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GPT-4 is a moving target that may eventually replace Gary.<br>
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Terry<br>
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Write a critique of ChatGPT in the style of Gary Marcus<br>
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<p>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.</p>
<p><strong>Critique of ChatGPT in the Style of Gary Marcus:</strong></p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
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-------- Forwarded Message --------
<table class="moz-email-headers-table" cellspacing="0"
cellpadding="0" border="0">
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<tr>
<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Subject:
</th>
<td>Connectionists: Statistics versus “Understanding” in
Generative AI.</td>
</tr>
<tr>
<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Date: </th>
<td>Tue, 13 Feb 2024 09:54:48 -0800</td>
</tr>
<tr>
<th valign="BASELINE" nowrap="nowrap" align="RIGHT">From: </th>
<td>Gary Marcus <a class="moz-txt-link-rfc2396E" href="mailto:gary.marcus@nyu.edu"><gary.marcus@nyu.edu></a></td>
</tr>
<tr>
<th valign="BASELINE" nowrap="nowrap" align="RIGHT">To: </th>
<td>Weng, Juyang <a class="moz-txt-link-rfc2396E" href="mailto:weng@msu.edu"><weng@msu.edu></a></td>
</tr>
<tr>
<th valign="BASELINE" nowrap="nowrap" align="RIGHT">CC: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:connectionists@mailman.srv.cs.cmu.edu">connectionists@mailman.srv.cs.cmu.edu</a></td>
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<div>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.</div>
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<div>Gary<br>
<br>
<img src="cid:part3.fuYvxhh3.pMKDF8Rm@snl.salk.edu" alt=""></div>
<br>
<br>
<div>Gary</div>
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