Connectionists: Fwd: Statistics versus “Understanding” in Generative AI.

[Terry Sejnowski]

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

Terry

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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>
To: 	Weng, Juyang <weng at msu.edu>
CC: 	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



Gary
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