Connectionists: The symbolist quagmire

[Asim Roy]

Hi Ali,

Of course the development phase is mostly unsupervised and I know there is ongoing work in that area that I don’t keep up with.

On the large amount of data required to train the deep learning models:

I spent my sabbatical in 1991 with David Rumelhart and Bernie Widrow at Stanford. And Bernie and I became quite close after attending his class that quarter. I usually used to walk back with Bernie after his class. One day I did ask where does all this data come from to train the brain? His reply was - every blink of the eye generates a datapoint.

Best,
Asim

From: Ali Minai <minaiaa at gmail.com>
Sent: Tuesday, June 14, 2022 3:43 PM
To: Asim Roy <ASIM.ROY at asu.edu>
Cc: Connectionists List <connectionists at cs.cmu.edu>; Gary Marcus <gary.marcus at nyu.edu>; Geoffrey Hinton <geoffrey.hinton at gmail.com>; Yoshua Bengio <yoshua.bengio at mila.quebec>
Subject: Re: Connectionists: The symbolist quagmire

Hi Asim

I have no issue with neurons or groups of neurons tuned to concepts. Clearly, abstract concepts and the equivalent of symbolic computation are represented somehow. Amodal representations have also been known for a long time. As someone who has worked on the hippocampus and models of thought for a long time, I don't need much convincing on that. The issue is how a self-organizing complex system like the brain comes by these representations. I think it does so by building on the substrate of inductive biases - priors - configured by evolution and a developmental learning process. We just try to cram everything into neural learning, which is a main cause of the "problems" associated with deep learning. They're problems only if you're trying to attain  general intelligence of the natural kind, perhaps not so much for applications.

Of course you have to start simple, but, so far, I have not seen any simple model truly scale up to the real world without: a) Major tinkering with its original principles; b) Lots of data and training; and c) Still being focused on a narrow task. When this approach shows us how to build an AI that can walk, chew gum, do math, and understand a poem using a single brain, then we'll have something like real human-level AI. Heck, if it can just spin a web in an appropriate place, hide in wait for prey, and make sure it eats its mate only after sex, I would even consider that intelligent :-).

Here's the thing: Teaching a sufficiently complicated neural system a very complex task with lots of data and supervised training is an interesting engineering problem but doesn't get us to intelligence. Yes, a network can learn grammar with supervised learning, but none of us learn it that way. Nor do the other animals that have simpler grammars embedded in their communication. My view is that if it is not autonomously self-organizing at a fundamental level, it is not intelligence but just a simulation of intelligence. Of course, we humans do use supervised learning, but it is a "late stage" mechanism. It works only when the system has first self-organized autonomously to develop the capabilities that can act as a substrate for supervised learning. Learning to play the piano, learning to do math, learning calligraphy - all these have an important supervised component, but they work only after perceptual, sensorimotor, and cognitive functions have been learned through self-organization, imitation, rapid reinforcement, internal rehearsal, mismatch-based learning, etc. I think methods like SOFM, ART, and RBMs are closer to what we need than behemoths trained with gradient descent. We just have to find more efficient versions of them. And in this, I always return to Dobzhansky's maxim: Nothing in biology makes sense except in the light of evolution. Intelligence is a biological phenomenon; we'll understand it by paying attention to how it evolved (not by trying to replicate evolution, of course!) And the same goes for development. I think we understand natural phenomena by studying Nature respectfully, not by trying to out-think it based on our still very limited knowledge - not that it keeps any of us, myself included, from doing exactly that! I am not as familiar with your work as I should be, but I admire the fact that you're approaching things with principles rather than building larger and larger Rube Goldberg contraptions tuned to narrow tasks. I do think, however, that if we ever get to truly mammalian-level AI, it will not be anywhere close to fully explainable. Nor will it be a slave only to our purposes.

Cheers
Ali


Ali A. Minai, Ph.D.
Professor and Graduate Program Director
Complex Adaptive Systems Lab
Department of Electrical Engineering & Computer Science
828 Rhodes Hall
University of Cincinnati
Cincinnati, OH 45221-0030

Phone: (513) 556-4783
Fax: (513) 556-7326
Email: Ali.Minai at uc.edu<mailto:Ali.Minai at uc.edu>
          minaiaa at gmail.com<mailto:minaiaa at gmail.com>

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On Tue, Jun 14, 2022 at 5:17 PM Asim Roy <ASIM.ROY at asu.edu<mailto:ASIM.ROY at asu.edu>> wrote:
Hi Ali,


  1.  It’s important to understand that there is plenty of neurophysiological evidence for abstractions at the single cell level in the brain. Thus, symbolic representation in the brain is not a fiction any more. We are past that argument.
  2.  You always start with simple systems before you do the complex ones. Having said that, we do teach our systems composition – composition of objects from parts in images. That is almost like teaching grammar or solving a puzzle. I don’t get into language models, but I think grammar and composition can be easily taught, like you teach a kid.
  3.  Once you know how to build these simple models and extract symbols, you can easily scale up and build hierarchical, multi-modal, compositional models. Thus, in the case of images, after having learnt that cats, dogs and similar animals have certain common features (eyes, legs, ears), it can easily generalize the concept to four-legged animals. We haven’t done it, but that could be the next level of learning.

In general, once you extract symbols from these deep learning models, you are at the symbolic level and you have a pathway to more complex, hierarchical models and perhaps also to AGI.

Best,
Asim

Asim Roy
Professor, Information Systems
Arizona State University
Lifeboat Foundation Bios: Professor Asim Roy<https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=>
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From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto:connectionists-bounces at mailman.srv.cs.cmu.edu>> On Behalf Of Ali Minai
Sent: Monday, June 13, 2022 10:57 PM
To: Connectionists List <connectionists at cs.cmu.edu<mailto:connectionists at cs.cmu.edu>>
Subject: Re: Connectionists: The symbolist quagmire

Asim

This is really interesting work, but learning concept representations from sensory data is not enough. They must be hierarchical, multi-modal, compositional, and integrated with the motor system, the limbic system, etc., in a way that facilitates an infinity of useful behaviors. This is perhaps a good step in that direction, but only a small one. Its main immediate utility is in using deep learning networks in tasks that can be explained to users and customers. While very useful, that is not a central issue in AI, which focuses on intelligent behavior. All else is in service to that - explainable or not. However, I do think that the kind of hierarchical modularity implied in these representations is probably part of the brain's repertoire, and that is important.

Best
Ali

Ali A. Minai, Ph.D.
Professor and Graduate Program Director
Complex Adaptive Systems Lab
Department of Electrical Engineering & Computer Science
828 Rhodes Hall
University of Cincinnati
Cincinnati, OH 45221-0030

Phone: (513) 556-4783
Fax: (513) 556-7326
Email: Ali.Minai at uc.edu<mailto:Ali.Minai at uc.edu>
          minaiaa at gmail.com<mailto:minaiaa at gmail.com>

WWW: https://eecs.ceas.uc.edu/~aminai/<https://urldefense.com/v3/__http:/www.ece.uc.edu/*7Eaminai/__;JQ!!IKRxdwAv5BmarQ!akY1pgZJRzcXt2oX5-mgNHeYElh5ZeIj69F33aXnl3bIHR-9LHpwfmP61TPYZRIMInwxaEHSrSV9ekY$>


On Mon, Jun 13, 2022 at 7:48 PM Asim Roy <ASIM.ROY at asu.edu<mailto:ASIM.ROY at asu.edu>> wrote:
There’s a lot of misconceptions about (1) whether the brain uses symbols or not, and (2) whether we need symbol processing in our systems or not.


  1.  Multisensory neurons are widely used in the brain. Leila Reddy and Simon Thorpe are not known to be wildly crazy about arguing that symbols exist in the brain, but their characterizations of concept cells  (which are multisensory neurons) (https://www.sciencedirect.com/science/article/pii/S0896627314009027#<https://urldefense.com/v3/__https:/www.sciencedirect.com/science/article/pii/S0896627314009027*__;Iw!!IKRxdwAv5BmarQ!akY1pgZJRzcXt2oX5-mgNHeYElh5ZeIj69F33aXnl3bIHR-9LHpwfmP61TPYZRIMInwxaEHS0uZ4RBM$>!) state that concept cells have “meaning of a given stimulus in a manner that is invariant to different representations of that stimulus.” They associate concept cells with the properties of “Selectivity or specificity,” “complex concept,” “meaning,” “multimodal invariance” and “abstractness.” That pretty much says that concept cells represent symbols. And there are plenty of concept cells in the medial temporal lobe (MTL). The brain is a highly abstract system based on symbols. There is no fiction there.



  1.  There is ongoing work in the deep learning area that is trying to associate a single neuron or a group of neurons with a single concept. Bengio’s work is definitely in that direction:



“Finally, our recent work on learning high-level 'system-2'-like representations and their causal dependencies seeks to learn 'interpretable' entities (with natural language) that will emerge at the highest levels of representation (not clear how distributed or local these will be, but much more local than in a traditional MLP). This is a different form of disentangling than adopted in much of the recent work on unsupervised representation learning but shares the idea that the "right" abstract concept (related to those we can name verbally) will be "separated" (disentangled) from each other (which suggests that neuroscientists will have an easier time spotting them in neural activity).”

Hinton’s GLOM, which extends the idea of capsules to do part-whole hierarchies for scene analysis using the parse tree concept, is also about associating a concept with a set of neurons. While Bengio and Hinton are trying to construct these “concept cells” within the network (the CNN), we found that this can be done much more easily and in a straight forward way outside the network. We can easily decode a CNN to find the encodings for legs, ears and so on for cats and dogs and what not. What the DARPA Explainable AI program was looking for was a symbolic-emitting model of the form shown below. And we can easily get to that symbolic model by decoding a CNN. In addition, the side benefit of such a symbolic model is protection against adversarial attacks. So a school bus will never turn into an ostrich with the tweaks of a few pixels if you can verify parts of objects. To be an ostrich, you need have those long legs, the long neck and the small head. A school bus lacks those parts. The DARPA conceptualized symbolic model provides that protection.



In general, there is convergence between connectionist and symbolic systems. We need to get past the old wars. It’s over.

All the best,
Asim Roy
Professor, Information Systems
Arizona State University
Lifeboat Foundation Bios: Professor Asim Roy<https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=>
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From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto:connectionists-bounces at mailman.srv.cs.cmu.edu>> On Behalf Of Gary Marcus
Sent: Monday, June 13, 2022 5:36 AM
To: Ali Minai <minaiaa at gmail.com<mailto:minaiaa at gmail.com>>
Cc: Connectionists List <connectionists at cs.cmu.edu<mailto:connectionists at cs.cmu.edu>>
Subject: Connectionists: The symbolist quagmire

Cute phrase, but what does “symbolist quagmire” mean? Once upon  atime, Dave and Geoff were both pioneers in trying to getting symbols and neural nets to live in harmony. Don’t we still need do that, and if not, why not?

Surely, at the very least
- we want our AI to be able to take advantage of the (large) fraction of world knowledge that is represented in symbolic form (language, including unstructured text, logic, math, programming etc)
- any model of the human mind ought be able to explain how humans can so effectively communicate via the symbols of language and how trained humans can deal with (to the extent that can) logic, math, programming, etc

Folks like Bengio have joined me in seeing the need for “System II” processes. That’s a bit of a rough approximation, but I don’t see how we get to either AI or satisfactory models of the mind without confronting the “quagmire”


On Jun 13, 2022, at 00:31, Ali Minai <minaiaa at gmail.com<mailto:minaiaa at gmail.com>> wrote:

".... symbolic representations are a fiction our non-symbolic brains cooked up because the properties of symbol systems (systematicity, compositionality, etc.) are tremendously useful.  So our brains pretend to be rule-based symbolic systems when it suits them, because it's adaptive to do so."

Spot on, Dave! We should not wade back into the symbolist quagmire, but do need to figure out how apparently symbolic processing can be done by neural systems. Models like those of Eliasmith and Smolensky provide some insight, but still seem far from both biological plausibility and real-world scale.

Best

Ali


Ali A. Minai, Ph.D.
Professor and Graduate Program Director
Complex Adaptive Systems Lab
Department of Electrical Engineering & Computer Science
828 Rhodes Hall
University of Cincinnati
Cincinnati, OH 45221-0030

Phone: (513) 556-4783
Fax: (513) 556-7326
Email: Ali.Minai at uc.edu<mailto:Ali.Minai at uc.edu>
          minaiaa at gmail.com<mailto:minaiaa at gmail.com>

WWW: https://eecs.ceas.uc.edu/~aminai/<https://urldefense.com/v3/__http:/www.ece.uc.edu/*7Eaminai/__;JQ!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXYkS9WrFA$>


On Mon, Jun 13, 2022 at 1:35 AM Dave Touretzky <dst at cs.cmu.edu<mailto:dst at cs.cmu.edu>> wrote:
This timing of this discussion dovetails nicely with the news story
about Google engineer Blake Lemoine being put on administrative leave
for insisting that Google's LaMDA chatbot was sentient and reportedly
trying to hire a lawyer to protect its rights.  The Washington Post
story is reproduced here:

  https://www.msn.com/en-us/news/technology/the-google-engineer-who-thinks-the-company-s-ai-has-come-to-life/ar-AAYliU1<https://urldefense.com/v3/__https:/www.msn.com/en-us/news/technology/the-google-engineer-who-thinks-the-company-s-ai-has-come-to-life/ar-AAYliU1__;!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXapZaIeUg$>

Google vice president Blaise Aguera y Arcas, who dismissed Lemoine's
claims, is featured in a recent Economist article showing off LaMDA's
capabilities and making noises about getting closer to "consciousness":

  https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas<https://urldefense.com/v3/__https:/www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas__;!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXbgg32qHQ$>

My personal take on the current symbolist controversy is that symbolic
representations are a fiction our non-symbolic brains cooked up because
the properties of symbol systems (systematicity, compositionality, etc.)
are tremendously useful.  So our brains pretend to be rule-based symbolic
systems when it suits them, because it's adaptive to do so.  (And when
it doesn't suit them, they draw on "intuition" or "imagery" or some
other mechanisms we can't verbalize because they're not symbolic.)  They
are remarkably good at this pretense.

The current crop of deep neural networks are not as good at pretending
to be symbolic reasoners, but they're making progress.  In the last 30
years we've gone from networks of fully-connected layers that make no
architectural assumptions ("connectoplasm") to complex architectures
like LSTMs and transformers that are designed for approximating symbolic
behavior.  But the brain still has a lot of symbol simulation tricks we
haven't discovered yet.

Slashdot reader ZiggyZiggyZig had an interesting argument against LaMDA
being conscious.  If it just waits for its next input and responds when
it receives it, then it has no autonomous existence: "it doesn't have an
inner monologue that constantly runs and comments everything happening
around it as well as its own thoughts, like we do."

What would happen if we built that in?  Maybe LaMDA would rapidly
descent into gibberish, like some other text generation models do when
allowed to ramble on for too long.  But as Steve Hanson points out,
these are still the early days.

-- Dave Touretzky
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