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Companies like Mythic and IBM have developed analog devices for energy- efficient deep learning. Noise and low precision are often used as part of ML training protocols anyway. Here they did careful side-by-side testing to quantify the impact of going
analog.
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<div><a href="https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full">https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full</a></div>
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<div>Or perhaps the proposition is that CS people (for example) can’t appreciate complexity that where the generative rules are unknown or obscure? Sure that horse is fast, but how can I put a new engine in it?<br>
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<blockquote type="cite">On Oct 1, 2021, at 12:58 AM, David Eric Smith <desmith@santafe.edu> wrote:<br>
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<div dir="ltr"> Doh!
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<div class="">I’m such a dolt, watching the pretty pictures.</div>
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<div class="">They’re both Turing complete, correct? Is there a natural sense of writing a program that, in that algorithmic representation, you know is somehow algorithmically deep in 110, which then becomes something algorithmically interesting under the
corresponding representation in Life? If one did that, would all the constructions be so forced that it was tedious and not elucidating of anything? One is better to run “analog” and just relish the evident generative complexity?</div>
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<div class="">Eric</div>
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<div class="">On Sep 30, 2021, at 5:33 AM, Steve Smith <<a href="mailto:sasmyth@swcp.com" class="">sasmyth@swcp.com</a>> wrote:</div>
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<div class="">Jon -<br class="">
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<p class="">I never cease to be surprised and fascinated watching simple rules generate complex structure and dynamics.</p>
<p class="">I had a lot of complex reactions to this but I won't waste anyone's bandwidth with my reflective rambling...
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<p class="">Thanks for sharing...<br class="">
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<p class="">- Steve<br class="">
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