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<p class="MsoNormal">I have a tendancy to agree with Leslie Smith concerning the philosophical disjunction that is occurring in this discussion.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">However, the distinction between an agent that statistically manipulates symbols (ie. can use language similar to humans) and an agent that has a grounded sense of concepts and relations (ie. knows about the world like humans) is not as
obvious as it was 20 years ago. Certainly, it has a lot to do with the sensory modalities the agent can perceive and its ability to integrate and act on information from those modalities. We are getting closer to an understanding of how such modalities
might be integrated. Recent work on LLMs that can input from, and output to, various modalities (text, video, audio, tactile) show evidence that they develop complex embedded representations of concepts (<i>conreps</i>) that include, or are closely connected
to, representations of symbols (<i>symreps</i>). There is a lot more to do here in terms of how these sensory modalities are integrated in real-time (in terms of architecture and learning), but my sense (pardon the pun) is that many AI researchers are on
the right path. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">For more on <i>symrep</i>s and<i> conreps</i>, please see our paper (Silver and Mitchell 2023) “<b><a href="https://arxiv.org/abs/2304.13626">The Roles of Symbols in Neural-based AI: They are Not What You Think</a>!” </b>from the<b>
</b>Compendium of Neurosymbolic Artificial Intelligence 2023: p.1-28. Our hypothesis and the conceptual architecture we present imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks
of thought, but because they are characterizations of subsymbolic processes that constitute thought.<b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span lang="EN-US" style="color:black">==========================</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="color:red">Daniel L. Silver </span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black">Professor, Jodrey School of Computer Science</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black">Data Scientist, Acadia Institute for Data Analytics</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black">Acadia University,</span><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US" style="color:black">Wolfville, Nova Scotia Canada B4P 2R6 </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black">Cell: (902) 679-9315 </span><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">From:
</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Prof Leslie Smith <l.s.smith@cs.stir.ac.uk><br>
<b>Date: </b>Wednesday, February 14, 2024 at 1:38 AM<br>
<b>To: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Statistics versus Understanding in Generative AI.<o:p></o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt">CAUTION: This email comes from outside Acadia. Verify the sender and use caution with any requests, links or attachments.<br>
<br>
Can I suggest that there's a deep philosophical disjunction here.<br>
<br>
I'd say that it hinges on whether "Understanding" implies grounded-ness or<br>
not. Does it require the understanding entity to have a model of the<br>
environment into which the information can be placed, or can understanding<br>
be achieved in an abstract system that only has access to a world of<br>
textual data?<br>
<br>
One one hand, there's a danger that we imply that understanding only<br>
becomes possible in living systems, whatever the capability of the<br>
synthetic system, and on the other hand there's a danger that we ascribe<br>
understanding to systems that we really shouldn't. Does a thermostat<br>
understand temperature?<br>
<br>
This is a difficult question altogether: at what point to we ascribe<br>
understanding to animals? They all behave in ways appropriate to their<br>
environment, from the single celled Paramecium upwards, but we don't tend<br>
to consider them to understand their environment until we consider much<br>
higher animals (like mice or rats).<br>
<br>
--Leslie Smith<br>
<br>
Gary Marcus wrote:<br>
<br>
<br>
--<br>
Prof Leslie Smith (Emeritus)<br>
Computing Science & Mathematics,<br>
University of Stirling, Stirling FK9 4LA<br>
Scotland, UK<br>
Mobile: +44 7913 219013<br>
Web: <a href="http://www.cs.stir.ac.uk/~lss">http://www.cs.stir.ac.uk/~lss</a><br>
Blog: <a href="http://lestheprof.com">http://lestheprof.com</a><o:p></o:p></p>
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