<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2024-05-01T23:19:56+00:00</updated><id>/feed.xml</id><title type="html">Anthropomorphic AI</title><subtitle>Wandering the spaces of anthropomorphic AI.</subtitle><entry><title type="html">Reboot, then wander</title><link href="/meta/2024/04/29/reboot-and-wander.html" rel="alternate" type="text/html" title="Reboot, then wander" /><published>2024-04-29T00:00:00+00:00</published><updated>2024-04-29T00:00:00+00:00</updated><id>/meta/2024/04/29/reboot-and-wander</id><content type="html" xml:base="/meta/2024/04/29/reboot-and-wander.html"><![CDATA[<p>Three years since the first (and last) post, time to look around and pick up the threads.</p>

<p>I was previously aiming at a more focused sequence, but I think now we’re better served by less focused wandering. The core feeling remains: there’s untapped hope in the space of anthropomorphic-by-design approaches to AI. Old threads of investigation remain too: <strong>mammals that mimic meaning</strong>, <strong>anthropomorphic ideals</strong>, and <strong>artificial life</strong>. But new threads have joined them, and the world has shifted. Large language models and generative image models have proved astoundingly proficient and popular, and both are anthropomorphic in important ways.</p>

<p>So, let’s reboot, then wander.</p>]]></content><author><name></name></author><category term="meta" /><summary type="html"><![CDATA[Three years since the first (and last) post, time to look around and pick up the threads.]]></summary></entry><entry><title type="html">Init</title><link href="/init/2021/01/15/init.html" rel="alternate" type="text/html" title="Init" /><published>2021-01-15T00:00:00+00:00</published><updated>2021-01-15T00:00:00+00:00</updated><id>/init/2021/01/15/init</id><content type="html" xml:base="/init/2021/01/15/init.html"><![CDATA[<p>Artificial Intelligence designs its systems according to two kinds of ideal. One kind involves doing something well: solving problems, maximizing expected utility, minimizing required training data, among more concrete ideals like winning Go games or driving cars well. The other involves being like us in some sense: looking like us, behaving like us, or being structured like us. Call the first kind <strong>adeptness ideals</strong> and the second <strong>anthropomorphic ideals</strong>.</p>

<p>Systems today, both in AI alignment and AI more broadly, are typically designed according to adeptness ideals, with humans used mainly for inspiration (outside of scientific efforts to understand humans through AI).</p>

<p>By contrast, in this blog I will argue that designing explicitly <strong>anthropomorphic AI</strong> will be crucial for developing aligned AGI, and give concrete research directions and comparisons to existing approaches. Befitting the medium, I’ll present the ideas in no particular order, pull it together as we go, and promise that all of your questions will be satisfactorily answered in some later post.</p>

<p>Any anthropomorphic AI approach rests on an understanding of what humans are. Among other things, we are <strong>mammals that mimic meanings</strong>. Anthropomorphic AI should be too, given an appropriate generalization of these concepts. I’ll elaborate on what this means and why it’s true in a later post.</p>

<p>Humans are a messy <a href="https://www.youtube.com/watch?v=7tScAyNaRdQ">meaty</a> outgrowth of biological evolution and historical accident, but there’s ultimately no way of escaping the fact that we are human. All of our highest dreams and aspirations, no matter how abstract and universal, are only human. This fact is not a bad thing, but any aligned AGI approach must take it into account.</p>]]></content><author><name></name></author><category term="init" /><summary type="html"><![CDATA[Artificial Intelligence designs its systems according to two kinds of ideal. One kind involves doing something well: solving problems, maximizing expected utility, minimizing required training data, among more concrete ideals like winning Go games or driving cars well. The other involves being like us in some sense: looking like us, behaving like us, or being structured like us. Call the first kind adeptness ideals and the second anthropomorphic ideals.]]></summary></entry></feed>