Connectionists: The symbolist quagmire
jose at rubic.rutgers.edu
jose at rubic.rutgers.edu
Mon Jun 13 13:37:00 EDT 2022
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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