Connectionists: Stephen Hanson in conversation with Geoff Hinton

Thu Jul 14 08:08:19 EDT 2022

This can still be improved on. Always happy to cite relevant predecessors.

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 Thu, Jul 14, 2022 at 2:03 PM Miroslav Karny <school at utia.cas.cz> wrote:

> Dear all,
>
> I am an external observer of your interesting discussions. It has been a
> bit surprising to me that the work of prof.  Nikolic does not  comment
> the work @book{Fus:05,  title={Cortex and mind: Unifying
> cognition}, author={J.M. Fuster}, year={2005}, publisher={Oxford university
> press}}, which* I feel *as the highly relevant predecessor of his work.
>
>                        Best regards  Miroslav Karny
>
> https://www.utia.cas.cz/people/karny
>
> Danko Nikolic wrote:
>
> 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
> (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
> http://www.danko-nikolic.com
> https://www.linkedin.com/in/danko-nikolic/
>
>
> 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
> ]
> > 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
> > http://www.danko-nikolic.com
> > <
> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=HwOLDw6UCRzU5-FPSceKjtpNm7C6sZQU5kuGAMVbPaI&e=
> >
> > https://www.linkedin.com/in/danko-nikolic/
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.linkedin.com_in_danko-2Dnikolic_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=b70c8lokmxM3Kz66OfMIM4pROgAhTJOAlp205vOmCQ8&e=
> >
> > --- A progress usually starts with an insight ---
> >
> >
> >
> > <
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> >
> >
> > 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://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=
> >
> >>
> >> Asim Roy | iSearch (asu.edu)
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=
> >
> >>
> >>
> >>
> >>
> >>
> >> *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://urldefense.com/v3/__https:/arxiv.org/abs/2201.02387__;!!IKRxdwAv5BmarQ!INA0AMmG3iD1B8MDtLfjWCwcBjxO-e-eM2Ci9KEO_XYOiIEgiywK-G_8j6L3bHA$
> >)
> >> 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://urldefense.proofpoint.com/v2/url?u=https-3A__aclanthology.org_2020.acl-2Dmain.463.pdf&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=K-Vl6vSvzuYtRMi-s4j7mzPkNRTb-I6Zmf7rbuKEBpk&e=
> >,
> >> 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://urldefense.proofpoint.com/v2/url?u=http-3A__AIhub.org&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=eOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A&e=
> >,
> >> 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://urldefense.proofpoint.com/v2/url?u=https-3A__aihub.org_2022_02_02_what-2Dis-2Dai-2Dstephen-2Dhanson-2Din-2Dconversation-2Dwith-2Dgeoff-2Dhinton_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=OY_RYGrfxOqV7XeNJDHuzE--aEtmNRaEyQ0VJkqFCWw&e=
> >
> >>
> >>
> >>
> >> 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://urldefense.proofpoint.com/v2/url?u=http-3A__AIhub.org&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=eOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A&e=
> >
> >> (https://aihub.org/
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__aihub.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=IKFanqeMi73gOiS7yD-X_vRx_OqDAwv1Il5psrxnhIA&e=
> >).
> >> 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://urldefense.proofpoint.com/v2/url?u=https-3A__aaai.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=wBvjOWTzEkbfFAGNj9wOaiJlXMODmHNcoWO5JYHugS0&e=
> >,
> >> NeurIPS
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__neurips.cc_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=3-lOHXyu8171pT_UE9hYWwK6ft4I-cvYkuX7shC00w0&e=
> >,
> >> ICML
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__icml.cc_imls_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=JJyjwIpPy9gtKrZzBMbW3sRMh3P3Kcw-SvtxG35EiP0&e=
> >,
> >> AIJ
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.journals.elsevier.com_artificial-2Dintelligence&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=eWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U&e=
> >
> >> /IJCAI
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.journals.elsevier.com_artificial-2Dintelligence&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=eWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U&e=
> >,
> >> ACM SIGAI
> >> <
> https://urldefense.proofpoint.com/v2/url?u=http-3A__sigai.acm.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=7rC6MJFaMqOms10EYDQwfnmX-zuVNhu9fz8cwUwiLGQ&e=
> >,
> >> EurAI/AICOMM, CLAIRE
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__claire-2Dai.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=66ZofDIhuDba6Fb0LhlMGD3XbBhU7ez7dc3HD5-pXec&e=
> >
> >> and RoboCup
> >> <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.robocup.org__&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=bBI6GRq--MHLpIIahwoVN8iyXXc7JAeH3kegNKcFJc0&e=
> >
> >> .
> >>
> >> Twitter: @aihuborg
> >>
> >>
> >
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=Ao9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4&e=>
> Virus-free.
> > http://www.avast.com
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=Ao9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4&e=
> >
> >
> >
> >
>
>
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