Connectionists: The symbolist quagmire
jose at rubic.rutgers.edu
jose at rubic.rutgers.edu
Mon Jun 13 11:44:14 EDT 2022
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://www.newworldai.com/system-1-deep-learning-system-2-deep-learning-yoshua-bengio/>
>
> 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://arxiv.org/search/cs?searchtype=author&query=Kerg%2C+G>,
> Sarthak Mittal
> <https://arxiv.org/search/cs?searchtype=author&query=Mittal%2C+S>,
> David Rolnick
> <https://arxiv.org/search/cs?searchtype=author&query=Rolnick%2C+D>,
> Yoshua Bengio
> <https://arxiv.org/search/cs?searchtype=author&query=Bengio%2C+Y>,
> Blake Richards
> <https://arxiv.org/search/cs?searchtype=author&query=Richards%2C+B>,
> Guillaume Lajoie
> <https://arxiv.org/search/cs?searchtype=author&query=Lajoie%2C+G>
>
> 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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