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<p class="MsoNormal">One of the ways out of the “understanding” dilemma is for the system to be able to explain its answer. And for that to happen, it can be “taught” how to explain its answer. That would effectively pull out what Geoff refers to as “discrete
symbolic information” that has been factored “into a very large number of features.” We do this very effectively for computer vision/image recognition. We get the causality out by saying it’s a cat because it has whiskers, claws and fur like a cat. And that’s
all at the symbolic level.<o:p></o:p></p>
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<p class="MsoNormal">Asim<o:p></o:p></p>
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<p class="MsoNormal">Asim Roy<o:p></o:p></p>
<p class="MsoNormal">Professor, Information Systems<o:p></o:p></p>
<p class="MsoNormal">Arizona State University<o:p></o:p></p>
<p class="MsoNormal"><a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=" target="_blank">Lifeboat
Foundation Bios: Professor Asim Roy</a><o:p></o:p></p>
<p class="MsoNormal"><a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=" target="_blank">Asim
Roy | iSearch (asu.edu)</a><o:p></o:p></p>
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<p class="MsoNormal"><b>From:</b> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu>
<b>On Behalf Of </b>Geoffrey Hinton<br>
<b>Sent:</b> Monday, March 20, 2023 10:59 AM<br>
<b>To:</b> Paul Cisek <paul.cisek@umontreal.ca><br>
<b>Cc:</b> connectionists@mailman.srv.cs.cmu.edu<br>
<b>Subject:</b> Re: Connectionists: Can LLMs think?<o:p></o:p></p>
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<p class="MsoNormal">LLM's do not do pattern matching in the sense that most people understand it. They use the data to create huge numbers of features and interactions between features such that these interactions can predict the next word.<o:p></o:p></p>
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<p class="MsoNormal">The first neural net language model (so far as I know) made bets about the third term of a triple using word embedding vectors with 6 components. Retrospectively, the components of these vectors could be interpreted as sensible features
for capturing the structure of the domain (which was very conventional family relationships). For example, there was a three-valued feature for a person's generation and the interactions between features ensured that the triple Victoria has-father ? took
the generation of Victoria and produced an answer that was of a higher generation because it understood that the relationship has-father requires this. Of course, in complicated domains there will be huge numbers of regularities which will make conflicting
predictions for the next word but the consensus can still be fairly reliable. I believe that factoring the discrete symbolic information into a very large number of features and interactions IS intuitive understanding and that this is true for both brains
and LLMs even though they may use different learning algorithms for arriving at these factorizations. I am dismayed that so many people fall prey to the well-known human disposition to think that there is something special about people.<o:p></o:p></p>
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<p class="MsoNormal">Geoff<o:p></o:p></p>
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<p class="MsoNormal">On Mon, Mar 20, 2023 at 3:53 AM Paul Cisek <<a href="mailto:paul.cisek@umontreal.ca">paul.cisek@umontreal.ca</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">I must say that I’m somewhat dismayed when I read these kinds of discussions, here or elsewhere. Sure, it’s understandable that many people are fooled into thinking that LLMs are
intelligent, just like many people were fooled by Eliza and Eugene Goostman. Humans are predisposed into ascribing intention and purpose to events in the world, which helped them construct complex societies by (often correctly) interpreting the actions of
other people around them. But this same predisposition also led them to believe that the volcano was angry when it erupted because they did something to offend the gods. Given how susceptible humans are to this false ascription of agency, it is not surprising
that they get fooled when something acts in a complex way.<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">But (most of) the people on this list know what’s under the hood! We know that LLMs are very good at pattern matching and completion, we know about the universal approximation theorem,
we know that there is a lot of structure in the pattern of human-written text, and we know that humans are predisposed to ascribe meaning and intention even where there are none. We should therefore not be surprised that LLMs can produce text patterns that
generalize well within-distribution but not so well out-of-distribution, and that when the former happens, people may be fooled into thinking they are speaking with a thinking being. Again, they were fooled by Eliza, and Eugene Goostman, and the Heider-Simmel
illusion (ascribing emotion to animated triangles and circles)… and the rumblings of volcanos. But we know how LLMs and volcanos do what they do, and can explain their behavior without any additional assumptions (of thinking, or sentience, or whatever). So
why add them?<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">In a sense, we are like a bunch of professional magicians, who know where all of the little strings and hidden compartments are, and who know how we just redirected the audience’s
attention to slip the card into our pocket… but then we are standing around backstage wondering: “Maybe there really is magic?”<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">I think it’s not that machines have passed the Turing Test, but rather that we failed it.<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Paul Cisek<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>From:</b> Rothganger, Fredrick <<a href="mailto:frothga@sandia.gov" target="_blank">frothga@sandia.gov</a>>
<br>
<b>Sent:</b> Thursday, March 16, 2023 11:39 AM<br>
<b>To:</b> <a href="mailto:connectionists@mailman.srv.cs.cmu.edu" target="_blank">
connectionists@mailman.srv.cs.cmu.edu</a><br>
<b>Subject:</b> Connectionists: Can LLMs think?<o:p></o:p></p>
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<span style="color:black">Noting the examples that have come up on this list over the last week, it's interesting that it takes some of the most brilliant AI researchers in the world to devise questions that break LLMs. Chatbots have always been able to fool
some people some of the time, ever since ELIZA. But we now have systems that can fool a lot of people a lot of the time, and even the occasional expert who loses their perspective and comes to believe the system is sentient. LLMs have either already passed
the classic Turning test, or are about to in the next generation.</span><o:p></o:p></p>
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<span style="color:black">What does that mean exactly? Turing's expectation was that "the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted". The ongoing
discussion here is an indication that we are approaching that threshold. For the average person, we've probably already passed it.</span><o:p></o:p></p>
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