Connectionists: The symbolist quagmire
jose at rubic.rutgers.edu
jose at rubic.rutgers.edu
Mon Jun 13 14:00:03 EDT 2022
Nope. But lets take this offline as one of us is confused.
On 6/13/22 1:58 PM, Gary Marcus wrote:
> I think you are conflating Bengio’s views with Kahneman’s
>
> Bengio wants to have a System I, which he thinks is not the same as
> System II. He doesn’t want System II to be symbol-based, but he does
> want to do many things that symbols have historically done. That is an
> ambition, and we can see how it goes. My impression is he is on a road
> towards recapitulating a lot of historically symbolic tools, such as
> key-value pairs and operations that work over their pairs. We will see
> where he gets to; it’s an interesting projects.
>
> Kahneman coined the terms; I prefer to call them Reflexive and
> Deliberative. In my view deliberation of that sort requires symbols.
> For what it’s worth Kahneman was enormously sympathetic (both publicly
> and in an email) to my paper the Next Decade in AI, in which I argued
> that one needed a neurosymbolic system with rich knowledge, and
> reasoning over detailed cognitive models.
>
> It’s all an empirical question as to what can be done.
>
> I guess “he” refers below to Bengio, but not to Kahneman who
> originated the System I/II distinction. Danny is open about how these
> things cache out, and would also be the first to tell you that the
> distinction is just a rough one, in any event.
>
> Gary
>
>> On Jun 13, 2022, at 10:37, jose at rubic.rutgers.edu wrote:
>>
>>
>>
>> Well. your conclusion is based on some hearsay and a talk he gave, I
>> talked with him directly and we discussed what
>>
>> you are calling SystemII which just means explicit memory/learning to
>> me and him.. he has no intention of incorporating anything like
>> symbols or
>>
>> hybrid Neural/Symbol systems.. he does intend on modeling
>> conscious symbol manipulation. more in the way Dave T. outlined.
>>
>> AND, I'm sure if he was seeing this.. he would say... "Steve's right".
>>
>> Steve
>>
>> On 6/13/22 1:10 PM, Gary Marcus wrote:
>>> I don’t think i need to read your conversation to have serious
>>> doubts about your conclusion, but feel free to reprise the arguments
>>> here.
>>>
>>>> On Jun 13, 2022, at 08:44, jose at rubic.rutgers.edu wrote:
>>>>
>>>>
>>>>
>>>> We prefer the explicit/implicit cognitive psych refs. but System II
>>>> is not symbolic.
>>>>
>>>> See the AIHUB conversation about this.. we discuss this specifically.
>>>>
>>>>
>>>> Steve
>>>>
>>>>
>>>> On 6/13/22 10:00 AM, Gary Marcus wrote:
>>>>> Please reread my sentence and reread his recent work. Bengio has
>>>>> absolutely joined in calling for System II processes. Sample is
>>>>> his 2019 NeurIPS keynote:
>>>>> https://www.newworldai.com/system-1-deep-learning-system-2-deep-learning-yoshua-bengio/
>>>>> <https://urldefense.com/v3/__https://www.newworldai.com/system-1-deep-learning-system-2-deep-learning-yoshua-bengio/__;!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVOgyztpc$>
>>>>>
>>>>> Whether he wants to call it a hybrid approach is his business but
>>>>> he certainly sees that traditional approaches are not covering
>>>>> things like causality and abstract generalization. Maybe he will
>>>>> find a new way, but he recognizes what has not been covered with
>>>>> existing ways.
>>>>>
>>>>> And he is emphasizing both relationships and out of distribution
>>>>> learning, just as I have been for a long time. From his most
>>>>> recent arXiv a few days ago, the first two sentences of which
>>>>> sounds almost exactly like what I have been saying for years:
>>>>>
>>>>> Submitted on 9 Jun 2022]
>>>>>
>>>>>
>>>>> On Neural Architecture Inductive Biases for Relational Tasks
>>>>>
>>>>> Giancarlo Kerg
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Kerg*2C*G__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEV3gZmAsw$>,
>>>>> Sarthak Mittal
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Mittal*2C*S__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVLC65Ftc$>,
>>>>> David Rolnick
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Rolnick*2C*D__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVsXExRpc$>,
>>>>> Yoshua Bengio
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Bengio*2C*Y__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVTTRf_9g$>,
>>>>> Blake Richards
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Richards*2C*B__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVnyKkuNY$>,
>>>>> Guillaume Lajoie
>>>>> <https://urldefense.com/v3/__https://arxiv.org/search/cs?searchtype=author&query=Lajoie*2C*G__;JSs!!BhJSzQqDqA!XG4zEf0hOZijhGBf_sFhhbkQzKlArmTaaBCbKV2h_BBa3TSeO_Be99dqthIiW9gcQf1n4qpT0YBNFXEVa03VLYM$>
>>>>>
>>>>> Current deep learning approaches have shown good
>>>>> in-distribution generalization performance, but struggle with
>>>>> out-of-distribution generalization. This is especially true in
>>>>> the case of tasks involving abstract relations like
>>>>> recognizing rules in sequences, as we find in many
>>>>> intelligence tests. Recent work has explored how forcing
>>>>> relational representations to remain distinct from sensory
>>>>> representations, as it seems to be the case in the brain, can
>>>>> help artificial systems. Building on this work, we further
>>>>> explore and formalize the advantages afforded by 'partitioned'
>>>>> representations of relations and sensory details, and how this
>>>>> inductive bias can help recompose learned relational structure
>>>>> in newly encountered settings. We introduce a simple
>>>>> architecture based on similarity scores which we name
>>>>> Compositional Relational Network (CoRelNet). Using this model,
>>>>> we investigate a series of inductive biases that ensure
>>>>> abstract relations are learned and represented distinctly from
>>>>> sensory data, and explore their effects on out-of-distribution
>>>>> generalization for a series of relational psychophysics tasks.
>>>>> We find that simple architectural choices can outperform
>>>>> existing models in out-of-distribution generalization.
>>>>> Together, these results show that partitioning relational
>>>>> representations from other information streams may be a simple
>>>>> way to augment existing network architectures' robustness when
>>>>> performing out-of-distribution relational computations.
>>>>>
>>>>>
>>>>> Kind of scandalous that he doesn’t ever cite me for having
>>>>> framed that argument, even if I have repeatedly called his
>>>>> attention to that oversight, but that’s another story for a
>>>>> day, in which I elaborate on some Schmidhuber’s observations
>>>>> on history.
>>>>>
>>>>>
>>>>> Gary
>>>>>
>>>>>> On Jun 13, 2022, at 06:44, jose at rubic.rutgers.edu wrote:
>>>>>>
>>>>>>
>>>>>>
>>>>>> No Yoshua has *not* joined you ---Explicit processes, memory,
>>>>>> problem solving. .are not Symbolic per se.
>>>>>>
>>>>>> These original distinctions in memory and learning were from
>>>>>> Endel Tulving and of course there are brain structures that
>>>>>> support the distinctions.
>>>>>>
>>>>>> and Yoshua is clear about that in discussions I had with him in AIHUB
>>>>>>
>>>>>> He's definitely not looking to create some hybrid approach..
>>>>>>
>>>>>> Steve
>>>>>>
>>>>>> On 6/13/22 8:36 AM, Gary Marcus wrote:
>>>>>>> Cute phrase, but what does “symbolist quagmire” mean? Once upon
>>>>>>> atime, Dave and Geoff were both pioneers in trying to getting
>>>>>>> symbols and neural nets to live in harmony. Don’t we still need
>>>>>>> do that, and if not, why not?
>>>>>>>
>>>>>>> Surely, at the very least
>>>>>>> - we want our AI to be able to take advantage of the (large)
>>>>>>> fraction of world knowledge that is represented in symbolic form
>>>>>>> (language, including unstructured text, logic, math, programming
>>>>>>> etc)
>>>>>>> - any model of the human mind ought be able to explain how
>>>>>>> humans can so effectively communicate via the symbols of
>>>>>>> language and how trained humans can deal with (to the extent
>>>>>>> that can) logic, math, programming, etc
>>>>>>>
>>>>>>> Folks like Bengio have joined me in seeing the need for “System
>>>>>>> II” processes. That’s a bit of a rough approximation, but I
>>>>>>> don’t see how we get to either AI or satisfactory models of the
>>>>>>> mind without confronting the “quagmire”
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 13, 2022, at 00:31, Ali Minai <minaiaa at gmail.com> wrote:
>>>>>>>>
>>>>>>>>
>>>>>>>> ".... symbolic representations are a fiction our non-symbolic
>>>>>>>> brains cooked up because the properties of symbol systems
>>>>>>>> (systematicity, compositionality, etc.) are tremendously
>>>>>>>> useful. So our brains pretend to be rule-based symbolic
>>>>>>>> systems when it suits them, because it's adaptive to do so."
>>>>>>>>
>>>>>>>> Spot on, Dave! We should not wade back into the symbolist
>>>>>>>> quagmire, but do need to figure out how apparently symbolic
>>>>>>>> processing can be done by neural systems. Models like those of
>>>>>>>> Eliasmith and Smolensky provide some insight, but still seem
>>>>>>>> far from both biological plausibility and real-world scale.
>>>>>>>>
>>>>>>>> Best
>>>>>>>>
>>>>>>>> Ali
>>>>>>>>
>>>>>>>>
>>>>>>>> *Ali A. Minai, Ph.D.*
>>>>>>>> Professor and Graduate Program Director
>>>>>>>> Complex Adaptive Systems Lab
>>>>>>>> Department of Electrical Engineering & Computer Science
>>>>>>>> 828 Rhodes Hall
>>>>>>>> University of Cincinnati
>>>>>>>> Cincinnati, OH 45221-0030
>>>>>>>>
>>>>>>>> Phone: (513) 556-4783
>>>>>>>> Fax: (513) 556-7326
>>>>>>>> Email: Ali.Minai at uc.edu <mailto:Ali.Minai at uc.edu>
>>>>>>>> minaiaa at gmail.com <mailto:minaiaa at gmail.com>
>>>>>>>>
>>>>>>>> WWW: https://eecs.ceas.uc.edu/~aminai/
>>>>>>>> <https://urldefense.com/v3/__http://www.ece.uc.edu/*7Eaminai/__;JQ!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXYkS9WrFA$>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Mon, Jun 13, 2022 at 1:35 AM Dave Touretzky <dst at cs.cmu.edu
>>>>>>>> <mailto:dst at cs.cmu.edu>> wrote:
>>>>>>>>
>>>>>>>> This timing of this discussion dovetails nicely with the
>>>>>>>> news story
>>>>>>>> about Google engineer Blake Lemoine being put on
>>>>>>>> administrative leave
>>>>>>>> for insisting that Google's LaMDA chatbot was sentient and
>>>>>>>> reportedly
>>>>>>>> trying to hire a lawyer to protect its rights. The
>>>>>>>> Washington Post
>>>>>>>> story is reproduced here:
>>>>>>>>
>>>>>>>> https://www.msn.com/en-us/news/technology/the-google-engineer-who-thinks-the-company-s-ai-has-come-to-life/ar-AAYliU1
>>>>>>>> <https://urldefense.com/v3/__https://www.msn.com/en-us/news/technology/the-google-engineer-who-thinks-the-company-s-ai-has-come-to-life/ar-AAYliU1__;!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXapZaIeUg$>
>>>>>>>>
>>>>>>>> Google vice president Blaise Aguera y Arcas, who dismissed
>>>>>>>> Lemoine's
>>>>>>>> claims, is featured in a recent Economist article showing
>>>>>>>> off LaMDA's
>>>>>>>> capabilities and making noises about getting closer to
>>>>>>>> "consciousness":
>>>>>>>>
>>>>>>>> https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas
>>>>>>>> <https://urldefense.com/v3/__https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas__;!!BhJSzQqDqA!UCEp_V8mv7wMFGacqyo0e5J8KbCnjHTDVRykqi1DQgMu87m5dBCpbcV6s4bv6xkTdlkwJmvlIXbgg32qHQ$>
>>>>>>>>
>>>>>>>> My personal take on the current symbolist controversy is
>>>>>>>> that symbolic
>>>>>>>> representations are a fiction our non-symbolic brains
>>>>>>>> cooked up because
>>>>>>>> the properties of symbol systems (systematicity,
>>>>>>>> compositionality, etc.)
>>>>>>>> are tremendously useful. So our brains pretend to be
>>>>>>>> rule-based symbolic
>>>>>>>> systems when it suits them, because it's adaptive to do
>>>>>>>> so. (And when
>>>>>>>> it doesn't suit them, they draw on "intuition" or "imagery"
>>>>>>>> or some
>>>>>>>> other mechanisms we can't verbalize because they're not
>>>>>>>> symbolic.) They
>>>>>>>> are remarkably good at this pretense.
>>>>>>>>
>>>>>>>> The current crop of deep neural networks are not as good at
>>>>>>>> pretending
>>>>>>>> to be symbolic reasoners, but they're making progress. In
>>>>>>>> the last 30
>>>>>>>> years we've gone from networks of fully-connected layers
>>>>>>>> that make no
>>>>>>>> architectural assumptions ("connectoplasm") to complex
>>>>>>>> architectures
>>>>>>>> like LSTMs and transformers that are designed for
>>>>>>>> approximating symbolic
>>>>>>>> behavior. But the brain still has a lot of symbol
>>>>>>>> simulation tricks we
>>>>>>>> haven't discovered yet.
>>>>>>>>
>>>>>>>> Slashdot reader ZiggyZiggyZig had an interesting argument
>>>>>>>> against LaMDA
>>>>>>>> being conscious. If it just waits for its next input and
>>>>>>>> responds when
>>>>>>>> it receives it, then it has no autonomous existence: "it
>>>>>>>> doesn't have an
>>>>>>>> inner monologue that constantly runs and comments
>>>>>>>> everything happening
>>>>>>>> around it as well as its own thoughts, like we do."
>>>>>>>>
>>>>>>>> What would happen if we built that in? Maybe LaMDA would
>>>>>>>> rapidly
>>>>>>>> descent into gibberish, like some other text generation
>>>>>>>> models do when
>>>>>>>> allowed to ramble on for too long. But as Steve Hanson
>>>>>>>> points out,
>>>>>>>> these are still the early days.
>>>>>>>>
>>>>>>>> -- Dave Touretzky
>>>>>>>>
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