Connectionists: [EXT]Re: Fwd: Statistics versus “Understanding” in Generative AI.

Fri Feb 16 11:09:54 EST 2024

Is it accurate though?
Some of us sure could us a concise summary of everyone's position/opinion!
Thanks,
Jean-Marc

________________________________
From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Gary Marcus <gary.marcus at nyu.edu>
Sent: Friday, February 16, 2024 5:11 AM
To: Terry Sejnowski <terry at snl.salk.edu>
Cc: connectionists at cs.cmu.edu <connectionists at cs.cmu.edu>
Subject: [EXT]Re: Connectionists: Fwd: Statistics versus “Understanding” in Generative AI.

External Email

Always with the childish ad hominem.

This field is something else.

On Feb 15, 2024, at 11:09 PM, Terry Sejnowski <terry at snl.salk.edu> wrote:

 GPT-4 is a moving target that may eventually replace Gary.

Terry

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[cid:162d7610-f7c9-451c-a9a6-5e7cb4028673 at namprd19.prod.outlook.com]

The request you've made is similar to one I've already fulfilled. The image generated earlier depicts an empty room without any elephant present, adhering to your specific instructions. If you need any further adjustments or have another request, feel free to let me know!

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[User]

Write a critique of ChatGPT in the style of Gary Marcus

ChatGPT

Writing a critique of ChatGPT in the style of Gary Marcus, a scientist known for his clear-eyed scrutiny of artificial intelligence's capabilities and limitations, presents an intriguing challenge. Marcus, with his deep understanding of both the potential and pitfalls of AI, often emphasizes the importance of grounding AI advancements in robust, generalized learning principles, rather than overreliance on data and statistical patterns.

Critique of ChatGPT in the Style of Gary Marcus:

ChatGPT, while a marvel of contemporary AI technology, epitomizes the quintessential limitations that besiege deep learning models. At its core, ChatGPT is a linguistic colossus, built upon the edifice of the transformer architecture, yet it is fundamentally constrained by the inherent limitations of its training methodology and underlying principles. The brilliance of generating coherent, contextually appropriate responses is overshadowed by its occasional lapses into confabulation, a glaring testament to its lack of true understanding.

Gary Marcus would likely point out that ChatGPT, for all its linguistic dexterity, lacks the nuanced grasp of the world that humans possess. It can mimic the form of human knowledge and discourse, but it doesn't truly comprehend the content in the way a human does. This distinction is crucial; it is the boundary between a sophisticated parrot and an entity capable of genuine thought and understanding. ChatGPT's responses, no matter how convincing, are ultimately derived from patterns in data it has been fed, not from an intrinsic understanding of those patterns.

Furthermore, Marcus might criticize ChatGPT's reliance on sheer volume of data as a substitute for a more nuanced, rule-based understanding of language and the world. This data-driven approach can lead to impressive performances in many scenarios but falls short when faced with novel situations or when required to apply common sense reasoning that hasn't been explicitly encoded in its training data. The model's occasional incoherence or its propensity to generate plausible but factually incorrect responses are symptomatic of this deeper issue.

Another point of critique would likely focus on the opacity of ChatGPT's decision-making process. Despite recent advances in making AI models more interpretable, understanding how ChatGPT arrives at a particular response remains a challenge. This opacity complicates efforts to debug or improve the model, especially when it produces erroneous or nonsensical outputs. For AI to truly advance, Marcus might argue, it must not only perform well but do so in a manner that is transparent and understandable to human overseers.

In the spirit of Marcus's advocacy for a hybrid approach to AI development, one could argue that integrating ChatGPT's statistical prowess with more structured, rule-based systems could ameliorate some of its shortcomings. By grounding the model's responses in a framework of logical reasoning and factual correctness, one might achieve a more reliable, genuinely intelligent system that better approximates human cognition.

In conclusion, while ChatGPT represents a significant achievement in natural language processing, its limitations underscore the need for a more comprehensive approach to AI. An approach that incorporates the strengths of both deep learning and symbolic AI, coupled with a commitment to transparency and understandability, may pave the way for the development of AI systems that are not only more capable but also more aligned with the nuanced complexities of human intelligence and reasoning.

-------- Forwarded Message --------
Subject:        Connectionists: Statistics versus “Understanding” in Generative AI.
Date:   Tue, 13 Feb 2024 09:54:48 -0800
From:   Gary Marcus <gary.marcus at nyu.edu><mailto:gary.marcus at nyu.edu>
To:     Weng, Juyang <weng at msu.edu><mailto:weng at msu.edu>
CC:     connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu>

...

A few sample visuals from those below (read the essays for sources). The argument in brief is that the systems do fine in statistically canonical situations, but lack depth in their comprehension of complex syntax, negation, unusual circumstances, etc. Much more context (and many more examples) in the essays themselves.

Gary

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Gary

[cid:8d75ad4a-90a5-4fde-9024-f47268d3bdac at namprd19.prod.outlook.com]
[cid:9fd611e9-abe6-45c1-9096-46d02dd0b19c at namprd19.prod.outlook.com]
[cid:f61a0e86-d924-4a35-af96-6b7445bf1fb9 at namprd19.prod.outlook.com]
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