<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">

<head>  <title>[2408.03943] Building Machines that Learn and Think with People</title>
  <meta name="viewport" content="width=device-width, initial-scale=1">
  <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png">
  <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png">
  <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png">
  <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest">
  <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5">
  <meta name="msapplication-TileColor" content="#da532c">
  <meta name="theme-color" content="#ffffff">
  <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20240822" />
  <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" />
  <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" />
  <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script>
  
    <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/slider.css?v=1.14" />
    <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script>
    <script type="text/javascript" src="/static/browse/0.3.4/js/donate.js?v=1.11"></script><link rel="canonical" href="/abs/2408.03943"/><meta property="og:type" content="website" />
<meta property="og:site_name" content="arXiv.org" />
<meta property="og:title" content="Building Machines that Learn and Think with People" />
<meta property="og:url" content="https://arxiv.org/abs/2408.03943v1" />
<meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" />
<meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" />
<meta property="og:image:width" content="1200" />
<meta property="og:image:height" content="700" />
<meta property="og:image:alt" content="arXiv logo"/>
<meta property="og:description" content="What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,&#39;&#39; systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world."/>
<meta name="twitter:site" content="@arxiv"/>
<meta name="twitter:card" content="summary"/>
<meta name="twitter:title" content="Building Machines that Learn and Think with People"/>
<meta name="twitter:description" content="What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy..."/>
<meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/>
<meta name="twitter:image:alt" content="arXiv logo"/>
  <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/>  <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script>  <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script>
  <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script>
  <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script>
  <script src="/static/browse/0.3.4/js/toggle-labs.js?20230622" type="text/javascript"></script>
  <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Building Machines that Learn and Think with People" /><meta name="citation_author" content="Collins, Katherine M." /><meta name="citation_author" content="Sucholutsky, Ilia" /><meta name="citation_author" content="Bhatt, Umang" /><meta name="citation_author" content="Chandra, Kartik" /><meta name="citation_author" content="Wong, Lionel" /><meta name="citation_author" content="Lee, Mina" /><meta name="citation_author" content="Zhang, Cedegao E." /><meta name="citation_author" content="Zhi-Xuan, Tan" /><meta name="citation_author" content="Ho, Mark" /><meta name="citation_author" content="Mansinghka, Vikash" /><meta name="citation_author" content="Weller, Adrian" /><meta name="citation_author" content="Tenenbaum, Joshua B." /><meta name="citation_author" content="Griffiths, Thomas L." /><meta name="citation_date" content="2024/07/22" /><meta name="citation_online_date" content="2024/07/22" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2408.03943" /><meta name="citation_arxiv_id" content="2408.03943" /><meta name="citation_abstract" content="What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,&#39;&#39; systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world." />
</head>

<body  class="with-cu-identity">
    <aside class="slider-wrapper bps-banner forum green">
    <a class="close-slider bps-banner" href="#"><img src="/static/browse/0.3.4/images/icons/close-slider.png" alt="close this message"></a>
    <div class="columns">
      <img role="presentation" class="bps-banner-image" src="/static/browse/0.3.4/images/icons/smileybones-infinity-large.png" alt="arXiv Accessibility Forum 2024">
      <div class="copy-donation bps-banner">
        <h2>The countdown begins!</h2>
        <p>The <a href="https://accessibility2024.arxiv.org/" target="_blank">Accessibility Forum</a> starts next week! <a href="https://cornell.ca1.qualtrics.com/jfe/form/SV_eEZ1d27LF2fVM7Y" target="_blank">Sign up</a> and join us in September.</p>
      </div>
      <div class="amount-donation bps-banner">
        <div class="donate-cta"><a class="banner_link banner-btn-grad" target="_blank" href="https://cornell.ca1.qualtrics.com/jfe/form/SV_eEZ1d27LF2fVM7Y"><b>Sign Up</b></a></div>
      </div>
    </div>
  </aside>
  <div class="flex-wrap-footer">
    <header>
      <a href="#content" class="is-sr-only">Skip to main content</a>
      <!-- start desktop header -->
      <div class="columns is-vcentered is-hidden-mobile" id="cu-identity">
        <div class="column" id="cu-logo">
          <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a>
        </div><div class="column banner-minimal forum">
            <!-- /from July 7 at 1:00 AM to Sept 2 at 1:00 AM --><p>arXiv's Accessibility Forum starts next month!</p>
            <a href="https://cornell.ca1.qualtrics.com/jfe/form/SV_eEZ1d27LF2fVM7Y" target="_blank">Sign Up</a>

            <!-- /from Sept 2 at 1:00 AM to Sept 13 at 11:30PM --></div><div class="column" id="support-ack">
          <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span>
          <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a>
        </div>
      </div>

      <div id="header" class="is-hidden-mobile">
<a aria-hidden="true" href="{url_path('ignore_me')}"></a>
  <div class="header-breadcrumbs is-hidden-mobile">
    <a href="/"><img src="/static/browse/0.3.4/images/arxiv-logo-one-color-white.svg" alt="arxiv logo" style="height:40px;"/></a> <span>&gt;</span> <a href="/list/cs/recent">cs</a> <span>&gt;</span> arXiv:2408.03943
  </div>
          <div class="search-block level-right">
    <form class="level-item mini-search" method="GET" action="https://arxiv.org/search">
      <div class="field has-addons">
        <div class="control">
          <input class="input is-small" type="text" name="query" placeholder="Search..." aria-label="Search term or terms" />
          <p class="help"><a href="https://info.arxiv.org/help">Help</a> | <a href="https://arxiv.org/search/advanced">Advanced Search</a></p>
        </div>
        <div class="control">
          <div class="select is-small">
            <select name="searchtype" aria-label="Field to search">
              <option value="all" selected="selected">All fields</option>
              <option value="title">Title</option>
              <option value="author">Author</option>
              <option value="abstract">Abstract</option>
              <option value="comments">Comments</option>
              <option value="journal_ref">Journal reference</option>
              <option value="acm_class">ACM classification</option>
              <option value="msc_class">MSC classification</option>
              <option value="report_num">Report number</option>
              <option value="paper_id">arXiv identifier</option>
              <option value="doi">DOI</option>
              <option value="orcid">ORCID</option>
              <option value="author_id">arXiv author ID</option>
              <option value="help">Help pages</option>
              <option value="full_text">Full text</option>
            </select>
          </div>
        </div>
        <input type="hidden" name="source" value="header">
        <button class="button is-small is-cul-darker">Search</button>
      </div>
    </form>
  </div>
     </div><!-- /end desktop header -->

      <div class="mobile-header">
        <div class="columns is-mobile">
          <div class="column logo-arxiv"><a href="https://arxiv.org/"><img src="/static/browse/0.3.4/images/arxiv-logomark-small-white.svg" alt="arXiv logo" style="height:60px;" /></a></div>
          <div class="column logo-cornell"><a href="https://www.cornell.edu/">
            <picture>
              <source media="(min-width: 501px)"
                srcset="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg  400w"
                sizes="400w" />
              <source srcset="/static/browse/0.3.4/images/icons/cu/cornell_seal_simple_black.svg 2x" />
              <img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University Logo" />
            </picture>
          </a></div>
          <div class="column nav" id="toggle-container" role="menubar">
            <button class="toggle-control"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-white"><title>open search</title><path d="M505 442.7L405.3 343c-4.5-4.5-10.6-7-17-7H372c27.6-35.3 44-79.7 44-128C416 93.1 322.9 0 208 0S0 93.1 0 208s93.1 208 208 208c48.3 0 92.7-16.4 128-44v16.3c0 6.4 2.5 12.5 7 17l99.7 99.7c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.6.1-34zM208 336c-70.7 0-128-57.2-128-128 0-70.7 57.2-128 128-128 70.7 0 128 57.2 128 128 0 70.7-57.2 128-128 128z"/></svg></button>
            <div class="mobile-toggle-block toggle-target">
              <form class="mobile-search-form" method="GET" action="https://arxiv.org/search">
                <div class="field has-addons">
                  <input class="input" type="text" name="query" placeholder="Search..." aria-label="Search term or terms" />
                  <input type="hidden" name="source" value="header">
                  <input type="hidden" name="searchtype" value="all">
                  <button class="button">GO</button>
                </div>
              </form>
            </div>

            <button class="toggle-control"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-white" role="menu"><title>open navigation menu</title><path d="M16 132h416c8.837 0 16-7.163 16-16V76c0-8.837-7.163-16-16-16H16C7.163 60 0 67.163 0 76v40c0 8.837 7.163 16 16 16zm0 160h416c8.837 0 16-7.163 16-16v-40c0-8.837-7.163-16-16-16H16c-8.837 0-16 7.163-16 16v40c0 8.837 7.163 16 16 16zm0 160h416c8.837 0 16-7.163 16-16v-40c0-8.837-7.163-16-16-16H16c-8.837 0-16 7.163-16 16v40c0 8.837 7.163 16 16 16z"/ ></svg></button>
            <div class="mobile-toggle-block toggle-target">
              <nav class="mobile-menu" aria-labelledby="mobilemenulabel">
                <h2 id="mobilemenulabel">quick links</h2>
                <ul>
                    <li><a href="https://arxiv.org/login">Login</a></li>
                    <li><a href="https://info.arxiv.org/help">Help Pages</a></li>
                    <li><a href="https://info.arxiv.org/about">About</a></li>
                </ul>
              </nav>
            </div>
          </div>
        </div>
      </div><!-- /end mobile-header -->
    </header>

    <main>
      <div id="content">
<!--
rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
         xmlns:dc="http://purl.org/dc/elements/1.1/"
         xmlns:trackback="http://madskills.com/public/xml/rss/module/trackback/">
    <rdf:Description
        rdf:about="/abs/2408.03943"
        dc:identifier="/abs/2408.03943"
        dc:title="Building Machines that Learn and Think with People"
        trackback:ping="/trackback/2408.03943" />
    </rdf:RDF>
--><div id="abs-outer">

  <div class="leftcolumn">
    <div class="subheader">
      <h1>Computer Science > Human-Computer Interaction</h1>
    </div>

    <div class="header-breadcrumbs-mobile">
      <strong>arXiv:2408.03943</strong> (cs)
    </div>
<link rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css">
<div id="content-inner">
  <div id="abs">
    <div class="dateline">
  [Submitted on 22 Jul 2024]</div>
    <h1 class="title mathjax"><span class="descriptor">Title:</span>Building Machines that Learn and Think with People</h1>
    <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Collins,+K+M">Katherine M. Collins</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Sucholutsky,+I">Ilia Sucholutsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Bhatt,+U">Umang Bhatt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Chandra,+K">Kartik Chandra</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Wong,+L">Lionel Wong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Lee,+M">Mina Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Zhang,+C+E">Cedegao E. Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Zhi-Xuan,+T">Tan Zhi-Xuan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Ho,+M">Mark Ho</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Mansinghka,+V">Vikash Mansinghka</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Weller,+A">Adrian Weller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Tenenbaum,+J+B">Joshua B. Tenenbaum</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Griffiths,+T+L">Thomas L. Griffiths</a></div>            <div id="download-button-info" hidden>View a PDF of the paper titled Building Machines that Learn and Think with People, by Katherine M. Collins and 12 other authors</div>
    <a class="mobile-submission-download" href="/pdf/2408.03943">View PDF</a>
    <a class="mobile-submission-download" href="https://arxiv.org/html/2408.03943v1">HTML (experimental)</a>



    <blockquote class="abstract mathjax">
            <span class="descriptor">Abstract:</span>What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,&#39;&#39; systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.
    </blockquote>

    <!--CONTEXT-->
    <div class="metatable">
      <table summary="Additional metadata"><tr>
          <td class="tablecell label">Subjects:</td>
          <td class="tablecell subjects">
            <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)</td>
        </tr><tr>
          <td class="tablecell label">Cite as:</td>
          <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2408.03943">arXiv:2408.03943</a> [cs.HC]</span></td>
        </tr>
        <tr>
          <td class="tablecell label">&nbsp;</td>
          <td class="tablecell arxividv">(or <span class="arxivid">
              <a href="https://arxiv.org/abs/2408.03943v1">arXiv:2408.03943v1</a> [cs.HC]</span> for this version)
          </td>
        </tr>
        <tr>
          <td class="tablecell label">&nbsp;</td>
          <td class="tablecell arxivdoi">              <a href="https://doi.org/10.48550/arXiv.2408.03943"  id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2408.03943</a><div class="button-and-tooltip">
              <button class="more-info" aria-describedby="more-info-desc-1">
                <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg>
                <span class="visually-hidden">Focus to learn more</span>
              </button>
              <!-- tooltip description -->
              <div role="tooltip" id="more-info-desc-1">
                <span class="left-corner"></span>                  arXiv-issued DOI via DataCite</div>
            </div>
          </td>
        </tr></table>
    </div>
  </div>
</div>
    <div class="submission-history">
      <h2>Submission history</h2> From: Katherine Collins [<a href="/show-email/8f3aa290/2408.03943">view email</a>]      <br/>    <strong>[v1]</strong>
        Mon, 22 Jul 2024 02:42:45 UTC (1,252 KB)<br/>
</div>
  </div>
  <!--end leftcolumn-->
<div class="extra-services">    <div class="full-text">
      <a name="other"></a>
      <span class="descriptor">Full-text links:</span>
      <h2>Access Paper:</h2>
      <ul>
  <div id="download-button-info" hidden>
View a PDF of the paper titled Building Machines that Learn and Think with People, by Katherine M. Collins and 12 other authors</div><li><a href="/pdf/2408.03943" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="https://arxiv.org/html/2408.03943v1" class="abs-button" id="latexml-download-link">HTML (experimental)</a></li><li><a href="/src/2408.03943" class="abs-button download-eprint">TeX Source</a></li><li><a href="/format/2408.03943" class="abs-button download-format">Other Formats</a></li></ul>
      <div class="abs-license"><a href="http://creativecommons.org/licenses/by/4.0/" title="Rights to this article" class="has_license">
          <img alt="license icon" role="presentation" src="https://arxiv.org/icons/licenses/by-4.0.png"/>
          <span>view license</span>
        </a></div>
    </div>
    <!--end full-text-->    <div class="browse">
    Current browse context: <div class="current">cs.HC</div>

  <div class="prevnext">
<span class="arrow">
      <a class="abs-button prev-url" href="/prevnext?id=2408.03943&amp;function=prev&amp;context=cs.HC"
         accesskey="p" title="previous in cs.HC (accesskey p)">&lt;&nbsp;prev</a>
    </span>
    <span class="is-hidden-mobile">&nbsp; | &nbsp;</span>    <span class="arrow">
      <a class="abs-button next-url" href="/prevnext?id=2408.03943&amp;function=next&amp;context=cs.HC" accesskey="n"
         title="next in cs.HC (accesskey n)">next&nbsp;&gt;</a>
    </span><br/>
  </div><div class="list">
    <a class="abs-button abs-button-grey abs-button-small context-new" href="/list/cs.HC/new">new</a>
    <span class="is-hidden-mobile"> | </span>
    <a class="abs-button abs-button-grey abs-button-small context-recent" href="/list/cs.HC/recent">recent</a>
    <span class="is-hidden-mobile"> | </span><a class="abs-button abs-button-grey abs-button-small context-id" href="/list/cs.HC/2024-08">2024-08</a>
  </div><div class="abs-switch-cat">
    Change to browse by:
    <div class="switch context-change">
        <a href="/abs/2408.03943?context=cs">cs</a><br class="is-hidden-mobile">
        <a class="subclass" href="/abs/2408.03943?context=cs.AI">cs.AI</a><br class="is-hidden-mobile">
        <a class="subclass" href="/abs/2408.03943?context=cs.LG">cs.LG</a><br class="is-hidden-mobile">
    </div>
  </div>

    </div>

    <div class="extra-ref-cite">
      <h3>References &amp; Citations</h3>
      <ul>
        <li><a  class="abs-button abs-button-small cite-ads" href="https://ui.adsabs.harvard.edu/abs/arXiv:2408.03943">NASA ADS</a></li><li><a  class="abs-button abs-button-small cite-google-scholar" href="https://scholar.google.com/scholar_lookup?arxiv_id=2408.03943" target="_blank" rel="noopener">Google Scholar</a></li>
        <li><a  class="abs-button abs-button-small cite-semantic-scholar" href="https://api.semanticscholar.org/arXiv:2408.03943" target="_blank" rel="noopener">Semantic Scholar</a></li>
      </ul>
      <div style="clear:both;"></div>
    </div>

<div class='extra-ref-cite'>
    <a id='bib-cite-css' hidden='true' href='/static/browse/0.3.4/css/cite.css'>a</a>

    <span id='bib-cite-trigger' class="bib-cite-button abs-button">export BibTeX citation</span>
    <span id='bib-cite-loading' hidden='true'>Loading...</span>
</div>

<div id='bib-cite-modal' class='bib-modal' hidden='true'>
    <div class='bib-modal-content'>
        <div class='bib-modal-title'>
            <h2>BibTeX formatted citation</h2>
            <span class='bib-modal-close' >&times;</span>
        </div>
        <div>
            <textarea id='bib-cite-target' class="bib-citation-content" aria-label="loading the citation">loading...</textarea>
        </div>
        <div>
            <span>Data provided by: </span>
            <a id='bib-cite-source-api'></a>
        </div>
    </div>
</div><div class="bookmarks">
  <div><h3>Bookmark</h3></div><a class="abs-button abs-button-grey abs-button-small" href="http://www.bibsonomy.org/BibtexHandler?requTask=upload&amp;url=https://arxiv.org/abs/2408.03943&amp;description=Building Machines that Learn and Think with People"
     title="Bookmark on BibSonomy">
    <img src="/static/browse/0.3.4/images/icons/social/bibsonomy.png"
         alt="BibSonomy logo"/>
  </a>
  <a class="abs-button abs-button-grey abs-button-small" href="https://reddit.com/submit?url=https://arxiv.org/abs/2408.03943&amp;title=Building Machines that Learn and Think with People"
     title="Bookmark on Reddit">
    <img src="/static/browse/0.3.4/images/icons/social/reddit.png"
         alt="Reddit logo"/>
  </a>
</div>  </div>
  <!--end extra-services-->
<!-- LABS AREA -->
<div id="labstabs">
  <div class="labstabs"><input type="radio" name="tabs" id="tabone"checked="checked">
    <label for="tabone">Bibliographic Tools</label>
    <div class="tab labs-display-bib">
      <h1>Bibliographic and Citation Tools</h1>
      <div class="toggle">
        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input id="bibex-toggle" type="checkbox" class="lab-toggle">
              <span class="slider"></span>
              <span class="is-sr-only">Bibliographic Explorer Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-bibex">Bibliographic Explorer</span> <em>(<a href="https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer">What is the Explorer?</a>)</em>
          </div>
        </div>
<div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="litmaps-toggle"
                type="checkbox"
                class="lab-toggle"
                data-script-url="/static/browse/0.3.4/js/litmaps.js?20210617"
                aria-labelledby="label-for-litmaps">
              <span class="slider"></span>
              <span class="is-sr-only">Litmaps Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-litmaps">Litmaps</span> <em>(<a href="https://www.litmaps.co/" target="_blank">What is Litmaps?</a>)</em>
          </div>
        </div>
        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="scite-toggle"
                type="checkbox"
                class="lab-toggle"
                data-script-url="/static/browse/0.3.4/js/scite.js?20210617"
                aria-labelledby="label-for-scite">
              <span class="slider"></span>
              <span class="is-sr-only">scite.ai Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-scite">scite Smart Citations</span> <em>(<a href="https://www.scite.ai/" target="_blank">What are Smart Citations?</a>)</em>
          </div>
        </div>
      </div>
        <div class="labs-content-placeholder labs-display" style="display: none;"></div>
        <div style="min-height: 15px" id="litmaps-open-in"></div>
        <div style="min-height: 15px" id="scite-open-in"></div>
    </div>


    <input type="radio" name="tabs" id="tabtwo">
    <label for="tabtwo">Code, Data, Media</label>
    <div class="tab">
      <h1>Code, Data and Media Associated with this Article</h1>
      <div class="toggle">
        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input        
                id="catalyzex-toggle"
                data-script-url="/static/browse/0.3.4/js/catalyzex.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-cx">
              <span class="slider"></span>
              <span class="is-sr-only">Links to Code Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-cx">CatalyzeX Code Finder for Papers</span> <em>(<a href="https://www.catalyzex.com" target="_blank">What is CatalyzeX?</a>)</em>
          </div>
        </div>

        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="dagshub-toggle"
                data-script-url="/static/browse/0.3.4/js/dagshub.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-dagshub">
              <span class="slider"></span>
              <span class="is-sr-only">DagsHub Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-dagshub">DagsHub</span> <em>(<a href="https://dagshub.com/" target="_blank">What is DagsHub?</a>)</em>
          </div>
        </div>
  
        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="gotitpub-toggle"
                data-script-url="/static/browse/0.3.4/js/gotitpub.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-gotitpub">
              <span class="slider"></span>
              <span class="is-sr-only">GotitPub Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-gotitpub">Gotit.pub</span> <em>(<a href="http://gotit.pub/faq" target="_blank">What is GotitPub?</a>)</em>
          </div>
        </div>

        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="paperwithcode-toggle"
                data-script-url="/static/browse/0.3.4/js/paperswithcode.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-pwc">
              <span class="slider"></span>
              <span class="is-sr-only">Links to Code Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-pwc">Papers with Code</span> <em>(<a href="https://paperswithcode.com/" target="_blank">What is Papers with Code?</a>)</em>
          </div>
        </div>


        <div class="columns is-mobile lab-row">
          <div class="column lab-switch">
            <label class="switch">
              <input
                id="sciencecast-toggle"
                data-script-url="/static/browse/0.3.4/js/sciencecast.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-sciencecast">
              <span class="slider"></span>
              <span class="is-sr-only">ScienceCast Toggle</span>
            </label>
          </div>
          <div class="column lab-name">
            <span id="label-for-sciencecast">ScienceCast</span> <em>(<a href="https://sciencecast.org/welcome" target="_blank">What is ScienceCast?</a>)</em>
          </div>
        </div>
      </div>

      <div id="catalyzex-output" style="display:none"></div>
      <div id="dagshub-output" style="display:none"></div>
      <div id="gotitpub-output" style="display:none"></div>
      <div id="pwc-output" style="display:none"></div>
      <div id="pwc-data-output" style="display:none"></div>
      <div id="sciencecast-output" style="display:none"></div>
    </div>


      <input type="radio" name="tabs" id="labstabs-demos-input">
      <label for="labstabs-demos-input" id="labstabs-demos-label">Demos</label>
      <div class="tab">
        <h1>Demos</h1>
        <div class="toggle">
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input
                  id="replicate-toggle"
                  data-script-url="/static/browse/0.3.4/js/replicate.js"
                  type="checkbox" class="lab-toggle" aria-labelledby="label-for-replicate">
                <span class="slider"></span>
                <span class="is-sr-only">Replicate Toggle</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-replicate">Replicate</span> <em>(<a href="https://replicate.com/docs/arxiv/about" target="_blank">What is Replicate?</a>)</em>
            </div>
          </div>
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input
                  id="spaces-toggle"
                  data-script-url="/static/browse/0.3.4/js/spaces.js"
                  type="checkbox" class="lab-toggle" aria-labelledby="label-for-spaces">
                <span class="slider"></span>
                <span class="is-sr-only">Spaces Toggle</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-spaces">Hugging Face Spaces</span> <em>(<a href="https://huggingface.co/docs/hub/spaces" target="_blank">What is Spaces?</a>)</em>
            </div>
          </div>
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input
                  id="txyz-toggle"
                  data-script-url="/static/browse/0.3.4/js/txyz.js"
                  type="checkbox" class="lab-toggle" aria-labelledby="label-for-txyz">
                <span class="slider"></span>
                <span class="is-sr-only">Spaces Toggle</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-txyz">TXYZ.AI</span> <em>(<a href="https://txyz.ai" target="_blank">What is TXYZ.AI?</a>)</em>
            </div>
          </div>
        </div>
        <div id="replicate-output"></div>
        <div id="spaces-output"></div>
        <div id="txyz-output"></div>
      </div>
      <input type="radio" name="tabs" id="tabfour">
      <label for="tabfour">Related Papers</label>
      <div class="tab">
        <h1>Recommenders and Search Tools</h1>
        <div class="toggle">
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input id="influenceflower-toggle"
                data-script-url="/static/browse/0.3.4/js/influenceflower.js"
                type="checkbox" class="lab-toggle" aria-labelledby="label-for-influenceflower">
                <span class="slider"></span>
                <span class="is-sr-only">Link to Influence Flower</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-influenceflower">Influence Flower</span> <em>(<a href="https://influencemap.cmlab.dev/" target="_blank">What are Influence Flowers?</a>)</em>
            </div>
          </div>
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input id="connectedpapers-toggle" type="checkbox" class="lab-toggle"
                       data-script-url="/static/browse/0.3.4/js/connectedpapers.js"
                       aria-labelledby="label-for-connected-papers" >
                <span class="slider"></span>
                <span class="is-sr-only">Connected Papers Toggle</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-connected-papers">Connected Papers</span> <em>(<a href="https://www.connectedpapers.com/about" target="_blank">What is Connected Papers?</a>)</em>
            </div>
          </div>
          <div class="columns is-mobile lab-row">
            <div class="column lab-switch">
              <label class="switch">
                <input id="core-recommender-toggle" type="checkbox" class="lab-toggle" aria-labelledby="label-for-core">
                <span class="slider"></span>
                <span class="is-sr-only">Core recommender toggle</span>
              </label>
            </div>
            <div class="column lab-name">
              <span id="label-for-core">CORE Recommender</span> <em>(<a href="https://core.ac.uk/services/recommender">What is CORE?</a>)</em>
            </div>
          </div></div>
        <div id="influenceflower-output"></div>
        <div id="influenceflower-output-graph" style="display:none">
          <ul class="flower-tabs">
            <li class="active"><a class="btn tab-btn" onclick="openTab(event, 'tab-author')">Author</a></li>
            <li><a class="btn tab-btn" onclick="openTab(event, 'tab-venue')">Venue</a></li>
            <li><a class="btn tab-btn" onclick="openTab(event, 'tab-inst')">Institution</a></li>
            <li><a class="btn tab-btn" onclick="openTab(event, 'tab-topic')">Topic</a></li>
          </ul>
          <div class="flower-tab-content">
            <div class="tab-flower active" id="tab-author"><svg id="flower-graph-author"></svg></div>
            <div class="tab-flower" id="tab-venue"><svg id="flower-graph-venue"></svg></div>
            <div class="tab-flower" id="tab-inst"><svg id="flower-graph-inst"></svg></div>
            <div class="tab-flower" id="tab-topic"><svg id="flower-graph-topic"></svg></div>
          </div>
        </div>
        <div style="min-height: 15px" id="connectedpapers-output"></div>
        <div id="coreRecommenderOutput"></div>
        <div id="iarxivOutput"></div>
      </div>

      <input type="radio" name="tabs" id="tabfive">
      <label for="tabfive">
        About arXivLabs
      </label>
      <div class="tab">
        <div class="columns">
          <div class="column">
            <h1>arXivLabs: experimental projects with community collaborators</h1>
            <p>arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.</p>
            <p>Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.</p>
            <p>Have an idea for a project that will add value for arXiv's community? <a href="https://info.arxiv.org/labs/index.html"><strong>Learn more about arXivLabs</strong></a>.</p>
          </div>
          <div class="column is-narrow is-full-mobile">
            <p class="icon-labs"><svg xmlns="http://www.w3.org/2000/svg" role="presentation" viewBox="0 0 635.572 811"><path d="M175.6 676v27h-27v-27zm-54 27v27h27v-27zm-27 27v27h27v-27zm396-54v27h-27v-27zm0 27v27h27v-27zm27 27v27h27v-27zm-27-414h27v27h-27zm27 0h27v-27h-27zm27-27h27v-27h-27zm-396 45h-27v-27h27zm-27-54h-27v27h27zm-27-27h-27v27h27z"/><path d="M94.6 730v27h-27v-27zm477 0v27h-27v-27zm-27-495h27v27h-27zm-450 18h-27v-27h27zm477 9h27v27h-27zm-54 495h27v27h-27zm-423 0h27v27h-27zm-54-504h27v27h-27z" fill="#666"/><path d="M67.6 730v27h-27v-27zm54 54v27h-27v-27zm0-108v27h27v-27zm-27 27v27h27v-27zm-81 0v27h27v-27zm585 27v27h-27v-27zm-108-54v27h27v-27zm27 27v27h27v-27zm81 0v27h27v-27zm-54-495h27v27h-27zm-54 108h27v-27h-27zm27-27h27v-27h-27zm0-81h27v-27h-27zm-423 18h-27v-27h27zm54 54h-27v27h27zm-27-27h-27v27h27zm0-81h-27v27h27zm423 612v27h-27v-27zm81-522v27h-27v-27zm-585-9v27h-27v-27z" fill="#999"/><path d="M94.6 784v27h-27v-27zm-27-27v27h27v-27zm-27-54v27h27v-27zm27 0v27h27v-27zm0-27v27h27v-27zm27 0v27h27v-27zm0-27v27h27v-27zm27 0v27h27v-27zm-108 81v27h27v-27zm558 54v27h-27v-27zm-27-27v27h27v-27zm27-54v27h27v-27zm-27 0v27h27v-27zm0-27v27h27v-27zm-27 0v27h27v-27zm0-27v27h27v-27zm-27 0v27h27v-27zm108 81v27h27v-27zm0-495h27v27h-27zm-27 27h27v-27h-27zm-54-27h27v-27h-27zm0 27h27v-27h-27zm-27 0h27v-27h-27zm0 27h27v-27h-27zm-27 0h27v-27h-27zm0 27h27v-27h-27zm81-108h27v-27h-27zm-504 45h-27v-27h27zm27-27h-27v27h27zm54-27h-27v27h27zm0 27h-27v27h27zm27 0h-27v27h27zm0 27h-27v27h27zm27 0h-27v27h27zm0 27h-27v27h27zm-81-108h-27v27h27z" fill="#ccc"/><path d="M598.6 665.1H41.5C-76.5 667 176 280.2 176 280.2h53a46.5 46.5 0 0162.8-56.3 29.2 29.2 0 1128.5 35.9h-1a46.5 46.5 0 01-1.5 20.3l142.5-.1s255.3 387 138.3 385.1zM291 181a29.3 29.3 0 10-29.2-29.3A29.3 29.3 0 00291 181zm65.4-66.8a22.4 22.4 0 10-22.5-22.4 22.4 22.4 0 0022.5 22.4z" fill="#fc0"/><path d="M245.5 172V10h153v162s324 495 198 495h-558c-126 0 207-495 207-495zm126 54h56m-13 72h56m-9 72h56m-20 72h56m-22 72h56m-29 72h56m-457-45c20.8 41.7 87.3 81 160.7 81 72.1 0 142.1-38.2 163.4-81" fill="none" stroke="#000" stroke-miterlimit="10" stroke-width="20"/><path d="M273.3 421.7c0 31-9.8 56.3-21.9 56.3s-21.8-25.2-21.8-56.3 9.8-56.3 21.8-56.3 21.9 25.2 21.9 56.3zm114.4-56.3c-12 0-21.8 25.2-21.8 56.3s9.7 56.3 21.8 56.3 21.9-25.2 21.9-56.3-9.8-56.3-21.9-56.3zM150.1 526.6c-18.2 6.7-27.5 22.9-23.2 30.2s14.8-5.5 33-12.2 37.4-4.9 33-12.2-24.5-12.6-42.8-5.8zm296 5.8c-4.2 7.3 14.9 5.5 33.1 12.2s28.7 19.5 33 12.2-5-23.5-23.2-30.2-38.5-1.5-42.8 5.8z"/></svg></p>
          </div>
        </div>
      </div>

    </div>
</div>
<!-- END LABS AREA -->
  <div class="endorsers">
    <a href="/auth/show-endorsers/2408.03943" class="endorser-who">Which authors of this paper are endorsers?</a> |
    <a id="mathjax_toggle" href="javascript:setMathjaxCookie()">Disable MathJax</a> (<a href="https://info.arxiv.org/help/mathjax.html">What is MathJax?</a>)
    <span class="help" style="font-style: normal; float: right; margin-top: 0; margin-right: 1em;"></span>
  </div>
  <script type="text/javascript" language="javascript">mathjaxToggle();</script>
</div>
      </div>
    </main>

    <footer style="clear: both;">
      <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">
        <!-- Macro-Column 1 -->
        <div class="column" style="padding: 0;">
          <div class="columns">
            <div class="column">
              <ul style="list-style: none; line-height: 2;">
                <li><a href="https://info.arxiv.org/about">About</a></li>
                <li><a href="https://info.arxiv.org/help">Help</a></li>
              </ul>
            </div>
            <div class="column">
              <ul style="list-style: none; line-height: 2;">
                <li>
                  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>
                  <a href="https://info.arxiv.org/help/contact.html"> Contact</a>
                </li>
                <li>
                  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg>
                  <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a>
                </li>
              </ul>
            </div>
          </div>
        </div>
        <!-- End Macro-Column 1 -->
        <!-- Macro-Column 2 -->
        <div class="column" style="padding: 0;">
          <div class="columns">
            <div class="column">
              <ul style="list-style: none; line-height: 2;">
                <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li>
                <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li>
              </ul>
            </div>
            <div class="column sorry-app-links">
              <ul style="list-style: none; line-height: 2;">
                <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li>
                <li>
                  <p class="help">
                    <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br>
                    Get status notifications via
                    <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a>
                    or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a>
                  </p>
                </li>
              </ul>
            </div>
          </div>
        </div> <!-- end MetaColumn 2 -->
        <!-- End Macro-Column 2 -->
      </div>
    </footer>
  </div>

  <script src="/static/base/1.0.1/js/member_acknowledgement.js"></script>

</body>

</html>
2408.03943
 What do we want from machine intelligence? We envision machines that are not just tools for thought, but *partners* in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think *with* us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called "thought partners," systems built to meet our expectations and complement our limitations.  We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world. 
abstract
Building Machines That Learn And Think With People
Abstract
2408.03943
 Computers have long been seen as tools for thought. Steve Jobs called computers "bicycles for the mind": tools that dramatically increase the efficiency, productivity, and joy of thinking. Now, thirty years later, this metaphor is beginning to change. Computer systems are increasingly referred to not as *vehicles* but as "copilots" 1,2: we have moved from designing *tools* for thought to actual *partners* in thought.  The current wave of AI technologies, particularly language models, have catalyzed this transition. Users no longer have to know how to write code to engage intimately with computers; we can now interface through the medium of natural language. Humans already think alone and together, at least communicated often through the medium of language 3. We long have – from developing new modes of thinking through questioning and debate to teaching and learning through language. The apparent power of these new systems - getting closer to the kind of artificial intelligence (AI) imagined in the field's early days 4–9 - as well as challenges faced by the current iterations of such systems - invites us to think about what it will take to build ∗Contributed equally.  †Contributed equally.  ‡Equal senior role.  ![1_image_0.png](1_image_0.png)  systems that truly act as effective thought partners. We argue that good thought partners are systems (1) which can understand us, (2) which we can understand, and (3) which have sufficient understanding of the world that we can engage on common ground.  One path to building such thought partners is to scale foundation models (e.g., LLMs 10) with large amounts of human demonstrations and feedback, along with "traces" of human thought scraped from web-scale data 11–13. While such an approach has produced systems that accurately mimic human *behavior* (e.g., producing fluent text), these machines do not robustly simulate human *cognition* (e.g., explicitly reasoning about the world or other minds) in ways expected by a true thought partner 3,14–20.  What would it take to design systems that meet our criteria? One promising path is to design systems that build explicit models of the task, world, and human (where these models are structured21, rather than distributionally learned from data) - drawing on formal frameworks grounded in cognitive psychology for understanding how humans think, alone and together. In this Perspective, we chart a new vision for the design of AI thought partners. Decades of work in the behavioral sciences provide valuable insights for designing human-centric, uncertainty-aware thought partners. Drawing on such research, we argue that effective thought partners are those which *build models of the human* and *the world*.  This toolkit includes foundation models 22–24, but is not limited to them. Indeed, foundation models like LLMs are fueling new motifs for thinking about human minds in computational terms (e.g., "rational meaning construction" 16) interleaved *alongside* techniques from probabilistic programming25–29, goal-directed search30–32, and other explicit, structured representations, e.g., of agents thinking about other agents 33–35. We already have tools that help us build machines that learn and think *like* people36. We propose applying that toolkit to collaborative cognition – to build machines that learn and think *with* people. 
introduction
Building Machines That Learn And Think With People
Introduction
2408.03943
 When we think, we draw coherent inferences, make predictions, and act on these predictions – from assessing what birthday present to gift a treasured friend, to formulating a new scientific hypothesis and experiment plan to evaluate a theory. We flexibly draw on prior knowledge and update our beliefs through experience (as we discuss below). We not only solve problems, but imagine new ones37. And we *think together*. For generations, humans have discussed and debated ideas, and developed ecosystems to disseminate such thoughts to new audiences. Much scientific innovation has come through collaboration, where advances are frequently fueled by engaging with diverse partners who offer new ideas yet share our values 38. 
body
Building Machines That Learn And Think With People
What Are Thought Partners?
2408.03943
 As an illustration of the many ways that people and machines might think with each other, we highlight a few *modes of collaborative thought* (Table 1). This set of modes, partly inspired by characterizations of thinking and reasoning in psychology 39,40, are not meant to be comprehensive of all aspects of thought. Rather, we see these modes as ripe for the further development of AI thought partners. 
body
Building Machines That Learn And Think With People
Modes Of Collaborative Thought
2408.03943
 We next outline a few diverse domains in which the development of AI thought partners able to truly collaborate with humans (Figure 1) may be particularly valuable. We highlight common computational challenges that arise when considering what effective partnership might look like in each domain, foreshadowing our proposed desiderata. We later return to these case studies with concrete human-centric thought partner instantiations.  Thought Partners for Programming. Programming is a cognitively-demanding activity that requires gaining fluency in translating human intentions into formal, machine-interpretable languages. It is no surprise that decades of effort have gone into designing tools to help people program41–45. New "programming assistant" tools like GitHub Copilot have rapidly gained enormous popularity and attention; however, these tools are often unreliable 46–48, e.g., failing to understand users' intentions 49 and generating bugs that may be particularly risky alongside beginner programmers 50. Programming involves much more than just accurate in-line code suggestions - which, at the time of writing, GitHub Copilot specializes in. Humans plan abstract, structural decisions and collaboratively learn, and need partners who can answer our questions – like why code behaves as it does, or fails to work. A good collaborative programming partner seeks to understand not only the programming language, but also their fellow *programmer*, inferring and reasoning about our overarching intentions, and adapting to both what we do and do not know.  Thought Partners for Embodied Assistance. Ensuring embodied agents can form accurate and physically-realizable plans is foundational for effective assistance we can trust - from guessing what a friend wants when we help them cook 51, to working with someone with different physical abilities52, or carrying out a high-stakes search-and-rescue mission53. While much current research on embodied AI and assistive robots focuses on learning specific skills or following simple instructions54–56, evaluations suggest that even state-of-the-art language models fine-tuned on extensive human feedback continue to struggle with tasks that require reliable, effective planning towards novel goals57,58. Instead, ideal assistive partners understand our actions, words, and instructions as expressions of goals, beliefs, and intentions 59–61 that are *grounded* in physical possibilities62, while also understanding that these can be *shared* across multiple minds 63–65.  In addition, effective partners account for each others' limitations in perception, planning, and world modeling, correcting for possible mistakes 66,67, and acting so as to make their intentions more legible68,69.  Thought Partners for Storytelling. Another domain in which we may want thought partners is storytelling - for writers, filmmakers, and even scientists. Storytelling is a complex, iterative cognitive process 70,71 with substantial opportunities for thought partners to collaboratively ideate and create with humans from helping brainstorm new ideas, generate storylines, and improve their writing style and tone 72–77. For this process to be productive, a thought partner needs to understand more than just our authorial intentions and dispositions - they also need to understand the *audience* we are speaking to (that is, to *understand the social world*), including audience expectations and likely interpretations of the stories we are crafting for them.  Thought Partners for Medicine. Doctors need to sensemake, plan, deliberate, and continually learn in the face of new medical evidence. A primary care doctor is not unlike Sherlock Holmes - collating and integrating disparate bits of evidence with their prior beliefs to make decisions under uncertainty. Yet, doctors rarely have enough time to engage deeply with each patient 78, driving high rates of burnout with knock-on effects on patient care quality 79. Can we develop safe, reliable thought partners that can free doctors up to spend more time and communicate better with their patients? Already, foundation models are becoming proficient in medical assessments80,81, seemingly capable of easing the heavy burden on doctors by assisting and partnering82,83, and even providing preferable responses to patients 84. Yet, it is not clear that these systems *understand us* (and our cognitive limitations), *understand the world* (underlying biology), and enable us to *understand them* (which in this context, may be important for transparency and reliability 85–88). 
body
Building Machines That Learn And Think With People
Example Domains
2408.03943
 What then do we want from thought partners? There are many criteria for tools for thought that are of course relevant: efficiency, accuracy, robustness, fairness, cost, scalability, etc. But the domains above illuminate that what is distinctive about a thought *partner* is its relationship to the *user* 89. Looking to ideas the behavioral sciences motivates three desiderata to guide the design of human-centered thought partners: 1. You understand me: We would like our thought partners to understand our goals, plans, (possibly false) beliefs, and resource limitations, taking into account what they have observed of us in the past and present in order to best collaborate with us in the future 90,91.  For example, a thought partner should adaptively change strategies when working with an expert, layperson, or child, meeting us where we are.  2. I understand you: We would like our thought partners to act in a way that is legible to us68,92, and communicate with us in the way we intuitively understand93–95.  3. We understand the world: We would like our thought partners to be tethered to reality96. This means being accurate and knowledgeable, but also working with a shared representation of the world, domain, or task 97–99. Further, our use of 'we' emphasizes that thought partnerships are fundamentally about *synergy*, moving beyond the sum of its parts. 
body
Building Machines That Learn And Think With People
Desiderata
2408.03943
 Our core proposal is that our three desiderata can be engineered explicitly, building on theoretical motifs from computational cognitive science and cognitively-informed AI (summarized in Table 2), rather than left as emergent and potentially brittle properties arising implicitly in systems trained for other ends 20. Here, we articulate a framework for engineering thought partners designed to robustly and explicitly function as cooperative, collaborative actors. Humans are far from homogeneous, perfectly rational oracles, nor are we so unpredictable that it is hopeless to model human behavior. We argue that models that explain human cognition and choice as approximately optimal solutions given goals and constraints provide an ideal starting point for designing thought partners, and that a Bayesian formalism provides a probabilistically-sound common conceptual language that facilitates cross-talk between different disciplines 22,155,156. 
body
Building Machines That Learn And Think With People
Engineering Human-Centered Thought Partners
2408.03943
 What does it take to engineer real systems that meet our desiderata? First, we propose that a thought partner that understands us should explicitly *model its human collaborator* as such - as a cooperative agent with structured internal beliefs, knowledge, and goals - and fundamental resource limitations. Second, engineering a thought partner that we can understand benefits from looking at how *humans model other humans*; just as a good human collaborator seeks to learn  | Mode                                                                                                  | Ongoing Challenges                                                                                | Sampling of Existing Systems                                                              |        |    | |-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|--------|----| | Collaborative planning - Joint decision-making - Decentralized cooperation - Goal and task assistance | Reliable goal inference Value and intent alignment Scalable multi-agent planning                  | Collaborative robots 68,100 Video game sidekicks 101,102 Language-based assistants 35,103 |        |    | | Collaborative learning - Pair & team problemsolving - Identification of knowledge gaps - New problem construction                                                                                                       | Strong                                                                                            | &                                                                                         | robust | problem    | | solving abilities Personalized curriculum pacing Problem construction of targeted difficulty                                                                                                       | Programming learning aids 104–107 Mathematics tutors 15,108,109                                   |                                                                                           |        |    | | Collaborative deliberation - Debate & argumentation - Critical review & discussion - Consensus formation                                                                                                       | Machine-assisted debating 110–112 Consensus writing & opinion mapping 113,114                                                                                                   |                                                                                           |        |    | | Opinion diversity Verifiable reasoning Formation of common ground                                     |                                                                                                   |                                                                                           |        |    | | Collaborative sensemaking - Explanation - Visualization - Data Analytics                              | Exponential increases in data produced Accessible communication Fidelity of insights to the world | Probabilistic data modeling 115–119 Machine-assisted theory discovery 120–122                                                                                           |        |    | | Collaborative                                                                                         | creation                                                                                          | &                                                                                         |        |    | | ideation - Co-design - Idea critiquing - Brainstorming                                                | Machine-assisted writing                                                                          |                                                                                           |        |    | | Generation diversity Style consistency Modular customizability                                        | 72,74,123 Prompted image creation 124–126 Collaborative sketching 127–129                         |                                                                                           |        |    |  Table 1: Modes of collaborative thought. Settings in which human-human and human-AI thought partners can engage.  and adapt to the relative strengths, imperfections, and computational bounds of their partner, we can build machine thought partners that also reason about the computational demands they are placing on another agent such that we can appropriately predict their behavior 18,157. Finally, to build thought partners that understand the world - and learn and think synergistically alongside us - we argue that it is valuable to build on structured computational toolkits for grounding shared goals and communication into the environment and domain in which collaboration takes place. 
body
Building Machines That Learn And Think With People
Implementing Our Desiderata
2408.03943
 We now (non-exhaustively) spotlight several key insights about modeling humans, modeling humans modeling humans, and modeling humans modeling the world from computational cognitive science - "motifs" for reverse engineering the mind (Table 2) - that we believe can inform engineering of human-centered thought partners. While we acknowledge that there are communities within cognitive science that may disagree with some of these theories, we emphasize that the computational underpinnings of the motifs hold tremendous engineering potential for building thought partners in practice.  | Motif                                                                                            | Description                                     | Sample References   | |--------------------------------------------------------------------------------------------------|-------------------------------------------------|---------------------| | Probabilistic Mental Models and Inference                                                        | Humans update beliefs and draw inferences consistent with probabilistic generative models representing the world.                                                 | 21,130,131          | | Structured                                                                                       | Humans have abstract, highly structured conceptual representations that include causality,                                                 |                     | | Knowledge Representations                                                                                                  | 132–134                                         |                     | | agents, and physical representations. Humans construct and update hierarchical representations that separate concrete knowledge                                                                                                  |                                                 |                     | | Hierarchical                                                                                     | 135–137                                         |                     | | Models                                                                                           | and belief from abstract ones. Humans minds can be viewed as growing and editing theories of the world, expressed as programs, to "improve" their codebase (world models).                                                 | 138–140             | | Theory Learning as Program Synthesis                                                                                                  | Humans make rational choices about how to       |                     | | ResourceRationality                                                                                                  | 141–143                                         |                     | | allocate finite computational resources, including time and memory. Humans are intentional actors, who plan to achieve goals by reasoning about the (uncertain) effects of their (possible) actions in the environment.                                                                                                  |                                                 |                     | | Goal-Directed Planning and Search                                                                | 144–146                                         |                     | | Humans represent other agents as intentional,                                                    | 147–149                                         |                     | | intelligent actors; and probabistically infer their mental states from observations of actions.  |                                                 |                     | | Bayesian Theory of Mind (BToM)                                                                   | Humans reason about language as an intentional, |                     | | Rational Speech                                                                                  | communicative action to infer a speakers' underlying goals.                                                 | 59,150,151          | | Acts (RSA)                                                                                       | Humans meta-learn (improve our overarching ability to learn) jointly with learning new concrete concepts and skills.                                                 | 36,152–154          | | Learning                                                                                         | to                                              |                     | | Learn                                                                                            |                                                 |                     | | Table 2: Bayesian Thought Partner Toolkit. A range of computational cognitive motifs for reverse |                                                 |                     |  Table 2: Bayesian Thought Partner Toolkit. A range of *computational cognitive motifs* for reverse engineering the mind in engineering terms, drawn from computational cognitive science, can be used to build human-centric thought partners that meet our desiderata.  Probabilistic Models of Cognition. Decades of work in computational cognitive science have demonstrated the power of modeling aspects of human cognition as Bayesian inference through structured probabilistic generative world models 21,131,137,158,159. Such approaches have found empirical success in capturing a diversity of facets of human cognition from early word learning160, to visual perception161,162, physical reasoning99,163,164, concept learning165–167, language processing and acquisition158,168–170, causal inference in children171,172 and adults173,174, memory reconstruction175, and theory formation176,177, among many others. Probabilistic models of cognition, particularly those built using a Bayesian approach, have offered principled formalisms in capturing rapid belief updating 178 and how we may integrate our commonsense world knowledge with new evidence to inform the actions and decisions we take in the world149. Probabilistic inference over structured representations, particularly drawing on Bayesian modeling and tools like meta-level Markov Decision Processes 179, has provided a computational account of how humans plan so flexibly, with the capability of forming rich hierarchical goals and subgoals, across varied timescales149,155,180–182.  Theory of Mind and Communication. In our quest to build systems for collaborative cognition, we are guided by the success of Bayesian accounts of how we reason about *others*' mental states, and how we communicate about them. In particular, Bayesian treatments of theory of mind (ToM) have offered strong accounts for how we may rapidly reason about each others' beliefs, desires, goals, and intentions 33,147,183–185. We may build mental models 186,187 of our thought partners, which can in turn be used to support communication and collaboration, informing the way we teach188–190, infer whether to rely on a partner for help191, and support rapid, flexible adaptation to new conversation partners 192,193. We call particular attention to the Rational Speech Act (RSA) framework 59,150, which models communicative partners as recursively reasoning about each others' minds to inform what to say (from the perspective of the speaker) and how to interpret a received utterance (as the listener). Bayesian models provide a useful framework for formalizing such rich cross-partner inferences, allowing both social cognition and communication to be modeled with the same computational toolbox 194,195.  Resource-Rationality and Tractable Theory-Building. Human brains also have limited resources such as time, memory, and attention that shape what we think about, how long we spend thinking, and even how we communicate our thoughts to others 196. Thus, we sometimes make systematically biased inferences197,198. Such "erroneous" judgments can be captured by modeling humans as making rational use of our finite resources; e.g., via approximate inference 178,199 or bounded planning67. Crucially, human cognition is tractable 200. Indeed, we can navigate large, potentially unbounded, hypothesis spaces to build theories of the world: a process that seems to demand some kind of heuristics and approximations, which may be resource-rational 17,142,143,182,196,201,202.  One approach to modeling minds advocates thinking about humans, as "world model builders" (or "hackers") - conducting experiments and updating our beliefs about compressed representations of the world, where these representations may be expressed as programs 138,176. Such representations - bolstered by tools like program synthesis - help explore suboptimal behavior 203. 
body
Building Machines That Learn And Think With People
Computational Cognitive Science Motifs
2408.03943
 If Bayesian thought partners are to reason over models of their human thought partner and the world, these models need to continually evolve as new facts come to light and as the human thought partner themselves grows in their expertise, beliefs, and needs. Probabilistic programming 26 provides one powerful methodology for building, scaling, and performing inference in these kinds of rich models. For example, probabilistic programs can be learned from data 116,204, and synthesized via LLMs that encode rich priors 16,118,205. Probabilistic programs also enable fast approximate inference in world models that cohere with human common-sense knowledge and domain expertise 115,206, where the learned models are themselves amenable to modular inspection and editing by humans. Modern probabilistic programming languages 25,27,207 offer not just generic inference but *programmable* inference, that is, they automate the math for hybrids of optimization208,209, dynamic programming 210, and Monte Carlo inference 211. While such frameworks are certainly not the only methods to handle uncertainty and build effective and robust thought partners, we believe they are one promising and cognitively-grounded approach to instantiating thought partners today, as we discuss in our case studies. 
body
Building Machines That Learn And Think With People
Scaling Thought Partners Via Probabilistic Programming
2408.03943
 The design of systems that learn and think with people necessitates not only careful construction of the thought partner (i.e., the machine itself), but also the *infrastructure* within which human and computational thought partners collaborate 157. Questions like "when and where should a human be able to engage a computational thought partner to ensure effective and appropriate use?" or "for a given problem, is the human or computational thought partner better suited to start first, in light of their respective strengths and weakness, costs of the task at hand, and particular mode of thought?" inform the design of the workflow that *surrounds* thought partnership. This sociotechnical ecosystem may be dictated by external regulations, organizational practices, or other principles73,212–215, and crucially informed by studies of human behavior. For example, 7  ![7_image_0.png](7_image_0.png)  Article 14 of the EU AI Act requires users of high-risk AI systems "to correctly interpret the high-risk AI system's output" and "to remain aware of the possible tendency of automatically relying or over-relying on the output." Satisfying such requirements begets not only careful design of thought partners (e.g., that we can understand), but demands careful design of the system of affordances216,217 and infrastructure around thought partnerships (for instance, communicating back to humans information about their reliance strategies). Disentangling thought partners from the infrastructure around them provides a *modular* scaffold for addressing unintentional thought partnership behavior, e.g, overreliance 218 and "illusions of understanding" 219. Bayesian modeling has already found success in inferring humans' reliance strategies 220 and regions of the task space where a human versus machine can complement one another 221. 
body
Building Machines That Learn And Think With People
Infrastructure Around Thought Partners
2408.03943
 We now return to the example domains previously introduced and discuss specific case studies (depicted in Figure 2). Our goal is to demonstrate the potential benefits of endowing thought partners with structured probabilistic models of the human and/or world, and provide a flavor of the kinds of infrastructure questions that may surround them to ensure that the thought partners we build work *with* people. 
body
Building Machines That Learn And Think With People
Case Studies In Engineering Thought Partners
2408.03943
 We highlighted some visions for effective programming partnerships, such as a partner that can address "why" questions. One recent idea, from Chandra et al. 106, is to apply the Bayesian toolkit to *explain surprising behavior* of computer programs in a human-like way. Chandra et al., apply Bayesian models of mental state inference and rational communication222 to design a system called "WatChat" that answers questions like "why did program p output result r?" in a principled, human-like way. WatChat infers what *erroneous mental model* might cause the programmer to have expected something different (partner understands user) and generates an explanation that "debugs" that mental model (user understands partner). WatChat represents possible mental models themselves as "programs" whose "bugs" correspond to possible misconceptions; mental models can thus be inferred by Bayesian program synthesis (see Table 2). Such a framework can also be inverted to help design new questions for teachers or self-driven learners to identify misconceptions. 
body
Building Machines That Learn And Think With People
Thought Partners For Programming
2408.03943
 Recall the challenge of collaboratively planning uncertain tasks, from a search-and-rescue mission to everyday cooking, wherein we typically want to infer shared goals and communicative intent from our partners. This cooperative logic can be modeled in a Bayesian architecture called Cooperative Language-Guided Inverse Plan Search (CLIPS) 35. By modeling humans as cooperative planners who use language to communicate *joint plans* to achieve their goals65, CLIPS is able to infer those plans and goals from both the actions and instructions of human collaborators.  This allows CLIPS to *pragmatically* follow human instructions, using context to disambiguate the multiple meanings that a request might have, while *pro-actively* assisting with the goals that underlie the instruction. For example, CLIPS can understand the likely intentions behind an instruction like "Can you prepare the vegetables while I knead the dough?", inferring the shared goal of making pizza. These capabilities are made possible by using probabilistic programming infrastructure25 to unite algorithms for Bayesian inverse planning 33,184 and human-AI alignment51,61,223 with LLMs. In particular, by using LLMs to evaluate the probability of a natural language instruction given a possible intention, CLIPS can infer intentions from natural language in a coherent Bayesian manner - demonstrating the power of combining tools from the Bayesian thought partner toolkit. 
body
Building Machines That Learn And Think With People
Thought Partners For Embodied Assistance
2408.03943
 Storytelling is about crafting *experience*. Can we also apply the toolkit to help storytellers design experiences from first principles? Recent work has shown that a system grounded in Bayesian ToM can predict and even design interventions on the audience's experience of a story 224,225.  Chandra et al. conceive of storytelling as "inverse inverse planning": that is, starting with human social cognition, modeled as Bayesian inverse planning 33, and then optimizing narrative events to shape the model's inferences over time. They show how a variety of storytelling techniques – from plot twists to stage mime - can be expressed in the language of inverse inverse planning to create animations that have a desired cognitive effect on viewers. Herein, we also highlight the breadth of thought partners for media beyond language, though the framework does nicely suggest a variety of natural extensions, such as integration into tools for creative writing 72–77. 
body
Building Machines That Learn And Think With People
Thought Partners For Storytelling
2408.03943
 Finally, we envision medical thought partners both understand us - reasoning about the doctor, patient, and care team as agents with goals, beliefs, and worries - and *complement* our capabilities, integrating swaths of evidence that exceed our cognitive capacities to inform diagnosis and treatment. While no system yet meets our desiderata for these criteria, we believe a range of motifs and tools from the Bayesian thought partner toolkit here can support the development of such systems for collaborative sensemaking and deliberation. We imagine Bayesian thought partners that can update their medical world knowledge in light of new insights in biology, e.g., editing a code snippet of the underlying probabilistic world model 16 or growing the representation in a non-parametric hierarchical Bayesian model 135. Such a model can then, similar to WatChat, synthesize new questions to ensure the human doctor's own medical world model is sound.  Early work demonstrates that we can employ elements of our toolkit, specifically probabilistic programming, to learn rich generative models for oncology and support efficient user queries 227.  Yet, effective medical thought partners beckon a broader view of the ecosystem in which they are deployed89,228. If a doctor is over-relying on the output of the thought partner, or overburdened amidst a surge in patient queries, infrastructure around the human and thought partner can modulate when a patient query is either routed to a human or the AI thought partner, or deemed necessary of collaborative planning 229. Systems for routing based on probabilistic modeling are already proving successful in simulation230. 
body
Building Machines That Learn And Think With People
Thought Partners For Medicine
2408.03943
 There is much exciting work to be done to characterize when and how to build thought partners across modes of collaborative thought, which can advance the dissemination and creation of new knowledge alongside humans. We next lay out several key challenges for researchers and designers intent on pursuing a human-centered program of building machines that learn and think *with* people. 
body
Building Machines That Learn And Think With People
Looking Ahead
2408.03943
 While there is substantial work to be done characterizing the space of possibilities for a single human and single AI thought partner ("dyadic"), we envision a future where *many* humans and many machines engage ("non-dyadic"), across roles and specialties in increasingly complex social systems231, engage in the realm of thought 232–234. Already, researchers are exploring non-dyadic versions of many of the modes of thought and case studies laid out above, including collaborative learning with groups of humans accompanied by an AI thought partner 235 and medical robot collision avoidance systems that need to account for multiple humans 236. As in the dyad setting, extensions to non-dyadic settings can be bolstered by a deepening understanding of human behavior in groups - expanding the Bayesian thought partner toolkit - as is already underway in the study of convention formation192,237. Looking ahead, citizen science is a promising example of the opportunities of creating large networks of humans and thought partners: Zooniverse, a large-scale galaxy classification crowdsourcing project, serves as a case study for exploring smart task allocation, blending human and machine classifications, and infrastructure changes that impact human participation and performance with outcomes including both iterative scientific progress and serendipitous scientific discovery 238. 
body
Building Machines That Learn And Think With People
Non-Dyad Settings
2408.03943
 The assessment of thought partners demands a multi-faceted, cross-disciplinary suite of approaches. At minimum, the evaluation of AI thought partners must include some element of *interactivity* 239. Recent works have highlighted deficits in static evaluation of foundation models15,240, demonstrating the need for considering the interaction *process* in addition to the final output, the *first-person* perspective in addition to the third-party perspective, and notions of *preference* beyond quality. In addition to interactive user studies, we posit that to study different kinds of thought partners across modes of collaborative thought would benefit from a controlled, yet rich, playspace; *games* provide one such domain. Games offer a good formalism for the study of repeated interactions between multiple agents and grounds to explore rich patterns of thought, in social collaborative settings 241–244. 
body
Building Machines That Learn And Think With People
Evaluation
2408.03943
 Computational thought partners are by no means a guaranteed nor universal good and come with certain risks. We call out three such spheres of risk: (i) reliance, critical thinking, and access, (ii) anthromorphization, and (iii) misalignment.  First, AI thought partners could induce over-reliance and impair the development of critical thinking skills 219,245–247, potentially acting as "steroids" for the mind 248. We are concerned about these risks; our emphasis on the *infrastructure* around thought partner use is explicitly intended to help practitioners take steps to address these challenges, motivating further design of infrastructure modifications like cognitive forcing functions 249,250. Conversely, it is possible that some people may *under-rely* on a thought partner, particularly if there is inadequate AI literacy training for how to best make use of new thought partners 251–253. Already, research has found that the kinds of queries people make of AI systems can be informed by the amount of prior experience they have interacting with chatbots 15 meaning students, researchers, and other practitioners in lower-income communities may be unable to maximize the value of thought partnering. It is important to ensure that the benefits of thought partners are not confined to an exclusive set of people.  Second, on the topic of anthromoprhization, we highlight an important distinction between human-centric and *human-like* thought partners 254. Our desiderata "I understand you" advocates for thought partners whose behavior we understand; while this could draw on how we understand other humans, however, we should be careful about *interpreting* such machine thought partners as we do humans. As Weizenbaum 6illuminated with the ELIZA system, there are risks to developing computer systems that present themselves as human-like in ways that they are not: for example, by leading users to attribute undue intention to systems' responses or (in the long run) leading society to devalue human intelligence 255. Human-like thought partners should maintain categorical delineation between humans and machines to prevent overreliance 245,256 and promote human dignity without encroaching on any partner's self-worth257. The term used to refer to a thought partner can affect the assumptions made about their capabilities (e.g., *teammate* implies the machine and human are on equal footing) or can detract from a partner's human-like nature (e.g., *tool* would be less anthropomorphic).  Lastly, we note that insufficiently accurate, robust, or cognitively-grounded models can yield misalignment with humans, leading intended AI thought partners to act towards the wrong goals258, provide wrong or misleading information259, or violate safety constraints 260. A Bayesian approach to thought partnership can address some of these issues, enabling uncertainty-aware decision-making that avoids overconfidence 223,261,262. Yet, while inferring human thoughts and behavior can be used to design better collaborators, models of humans are inherently dual-use and can also be used to mislead, surveil, or manipulate 263. It is crucial to consider whether thought partners are aligned with society at large, or merely superficially aligned with users while serving more powerful interests 264. 
body
Building Machines That Learn And Think With People
Risks And Important Considerations
2408.03943
 If we are to build helpful and reliable human-AI thought partnerships, we advocate for design that explicitly recognizes and engages with the richness and diversity of human thought in an often unpredictable world. We have argued, supported by several case studies, that those engineering thought partners and the infrastructure around their use can benefit from drawing on motifs from computational cognitive science and cognitive-AI. The future of collaborative cognition is bright, but not without risk; continual collaboration and knowledge sharing amongst behavioral scientists, AI practitioners, domain experts, and related disciplines is crucial as we strive to build machines that truly learn and think *with* people.  Glossary of main terms  - Collaborative cognition: the process by which two or more agents work together in some aspect(s) of thinking (e.g., planning together, learning together, creating together).  - Thought partner: another entity (human or AI) that works with an agent to push forward some aspect(s) of thinking.  - Artificial Intelligence (AI): computational systems that are able to process inputs and engage in some aspect of learning, planning, reasoning, and/or decision-making. Used interchangeably with machines.  - Large language model (LLM): a particular kind of AI system which learns a distribution over text, often trained on large amounts of web-scale text data. LLMs are a class of large-scale foundation models.  - Agent: an entity that can process inputs, make decisions, and take actions in some environment.  - Dyad: a system with two agents (e.g., human-human, human-AI, AI-AI).  - Resource-rationality: the idea that human behavior and cognition can be viewed as *rational* under bounded constraints (e.g., under limited working memory).  - Probabilistic generative model: a model of how the data one observes about the world is generated by some probabilistic process, from which one can sample new observations and make queries about existing observations.  - Probabilistic programming language (PPL): a language for expressing probabilistic generative models as computer programs that interleave deterministic code (e.g. arithmetic, logic, or artificial neural networks) with random choices. PPLs allow users to specify probabilistic models and inference algorithms in a modular and compositional manner.  - Bayesian inference: a method for updating one's *beliefs* over various aspects of the world, grounded in probability theory; in Bayesian inference, an agent updates their beliefs by assigning higher credence to hypotheses that better explain the evidence, weighted against the backdrop of their prior beliefs.  - Affordance: design features of a system that inform use. 
conclusion
Building Machines That Learn And Think With People
Conclusion
2408.03943
 We thank Richard Turner, Laura Schulz, Tyler Brooke-Wilson, Valerie Chen, Alena Rote, Lance Ying, Tony Chen, Matt Ashman, Mike Walmsley, Albert Jiang, Mateja Jamnik, Dj Dvijotham, Jonathan Ragan-Kelley, Will Crichton, Alex Lew, Tim O'Donnell, Joao Loula, Marty Tenenbaum, Mary McNaughton-Collins, and Jim Collins for valuable conversations that informed this work. KMC gratefully acknowledges support from the Marshall Commission and the Cambridge Trust.  UB acknowledges support by ELSA (European Lighthouse on Secure and Safe AI) funded by the European Union under grant agreement No. 101070617; IS acknowledges funding from an NSERC fellowship (567554-2022); KC is supported by the Hertz Foundation, the Paul and Daisy Soros Fellowship, and an NSF Graduate Research Fellowship under grant \#1745302.; ML acknowledges funding from MSR; TZX acknowledges support from the OpenPhilanthropy AI Fellowship. VM acknowledges a gift from the Siegel Family Foundation. AW acknowledges support from a Turing AI Fellowship under grant EP/V025279/1, The Alan Turing Institute, and the Leverhulme Trust via CFI. TLG acknowledges support from ONR grant N00014-22-1-2813. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the institutions listed above. 
body
Building Machines That Learn And Think With People
Acknowledgments
2408.03943
 [1] GitHub Copilot · Your AI pair programmer. https://github.com/features/copilot.  [2] Copilot for Microsoft 365 - Microsoft Adoption. https://adoption.microsoft.com/ en-us/copilot/.  [3] Evelina Fedorenko, Steven T Piantadosi, and Edward AF Gibson. Language is primarily a tool for communication rather than thought. *Nature*, 630(8017) :575–586, 2024.  [4] Alan Turing. Computing machinery and intelligence. *Mind*, 59(236) :433, 1950.  [5] Manfred E Clynes and Nathan S Kline. Cyborgs and space. *Astronautics*, 14(9) :26–27, 1960.  [6] Joseph Weizenbaum. Eliza—a computer program for the study of natural language communication between man and machine. *Communications of the ACM*, 9(1) :36–45, 1966.  [7] Ben Shneiderman. . In *Human-Centered AI*. Oxford University Press, 01 2022. ISBN 9780192845290. doi: 10.1093/oso/9780192845290.001.0001.  [8] Alan Bundy. The computer modelling of mathematical reasoning. 1983.  [9] John R Anderson, C Franklin Boyle, Albert T Corbett, and Matthew W Lewis. Cognitive modeling and intelligent tutoring. 1990.  [10] Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, and Simran Arora et al.  On the opportunities and risks of foundation models. *CoRR*, abs/2108.07258, 2021.  [11] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, and Carroll et al Wainwright.  Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022.  [12] Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, and Shane et al Legg. Deep reinforcement learning from human preferences. *Advances in neural information processing* systems, 30, 2017.  [13] Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, and Yuqing et al Du. Aligning text-to-image models using human feedback. *arXiv preprint arXiv:2302.12192*, 2023.  [14] Tomer Ullman. Large language models fail on trivial alterations to theory-of-mind tasks.  arXiv preprint arXiv:2302.08399, 2023.  [15] Katherine M Collins, Albert Q Jiang, Simon Frieder, Lionel Wong, and Miri et al Zilka.  Evaluating language models for mathematics through interactions. *Proceedings of the* National Academy of Sciences, 121(24) :e2318124121, 2024.  [16] Lionel Wong, Gabriel Grand, Alexander K Lew, Noah D Goodman, and Vikash K et al Mansinghka. From word models to world models: Translating from natural language to the probabilistic language of thought. *arXiv preprint arXiv:2306.12672*, pages arXiv–2306, 2023.  [17] Cedegao E Zhang, Katherine M Collins, Adrian Weller, and Joshua B Tenenbaum. Ai for mathematics: A cognitive science perspective. *arXiv preprint arXiv:2310.13021*, 2023.  [18] Hyowon Gweon, Judith Fan, and Been Kim. Socially intelligent machines that learn from humans and help humans learn. *Philosophical Transactions of the Royal Society A*, 381 (2251) :20220048, 2023.  [19] Kyle Mahowald, Anna A Ivanova, Idan A Blank, Nancy Kanwisher, and Joshua B et al Tenenbaum. Dissociating language and thought in large language models. *Trends in* Cognitive Sciences, 2024.  [20] R Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L Griffiths.  Embers of autoregression: Understanding large language models through the problem they are trained to solve. *arXiv preprint arXiv:2309.13638*, 2023.  [21] Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman. How to grow a mind: Statistics, structure, and abstraction. *science*, 331(6022) :1279–1285, 2011.  [22] Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, and R. Thomas McCoy. Bayes in the age of intelligent machines, 2023.  [23] Theodore Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. Cognitive architectures for language agents. *arXiv preprint arXiv:2309.02427*, 2023.  [24] Marcel Binz and Eric Schulz. Turning large language models into cognitive models. arXiv preprint arXiv:2306.03917, 2023.  [25] Marco F Cusumano-Towner, Feras A Saad, Alexander K Lew, and Vikash K Mansinghka.  Gen: a general-purpose probabilistic programming system with programmable inference. In Proceedings of the 40th acm sigplan conference on programming language design and implementation, pages 221–236, 2019.  [26] Noah D Goodman, Vikash K Mansinghka, Daniel Roy, Keith Bonawitz, and Joshua B Tenenbaum. Church: a language for generative models. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 220–229, 2008.  [27] Eli Bingham, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, and Neeraj et al Pradhan. Pyro: Deep universal probabilistic programming. *The Journal of Machine* Learning Research, 20(1) :973–978, 2019.  [28] Hong Ge, Kai Xu, and Zoubin Ghahramani. Turing: a language for flexible probabilistic inference. In *International conference on artificial intelligence and statistics*, pages 1682– 1690. PMLR, 2018.  [29] Noah D Goodman, Joshua B Tenenbaum, and Tobias Gerstenberg. Concepts in a probabilistic language of thought. Technical report, Center for Brains, Minds and Machines (CBMM) , 2014.  [30] Bas van Opheusden, Ionatan Kuperwajs, Gianni Galbiati, Zahy Bnaya, and Yunqi et al Li. Expertise increases planning depth in human gameplay. *Nature*, 618(7967) :1000–1005, 2023.  [31] Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. Solving olympiad geometry without human demonstrations. *Nature*, 625(7995) :476–482, 2024.  [32] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, and Tom et al Griffiths. Tree of thoughts: Deliberate problem solving with large language models. *Advances in Neural* Information Processing Systems, 36, 2024.  [33] Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum. Action understanding as inverse planning. *Cognition*, 113(3) :329–349, 2009.  [34] Julian Jara-Ettinger, Laura E Schulz, and Joshua B Tenenbaum. The naive utility calculus as a unified, quantitative framework for action understanding. *Cognitive Psychology*, 123: 101334, 2020.  [35] Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, and Joshua B Tenenbaum. Pragmatic instruction following and goal assistance via cooperative language-guided inverse planning.  In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, pages 2094–2103, 2024.  [36] Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman.  Building machines that learn and think like people. *Behavioral and Brain Sciences*, 40: e253, 2017. doi: 10.1017/S0140525X16001837.  [37] Junyi Chu and Laura E Schulz. Play, curiosity, and cognition. *Annual Review of Developmental Psychology*, 2(1) :317–343, 2020.  [38] Itai Yanai and Martin J Lercher. It takes two to think. *Nature Biotechnology*, pages 1–2, 2024.  [39] Keith J Holyoak and Robert G Morrison. *The Cambridge handbook of thinking and* reasoning. Cambridge University Press, 2005.  [40] Keith J Holyoak and Robert G Morrison. *The Oxford handbook of thinking and reasoning*.  Oxford University Press, 2012.  [41] Amy J Ko and Brad A Myers. Designing the whyline: a debugging interface for asking questions about program behavior. In *Proceedings of the SIGCHI conference on Human* factors in computing systems, pages 151–158, 2004.  [42] Amy J Ko, Robin Abraham, Laura Beckwith, Alan Blackwell, and Margaret et al Burnett.  The state of the art in end-user software engineering. *ACM Computing Surveys (CSUR) *, 43(3) :1–44, 2011.  [43] Stephen Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods.  The Journal of Logic Programming, 19:629–679, 1994.  [44] John R Anderson and Brian J Reiser. The lisp tutor. *Byte*, 10(4) :159–175, 1985.  [45] John R Anderson, Albert T Corbett, Kenneth R Koedinger, and Ray Pelletier. Cognitive tutors: Lessons learned. *The journal of the learning sciences*, 4(2) :167–207, 1995.  [46] Saki Imai. Is github copilot a substitute for human pair-programming? an empirical study.  In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pages 319–321, 2022.  [47] Nhan Nguyen and Sarah Nadi. An empirical evaluation of github copilot's code suggestions.  In *Proceedings of the 19th International Conference on Mining Software Repositories*, pages 1–5, 2022.  [48] Michel Wermelinger. Using github copilot to solve simple programming problems. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, pages 172–178, 2023.  [49] Shraddha Barke, Michael B James, and Nadia Polikarpova. Grounded copilot: How programmers interact with code-generating models. *Proceedings of the ACM on Programming* Languages, 7(OOPSLA1) :85–111, 2023.  [50] Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, and Michel C et al Desmarais. Github copilot ai pair programmer: Asset or liability? Journal of Systems and Software, 203:111734, 2023.  [51] Jaime F Fisac, Monica A Gates, Jessica B Hamrick, Chang Liu, and Dylan et al HadfieldMenell. Pragmatic-pedagogic value alignment. In Robotics Research: The 18th International Symposium ISRR, pages 49–57. Springer, 2020.  [52] Fabian Ranz, Vera Hummel, and Wilfried Sihn. Capability-based task allocation in human-robot collaboration. *Procedia Manufacturing*, 9:182–189, 2017.  [53] Jennifer Casper and Robin R. Murphy. Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. *IEEE Transactions on Systems,* Man, and Cybernetics, Part B (Cybernetics) , 33(3) :367–385, 2003.  [54] Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, and Winson et al Han.  Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pages 10740–10749, 2020.  [55] Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, and Omar et al Cortes.  Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691, 2022. Last Accessed: 2024–07-07.  [56] Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, and Andrew et al Bolt.  Scaling instructable agents across many simulated worlds. *arXiv preprint arXiv:2404.10179*, 2024.  [57] Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati.  Large language models still can't plan (a benchmark for llms on planning and reasoning about change) . *arXiv preprint arXiv:2206.10498*, 2022.  [58] Ida Momennejad, Hosein Hasanbeig, Felipe Vieira Frujeri, Hiteshi Sharma, and Nebojsa et al Jojic. Evaluating cognitive maps and planning in large language models with cogeval.  Advances in Neural Information Processing Systems, 36, 2024.  [59] Noah D Goodman and Michael C Frank. Pragmatic language interpretation as probabilistic inference. *Trends in cognitive sciences*, 20(11) :818–829, 2016.  [60] Theodore R Sumers, Mark K Ho, Thomas L Griffiths, and Robert D Hawkins. Reconciling truthfulness and relevance as epistemic and decision-theoretic utility. *Psychological Review*, 2023.  [61] Hong Jun Jeon, Smitha Milli, and Anca Dragan. Reward-rational (implicit)  choice: A unifying formalism for reward learning. *Advances in Neural Information Processing Systems*, 33:4415–4426, 2020.  [62] Thomas Kollar, Stefanie Tellex, Matthew R Walter, Albert Huang, and Abraham et al Bachrach. Generalized grounding graphs: A probabilistic framework for understanding grounded language. *Journal of Artificial Intelligence Research*, pages 1–35, 2013.  [63] Michael E Bratman. *Shared agency: A planning theory of acting together*. Oxford University Press, 2013.  [64] Stephanie Stacy, Chenfei Li, Minglu Zhao, Yiling Yun, and Qingyi et al Zhao. Modeling communication to coordinate perspectives in cooperation. In *Proceedings of the Annual* Meeting of the Cognitive Science Society, volume 43, 2021.  [65] Sarah A Wu, Rose E Wang, James A Evans, Joshua B Tenenbaum, and David C et al Parkes. Too many cooks: Bayesian inference for coordinating multi-agent collaboration.  Topics in Cognitive Science, 13(2) :414–432, 2021.  [66] Sid Reddy, Anca Dragan, and Sergey Levine. Where do you think you're going?: Inferring beliefs about dynamics from behavior. *Advances in Neural Information Processing Systems*, 31, 2018.  [67] Arwa Alanqary, Gloria Z Lin, Joie Le, Tan Zhi-Xuan, and Vikash K et al Mansinghka.  Modeling the mistakes of boundedly rational agents within a bayesian theory of mind.  arXiv preprint arXiv:2106.13249, 2021.  [68] Anca D Dragan, Kenton CT Lee, and Siddhartha S Srinivasa. Legibility and predictability of robot motion. In *2013 8th ACM/IEEE International Conference on Human-Robot* Interaction (HRI) , pages 301–308. IEEE, 2013.  [69] Shuwa Miura and Shlomo Zilberstein. A unifying framework for observer-aware planning and its complexity. In *Uncertainty in Artificial Intelligence*, pages 610–620. PMLR, 2021.  [70] Linda Flower and John R. Hayes. A cognitive process theory of writing. College Composition and Communication, 32(4) :365–387, 1981. ISSN 0010096X.  [71] John R Hayes. Modeling and remodeling writing. *Written communication*, 29(3) :369–388, 2012.  [72] Mina Lee, Percy Liang, and Qian Yang. Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In *Proceedings of the 2022 CHI* Conference on Human Factors in Computing Systems, pages 1–19, 2022.  [73] Mina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum, and Vipul et al Raheja. A design space for intelligent and interactive writing assistants. CHI, 2024.  [74] Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam. Creative writing with an ai-powered writing assistant: Perspectives from professional writers, 2022.  [75] Katy Ilonka Gero, Vivian Liu, and Lydia Chilton. Sparks: Inspiration for science writing using language models. In *Proceedings of the 2022 ACM Designing Interactive Systems* Conference, pages 1002–1019, 2022.  [76] Katy Ilonka Gero, Tao Long, and Lydia B Chilton. Social dynamics of ai support in creative writing. In *Proceedings of the 2023 CHI Conference on Human Factors in Computing* Systems, pages 1–15, 2023.  [77] Fabrizio Dell'Acqua, Edward McFowland, Ethan R Mollick, Hila Lifshitz-Assaf, and Katherine et al Kellogg. Navigating the jagged technological frontier: Field experimental evidence of the effects of ai on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (24-013) , 2023.  [78] Justin Porter, Cynthia Boyd, M Reza Skandari, and Neda Laiteerapong. Revisiting the time needed to provide adult primary care. *Journal of general internal medicine*, 38(1) : 147–155, 2023.  [79] Carolyn S Dewa, Desmond Loong, Sarah Bonato, and Lucy Trojanowski. The relationship between physician burnout and quality of healthcare in terms of safety and acceptability: a systematic review. *BMJ open*, 7(6) :e015141, 2017.  [80] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, and Gaurav et al Mishra. Palm: Scaling language modeling with pathways. *arXiv preprint arXiv:2204.02311*, 2022.  [81] Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, and Jason et al Wei. Large language models encode clinical knowledge. *Nature*, 620(7972) :172–180, 2023.  [82] Eric J Topol. High-performance medicine: the convergence of human and artificial intelligence. *Nature medicine*, 25(1) :44–56, 2019.  [83] Tao Tu, Anil Palepu, Mike Schaekermann, Khaled Saab, and Jan Freyberg et al. Towards conversational diagnostic ai, 2024.  [84] John W. Ayers, Adam Poliak, Mark Dredze, Eric C. Leas, and Zechariah et al Zhu.  Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. *JAMA Internal Medicine*, 04 2023. ISSN 2168-6106.  doi: 10.1001/jamainternmed.2023.1838.  [85] Alfredo Vellido. The importance of interpretability and visualization in machine learning for applications in medicine and health care. *Neural computing and applications*, 32(24) : 18069–18083, 2020.  [86] Pranav Rajpurkar, Emma Chen, Oishi Banerjee, and Eric J Topol. Ai in health and medicine. *Nature medicine*, 28(1) :31–38, 2022.  [87] Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, and Irene Y et al Chen. A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020:191, 2020.  [88] Roxana Daneshjou, Mary P Smith, Mary D Sun, Veronica Rotemberg, and James Zou.  Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. *JAMA dermatology*, 157(11) :1362–1369, 2021.  [89] Federico Cabitza and Jean-David Zeitoun. The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. *Annals of translational medicine*, 7(8) , 2019.  [90] Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, and Yuan-Hong et al Liao. Watchand-help: A challenge for social perception and human-ai collaboration. arXiv preprint arXiv:2010.09890, 2020.  [91] Kartik Chandra, Tony Chen, Tzu-Mao Li, Jonathan Ragan-Kelley, and Josh Tenenbaum.  Inferring the future by imagining the past. Advances in Neural Information Processing Systems, 36, 2024.  [92] Jaime F Fisac, Chang Liu, Jessica B Hamrick, Shankar Sastry, and J Karl et al Hedrick.  Generating plans that predict themselves. In Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics, pages 144–159. Springer, 2020.  [93] Herbert P Grice. Logic and conversation. In *Speech acts*, pages 41–58. Brill, 1975.  [94] Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. *arXiv preprint arXiv:1702.08608*, 2017.  [95] Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267:1–38, 2019.  [96] Brian Cantwell Smith. *The promise of artificial intelligence: reckoning and judgment*. Mit Press, 2019.  [97] Ilia Sucholutsky and Thomas L Griffiths. Alignment with human representations supports robust few-shot learning. *NeurIPS*, 2023.  [98] Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, and Andreea Bobu et al.  Getting aligned on representational alignment, 2023.  [99] Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum. Simulation as an engine of physical scene understanding. *Proceedings of the National Academy of Sciences*, 110(45) : 18327–18332, 2013.  [100] Alessandro Roncone, Olivier Mangin, and Brian Scassellati. Transparent role assignment and task allocation in human robot collaboration. In 2017 IEEE International Conference on Robotics and Automation (ICRA) , pages 1014–1021. IEEE, 2017.  [101] Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, and Sanjit et al Seshia. On the utility of learning about humans for human-ai coordination. Advances in neural information processing systems, 32, 2019.  [102] Owen Macindoe, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Pomcop: Belief space planning for sidekicks in cooperative games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 8, pages 38–43, 2012.  [103] Jessy Lin, Daniel Fried, Dan Klein, and Anca Dragan. Inferring rewards from language in context. *arXiv preprint arXiv:2204.02515*, 2022.  [104] Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education (TOCE) , 19(1) :1–43, 2018.  [105] Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. Automatic generation of programming exercises and code explanations using large language models. In *Proceedings* of the 2022 ACM Conference on International Computing Education Research-Volume 1, pages 27–43, 2022.  [106] Kartik Chandra, Tzu-Mao Li, Rachit Nigam, Joshua Tenenbaum, and Jonathan RaganKelley. Watchat: Explaining perplexing programs by debugging mental models, 2024.  [107] Andrew Head, Codanda Appachu, Marti A Hearst, and Björn Hartmann. Tutorons: Generating context-relevant, on-demand explanations and demonstrations of online code.  In *2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) *, pages 3–12. IEEE, 2015.  [108] Anna N Rafferty, Rachel A Jansen, and Thomas L Griffiths. Assessing mathematics misunderstandings via bayesian inverse planning. *Cognitive science*, 44(10) :e12900, 2020.  [109] Gabriel Poesia and Noah D Goodman. Peano: learning formal mathematical reasoning.  Philosophical Transactions of the Royal Society A, 381(2251) :20220044, 2023.  [110] Noam Slonim, Yonatan Bilu, Carlos Alzate, Roy Bar-Haim, and Ben et al Bogin. An autonomous debating system. *Nature*, 591(7850) :379–384, 2021.  [111] Daniel Jarrett, Miruna Pislar, Michiel A Bakker, Michael Henry Tessler, and Raphael et al Koster. Language agents as digital representatives in collective decision-making. In NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023.  [112] Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. *arXiv* preprint arXiv:2305.14325, 2023.  [113] Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, and Lucy et al Campbell-Gillingham. Fine-tuning language models to find agreement among humans with diverse preferences. *Advances in Neural Information Processing Systems*, 35:38176–38189, 2022.  [114] Christopher Small, Michael Bjorkegren, Timo Erkkilä, Lynette Shaw, and Colin Megill.  Polis: Scaling deliberation by mapping high dimensional opinion spaces. *Recerca: revista* de pensament i anàlisi, 26(2) , 2021.  [115] Alexander Lew, Monica Agrawal, David Sontag, and Vikash Mansinghka. Pclean: Bayesian data cleaning at scale with domain-specific probabilistic programming. In *International* Conference on Artificial Intelligence and Statistics, pages 1927–1935. PMLR, 2021.  [116] Feras A Saad, Marco F Cusumano-Towner, Ulrich Schaechtle, Martin C Rinard, and Vikash K Mansinghka. Bayesian synthesis of probabilistic programs for automatic data modeling. *Proceedings of the ACM on Programming Languages*, 3(POPL) :1–32, 2019.  [117] Mathieu Huot, Matin Ghavami, Alexander K Lew, Ulrich Schaechtle, and Cameron E et al Freer. Gensql: A probabilistic programming system for querying generative models of database tables. *Proceedings of the ACM on Programming Languages*, 8(PLDI) :790–815, 2024.  [118] Michael Y Li, Emily B Fox, and Noah D Goodman. Automated statistical model discovery with language models. *arXiv preprint arXiv:2402.17879*, 2024.  [119] Christian Steinruecken, Emma Smith, David Janz, James Lloyd, and Zoubin Ghahramani.  The automatic statistician. *Automated machine learning: Methods, systems, challenges*, pages 161–173, 2019.  [120] Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, and Daniel et al Zheng.  Advancing mathematics by guiding human intuition with ai. *Nature*, 600(7887) :70–74, 2021.  [121] Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, and Kyle et al Cranmer.  Discovering symbolic models from deep learning with inductive biases. Advances in neural information processing systems, 33:17429–17442, 2020.  [122] Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, and M Pawan et al Kumar. Mathematical discoveries from program search with large language models. *Nature*, 625(7995) :468–475, 2024.  [123] Joshua Ashkinaze, Julia Mendelsohn, Li Qiwei, Ceren Budak, and Eric Gilbert. How ai ideas affect the creativity, diversity, and evolution of human ideas: Evidence from a large, dynamic experiment, 2024.  [124] Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, and Mengting et al Wan. The use of generative search engines for knowledge work and complex tasks. Technical Report MSR-TR-2024-9, Microsoft, March 2024.  [125] Henriikka Vartiainen and Matti Tedre. Using artificial intelligence in craft education: crafting with text-to-image generative models. *Digital Creativity*, 34(1) :1–21, 2023.  [126] Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, and Devi et al Parikh. Make-ascene: Scene-based text-to-image generation with human priors. In European Conference on Computer Vision, pages 89–106. Springer, 2022.  [127] Judith E Fan, Monica Dinculescu, and David Ha. Collabdraw: an environment for collaborative sketching with an artificial agent. In Proceedings of the 2019 Conference on Creativity and Cognition, pages 556–561, 2019.  [128] Songwei Ge, Vedanuj Goswami, C Lawrence Zitnick, and Devi Parikh. Creative sketch generation. *arXiv preprint arXiv:2011.10039*, 2020.  [129] Marek Dvorožňák, Daniel S`ykora, Cassidy Curtis, Brian Curless, and Olga et al SorkineHornung. Monster mash: a single-view approach to casual 3d modeling and animation.  ACM Transactions on Graphics (ToG) , 39(6) :1–12, 2020.  [130] Nick Chater and Mike Oaksford, editors. *The Probabilistic Mind: Prospects for Bayesian* Cognitive Science. Oxford University Press, Oxford, UK, 2008.  [131] Thomas L. Griffiths, Charles Kemp, and Joshua B. Tenenbaum. Bayesian models of cognition. In Ron Sun, editor, *The Cambridge handbook of computational psychology*, chapter 3, pages 59–100. Cambridge University Press, 2008.  [132] Elizabeth S Spelke. Core knowledge. *American psychologist*, 55(11) :1233, 2000.  [133] Steven T Piantadosi. The computational origin of representation. *Minds and machines*, 31 (1) :1–58, 2021.  [134] Jake Quilty-Dunn, Nicolas Porot, and Eric Mandelbaum. The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences. *Behavioral* and Brain Sciences, 46:e261, 2023.  [135] Thomas Griffiths, Michael Jordan, Joshua Tenenbaum, and David Blei. Hierarchical topic models and the nested chinese restaurant process. *Advances in neural information* processing systems, 16, 2003.  [136] Charles Kemp and Joshua B Tenenbaum. The discovery of structural form. *Proceedings of* the National Academy of Sciences, 105(31) :10687–10692, 2008.  [137] Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. Human-level concept learning through probabilistic program induction. *Science*, 350(6266) :1332–1338, 2015.  [138] Joshua S Rule, Joshua B Tenenbaum, and Steven T Piantadosi. The child as hacker. Trends in cognitive sciences, 24(11) :900–915, 2020.  [139] Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, and Lucas et al Morales.  Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning. In *Proceedings of the 42nd acm sigplan international conference on programming language* design and implementation, pages 835–850, 2021.  [140] Tomer D Ullman and Joshua B Tenenbaum. Bayesian models of conceptual development: Learning as building models of the world. *Annual Review of Developmental Psychology*, 2 (1) :533–558, 2020.  [141] Falk Lieder, Owen X Chen, Paul M Krueger, and Thomas L Griffiths. Cognitive prostheses for goal achievement. *Nature human behaviour*, 3(10) :1096–1106, 2019.  [142] Falk Lieder and Thomas L Griffiths. Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. *Behavioral and brain* sciences, 43:e1, 2020.  [143] Thomas Icard. Resource rationality. Book manuscript, 2023.  [144] Allen Newell and Herbert A. Simon. *Human problem solving*. Prentice-Hall, 1972.  [145] Marcelo G Mattar and Máté Lengyel. Planning in the brain. *Neuron*, 110(6) :914–934, 2022.  [146] Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, and Jonathan D. et al Cohen. People construct simplified mental representations to plan. *Nature*, 606(7912) : 129–136, 2022.  [147] Julian Jara-Ettinger, Hyowon Gweon, Laura E Schulz, and Joshua B Tenenbaum. The naïve utility calculus: Computational principles underlying commonsense psychology. *Trends in* cognitive sciences, 20(8) :589–604, 2016.  [148] Chris Baker, Rebecca Saxe, and Joshua Tenenbaum. Bayesian theory of mind: Modeling joint belief-desire attribution. In *Proceedings of the annual meeting of the cognitive science* society, volume 33, 2011.  [149] Mark K Ho, Rebecca Saxe, and Fiery Cushman. Planning with theory of mind. Trends in Cognitive Sciences, 26(11) :959–971, 2022.  [150] Michael C Frank and Noah D Goodman. Predicting pragmatic reasoning in language games. *Science*, 336(6084) :998–998, 2012.  [151] Judith Degen. The rational speech act framework. *Annual Review of Linguistics*, 9:519–540, 2023.  [152] Marcel Binz, Ishita Dasgupta, Akshay K Jagadish, Matthew Botvinick, and Jane X et al Wang. Meta-learned models of cognition. *Behavioral and Brain Sciences*, pages 1–38, 2023.  [153] Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths. Recasting gradient-based meta-learning as hierarchical bayes. In 6th International Conference on Learning Representations, ICLR 2018, 2018.
references
Building Machines That Learn And Think With People
References