling all training data and having the same speed/accuracy on the random labels as on the real ones. Edit: I worded the above poorly. They showed the models were capable of merely learning the training data. Despite that, they also showed that moving the trained models from test to validation did not have a linear relationship with the amount of noise they added to the data. That is, the training methods seem indistinguishable from memorization, but also seem to learn some generalisations.
    - anaerobicover 6y ago
      Thanks! I will read it later.
Rygian 6y ago
I read GEB a long time ago, followed by "I Am A Strange Loop", and the overarching impression they left on me is that we should focus on emergent behaviour and feedback loops, because they seem to be pointing to the direction where "high-minded embodiment of abstraction" probably lives. So instead of believing GEB+IAASL haven't stood the test of time, I prefer to believe that current technology is akin to banging raw power together in hopes of seeing a spark of cool in the minor league of "this AI does this nifty (and maybe useful) thing better than humans" but we haven't yet upgraded to major league of AI choosing their own goals and what ever may emerge from that. (It may be that I'm due for a re-read!)
- galaxyLogic 6y ago
  Good point about feedback loops. I remember there was some material in GEB about turning a video-camera on its output on the screen. Aren't neural networks and their training basically all about feedback?
bidirectional 6y ago
I've heard this said many times. Most people I know who have loved the book read it before turning 20. It's a great read, but I think there's something in the format that means it's best for bright but fertile minds.
- T-hawk 6y ago
  And a similar data point: I read GEB around age 30 and liked it but didn't love it. About the first third of the book was interesting in new ways of thinking about symbols and self-reference. After that it kept looping back around the same topics without really adding anything more. The dialogues were somewhat entertaining but I found myself wishing to cut past the rhetorical fluff and get to the point.
- mindcrime 6y ago
  I read it for the first time about 2 years ago, when I was about 45. I don't know if it would be correct to say that I "loved" it, but I did rather enjoy reading it, and I found a lot of the ideas espoused within really resonated with me. All in all, I would say that I walked away thinking that it will get a second read at some point. Not sure that this anecdote proves anything, so take it for what it's worth.
- cgh 6y ago
  Yeah, I agree with you. I first read it when I was around fifteen years old and didn't quite grasp it all at first but it sure fired me up. I read it a couple of times after that, the last time when I was around 21 or so. I'm kind of afraid to read it now because I'm afraid I'll process it with my older, cynical, more "knowing" brain and it will tarnish my wonderful memories of being glued to it as a teenager. It's a unique work that seems magnetically attractive to a certain sort of young, imaginative mind.
- jkhdigital 6y ago
  I read it three months ago, at age 37, shortly after completing a course on computation theory (currently working on a PhD in CS). It was one of those books that somehow stole entire days from me, simply because I couldn’t put it down... along the way I also spent a lot of time browsing Wikipedia and Stanford Encyclopedia of Philosophy entries for a more rigorous understanding of Godel’s contributions. I’d agree that it feels like it was intended for a more youthful audience, but I doubt I would have made all of the mental connections that I did without the life experience and broader knowledge of my 30s.
mcphage 6y ago
> In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. It doesn't seem completely off. What the author is describing—and what we're seeing—is increasingly sophisticated algorithms, that are getting better and better at answering more and more narrowly defined questions: https://www.theguardian.com/technology/2021/mar/08/typographic-attack-pen-paper-fool-ai-thinking-apple-ipod-clip https://www.theguardian.com/technology/2021/mar/08/typograph...
cornel_io 6y ago
> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. The dead horse that I like to beat is that there has been no progress in AI so far, the past 5-10 years of successes are all about mere perception. If you like the Kahneman "fast and slow" framework, almost without exception the systems we're seeing today are fast, "system 1" responders, which take input and need to make an instant decision. That's freaking awesome, and was way out of reach even 15 years ago, so I'm throwing no shade at all on that achievement! It's astounding how much turns out to be possible in systems like this - a priori, I never would have thought that transformers could do as much as they've proven to be able to, because frankly the architecture is super limited and it's super unlikely that humans are able to extract anywhere near as much meaning from an instant, non-reflective pass over a paragraph of text as GPT-3 does. But there's a lot about those systems that makes them much easier to design and train, not the least of which is that descent via backprop works great as a training strategy when the input and target output are available at the same time. Real "system 2" thought can be spread over minutes, hour, or days, and I don't care how far you unroll backprop-through-time, you're not going to train that effectively without some other innovation by simply following the obvious error metrics. If we can get there we will almost certainly see big data lose its pedestal: it's great to have, but humans don't need to read the entire Internet to understand a web page, that's an artifact of forcing this to be done by a model that doesn't have dynamics. I disagree with Hofstadter's view (at least when he wrote GEB and some of his other classics) that explicit abstract reasoning is the right way to solve any of this; my gut tells me that in the end we're going to end up still using some sort of neural architecture and abstract reasoning rules will be implicitly learned mostly locally based on internal "error" signals that guide state transitions. "Learning how to learn" is probably going to be a big part of that, because current systems don't learn, they are merely trained to perceive by brute forcing weights down the stack. But some serious shifts in research focus will be required in order to break through the weaknesses of today's systems, and they all point more in the direction of reasoning, which is woefully underprioritized in all but a very small handful of niche AI labs today.
- jrumbut 6y ago
fulafel 6y ago
I feel the oft cited idea of GPUs being the key to the change is a bit exaggerated. They give a modest constant factor of speedup in exchange for more difficult programming model and esoteric hardware requirements, and as such give a chance of frontfrunning CPU computation a bit, but is it really significant if we zoom out a bit in the historical perspective?
- joefourier 6y ago
  CPU performance, especially single-core, has stalled in the last decade while GPU performance has kept improving. On paper, a 3080 has about 100x faster FP32 performance versus a modern gaming CPU, and in practice even considering memory bandwidth you do fully get a speed-bump of one or two orders of magnitude. I would not call that a "modest constant factor". And a lot of the recent CPU performance gains are due to SIMD and multithreading, which are basically making CPUs more GPU-like, rather than the continuous improvement of serial performance as we've had up until ~2005.
mindcrime 6y ago
Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. Just to play Devil's Advocate ever so slightly: there are people out there who would say that there hasn't been any "progress in AI" for quite some time, or at least very little so. And they would probably argue further that the apparent progress you are referring to is just progress in "mere pattern recognition" or something like that. I'm not sure myself. I do lean at least a little towards the idea that there is a qualitative difference between most of modern ML and many aspects of what we would call "intelligence". As such, my interests in all of this remain around the intersection of ML and GOFAI, and the possibility of hybrid systems that use elements of both. But I can't rule out the possibility that it will eventually be found to be the case that all of "intelligence" does indeed reduce to "mere pattern recognition" in some sense. And Geoffrey Hinton may be correct in saying that we don't need, and never will need, any kind of "hybridization" and that neural networks can do it all.
- galaxyLogic 6y ago
  Neural Networks can do it all after they emerge symbolic representations, a way to represent logic and mathematics. A neural network that just "feels" that Fermat's Last Theorem is true, is much less intelligent than one that can produce the proof of it and present that to us, so we can trust that what it's saying is true. If you can't do symbolic manipulation, you are not really intelligent, artificial or otherwise, I would say.
  - The_rationalist 6y ago
    neural network are extremely inefficient at learning symbolic reasoning by design and I doubt a radically new kind of neural network will be discovered, we have pretty much made an exhaustive analysis.
credit_guy 6y ago
> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power Maybe it's a bit of both. Sure, large DL models use lots of compute, but successful DL applications require some insight into the problem. For some reason, people like to de-emphasize this insight. The story is that a DL model will discover by itself which features are important, which are not, and you just provide the training data, and press a button. Thousands of people do just that, and end up with mediocre results. Thousands and thousands of absolutely mediocre papers get published, and receive acclaim instead of derision. The truly boundary shifting results always use deep insight. Like what comes out of DeepMind (AlphaGo, AlphaZero, AlphaFold).
breck 6y ago
> we will discover that large datasets are in some ways overrated In the short run, expand then compress, expand then compress. In the long run, the size of the model will always compress toward the capacity of the most intelligent individual.
mellosouls 6y ago
"Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power[...]" As has been argued in these pages many times before there has been no obvious progress in AGI, although certainly progress in AI in its weak sense has been impressive in the last few years. I know you didn't say AGI but its important to make that distinction as the book was very interested in that as a subject.
- ProfHewitt 6y ago
  Fundamental progress in Intelligent Systems will require that systems become facile in constructing and using abstractions. Intelligent Systems will be developed and deployed in this decade. However, full equivalence to humans will be probably not be achieved in this decade. See the following for more information: "Robust Inference for Universal Intelligent Systems" https://papers.ssrn.com/abstract=3789701 https://papers.ssrn.com/abstract=3789701
deleted 6y ago
[deleted]
ProfHewitt 6y ago
The aspect of GEB that has not withstood the test of time is the Gödel part. [Gödel 1931] seemed to have settled the issue of inferential undecidablity{∃[Ψ:Proposition](⊬Ψ)∧(⊬¬Ψ)} in the positive using the proposition I'mUnprovable, such that I’mUnprovable⇔⊬I’mUnprovable. However, existence of I’mUnprovable would enable the following cyberattack [cf. Wittgenstein 1937]: Proof of a contradiction in foundations: First prove I’mUnprovable using proof by contradictions as follows: In order to obtain a contradiction, hypothesize ¬I’mUnprovable. Therefore ⊢I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable). Consequently, ⊢⊢I’mUnprovable using ByProvabilityOfProofs {⊢∀[Ψ:Proposition](⊢Ψ)⇒⊢⊢Ψ}. However, ⊢¬I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable), which is the desired contradiction. Using proof by contradiction, ⊢I’mUnprovable meaning ⊢⊢I’mUnprovable using ByProvabilityOfProofs. However, ⊢¬I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable), which is a contradiction in foundations. Strong types prevent construction of I’mUnprovable using the following recursive definition: I’mUnprovable:Proposition≡⊬I’mUnprovable. Note that (⊬I’mUnprovable):Proposition<i+1> because I’mUnprovable is a propositional variable in the right hand side of the definition of I’mUnprovable:Proposition. Consequently, I’mUnprovable:Proposition⇒I’mUnprovable:Proposition<i+1>, which is a contradiction. The crucial issue with the proofs in [Gödel 1931] is that the Gödel number of a proposition does not capture its order. Because of orders of propositions, the Diagonal Lemma [Gödel 1931] fails to construct the proposition I’mUnprovable. See the following for more explanation: "Epistemology Cyberattacks" https://papers.ssrn.com/abstract=3603021 https://papers.ssrn.com/abstract=3603021
andrepd 6y ago
There is exactly zero progress in AI coming from neural networks. They are nonlinear classifiers, nothing more. To see an example of a task which requires actual artificial intelligence reasoning, see this article by none other than Hofstadter himself :p https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...

by lacker 6y ago

colordrops 6y ago

Why is there a pendulum? Aren't both necessary and two sides of the same coin? I know little of modern AI, but I've seen work where both low level raw power for NNs is combined with a symbolic organization of many NNs.

ghaff 6y ago
To some degree. But an enormous part of the (useful but narrow) success of AI is a specific set of ML tech, especially supervised learning. And the amount of computational horsepower and data required gives one at least some pause given what the human brain can do with apparently so much less. Which implies there are probably significant advances required in cognitive science and neurophysiology and perhaps other fields of studies we haven't even considered. We may need other ways to solve problems that have proven more elusive to brute force than it looked like 5 years or so ago (e.g. general purpose full self-driving in busy cities).
nextos 6y ago
Do you have any references to that work?
- heuroci 6y ago
  I personally designed and participated in the implementation of a hybrid cognitive architecture which combined both NNs and GOFAI. We (http://www.heurolabs.com http://www.heurolabs.com) are contemplating open sourcing it, or at least publish the design documents

jdonaldson 6y ago

I might offer a slight wrinkle to this assertion. While raw power has driven an advancement, what Hofstadter is asserting is that symbolic reasoning (particularly recursion) is "unsolvable" for certain classes of problems. In other words, ML lives in a "box" that Hofstadter has defined. His work is still useful as a "lens" to understand the limits of AI, and what else it could possibly accomplish.

PixyMisa 6y ago
Hofstadter not only asserted that symbolic reasoning is solvable, he explained how to do it. In very broad terms, yes.
- jdonaldson 6y ago
  Can you be more specific? Hofstadter specifically includes Cantor and Gödel as a way of showing how certain types of problems can't be solved with symbolic logic.

OskarS 6y ago

> I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. I still see this as an open question. You’re certainly correct that this kind of AI research is seriously out of vogue, but it seems to me that while “modern” brute force compute AI puts up impressive results and is a hugely useful technique, it has made exactly zero progress on anything that could be conceived of as “general intelligence”. It just doesn’t seem to me to be a thing AI researchers are even interested in any more. Like, the Twitter program that uses AI to crop images based on a dataset of a gazillion cropped images is pretty far from Turing’s thinking machines. I don’t know the way there, but it always seemed to me that the old-style AI research in the GEB style is still a rich vein we haven’t come close to mining out.

strangegecko 6y ago
> this kind of AI research is seriously out of vogue I would think this is a consequence of it being a very hard problem. ML gets all the industry funding and publicity because you get results that are immediately useful. You can work on General AI for decades and come up empty handed since there doesn't seem to be an incremental approach where intermediate results are useful. So it's closer to esoteric mathematics or philosophy in terms of "being en vogue". So I see this mostly as a reflection of the academic landscape in general. Funding is more focused on application and less on theoretical / fundamental research.
joseluis 6y ago
I very much agree. The recent outstanding advances in AI since at least the 90s seems to be in the spectrum of recognizing patterns bruteforcing through an insurmountable amount of data, arriving at black boxes that provide narrow solutions, with its inner structure remaining inescrutable to human understanding. I'm currently reading Surfaces and Essences from the same author, and it's so far been most illuminating. He very convincingly presents the thesis of the analogy being the foundation of each nd every concept and human thought. If someone could manage to apply and translate those insights into real algorithms and data structures to play with, that would be IMHO a big step towards general AI, or the very least a much more human like AI approach with its particular applications.

moonchild 6y ago

Is that conventional wisdom? Computational power is cheap, and it's certainly a tack that many are trying, but not exclusively. See, for instance, logicmoo.

taeric 6y ago

The article making the rounds a few weeks ago about rethinking general thinking feels relevant. By showing that many large neural networks are ultimately really good at memorizing the training set, in curious how much the two views you are showing are in conflict. It is the age old "rote memorization" versus "learning". In large, I suspect those do not have a clear line between them. Such that emergent behaviors are expected and can be taught.

anaerobicover 6y ago
Could you link the article you mention? I missed it making its rounds, but I would love to read it.
- taeric 6y ago
  Certainly, https://news.ycombinator.com/item?id=26346226 https://news.ycombinator.com/item?id=26346226 is the post I was thinking of. Pretty sure the article is older. Do let me know if I misrepresented it. I thought it was a clever way to show that the models are not finding inherent relations in the data, by just randomly labeling all training data and having the same speed/accuracy on the random labels as on the real ones. Edit: I worded the above poorly. They showed the models were capable of merely learning the training data. Despite that, they also showed that moving the trained models from test to validation did not have a linear relationship with the amount of noise they added to the data. That is, the training methods seem indistinguishable from memorization, but also seem to learn some generalisations.
  - anaerobicover 6y ago
    Thanks! I will read it later.

Rygian 6y ago

I read GEB a long time ago, followed by "I Am A Strange Loop", and the overarching impression they left on me is that we should focus on emergent behaviour and feedback loops, because they seem to be pointing to the direction where "high-minded embodiment of abstraction" probably lives. So instead of believing GEB+IAASL haven't stood the test of time, I prefer to believe that current technology is akin to banging raw power together in hopes of seeing a spark of cool in the minor league of "this AI does this nifty (and maybe useful) thing better than humans" but we haven't yet upgraded to major league of AI choosing their own goals and what ever may emerge from that. (It may be that I'm due for a re-read!)

galaxyLogic 6y ago
Good point about feedback loops. I remember there was some material in GEB about turning a video-camera on its output on the screen. Aren't neural networks and their training basically all about feedback?

bidirectional 6y ago

I've heard this said many times. Most people I know who have loved the book read it before turning 20. It's a great read, but I think there's something in the format that means it's best for bright but fertile minds.

T-hawk 6y ago
And a similar data point: I read GEB around age 30 and liked it but didn't love it. About the first third of the book was interesting in new ways of thinking about symbols and self-reference. After that it kept looping back around the same topics without really adding anything more. The dialogues were somewhat entertaining but I found myself wishing to cut past the rhetorical fluff and get to the point.
mindcrime 6y ago
I read it for the first time about 2 years ago, when I was about 45. I don't know if it would be correct to say that I "loved" it, but I did rather enjoy reading it, and I found a lot of the ideas espoused within really resonated with me. All in all, I would say that I walked away thinking that it will get a second read at some point. Not sure that this anecdote proves anything, so take it for what it's worth.
cgh 6y ago
Yeah, I agree with you. I first read it when I was around fifteen years old and didn't quite grasp it all at first but it sure fired me up. I read it a couple of times after that, the last time when I was around 21 or so. I'm kind of afraid to read it now because I'm afraid I'll process it with my older, cynical, more "knowing" brain and it will tarnish my wonderful memories of being glued to it as a teenager. It's a unique work that seems magnetically attractive to a certain sort of young, imaginative mind.
jkhdigital 6y ago
I read it three months ago, at age 37, shortly after completing a course on computation theory (currently working on a PhD in CS). It was one of those books that somehow stole entire days from me, simply because I couldn’t put it down... along the way I also spent a lot of time browsing Wikipedia and Stanford Encyclopedia of Philosophy entries for a more rigorous understanding of Godel’s contributions. I’d agree that it feels like it was intended for a more youthful audience, but I doubt I would have made all of the mental connections that I did without the life experience and broader knowledge of my 30s.

mcphage 6y ago

> In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. It doesn't seem completely off. What the author is describing—and what we're seeing—is increasingly sophisticated algorithms, that are getting better and better at answering more and more narrowly defined questions: https://www.theguardian.com/technology/2021/mar/08/typographic-attack-pen-paper-fool-ai-thinking-apple-ipod-clip https://www.theguardian.com/technology/2021/mar/08/typograph...

cornel_io 6y ago

> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. The dead horse that I like to beat is that there has been no progress in AI so far, the past 5-10 years of successes are all about mere perception. If you like the Kahneman "fast and slow" framework, almost without exception the systems we're seeing today are fast, "system 1" responders, which take input and need to make an instant decision. That's freaking awesome, and was way out of reach even 15 years ago, so I'm throwing no shade at all on that achievement! It's astounding how much turns out to be possible in systems like this - a priori, I never would have thought that transformers could do as much as they've proven to be able to, because frankly the architecture is super limited and it's super unlikely that humans are able to extract anywhere near as much meaning from an instant, non-reflective pass over a paragraph of text as GPT-3 does. But there's a lot about those systems that makes them much easier to design and train, not the least of which is that descent via backprop works great as a training strategy when the input and target output are available at the same time. Real "system 2" thought can be spread over minutes, hour, or days, and I don't care how far you unroll backprop-through-time, you're not going to train that effectively without some other innovation by simply following the obvious error metrics. If we can get there we will almost certainly see big data lose its pedestal: it's great to have, but humans don't need to read the entire Internet to understand a web page, that's an artifact of forcing this to be done by a model that doesn't have dynamics. I disagree with Hofstadter's view (at least when he wrote GEB and some of his other classics) that explicit abstract reasoning is the right way to solve any of this; my gut tells me that in the end we're going to end up still using some sort of neural architecture and abstract reasoning rules will be implicitly learned mostly locally based on internal "error" signals that guide state transitions. "Learning how to learn" is probably going to be a big part of that, because current systems don't learn, they are merely trained to perceive by brute forcing weights down the stack. But some serious shifts in research focus will be required in order to break through the weaknesses of today's systems, and they all point more in the direction of reasoning, which is woefully underprioritized in all but a very small handful of niche AI labs today.

jrumbut 6y ago

fulafel 6y ago

I feel the oft cited idea of GPUs being the key to the change is a bit exaggerated. They give a modest constant factor of speedup in exchange for more difficult programming model and esoteric hardware requirements, and as such give a chance of frontfrunning CPU computation a bit, but is it really significant if we zoom out a bit in the historical perspective?

joefourier 6y ago
CPU performance, especially single-core, has stalled in the last decade while GPU performance has kept improving. On paper, a 3080 has about 100x faster FP32 performance versus a modern gaming CPU, and in practice even considering memory bandwidth you do fully get a speed-bump of one or two orders of magnitude. I would not call that a "modest constant factor". And a lot of the recent CPU performance gains are due to SIMD and multithreading, which are basically making CPUs more GPU-like, rather than the continuous improvement of serial performance as we've had up until ~2005.

mindcrime 6y ago

Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. Just to play Devil's Advocate ever so slightly: there are people out there who would say that there hasn't been any "progress in AI" for quite some time, or at least very little so. And they would probably argue further that the apparent progress you are referring to is just progress in "mere pattern recognition" or something like that. I'm not sure myself. I do lean at least a little towards the idea that there is a qualitative difference between most of modern ML and many aspects of what we would call "intelligence". As such, my interests in all of this remain around the intersection of ML and GOFAI, and the possibility of hybrid systems that use elements of both. But I can't rule out the possibility that it will eventually be found to be the case that all of "intelligence" does indeed reduce to "mere pattern recognition" in some sense. And Geoffrey Hinton may be correct in saying that we don't need, and never will need, any kind of "hybridization" and that neural networks can do it all.

galaxyLogic 6y ago
Neural Networks can do it all after they emerge symbolic representations, a way to represent logic and mathematics. A neural network that just "feels" that Fermat's Last Theorem is true, is much less intelligent than one that can produce the proof of it and present that to us, so we can trust that what it's saying is true. If you can't do symbolic manipulation, you are not really intelligent, artificial or otherwise, I would say.
- The_rationalist 6y ago
  neural network are extremely inefficient at learning symbolic reasoning by design and I doubt a radically new kind of neural network will be discovered, we have pretty much made an exhaustive analysis.

credit_guy 6y ago

> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power Maybe it's a bit of both. Sure, large DL models use lots of compute, but successful DL applications require some insight into the problem. For some reason, people like to de-emphasize this insight. The story is that a DL model will discover by itself which features are important, which are not, and you just provide the training data, and press a button. Thousands of people do just that, and end up with mediocre results. Thousands and thousands of absolutely mediocre papers get published, and receive acclaim instead of derision. The truly boundary shifting results always use deep insight. Like what comes out of DeepMind (AlphaGo, AlphaZero, AlphaFold).

breck 6y ago

> we will discover that large datasets are in some ways overrated In the short run, expand then compress, expand then compress. In the long run, the size of the model will always compress toward the capacity of the most intelligent individual.

mellosouls 6y ago

"Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power[...]" As has been argued in these pages many times before there has been no obvious progress in AGI, although certainly progress in AI in its weak sense has been impressive in the last few years. I know you didn't say AGI but its important to make that distinction as the book was very interested in that as a subject.

ProfHewitt 6y ago
Fundamental progress in Intelligent Systems will require that systems become facile in constructing and using abstractions. Intelligent Systems will be developed and deployed in this decade. However, full equivalence to humans will be probably not be achieved in this decade. See the following for more information: "Robust Inference for Universal Intelligent Systems" https://papers.ssrn.com/abstract=3789701 https://papers.ssrn.com/abstract=3789701

deleted 6y ago

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ProfHewitt 6y ago

The aspect of GEB that has not withstood the test of time is the Gödel part. [Gödel 1931] seemed to have settled the issue of inferential undecidablity{∃[Ψ:Proposition](⊬Ψ)∧(⊬¬Ψ)} in the positive using the proposition I'mUnprovable, such that I’mUnprovable⇔⊬I’mUnprovable. However, existence of I’mUnprovable would enable the following cyberattack [cf. Wittgenstein 1937]: Proof of a contradiction in foundations: First prove I’mUnprovable using proof by contradictions as follows: In order to obtain a contradiction, hypothesize ¬I’mUnprovable. Therefore ⊢I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable). Consequently, ⊢⊢I’mUnprovable using ByProvabilityOfProofs {⊢∀[Ψ:Proposition](⊢Ψ)⇒⊢⊢Ψ}. However, ⊢¬I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable), which is the desired contradiction. Using proof by contradiction, ⊢I’mUnprovable meaning ⊢⊢I’mUnprovable using ByProvabilityOfProofs. However, ⊢¬I’mUnprovable (using I’mUnprovable⇔⊬I’mUnprovable), which is a contradiction in foundations. Strong types prevent construction of I’mUnprovable using the following recursive definition: I’mUnprovable:Proposition≡⊬I’mUnprovable. Note that (⊬I’mUnprovable):Proposition<i+1> because I’mUnprovable is a propositional variable in the right hand side of the definition of I’mUnprovable:Proposition. Consequently, I’mUnprovable:Proposition⇒I’mUnprovable:Proposition<i+1>, which is a contradiction. The crucial issue with the proofs in [Gödel 1931] is that the Gödel number of a proposition does not capture its order. Because of orders of propositions, the Diagonal Lemma [Gödel 1931] fails to construct the proposition I’mUnprovable. See the following for more explanation: "Epistemology Cyberattacks" https://papers.ssrn.com/abstract=3603021 https://papers.ssrn.com/abstract=3603021

andrepd 6y ago

There is exactly zero progress in AI coming from neural networks. They are nonlinear classifiers, nothing more. To see an example of a task which requires actual artificial intelligence reasoning, see this article by none other than Hofstadter himself :p https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...