Connectionists: Stephen Hanson in conversation with Geoff Hinton
Danko Nikolic
danko.nikolic at gmail.com
Sun Feb 6 05:27:39 EST 2022
Hi Gary,
you said: "Please avert your gaze while I apply Ockham’s Razor…"
I dare you to apply Ockham's razor. Practopoiesis is designed with the
Ockham's razor in mind: To account for as many mental phenomena as possible
by making as few assumptions as possible.
Danko
Dr. Danko Nikolić
www.danko-nikolic.com
https://www.linkedin.com/in/danko-nikolic/
--- A progress usually starts with an insight ---
On Sat, Feb 5, 2022 at 8:05 PM gary at ucsd.edu <gary at eng.ucsd.edu> wrote:
> Please avert your gaze while I apply Ockham’s Razor…
>
> On Sat, Feb 5, 2022 at 2:12 AM Danko Nikolic <danko.nikolic at gmail.com>
> wrote:
>
>> Gary, you wrote: "What are the alternatives?"
>>
>> There is at least one alternative: the theory of practopoiesis which
>> suggests that it is not the neural networks that "compute" the mental
>> operations.
>> It is instead the quick adaptations of neurons who are responsible for
>> thinking and perceiving. The network only serves the function of bringing
>> in the information and sending it out.
>>
>> The adaptations are suggested to do the central part of the cognition.
>>
>> So far, this is all hypothetical. If we develop these ideas into a
>> working system, this would be an entirely new paradigm. It would be like
>> the third paradigm:
>> 1) manipulation of symbols
>> 2) neural net
>> 3) fast adaptations
>>
>>
>> Danko
>>
>> Dr. Danko Nikolić
>> www.danko-nikolic.com
>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=8RJsdJD2SsrrPj_IZmgVEQXJT9dvaULicZp8q_rzKYc&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=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=MaN2MbUvhlQAp0jsn9LMCu21V0PDAtTwbqvdr3uycSE&e=>
>> --- A progress usually starts with an insight ---
>>
>>
>> On Fri, Feb 4, 2022 at 7:19 PM gary at ucsd.edu <gary at eng.ucsd.edu> wrote:
>>
>>> This is an argument from lack of imagination, as Pat Churchland used to
>>> say. All you have to notice, is that your brain is a neural net work. What
>>> are the alternatives?
>>>
>>> On Fri, Feb 4, 2022 at 4:08 AM Danko Nikolic <danko.nikolic at gmail.com>
>>> wrote:
>>>
>>>>
>>>> I suppose everyone agrees that "the brain is a physical system",
>>>> and that "There is no “magic” inside the brain",
>>>> and that '“understanding” is just part of “learning.”'
>>>>
>>>> Also, we can agree that some sort of simulation takes place behind
>>>> understanding.
>>>>
>>>> However, there still is a problem: Neural network's can't implement the
>>>> needed simulations; they cannot achieve the same cognitive effect that
>>>> human minds can (or animal minds can).
>>>>
>>>> We don't know a way of wiring a neural network such that it could
>>>> perform the simulations (understandings) necessary to find the answers to
>>>> real-life questions, such as the hamster with a hat problem.
>>>>
>>>> In other words, neural networks, as we know them today, cannot:
>>>>
>>>> 1) learn from a small number of examples (simulation or not)
>>>> 2) apply the knowledge to a wide range of situations
>>>>
>>>>
>>>> We, as scientists, do not understand understanding. Our technology's
>>>> simulations (their depth of understanding) are no match for the simulations
>>>> (depth of understanding) that the biological brain performs.
>>>>
>>>> I think that scientific integrity also covers acknowledging when we did
>>>> not (yet) succeed in solving a certain problem. There is still significant
>>>> work to be done.
>>>>
>>>>
>>>> Danko
>>>>
>>>> Dr. Danko Nikolić
>>>> www.danko-nikolic.com
>>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=8RJsdJD2SsrrPj_IZmgVEQXJT9dvaULicZp8q_rzKYc&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=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=MaN2MbUvhlQAp0jsn9LMCu21V0PDAtTwbqvdr3uycSE&e=>
>>>> --- A progress usually starts with an insight ---
>>>>
>>>>
>>>>
>>>> <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=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=SiUMr7UUUNTm7PSe4xLERduI7qNxagFZ0V21tq0hJE4&e=> Virenfrei.
>>>> www.avast.com
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>>>> <#m_-134745596574091214_m_8423976727351221435_m_-3229424020171779455_m_-1469727422087267219_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
>>>>
>>>> On Thu, Feb 3, 2022 at 9:35 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>>>>
>>>>> First of all, the brain is a physical system. There is no “magic”
>>>>> inside the brain that does the “understanding” part. Take for example
>>>>> learning to play tennis. You hit a few balls - some the right way and some
>>>>> wrong – but you fairly quickly learn to hit them right most of the time. So
>>>>> there is obviously some simulation going on in the brain about hitting the
>>>>> ball in different ways and “learning” its consequences. What you are
>>>>> calling “understanding” is really these simulations about different
>>>>> scenarios. It’s also very similar to augmentation used to train image
>>>>> recognition systems where you rotate images, obscure parts and so on, so
>>>>> that you still can say it’s a cat even though you see only the cat’s face
>>>>> or whiskers or a cat flipped on its back. So, if the following questions
>>>>> relate to “understanding,” you can easily resolve this by simulating such
>>>>> scenarios when “teaching” the system. There’s nothing “magical” about
>>>>> “understanding.” As I said, bear in mind that the brain, after all, is a
>>>>> physical system and “teaching” and “understanding” is embodied in that
>>>>> physical system, not outside it. So “understanding” is just part of
>>>>> “learning,” nothing more.
>>>>>
>>>>>
>>>>>
>>>>> DANKO:
>>>>>
>>>>> 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?)
>>>>>
>>>>>
>>>>>
>>>>> 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:* Gary Marcus <gary.marcus at nyu.edu>
>>>>> *Sent:* Thursday, February 3, 2022 9:26 AM
>>>>> *To:* Danko Nikolic <danko.nikolic at gmail.com>
>>>>> *Cc:* Asim Roy <ASIM.ROY at asu.edu>; Geoffrey Hinton <
>>>>> geoffrey.hinton at gmail.com>; AIhub <aihuborg at gmail.com>;
>>>>> connectionists at mailman.srv.cs.cmu.edu
>>>>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with
>>>>> Geoff Hinton
>>>>>
>>>>>
>>>>>
>>>>> 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://urldefense.com/v3/__https:/static.googleusercontent.com/media/research.google.com/en/*archive/unsupervised_icml2012.pdf__;Lw!!IKRxdwAv5BmarQ!PFl2URDWVshfy1BPSwAMXKYyn1wszxpN4EPzShAm3sX83AOt05MQX07oVyVLEqo$>]
>>>>> 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://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 ---
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> <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. 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=>
>>>>>
>>>>>
>>>>>
>>>>> 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. 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=>
>>>>>
>>>>>
>>>>>
>>>> --
>>> Gary Cottrell 858-534-6640 FAX: 858-534-7029
>>> Computer Science and Engineering 0404
>>> IF USING FEDEX INCLUDE THE FOLLOWING LINE:
>>> CSE Building, Room 4130
>>> University of California San Diego -
>>> 9500 Gilman Drive # 0404
>>> <https://www.google.com/maps/search/9500+Gilman+Drive+%23+0404+La+Jolla,+Ca.+92093-0404?entry=gmail&source=g>
>>> La Jolla, Ca. 92093-0404
>>> <https://www.google.com/maps/search/9500+Gilman+Drive+%23+0404+La+Jolla,+Ca.+92093-0404?entry=gmail&source=g>
>>>
>>> Email: gary at ucsd.edu
>>> Home page: http://www-cse.ucsd.edu/~gary/
>>> Schedule: http://tinyurl.com/b7gxpwo
>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__tinyurl.com_b7gxpwo&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=GGPEyfU2SeT7eAbyCFhOW_DUw2QV8OKHoJ7OL7vHdds&e=>
>>>
>>> Blind certainty - a close-mindedness that amounts to an imprisonment so
>>> total, that the prisoner doesn’t even know that he’s locked up. -David
>>> Foster Wallace
>>>
>>>
>>> Power to the people! —Patti Smith
>>>
>>> Except when they’re delusional —Gary Cottrell
>>>
>>>
>>> This song makes me nostalgic for a memory I don't have -- Tess Cottrell
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *Listen carefully,Neither the VedasNor the Qur'anWill teach you this:Put
>>> the bit in its mouth,The saddle on its back,Your foot in the stirrup,And
>>> ride your wild runaway mindAll the way to heaven.*
>>>
>>> -- Kabir
>>>
>> --
> Gary Cottrell 858-534-6640 FAX: 858-534-7029
> Computer Science and Engineering 0404
> IF USING FEDEX INCLUDE THE FOLLOWING LINE:
> CSE Building, Room 4130
> University of California San Diego -
> 9500 Gilman Drive # 0404
> La Jolla, Ca. 92093-0404
>
> Email: gary at ucsd.edu
> Home page: http://www-cse.ucsd.edu/~gary/
> Schedule: http://tinyurl.com/b7gxpwo
>
> Blind certainty - a close-mindedness that amounts to an imprisonment so
> total, that the prisoner doesn’t even know that he’s locked up. -David
> Foster Wallace
>
>
> Power to the people! —Patti Smith
>
> Except when they’re delusional —Gary Cottrell
>
>
> This song makes me nostalgic for a memory I don't have -- Tess Cottrell
>
>
>
>
>
>
>
>
>
>
> *Listen carefully,Neither the VedasNor the Qur'anWill teach you this:Put
> the bit in its mouth,The saddle on its back,Your foot in the stirrup,And
> ride your wild runaway mindAll the way to heaven.*
>
> -- Kabir
>
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