<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div dir="ltr"></div><div dir="ltr">Selectively looking at a single example (which happens to involve images) and ignoring all the other language-internal failures that I and others have presented is not a particularly effective way of getting to a general truth. </div><div dir="ltr"><br></div><div dir="ltr">More broadly, you are, in my judgement, mistaking correlation for a deeper level of understanding.</div><div dir="ltr"><br></div><div dir="ltr">Gary</div><div dir="ltr"><br><blockquote type="cite">On Feb 15, 2024, at 07:05, Iam Palatnik <iam.palat@gmail.com> wrote:<br><br></blockquote></div><blockquote type="cite"><div dir="ltr"><div dir="ltr"><div>Dear all,</div><div><br></div><div>
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</div><div><br></div><div>Copy pasting just the above paragraph onto GPT-4 should show the kind of behavior that makes some researchers say LLMs understand something, in some form. </div><div>We already use words such as 'intelligence' in AI and 'learning' in ML. This is not to say it's the same as human intelligence/learning. It is to say it's a similar enough behavior that the same word fits, while specifically qualifying the machine word-counterpart as something different (artificial/machine).</div><div><br></div><div>Can this debate be solved by coining a concept such as 'artificial/machine understanding'? GPT-4 then 'machine understands' the paragraph above. It 'machine understands' arbitrary scrambled text better than humans 'human understand' it. Matrix multiplying rotational semantic embeddings of byte pair encoded tokens is part of 'machine understanding' but not of 'human understanding'. At the same time, there are plenty of examples of things we 'human understand' and GPT-4 doesn't 'machine understand', or doesn't understand without tool access and self reflective prompts.<br></div><div><br></div><div>As to the map generation example, there are multiple tasks overlaid there. The language component of GPT-4 seems to have 'machine understood' it has to generate an image, and what the contents of the image have to be. It understood what tool it has to call to create the image. The tool generated an infograph style map of the correct country, but the states and landmarks are wrong. The markers are on the wrong cities and some of the drawings are bad. Is it too far fetched to say GPT-4 'machine understood' the assignment (generating a map with markers in the style of infograph), but its image generation component (Dall-E) is bad at detailed accurate geography knowledge?</div><div><br></div><div>I'm also confused why the linguistic understanding capabilities of GPT-4 are being tested by asking Dall-E 3 to generate images. Aren't these two completely separate models, and GPT-4 just function-calls Dall-E3 for image generation? Isn't this actually a sign GPT-4 did its job by 'machine understanding' what the user wanted, making the correct function call, creating and sending the correct prompt to Dall-E 3, but Dall-E 3 fumbled it because it's not good at generating detailed accurate maps?</div><div><br></div><div>Cheers,</div><div><br></div><div>Iam<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Feb 15, 2024 at 5:20 AM Gary Marcus <<a href="mailto:gary.marcus@nyu.edu">gary.marcus@nyu.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">I am having a genuinely hard time comprehending some of the claims recently made in this forum. (Not one of which engaged with any of the specific examples or texts I linked.)<br>
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Here’s yet another example, a dialog about geography that was just sent to me by entrepreneur Phil Libin. Do we really want to call outputs like these (to two prompts, with three generated responses zoomed in below) understanding? <br>
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In what sense do these responses exemplify the word “understanding”? <br>
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I am genuinely baffled. To me a better word would be “approximations”, and poor approximations at that. <br>
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Worse, I don’t see any AI system on the horizon that could reliably do better, across a broad range of related questions. If these kinds of outputs are any indication at all, we are still a very long away from reliable general-purpose AI. <br>
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Gary<br>
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