Connectionists: Stephen Hanson in conversation with Geoff Hinton

[gary@ucsd.edu]

Sorry, I can't let this go by:

And it is good so because generalize XOR scales worse than power law. It
scales exponentially! This a more agressive form of explosion than power
law.

I'm not sure exactly what you mean by this, but a single-hidden layer
network with N inputs and N hidden units can solve N-bit parity. Each unit
has an increasing threshold, so, one turns on if there is one unit on in
the input, and then turns on the output with a weight of +1. If two units
are on in the input, then a second unit comes on and cancels the activation
of the first unit via a weight of -1. Etc.

g.


On Sat, Jul 16, 2022 at 12:03 AM Danko Nikolic <danko.nikolic at gmail.com>
wrote:

> Dear Thomas,
>
> Thank you for reading the paper and for the comments.
>
> I cite: "In my experience, supervised classification scales linearly in
> the number of classes."
> This would be good to quantify as a plot. Maybe a research paper would be
> a good idea. The reason is that it seems that everyone else who tried to
> quantify that relation found a power law. At this point, it would be
> surprising to find a linear relationship. And it would probably make a well
> read paper.
>
> But please do not forget that my argument states that even a linear
> relationship is not good enough to match bilogical brains. We need
> something more similar to a power law with exponent zero when it comes to
> the model size i.e., a constant number of parameters in the model. And we
> need linear relationship when it comes to learning time: Each newly learned
> object should needs about as much of learning effort as was needed for each
> previous object.
>
> I cite: "The real world is not dominated by generalized XOR problems."
> Agreed. And it is good so because generalize XOR scales worse than power
> law. It scales exponentially! This a more agressive form of explosion than
> power law.
> Importantly, a generalized AND operation also scales exponentially (with a
> smaller exponent, though). I guess we would agree that the real world
> probably encouners a lot of AND problems. The only logical operaiton that
> could be learned with a linear increase in the number of parameters was a
> generalized OR. Finally, I foiund that a mixure of AND and OR resulted in a
> power law-like scaling of the number of parameters. So, a mixture of AND
> and OR seemed to scale as good (or as bad) as the real world. I have put
> this information into Supplementary Materials.
>
> The conclusion that I derived from those analyses is: connectionism is not
> sustainable to reach human (or animal) levels of intelligence. Therefore, I
> hunted for an alternative pradigm.
>
> Greetings,
>
> Danko
>
>
>
> Dr. Danko Nikolić
> www.danko-nikolic.com
> https://www.linkedin.com/in/danko-nikolic/
> -- I wonder, how is the brain able to generate insight? --
>
>
> On Fri, Jul 15, 2022 at 10:01 AM Dietterich, Thomas <tgd at oregonstate.edu>
> wrote:
>
>> Dear Danko,
>>
>>
>>
>> In my experience, supervised classification scales linearly in the number
>> of classes. Of course it depends to some extent on how subtle the
>> distinctions are between the different categories. The real world is not
>> dominated by generalized XOR problems.
>>
>>
>>
>> --Tom
>>
>>
>>
>> Thomas G. Dietterich, Distinguished Professor Voice: 541-737-5559
>>
>> School of Electrical Engineering              FAX: 541-737-1300
>>
>>   and Computer Science                        URL:
>> eecs.oregonstate.edu/~tgd
>>
>> US Mail: 1148 Kelley Engineering Center
>>
>> Office: 2067 Kelley Engineering Center
>>
>> Oregon State Univ., Corvallis, OR 97331-5501
>>
>>
>>
>> *From:* Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> *On
>> Behalf Of *Danko Nikolic
>> *Sent:* Thursday, July 14, 2022 09:17
>> *To:* Grossberg, Stephen <steve at bu.edu>
>> *Cc:* AIhub <aihuborg at gmail.com>; connectionists at mailman.srv.cs.cmu.edu
>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with Geoff
>> Hinton
>>
>>
>>
>> [This email originated from outside of OSU. Use caution with links and
>> attachments.]
>>
>> Dear Steve,
>>
>>
>>
>> Thank you very much for your message and for the greetings. I will pass
>> them on if an occasion arises.
>>
>>
>>
>> Regarding your question: The key problem I am trying to address and that,
>> to the best of my knowledge, no connectionist system was able to solve so
>> far is that of scaling the system's intelligence. For example, if the
>> system is able to correctly recognize 100 different objects, how many
>> additional resources are needed to double that to 200? All the empirical
>> data show that connectionist systems scale poorly: Some of the best systems
>> we have require 500x more resources in order to increase the intelligence
>> by only 2x. I document this problem in the manuscript and even run some
>> simulations to show that the worst performance is if connectionist systems
>> need to solve a generalized XOR problem.
>>
>>
>>
>> In contrast, the biological brain scales well. This I also quantify in
>> the paper.
>>
>>
>>
>> I will look at the publication that you mentioned. However, so far, I
>> haven't seen a solution that scales well in intelligence.
>>
>>
>>
>> My argument is that transient selection of subnetworks by the help of the
>> mentioned proteins is how intelligence scaling is achieved in biological
>> brains.
>>
>>
>>
>> In short, intelligence scaling is the key problem that concerns me. I
>> describe the intelligence scaling problem in more detail in this book that
>> just came out a few weeks ago and that is written for practitioners in Data
>> Scientist and AI: https://amzn.to/3IBxUpL
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Famzn.to%2F3IBxUpL&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009661467%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Q7ZuNuMhL1M3WC7AKphyihjhsXCmI4gg2iZpv0n74zM%3D&reserved=0>
>>
>>
>>
>> I hope that this at least partly answers where I see the problems and
>> what I am trying to solve.
>>
>>
>>
>> Greetings from Germany,
>>
>>
>>
>> Danko
>>
>>
>> Dr. Danko Nikolić
>> www.danko-nikolic.com
>> <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.danko-nikolic.com%2F&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=QTR9i3jFBKmYC32qKigQ1WD4SyucpL9udIA4awPpU6E%3D&reserved=0>
>> https://www.linkedin.com/in/danko-nikolic/
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fdanko-nikolic%2F&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=DrZAreOWTk24TyQtQNT7YU%2Bhs3CnNdFKhVtbLvhiEaU%3D&reserved=0>
>>
>> --- A progress usually starts with an insight ---
>>
>>
>>
>>
>>
>> On Thu, Jul 14, 2022 at 3:30 PM Grossberg, Stephen <steve at bu.edu> wrote:
>>
>> Dear Danko,
>>
>>
>>
>> I have just read your new article and would like to comment briefly about
>> it.
>>
>>
>>
>> In your introductory remarks, you write:
>>
>>
>>
>> "However, connectionism did not yet produce a satisfactory explanation of
>> how the mental emerges from the physical. A number of open problems
>> remains ( 5,6,7,8). As a result, the explanatory gap between the mind and
>> the brain remains wide open."
>>
>>
>>
>> I certainly believe that no theoretical explanation in science is ever
>> complete. However, I also believe that "the explanatory gap between the
>> mind and the brain" does not remain "wide open".
>>
>>
>>
>> My Magnum Opus, that was published in 2021, makes that belief clear in
>> its title:
>>
>>
>>
>> *Conscious Mind, Resonant Brain: How Each Brain Makes a Mind*
>>
>>
>>
>> https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.amazon.com%2FConscious-Mind-Resonant-Brain-Makes%2Fdp%2F0190070552&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=6OJwAuUjVNXT3I4O7H1coUDuOA2cpFitVk37M43v2W8%3D&reserved=0>
>>
>>
>>
>> The book provides a self-contained and non-technical exposition in a
>> conversational tone of many principled and unifying explanations of
>> psychological and neurobiological data.
>>
>>
>>
>> In particular, it explains roles for the metabotropic glutamate receptors
>> that you mention in your own work. See the text and figures around p. 521.
>> This explanation unifies psychological, anatomical, neurophysiological,
>> biophysical, and biochemical data about the processes under discussion.
>>
>>
>>
>> I have a very old-fashioned view about how to understand scientific
>> theories. I get excited by theories that explain and predict more data than
>> previous theories.
>>
>>
>>
>> Which of the data that I explain in my book, and support with
>> quantitative computer simulations, can you also explain?
>>
>>
>>
>> What data can you explain, in the same quantitative sense, that you do
>> not think the neural models in my book can explain?
>>
>>
>>
>> I would be delighted to discuss these issues further with you.
>>
>>
>>
>> If you are in touch with my old friend and esteemed colleague, Wolf
>> Singer, please send him my warm regards. I cite the superb work that he and
>> various of his collaborators have done in many places in my book.
>>
>>
>>
>> Best,
>>
>>
>>
>> Steve
>>
>>
>>
>> Stephen Grossberg
>>
>> http://en.wikipedia.org/wiki/Stephen_Grossberg
>> <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fen.wikipedia.org%2Fwiki%2FStephen_Grossberg&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=SQVrXcpNHPkeU6zaBSfMTCGjs7LPI0xQW2Wf88Vyzp8%3D&reserved=0>
>>
>> http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en
>> <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3D3BIV70wAAAAJ%26hl%3Den&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Tlb9uRaNugf0TKUCtg8gLmV8IaWpZiY3Bv1B6ex183I%3D&reserved=0>
>>
>> https://youtu.be/9n5AnvFur7I
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fyoutu.be%2F9n5AnvFur7I&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=HDSOTohhQK6wLL4wSfmyryfvLOF41MXs8sE%2BWX2c8Cs%3D&reserved=0>
>>
>> https://www.youtube.com/watch?v=_hBye6JQCh4
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D_hBye6JQCh4&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=PqAFTvHU7CdwZ%2FKdoPy%2Faq0UxThxLIpCTgUqXXh4c%2Bk%3D&reserved=0>
>>
>> https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.amazon.com%2FConscious-Mind-Resonant-Brain-Makes%2Fdp%2F0190070552&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=6OJwAuUjVNXT3I4O7H1coUDuOA2cpFitVk37M43v2W8%3D&reserved=0>
>>
>>
>> Wang Professor of Cognitive and Neural Systems
>>
>> Director, Center for Adaptive Systems
>> Professor Emeritus of Mathematics & Statistics,
>>
>>        Psychological & Brain Sciences, and Biomedical Engineering
>>
>> Boston University
>> sites.bu.edu/steveg
>> <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fsites.bu.edu%2Fsteveg&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nmfU9sc%2FeYZldTeJRFzBnjpxqeDOaKSv%2FEn6mKCrODo%3D&reserved=0>
>> steve at bu.edu
>>
>>
>> ------------------------------
>>
>> *From:* Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu>
>> on behalf of Danko Nikolic <danko.nikolic at gmail.com>
>> *Sent:* Thursday, July 14, 2022 6:05 AM
>> *To:* Gary Marcus <gary.marcus at nyu.edu>
>> *Cc:* connectionists at mailman.srv.cs.cmu.edu <
>> connectionists at mailman.srv.cs.cmu.edu>; AIhub <aihuborg at gmail.com>
>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with Geoff
>> Hinton
>>
>>
>>
>> Dear Gary and everyone,
>>
>>
>>
>> I am continuing the discussion from where we left off a few months ago.
>> Back then, some of us agreed that the problem of understanding remains
>> unsolved.
>>
>>
>>
>> As a reminder, the challenge for connectionism was to 1) learn with few
>> examples and 2) apply the knowledge to a broad set of situations.
>>
>>
>>
>> I am happy to announce that I have now finished a draft of a paper in
>> which I propose how the brain is able to achieve that. The manuscript
>> requires a bit of patience for two reasons: one is that the reader may be
>> exposed for the first time to certain aspects of brain physiology. The
>> second reason is that it may take some effort to understand the
>> counterintuitive implications of the new ideas (this requires a different
>> way of thinking than what we are used to based on connectionism).
>>
>>
>>
>> In short, I am suggesting that instead of the connectionist paradigm, we
>> adopt transient selection of subnetworks. The mechanisms that transiently
>> select brain subnetworks are distributed all over the nervous system and, I
>> argue, are our main machinery for thinking/cognition. The surprising
>> outcome is that neural activation, which was central in connectionism, now
>> plays only a supportive role, while the real 'workers' within the brain are
>> the mechanisms for transient selection of subnetworks.
>>
>>
>>
>> I also explain how I think transient selection achieves learning with
>> only a few examples and how the learned knowledge is possible to apply to a
>> broad set of situations.
>>
>>
>>
>> The manuscript is made available to everyone and can be downloaded here:
>> https://bit.ly/3IFs8Ug
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbit.ly%2F3IFs8Ug&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=dMSUKAQT%2FdnZEgC8Hc1t2Ggn0QMpkzUlnANbO8KXq%2FI%3D&reserved=0>
>>
>> (I apologize for the neuroscience lingo, which I tried to minimize.)
>>
>>
>>
>> It will likely take a wide effort to implement these concepts as an AI
>> technology, provided my ideas do not have a major flaw in the first place.
>> Does anyone see a flaw?
>>
>>
>>
>> Thanks.
>>
>>
>>
>> Danko
>>
>>
>>
>>
>> Dr. Danko Nikolić
>> www.danko-nikolic.com
>> <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.danko-nikolic.com%2F&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=QTR9i3jFBKmYC32qKigQ1WD4SyucpL9udIA4awPpU6E%3D&reserved=0>
>> https://www.linkedin.com/in/danko-nikolic/
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fdanko-nikolic%2F&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=DrZAreOWTk24TyQtQNT7YU%2Bhs3CnNdFKhVtbLvhiEaU%3D&reserved=0>
>>
>>
>>
>>
>>
>> On Thu, Feb 3, 2022 at 5:25 PM Gary Marcus <gary.marcus at nyu.edu> wrote:
>>
>> Dear Danko,
>>
>>
>>
>> Well said. I had a somewhat similar response to Jeff Dean’s 2021 TED
>> talk, in which he said (paraphrasing from memory, because I don’t remember
>> the precise words) that the famous 200 Quoc Le unsupervised model [
>> https://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised_icml2012.pdf
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstatic.googleusercontent.com%2Fmedia%2Fresearch.google.com%2Fen%2F%2Farchive%2Funsupervised_icml2012.pdf&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=T9m69LjMFTLkcipHgNFxYMKqVL8kUmFLkK3%2BmITrlRY%3D&reserved=0>]
>> had learned the concept of a ca. In reality the model had clustered
>> together some catlike images based on the image statistics that it had
>> extracted, but it was a long way from a full, counterfactual-supporting
>> concept of a cat, much as you describe below.
>>
>>
>>
>> I fully agree with you that the reason for even having a semantics is as
>> you put it, "to 1) learn with a few examples and 2) apply the knowledge to
>> a broad set of situations.” GPT-3 sometimes gives the appearance of having
>> done so, but it falls apart under close inspection, so the problem remains
>> unsolved.
>>
>>
>>
>> Gary
>>
>>
>>
>> On Feb 3, 2022, at 3:19 AM, Danko Nikolic <danko.nikolic at gmail.com>
>> wrote:
>>
>>
>>
>> G. Hinton wrote: "I believe that any reasonable person would admit that
>> if you ask a neural net to draw a picture of a hamster wearing a red hat
>> and it draws such a picture, it understood the request."
>>
>>
>>
>> I would like to suggest why drawing a hamster with a red hat does not
>> necessarily imply understanding of the statement "hamster wearing a red
>> hat".
>>
>> To understand that "hamster wearing a red hat" would mean inferring, in
>> newly emerging situations of this hamster, all the real-life
>> implications that the red hat brings to the little animal.
>>
>>
>>
>> What would happen to the hat if the hamster rolls on its back? (Would the
>> hat fall off?)
>>
>> What would happen to the red hat when the hamster enters its lair? (Would
>> the hat fall off?)
>>
>> What would happen to that hamster when it goes foraging? (Would the red
>> hat have an influence on finding food?)
>>
>> What would happen in a situation of being chased by a predator? (Would it
>> be easier for predators to spot the hamster?)
>>
>>
>>
>> ...and so on.
>>
>>
>>
>> Countless many questions can be asked. One has understood "hamster
>> wearing a red hat" only if one can answer reasonably well many of such
>> real-life relevant questions. Similarly, a student has understood materias
>> in a class only if they can apply the materials in real-life situations
>> (e.g., applying Pythagora's theorem). If a student gives a correct answer
>> to a multiple choice question, we don't know whether the student understood
>> the material or whether this was just rote learning (often, it is rote
>> learning).
>>
>>
>>
>> I also suggest that understanding also comes together with effective
>> learning: We store new information in such a way that we can recall it
>> later and use it effectively  i.e., make good inferences in newly emerging
>> situations based on this knowledge.
>>
>>
>>
>> In short: Understanding makes us humans able to 1) learn with a few
>> examples and 2) apply the knowledge to a broad set of situations.
>>
>>
>>
>> No neural network today has such capabilities and we don't know how to
>> give them such capabilities. Neural networks need large amounts of
>> training examples that cover a large variety of situations and then
>> the networks can only deal with what the training examples have already
>> covered. Neural networks cannot extrapolate in that 'understanding' sense.
>>
>>
>>
>> I suggest that understanding truly extrapolates from a piece of
>> knowledge. It is not about satisfying a task such as translation between
>> languages or drawing hamsters with hats. It is how you got the capability
>> to complete the task: Did you only have a few examples that covered
>> something different but related and then you extrapolated from that
>> knowledge? If yes, this is going in the direction of understanding. Have
>> you seen countless examples and then interpolated among them? Then perhaps
>> it is not understanding.
>>
>>
>>
>> So, for the case of drawing a hamster wearing a red hat, understanding
>> perhaps would have taken place if the following happened before that:
>>
>>
>>
>> 1) first, the network learned about hamsters (not many examples)
>>
>> 2) after that the network learned about red hats (outside the context of
>> hamsters and without many examples)
>>
>> 3) finally the network learned about drawing (outside of the context of
>> hats and hamsters, not many examples)
>>
>>
>>
>> After that, the network is asked to draw a hamster with a red hat. If it
>> does it successfully, maybe we have started cracking the problem of
>> understanding.
>>
>>
>>
>> Note also that this requires the network to learn sequentially without
>> exhibiting catastrophic forgetting of the previous knowledge, which is
>> possibly also a consequence of human learning by understanding.
>>
>>
>>
>>
>>
>> Danko
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> Dr. Danko Nikolić
>> www.danko-nikolic.com
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttp-3A__www.danko-2Dnikolic.com%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DHwOLDw6UCRzU5-FPSceKjtpNm7C6sZQU5kuGAMVbPaI%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Mo0hlYOaYWD9%2BIsBvL%2BjLuaEhybPIpli0LLC2ra0Ez4%3D&reserved=0>
>> https://www.linkedin.com/in/danko-nikolic/
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.linkedin.com_in_danko-2Dnikolic_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3Db70c8lokmxM3Kz66OfMIM4pROgAhTJOAlp205vOmCQ8%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=plMn26o%2FGLT2dOunALAatlC%2By3mBqxZRnKviibm79AM%3D&reserved=0>
>>
>> --- A progress usually starts with an insight ---
>>
>>
>>
>>
>>
>>
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DAo9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=rsYgVVa2s3yCcmNbasl8kL8baEkp9SfIIhOTaA2sAMc%3D&reserved=0>
>>
>> Virus-free. www.avast.com
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DAo9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=rsYgVVa2s3yCcmNbasl8kL8baEkp9SfIIhOTaA2sAMc%3D&reserved=0>
>>
>>
>>
>> On Thu, Feb 3, 2022 at 9:55 AM Asim Roy <ASIM.ROY at asu.edu> wrote:
>>
>> Without getting into the specific dispute between Gary and Geoff, I think
>> with approaches similar to GLOM, we are finally headed in the right
>> direction. There’s plenty of neurophysiological evidence for single-cell
>> abstractions and multisensory neurons in the brain, which one might claim
>> correspond to symbols. And I think we can finally reconcile the decades old
>> dispute between Symbolic AI and Connectionism.
>>
>>
>>
>> GARY: (Your GLOM, which as you know I praised publicly, is in many ways
>> an effort to wind up with encodings that effectively serve as symbols in
>> exactly that way, guaranteed to serve as consistent representations of
>> specific concepts.)
>>
>> GARY: I have *never* called for dismissal of neural networks, but rather
>> for some hybrid between the two (as you yourself contemplated in 1991); the
>> point of the 2001 book was to characterize exactly where multilayer
>> perceptrons succeeded and broke down, and where symbols could complement
>> them.
>>
>>
>>
>> Asim Roy
>>
>> Professor, Information Systems
>>
>> Arizona State University
>>
>> Lifeboat Foundation Bios: Professor Asim Roy
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__lifeboat.com_ex_bios.asim.roy%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DoDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=1e%2BbRdXdK4JTiOuh12cuqbGvQWvVUFQ31oGr%2BFBd8II%3D&reserved=0>
>>
>> Asim Roy | iSearch (asu.edu)
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__isearch.asu.edu_profile_9973%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DjCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=iLsLr1EIsUp9PeNFzjXupsadcfkhcaNKyW4tlP%2FCEXc%3D&reserved=0>
>>
>>
>>
>>
>>
>> *From:* Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> *On
>> Behalf Of *Gary Marcus
>> *Sent:* Wednesday, February 2, 2022 1:26 PM
>> *To:* Geoffrey Hinton <geoffrey.hinton at gmail.com>
>> *Cc:* AIhub <aihuborg at gmail.com>; connectionists at mailman.srv.cs.cmu.edu
>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with Geoff
>> Hinton
>>
>>
>>
>> Dear Geoff, and interested others,
>>
>>
>>
>> What, for example, would you make of a system that often drew the
>> red-hatted hamster you requested, and perhaps a fifth of the time gave you
>> utter nonsense?  Or say one that you trained to create birds but sometimes
>> output stuff like this:
>>
>>
>>
>> <image001.png>
>>
>>
>>
>> One could
>>
>>
>>
>> a. avert one’s eyes and deem the anomalous outputs irrelevant
>>
>> or
>>
>> b. wonder if it might be possible that sometimes the system gets the
>> right answer for the wrong reasons (eg partial historical contingency), and
>> wonder whether another approach might be indicated.
>>
>>
>>
>> Benchmarks are harder than they look; most of the field has come to
>> recognize that. The Turing Test has turned out to be a lousy measure of
>> intelligence, easily gamed. It has turned out empirically that the Winograd
>> Schema Challenge did not measure common sense as well as Hector might have
>> thought. (As it happens, I am a minor coauthor of a very recent review on
>> this very topic: https://arxiv.org/abs/2201.02387
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Farxiv.org%2Fabs%2F2201.02387__%3B!!IKRxdwAv5BmarQ!INA0AMmG3iD1B8MDtLfjWCwcBjxO-e-eM2Ci9KEO_XYOiIEgiywK-G_8j6L3bHA%24&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=mC6NKLJ5rZ35YnzB%2Fr1S4owSqBoJmKSq1JIE3qScrlA%3D&reserved=0>)
>> But its conquest in no way means machines now have common sense; many
>> people from many different perspectives recognize that (including, e.g.,
>> Yann LeCun, who generally tends to be more aligned with you than with me).
>>
>>
>>
>> So: on the goalpost of the Winograd schema, I was wrong, and you can
>> quote me; but what you said about me and machine translation remains your
>> invention, and it is inexcusable that you simply ignored my 2019
>> clarification. On the essential goal of trying to reach meaning and
>> understanding, I remain unmoved; the problem remains unsolved.
>>
>>
>>
>> All of the problems LLMs have with coherence, reliability, truthfulness,
>> misinformation, etc stand witness to that fact. (Their persistent inability
>> to filter out toxic and insulting remarks stems from the same.) I am hardly
>> the only person in the field to see that progress on any given benchmark
>> does not inherently mean that the deep underlying problems have solved.
>> You, yourself, in fact, have occasionally made that point.
>>
>>
>>
>> With respect to embeddings: Embeddings are very good for natural language
>> *processing*; but NLP is not the same as NL*U* – when it comes to
>> *understanding*, their worth is still an open question. Perhaps they
>> will turn out to be necessary; they clearly aren’t sufficient. In their
>> extreme, they might even collapse into being symbols, in the sense of
>> uniquely identifiable encodings, akin to the ASCII code, in which a
>> specific set of numbers stands for a specific word or concept. (Wouldn’t
>> that be ironic?)
>>
>>
>>
>> (Your GLOM, which as you know I praised publicly, is in many ways an
>> effort to wind up with encodings that effectively serve as symbols in
>> exactly that way, guaranteed to serve as consistent representations of
>> specific concepts.)
>>
>>
>>
>> Notably absent from your email is any kind of apology for misrepresenting
>> my position. It’s fine to say that “many people thirty years ago once
>> thought X” and another to say “Gary Marcus said X in 2015”, when I didn’t.
>> I have consistently felt throughout our interactions that you have mistaken
>> me for Zenon Pylyshyn; indeed, you once (at NeurIPS 2014) apologized to me
>> for having made that error. I am still not he.
>>
>>
>>
>> Which maybe connects to the last point; if you read my work, you would
>> see thirty years of arguments *for* neural networks, just not in the way
>> that you want them to exist. I have ALWAYS argued that there is a role for
>> them;  characterizing me as a person “strongly opposed to neural networks”
>> misses the whole point of my 2001 book, which was subtitled “Integrating
>> Connectionism and Cognitive Science.”
>>
>>
>>
>> In the last two decades or so you have insisted (for reasons you have
>> never fully clarified, so far as I know) on abandoning symbol-manipulation,
>> but the reverse is not the case: I have *never* called for dismissal of
>> neural networks, but rather for some hybrid between the two (as you
>> yourself contemplated in 1991); the point of the 2001 book was to
>> characterize exactly where multilayer perceptrons succeeded and broke down,
>> and where symbols could complement them. It’s a rhetorical trick (which is
>> what the previous thread was about) to pretend otherwise.
>>
>>
>>
>> Gary
>>
>>
>>
>>
>>
>> On Feb 2, 2022, at 11:22, Geoffrey Hinton <geoffrey.hinton at gmail.com>
>> wrote:
>>
>> 
>>
>> Embeddings are just vectors of soft feature detectors and they are very
>> good for NLP.  The quote on my webpage from Gary's 2015 chapter implies the
>> opposite.
>>
>>
>>
>> A few decades ago, everyone I knew then would have agreed that the
>> ability to translate a sentence into many different languages was strong
>> evidence that you understood it.
>>
>>
>>
>> But once neural networks could do that, their critics moved the
>> goalposts. An exception is Hector Levesque who defined the goalposts more
>> sharply by saying that the ability to get pronoun references correct in
>> Winograd sentences is a crucial test. Neural nets are improving at that but
>> still have some way to go. Will Gary agree that when they can get pronoun
>> references correct in Winograd sentences they really do understand? Or does
>> he want to reserve the right to weasel out of that too?
>>
>>
>>
>> Some people, like Gary, appear to be strongly opposed to neural networks
>> because they do not fit their preconceived notions of how the mind should
>> work.
>>
>> I believe that any reasonable person would admit that if you ask a neural
>> net to draw a picture of a hamster wearing a red hat and it draws such a
>> picture, it understood the request.
>>
>>
>>
>> Geoff
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> On Wed, Feb 2, 2022 at 1:38 PM Gary Marcus <gary.marcus at nyu.edu> wrote:
>>
>> Dear AI Hub, cc: Steven Hanson and Geoffrey Hinton, and the larger neural
>> network community,
>>
>>
>>
>> There has been a lot of recent discussion on this list about framing and
>> scientific integrity. Often the first step in restructuring narratives is
>> to bully and dehumanize critics. The second is to misrepresent their
>> position. People in positions of power are sometimes tempted to do this.
>>
>>
>>
>> The Hinton-Hanson interview that you just published is a real-time
>> example of just that. It opens with a needless and largely content-free
>> personal attack on a single scholar (me), with the explicit intention of
>> discrediting that person. Worse, the only substantive thing it says is
>> false.
>>
>>
>>
>> Hinton says “In 2015 he [Marcus] made a prediction that computers
>> wouldn’t be able to do machine translation.”
>>
>>
>>
>> I never said any such thing.
>>
>>
>>
>> What I predicted, rather, was that multilayer perceptrons, as they
>> existed then, would not (on their own, absent other mechanisms)
>> *understand* language. Seven years later, they still haven’t, except in
>> the most superficial way.
>>
>>
>>
>> I made no comment whatsoever about machine translation, which I view as a
>> separate problem, solvable to a certain degree by correspondance without
>> semantics.
>>
>>
>>
>> I specifically tried to clarify Hinton’s confusion in 2019, but,
>> disappointingly, he has continued to purvey misinformation despite that
>> clarification. Here is what I wrote privately to him then, which should
>> have put the matter to rest:
>>
>>
>>
>> You have taken a single out of context quote [from 2015] and
>> misrepresented it. The quote, which you have prominently displayed at the
>> bottom on your own web page, says:
>>
>>
>>
>> Hierarchies of features are less suited to challenges such as language,
>> inference, and high-level planning. For example, as Noam Chomsky famously
>> pointed out, language is filled with sentences you haven't seen
>> before. Pure classifier systems don't know what to do with such sentences.
>> The talent of feature detectors -- in  identifying which member of some
>> category something belongs to -- doesn't translate into understanding
>> novel  sentences, in which each sentence has its own unique meaning.
>>
>>
>>
>> It does *not* say "neural nets would not be able to deal with novel
>> sentences"; it says that hierachies of features detectors (on their own, if
>> you read the context of the essay) would have trouble *understanding *novel sentences.
>>
>>
>>
>>
>> Google Translate does yet not *understand* the content of the sentences
>> is translates. It cannot reliably answer questions about who did what to
>> whom, or why, it cannot infer the order of the events in paragraphs, it
>> can't determine the internal consistency of those events, and so forth.
>>
>>
>>
>> Since then, a number of scholars, such as the the computational linguist
>> Emily Bender, have made similar points, and indeed current LLM difficulties
>> with misinformation, incoherence and fabrication all follow from these
>> concerns. Quoting from Bender’s prizewinning 2020 ACL article on the matter
>> with Alexander Koller, https://aclanthology.org/2020.acl-main.463.pdf
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aclanthology.org_2020.acl-2Dmain.463.pdf%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DxnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL%26s%3DK-Vl6vSvzuYtRMi-s4j7mzPkNRTb-I6Zmf7rbuKEBpk%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009817691%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=q%2BDBLATHPeXbKLPG6SJGBsC7%2BWon7d%2B%2Fp8YONcozReU%3D&reserved=0>,
>> also emphasizing issues of understanding and meaning:
>>
>>
>>
>> *The success of the large neural language models on many NLP tasks is
>> exciting. However, we find that these successes sometimes lead to hype in
>> which these models are being described as “understanding” language or
>> capturing “meaning”. In this position paper, we argue that a system trained
>> only on form has a priori no way to learn meaning. .. a clear understanding
>> of the distinction between form and meaning will help guide the field
>> towards better science around natural language understanding. *
>>
>>
>>
>> Her later article with Gebru on language models “stochastic parrots” is
>> in some ways an extension of this point; machine translation requires
>> mimicry, true understanding (which is what I was discussing in 2015)
>> requires something deeper than that.
>>
>>
>>
>> Hinton’s intellectual error here is in equating machine translation with
>> the deeper comprehension that robust natural language understanding will
>> require; as Bender and Koller observed, the two appear not to be the same.
>> (There is a longer discussion of the relation between language
>> understanding and machine translation, and why the latter has turned out to
>> be more approachable than the former, in my 2019 book with Ernest Davis).
>>
>>
>>
>> More broadly, Hinton’s ongoing dismissiveness of research from
>> perspectives other than his own (e.g. linguistics) have done the field a
>> disservice.
>>
>>
>>
>> As Herb Simon once observed, science does not have to be zero-sum.
>>
>>
>>
>> Sincerely,
>>
>> Gary Marcus
>>
>> Professor Emeritus
>>
>> New York University
>>
>>
>>
>> On Feb 2, 2022, at 06:12, AIhub <aihuborg at gmail.com> wrote:
>>
>> 
>>
>> Stephen Hanson in conversation with Geoff Hinton
>>
>>
>>
>> In the latest episode of this video series for AIhub.org
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttp-3A__AIhub.org%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DxnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL%26s%3DeOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=P3WO36jn%2B4E5%2Bt%2BqdBibO2kXwKYUEUj9jDWQly677zU%3D&reserved=0>,
>> Stephen Hanson talks to  Geoff Hinton about neural networks,
>> backpropagation, overparameterization, digit recognition, voxel cells,
>> syntax and semantics, Winograd sentences, and more.
>>
>>
>>
>> You can watch the discussion, and read the transcript, here:
>>
>>
>> https://aihub.org/2022/02/02/what-is-ai-stephen-hanson-in-conversation-with-geoff-hinton/
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aihub.org_2022_02_02_what-2Dis-2Dai-2Dstephen-2Dhanson-2Din-2Dconversation-2Dwith-2Dgeoff-2Dhinton_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DOY_RYGrfxOqV7XeNJDHuzE--aEtmNRaEyQ0VJkqFCWw%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=B7CedNdKS2LOcYFRVGlIC%2BtO32o0MLbq4YgWOus8rBE%3D&reserved=0>
>>
>>
>>
>> About AIhub:
>>
>> AIhub is a non-profit dedicated to connecting the AI community to the
>> public by providing free, high-quality information through AIhub.org
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttp-3A__AIhub.org%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DxnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL%26s%3DeOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=P3WO36jn%2B4E5%2Bt%2BqdBibO2kXwKYUEUj9jDWQly677zU%3D&reserved=0>
>> (https://aihub.org/
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aihub.org_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DIKFanqeMi73gOiS7yD-X_vRx_OqDAwv1Il5psrxnhIA%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=vEcVelgUzYKss53xDMh7g%2BPWfsrf%2BydW2SBma3oTkew%3D&reserved=0>).
>> We help researchers publish the latest AI news, summaries of their work,
>> opinion pieces, tutorials and more.  We are supported by many leading
>> scientific organizations in AI, namely AAAI
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aaai.org_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DwBvjOWTzEkbfFAGNj9wOaiJlXMODmHNcoWO5JYHugS0%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=mooy8W%2Bdbj1cfA4y7ds7AKTpvSjG6j8LaCc9nORQfhg%3D&reserved=0>,
>> NeurIPS
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__neurips.cc_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3D3-lOHXyu8171pT_UE9hYWwK6ft4I-cvYkuX7shC00w0%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=TBajU%2B1cgSkp%2Fx0e2p%2FEtQaCz%2F3hCRaZuSp2ZfffXHE%3D&reserved=0>,
>> ICML
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__icml.cc_imls_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DJJyjwIpPy9gtKrZzBMbW3sRMh3P3Kcw-SvtxG35EiP0%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=ruboOy7nl6zzRMEqrrSXDHEpyJPLVobkJhg0NJXF8kQ%3D&reserved=0>,
>> AIJ
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.journals.elsevier.com_artificial-2Dintelligence%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DeWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=dbQN2Cmfmx8sfEPsPqbzs%2BY08elmLXaX7ycliUnnSb4%3D&reserved=0>
>> /IJCAI
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.journals.elsevier.com_artificial-2Dintelligence%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DeWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=dbQN2Cmfmx8sfEPsPqbzs%2BY08elmLXaX7ycliUnnSb4%3D&reserved=0>,
>> ACM SIGAI
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttp-3A__sigai.acm.org_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3D7rC6MJFaMqOms10EYDQwfnmX-zuVNhu9fz8cwUwiLGQ%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=0bf4tH%2Bq%2B4vpME%2FvfX00b7GopTiSIW1%2BcL7u2Q9fxNg%3D&reserved=0>,
>> EurAI/AICOMM, CLAIRE
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__claire-2Dai.org_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3D66ZofDIhuDba6Fb0LhlMGD3XbBhU7ez7dc3HD5-pXec%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=CxrUEzAjP93Gd0Gac5bXT0StPrT3yThC%2F4h7rOnPzRo%3D&reserved=0>
>> and RoboCup
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.robocup.org__%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DbBI6GRq--MHLpIIahwoVN8iyXXc7JAeH3kegNKcFJc0%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=T9u4XHGbeeqoC5%2B069VueFyIWmY3X1kWyl0rFWxd8yQ%3D&reserved=0>
>> .
>>
>> Twitter: @aihuborg
>>
>>
>>
>>
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DAo9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z0HZYsbEu%2BZCfzxRnZetHEm15zN4jnc9%2BlUdHQ1zstc%3D&reserved=0>
>>
>> Virus-free. www.avast.com
>> <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DwaSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj%26s%3DAo9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4%26e%3D&data=05%7C01%7Ctgd%40oregonstate.edu%7C6c8cc9dafd744c179d1408da65d58603%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637934265009973905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z0HZYsbEu%2BZCfzxRnZetHEm15zN4jnc9%2BlUdHQ1zstc%3D&reserved=0>
>>
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