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On 2016-03-10 18:26, William Flynn Wallace wrote:<br>
<blockquote
cite="mid:CAO+xQEaPeQLvDHJ9XxqC8sVfjFtYwVkHhTcsPjh-8S4X8ZqV4A@mail.gmail.com"
type="cite">
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<div class="gmail_default" style="font-family:'comic sans
ms',sans-serif;font-size:small;color:rgb(0,0,0)"><span
style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:12.8px">Anders:
On page 29 I have some plots from Katja Grace's excellent
review, which points out that algorithmic improvements
typically produce pretty drastic jumps in capability, unlike
the individual learning curve or the gradual collective
improvement in chess.</span></div>
<div class="gmail_default" style="font-family:'comic sans
ms',sans-serif;font-size:small;color:rgb(0,0,0)"><span
style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:12.8px"><br>
</span></div>
<div class="gmail_default" style="font-family:'comic sans
ms',sans-serif;font-size:small;color:rgb(0,0,0)"><span
style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:12.8px">Anders,
does this mean that an AI can exhibit insight learning?
That is, learning that proceeds through saltatory leaps - in
humans we would call those aha moments - insight? Or did
the paragraph above say just that? For a true insight curve
it would have to start out rather flat (meaning no insight
yet).</span></div>
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<br>
Nah, the page 29 diagram is what algorithms do over time as humans
have insights and improve them. <br>
<br>
But actually, when you run some learning algorithms on some
problems, you see jumps due to "insights" with flattish plateaus
between. My favorite example is genetic algorithms like Avida on
complex fitness landscapes - when they find good tricks they jump. <br>
<br>
Now, proper insight learning in AI is mainly something people are
modelling rather than doing, as far as I know.<br>
<br>
<pre class="moz-signature" cols="72">--
Anders Sandberg
Future of Humanity Institute
Oxford Martin School
Oxford University</pre>
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