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    <title>On Wisdom - Episodes Tagged with “Alignment”</title>
    <link>https://www.onwisdompodcast.com/tags/alignment</link>
    <pubDate>Sun, 23 Feb 2025 16:00:00 -0500</pubDate>
    <description>What does it actually take to think wisely? On Wisdom digs into the empirical science of wisdom, judgment, and decision-making, and what it means for how we live. Igor Grossmann runs the Wisdom &amp;amp; Culture Lab at the University of Waterloo. Charles Cassidy runs the Evidence-Based Wisdom project in London. Each episode pairs freewheeling conversation with guest spots from leading behavioural scientists working on reasoning, wellbeing, culture, and society. Past guests include Adam Grant, Jonathan Haidt, Paul Bloom, Shannon Vallor, Lisa Barrett, David Dunning, Mara Mather, Tom Gilovich, Laura Carstensen, Oliver Burkeman, Simine Vazire, Dacher Keltner, and more. This autumn: a five-episode special series built around the first Annual Review of Psychology chapter on wisdom since 2011, featuring fifteen scholars on narrative, metacognition, measurement, training, and the neuroscience of wise decisions.</description>
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    <itunes:subtitle>What does science tell us about wisdom?</itunes:subtitle>
    <itunes:author>Charles Cassidy and Igor Grossmann</itunes:author>
    <itunes:summary>What does it actually take to think wisely? On Wisdom digs into the empirical science of wisdom, judgment, and decision-making, and what it means for how we live. Igor Grossmann runs the Wisdom &amp;amp; Culture Lab at the University of Waterloo. Charles Cassidy runs the Evidence-Based Wisdom project in London. Each episode pairs freewheeling conversation with guest spots from leading behavioural scientists working on reasoning, wellbeing, culture, and society. Past guests include Adam Grant, Jonathan Haidt, Paul Bloom, Shannon Vallor, Lisa Barrett, David Dunning, Mara Mather, Tom Gilovich, Laura Carstensen, Oliver Burkeman, Simine Vazire, Dacher Keltner, and more. This autumn: a five-episode special series built around the first Annual Review of Psychology chapter on wisdom since 2011, featuring fifteen scholars on narrative, metacognition, measurement, training, and the neuroscience of wise decisions.</itunes:summary>
    <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/6/6e7bd116-2782-4422-a140-42f329164842/cover.jpg?v=1"/>
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    <itunes:keywords>psychology, science, happiness, philosophy, wisdom, decision-making, reasoning, society; judgment; metacognition</itunes:keywords>
    <itunes:owner>
      <itunes:name>Charles Cassidy and Igor Grossmann</itunes:name>
      <itunes:email>charlesdavidcassidy@gmail.com</itunes:email>
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  <title>63: The AI Mirror: Why Machines Reflect Us More Than They Think (with Shannon Vallor)</title>
  <link>https://www.onwisdompodcast.com/63</link>
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  <pubDate>Sun, 23 Feb 2025 16:00:00 -0500</pubDate>
  <author>Charles Cassidy and Igor Grossmann</author>
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  <itunes:episode>63</itunes:episode>
  <itunes:title>The AI Mirror: Why Machines Reflect Us More Than They Think (with Shannon Vallor)</itunes:title>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Charles Cassidy and Igor Grossmann</itunes:author>
  <itunes:subtitle>Can AI ever be truly wise, or are we just seeing reflections of ourselves? Philosopher Shannon Vallor joins Igor and Charles to explore how technology shapes human wisdom, why we’ve been thinking about AI all wrong, and what it really means to align machines with our values. Shannon unpacks the AI Mirror metaphor, suggesting that today’s AI isn’t a thinking mind but a reflection of human data, Igor considers whether technology could ever help us become wiser rather than just more efficient, and Charles wonders if philosophy can guide better decisions in a world increasingly shaped by algorithms. Welcome to Episode 63.</itunes:subtitle>
  <itunes:duration>44:30</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
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  <description>&lt;p&gt;Can AI ever be truly wise, or are we just seeing reflections of ourselves? Philosophy Professor Shannon Vallor joins Igor and Charles to explore how technology shapes human wisdom, why we’ve been thinking about AI all wrong, and what it really means to align machines with our values. Shannon unpacks the AI Mirror metaphor, suggesting that today’s AI isn’t a thinking mind but a reflection of human data, Igor considers whether technology could ever help us become wiser rather than just more efficient, and Charles wonders if philosophy can guide better decisions in a world increasingly shaped by algorithms. Welcome to Episode 63. Special Guest: Shannon Vallor.&lt;/p&gt;
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  <itunes:keywords>wisdom, psychology, philosophy, social science, happiness, well being, meaning, reasoning, emotions, purpose, artificial intelligence, AI, alignment, The AI Mirror, Shannon Vallor, Value Alignment, Virtue Embodiment, Moral Machines, Technomoral Virtues, Technomoral Wisdom</itunes:keywords>
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    <![CDATA[<p>Can AI ever be truly wise, or are we just seeing reflections of ourselves? Philosophy Professor Shannon Vallor joins Igor and Charles to explore how technology shapes human wisdom, why we’ve been thinking about AI all wrong, and what it really means to align machines with our values. Shannon unpacks the AI Mirror metaphor, suggesting that today’s AI isn’t a thinking mind but a reflection of human data, Igor considers whether technology could ever help us become wiser rather than just more efficient, and Charles wonders if philosophy can guide better decisions in a world increasingly shaped by algorithms. Welcome to Episode 63.</p><p>Special Guest: Shannon Vallor.</p><p>Links:</p><ul><li><a title="Shannon Vallor | University of Edinburgh" rel="nofollow" href="https://edwebprofiles.ed.ac.uk/profile/shannon-vallor">Shannon Vallor | University of Edinburgh
</a></li><li><a title="Shannon Vallor | Edinburgh Futures Institute, The University of Edinburgh" rel="nofollow" href="https://efi.ed.ac.uk/people/shannon-vallor/">Shannon Vallor | Edinburgh Futures Institute, The University of Edinburgh
</a></li><li><a title="The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking - Shannon Vallor (2024)" rel="nofollow" href="https://global.oup.com/academic/product/the-ai-mirror-9780197759066?cc=gb&amp;lang=en&amp;">The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking - Shannon Vallor (2024)
</a></li><li><a title="How philosopher Shannon Vallor delivered the year’s best critique of AI - Fast Company (2024)" rel="nofollow" href="https://www.fastcompany.com/91240425/how-philosopher-shannon-vallor-delivered-the-years-best-critique-of-ai">How philosopher Shannon Vallor delivered the year’s best critique of AI - Fast Company (2024)
</a></li><li><a title="The Turing Lectures: Can we live with AI? - Shannon Vallor" rel="nofollow" href="https://www.youtube.com/watch?v=7iX-wiKvYHs">The Turing Lectures: Can we live with AI? - Shannon Vallor
</a></li><li><a title="The Danger Of Superhuman AI Is Not What You Think | Noema - Shannon Vallor" rel="nofollow" href="https://www.noemamag.com/the-danger-of-superhuman-ai-is-not-what-you-think/">The Danger Of Superhuman AI Is Not What You Think | Noema - Shannon Vallor
</a></li><li><a title="The Thoughts The Civilized Keep | Noema - Shannon Vallor" rel="nofollow" href="https://www.noemamag.com/the-thoughts-the-civilized-keep/">The Thoughts The Civilized Keep | Noema - Shannon Vallor
</a></li><li><a title="AI Is the Black Mirror | Nautilus - Philip Ball" rel="nofollow" href="https://nautil.us/ai-is-the-black-mirror-1169121/">AI Is the Black Mirror | Nautilus - Philip Ball
</a></li><li><a title="Technology and the Virtues A Philosophical Guide to a Future Worth Wanting - Shannon Vallor (Book)" rel="nofollow" href="https://www.google.com/books/edition/Technology_and_the_Virtues/RaCkDAAAQBAJ?hl=en&amp;gbpv=0">Technology and the Virtues A Philosophical Guide to a Future Worth Wanting - Shannon Vallor (Book)
</a></li><li><a title="Moral Machines: From Value Alignment to Embodied Virtue - Wendell Wallach, Shannon Vallor (2020)" rel="nofollow" href="https://academic.oup.com/book/33540/chapter-abstract/287906775?redirectedFrom=fulltext&amp;login=false">Moral Machines: From Value Alignment to Embodied Virtue - Wendell Wallach, Shannon Vallor (2020)
</a></li><li><a title="AI and the Automation of Wisdom - Shannon Vallor (2017)" rel="nofollow" href="https://link.springer.com/chapter/10.1007/978-3-319-61043-6_8">AI and the Automation of Wisdom - Shannon Vallor (2017)
</a></li><li><a title="The AI Mirror — how technology blocks human potential | FT (Subscription Required)" rel="nofollow" href="https://www.ft.com/content/67d38081-82d3-4979-806a-eba0099f8011">The AI Mirror — how technology blocks human potential | FT (Subscription Required)
</a></li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Can AI ever be truly wise, or are we just seeing reflections of ourselves? Philosophy Professor Shannon Vallor joins Igor and Charles to explore how technology shapes human wisdom, why we’ve been thinking about AI all wrong, and what it really means to align machines with our values. Shannon unpacks the AI Mirror metaphor, suggesting that today’s AI isn’t a thinking mind but a reflection of human data, Igor considers whether technology could ever help us become wiser rather than just more efficient, and Charles wonders if philosophy can guide better decisions in a world increasingly shaped by algorithms. Welcome to Episode 63.</p><p>Special Guest: Shannon Vallor.</p><p>Links:</p><ul><li><a title="Shannon Vallor | University of Edinburgh" rel="nofollow" href="https://edwebprofiles.ed.ac.uk/profile/shannon-vallor">Shannon Vallor | University of Edinburgh
</a></li><li><a title="Shannon Vallor | Edinburgh Futures Institute, The University of Edinburgh" rel="nofollow" href="https://efi.ed.ac.uk/people/shannon-vallor/">Shannon Vallor | Edinburgh Futures Institute, The University of Edinburgh
</a></li><li><a title="The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking - Shannon Vallor (2024)" rel="nofollow" href="https://global.oup.com/academic/product/the-ai-mirror-9780197759066?cc=gb&amp;lang=en&amp;">The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking - Shannon Vallor (2024)
</a></li><li><a title="How philosopher Shannon Vallor delivered the year’s best critique of AI - Fast Company (2024)" rel="nofollow" href="https://www.fastcompany.com/91240425/how-philosopher-shannon-vallor-delivered-the-years-best-critique-of-ai">How philosopher Shannon Vallor delivered the year’s best critique of AI - Fast Company (2024)
</a></li><li><a title="The Turing Lectures: Can we live with AI? - Shannon Vallor" rel="nofollow" href="https://www.youtube.com/watch?v=7iX-wiKvYHs">The Turing Lectures: Can we live with AI? - Shannon Vallor
</a></li><li><a title="The Danger Of Superhuman AI Is Not What You Think | Noema - Shannon Vallor" rel="nofollow" href="https://www.noemamag.com/the-danger-of-superhuman-ai-is-not-what-you-think/">The Danger Of Superhuman AI Is Not What You Think | Noema - Shannon Vallor
</a></li><li><a title="The Thoughts The Civilized Keep | Noema - Shannon Vallor" rel="nofollow" href="https://www.noemamag.com/the-thoughts-the-civilized-keep/">The Thoughts The Civilized Keep | Noema - Shannon Vallor
</a></li><li><a title="AI Is the Black Mirror | Nautilus - Philip Ball" rel="nofollow" href="https://nautil.us/ai-is-the-black-mirror-1169121/">AI Is the Black Mirror | Nautilus - Philip Ball
</a></li><li><a title="Technology and the Virtues A Philosophical Guide to a Future Worth Wanting - Shannon Vallor (Book)" rel="nofollow" href="https://www.google.com/books/edition/Technology_and_the_Virtues/RaCkDAAAQBAJ?hl=en&amp;gbpv=0">Technology and the Virtues A Philosophical Guide to a Future Worth Wanting - Shannon Vallor (Book)
</a></li><li><a title="Moral Machines: From Value Alignment to Embodied Virtue - Wendell Wallach, Shannon Vallor (2020)" rel="nofollow" href="https://academic.oup.com/book/33540/chapter-abstract/287906775?redirectedFrom=fulltext&amp;login=false">Moral Machines: From Value Alignment to Embodied Virtue - Wendell Wallach, Shannon Vallor (2020)
</a></li><li><a title="AI and the Automation of Wisdom - Shannon Vallor (2017)" rel="nofollow" href="https://link.springer.com/chapter/10.1007/978-3-319-61043-6_8">AI and the Automation of Wisdom - Shannon Vallor (2017)
</a></li><li><a title="The AI Mirror — how technology blocks human potential | FT (Subscription Required)" rel="nofollow" href="https://www.ft.com/content/67d38081-82d3-4979-806a-eba0099f8011">The AI Mirror — how technology blocks human potential | FT (Subscription Required)
</a></li></ul>]]>
  </itunes:summary>
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<item>
  <title>55: Wise of the Machines (with Sina Fazelpour)</title>
  <link>https://www.onwisdompodcast.com/55</link>
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  <pubDate>Sat, 05 Aug 2023 12:00:00 -0400</pubDate>
  <author>Charles Cassidy and Igor Grossmann</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/6e7bd116-2782-4422-a140-42f329164842/fdc73ee1-e7d8-47ad-9d27-9ff1aadc7f2e.mp3" length="38604716" type="audio/mpeg"/>
  <itunes:episode>55</itunes:episode>
  <itunes:title>Wise of the Machines (with Sina Fazelpour)</itunes:title>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Charles Cassidy and Igor Grossmann</itunes:author>
  <itunes:subtitle>How can we make AI wiser? And could AI make us wiser in return? Sina Fazelpour joins Igor and Charles to discuss the problem of bias in algorithms, how we might make machine learning systems more diverse, and the thorny challenge of alignment. Igor considers whether interacting with AIs might help us achieve higher levels of understanding, Sina suggests that setting up AIs to promote certain values may be problematic in a pluralistic society, and Charles is intrigued to learn about the opportunities offered by teaming up with our machine friends. Welcome to Episode 55.</itunes:subtitle>
  <itunes:duration>1:04:20</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/6/6e7bd116-2782-4422-a140-42f329164842/cover.jpg?v=1"/>
  <description>&lt;p&gt;How can we make AI wiser? And could AI make us wiser in return? Sina Fazelpour joins Igor and Charles to discuss the problem of bias in algorithms, how we might make machine learning systems more diverse, and the thorny challenge of alignment. Igor considers whether interacting with AIs might help us achieve higher levels of understanding, Sina suggests that setting up AIs to promote certain values may be problematic in a pluralistic society, and Charles is intrigued to learn about the opportunities offered by teaming up with our machine friends. Welcome to Episode 55. Special Guest: Sina Fazelpour.&lt;/p&gt;
</description>
  <itunes:keywords>wisdom, psychology, philosophy, social science, happiness, well being, meaning, reasoning, emotions, purpose, Sina Fazelpour, Artificial Intelligence, AI, Machine Learning, Bias, Algorithms, Alignment, Diversity, Constitutional AI, AlphaGo, Lee Sedols, God’s touch, ChatGPT, LLM, large language model</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>How can we make AI wiser? And could AI make us wiser in return? Sina Fazelpour joins Igor and Charles to discuss the problem of bias in algorithms, how we might make machine learning systems more diverse, and the thorny challenge of alignment. Igor considers whether interacting with AIs might help us achieve higher levels of understanding, Sina suggests that setting up AIs to promote certain values may be problematic in a pluralistic society, and Charles is intrigued to learn about the opportunities offered by teaming up with our machine friends. Welcome to Episode 55.</p><p>Special Guest: Sina Fazelpour.</p><p>Links:</p><ul><li><a title="Sina Fazelpour&#39;s Website" rel="nofollow" href="https://sinafazelpour.com/">Sina Fazelpour's Website
</a></li><li><a title="AI and the transformation of social science research | Science - Igor Grossmann, Matthew Feinberg, Dawn C. Parker, Nicholas A. Christakis, Philip E. Tetlock,  Willian A. Cunningham (2023)" rel="nofollow" href="https://www.science.org/stoken/author-tokens/ST-1256/full">AI and the transformation of social science research | Science - Igor Grossmann, Matthew Feinberg, Dawn C. Parker, Nicholas A. Christakis, Philip E. Tetlock,  Willian A. Cunningham (2023)
</a></li><li><a title="Algorithmic Fairness from a Non-ideal Perspective - Sina Fazelpour, ZacharyC.Lipton (2020" rel="nofollow" href="https://dl.acm.org/doi/pdf/10.1145/3375627.3375828">Algorithmic Fairness from a Non-ideal Perspective - Sina Fazelpour, ZacharyC.Lipton (2020
</a></li><li><a title="Diversity in sociotechnical machine learning systems - Sina Fazelpour, Maria De-Arteaga (2022)" rel="nofollow" href="https://journals.sagepub.com/doi/10.1177/20539517221082027">Diversity in sociotechnical machine learning systems - Sina Fazelpour, Maria De-Arteaga (2022)
</a></li><li><a title="Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang (2022)" rel="nofollow" href="https://arxiv.org/abs/2211.13972">Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang (2022)
</a></li><li><a title="Algorithmic bias: Senses, sources, solutions - Sina Fazelpour, David Danks (2021)" rel="nofollow" href="https://compass.onlinelibrary.wiley.com/doi/full/10.1111/phc3.12760">Algorithmic bias: Senses, sources, solutions - Sina Fazelpour, David Danks (2021)
</a></li><li><a title="Constitutional AI: Harmlessness from AI Feedback - Yuntao Bai et al (2022)" rel="nofollow" href="https://arxiv.org/abs/2212.08073">Constitutional AI: Harmlessness from AI Feedback - Yuntao Bai et al (2022)
</a></li><li><a title="Taxonomy of Risks posed by Language Models - Laura Weidinger at Al (2022)" rel="nofollow" href="https://dl.acm.org/doi/10.1145/3531146.3533088">Taxonomy of Risks posed by Language Models - Laura Weidinger at Al (2022)
</a></li><li><a title="Large pre-trained language models contain human-like biases of what is right and wrong to do - Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf &amp; Kristian Kersting (2022)" rel="nofollow" href="https://www.nature.com/articles/s42256-022-00458-8">Large pre-trained language models contain human-like biases of what is right and wrong to do - Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf &amp; Kristian Kersting (2022)
</a></li><li><a title="On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - Emily M. Bender  ,  Timnit Gebru  ,  Angelina McMillan-Major  ,  Shmargaret Shmitchell (2021)  " rel="nofollow" href="https://dl.acm.org/doi/10.1145/3442188.3445922">On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - Emily M. Bender  ,  Timnit Gebru  ,  Angelina McMillan-Major  ,  Shmargaret Shmitchell (2021)  
</a></li><li><a title="In Two Moves, AlphaGo and Lee Sedol Redefined the Future | Wired Magazine (2016)" rel="nofollow" href="https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/">In Two Moves, AlphaGo and Lee Sedol Redefined the Future | Wired Magazine (2016)
</a></li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>How can we make AI wiser? And could AI make us wiser in return? Sina Fazelpour joins Igor and Charles to discuss the problem of bias in algorithms, how we might make machine learning systems more diverse, and the thorny challenge of alignment. Igor considers whether interacting with AIs might help us achieve higher levels of understanding, Sina suggests that setting up AIs to promote certain values may be problematic in a pluralistic society, and Charles is intrigued to learn about the opportunities offered by teaming up with our machine friends. Welcome to Episode 55.</p><p>Special Guest: Sina Fazelpour.</p><p>Links:</p><ul><li><a title="Sina Fazelpour&#39;s Website" rel="nofollow" href="https://sinafazelpour.com/">Sina Fazelpour's Website
</a></li><li><a title="AI and the transformation of social science research | Science - Igor Grossmann, Matthew Feinberg, Dawn C. Parker, Nicholas A. Christakis, Philip E. Tetlock,  Willian A. Cunningham (2023)" rel="nofollow" href="https://www.science.org/stoken/author-tokens/ST-1256/full">AI and the transformation of social science research | Science - Igor Grossmann, Matthew Feinberg, Dawn C. Parker, Nicholas A. Christakis, Philip E. Tetlock,  Willian A. Cunningham (2023)
</a></li><li><a title="Algorithmic Fairness from a Non-ideal Perspective - Sina Fazelpour, ZacharyC.Lipton (2020" rel="nofollow" href="https://dl.acm.org/doi/pdf/10.1145/3375627.3375828">Algorithmic Fairness from a Non-ideal Perspective - Sina Fazelpour, ZacharyC.Lipton (2020
</a></li><li><a title="Diversity in sociotechnical machine learning systems - Sina Fazelpour, Maria De-Arteaga (2022)" rel="nofollow" href="https://journals.sagepub.com/doi/10.1177/20539517221082027">Diversity in sociotechnical machine learning systems - Sina Fazelpour, Maria De-Arteaga (2022)
</a></li><li><a title="Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang (2022)" rel="nofollow" href="https://arxiv.org/abs/2211.13972">Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang (2022)
</a></li><li><a title="Algorithmic bias: Senses, sources, solutions - Sina Fazelpour, David Danks (2021)" rel="nofollow" href="https://compass.onlinelibrary.wiley.com/doi/full/10.1111/phc3.12760">Algorithmic bias: Senses, sources, solutions - Sina Fazelpour, David Danks (2021)
</a></li><li><a title="Constitutional AI: Harmlessness from AI Feedback - Yuntao Bai et al (2022)" rel="nofollow" href="https://arxiv.org/abs/2212.08073">Constitutional AI: Harmlessness from AI Feedback - Yuntao Bai et al (2022)
</a></li><li><a title="Taxonomy of Risks posed by Language Models - Laura Weidinger at Al (2022)" rel="nofollow" href="https://dl.acm.org/doi/10.1145/3531146.3533088">Taxonomy of Risks posed by Language Models - Laura Weidinger at Al (2022)
</a></li><li><a title="Large pre-trained language models contain human-like biases of what is right and wrong to do - Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf &amp; Kristian Kersting (2022)" rel="nofollow" href="https://www.nature.com/articles/s42256-022-00458-8">Large pre-trained language models contain human-like biases of what is right and wrong to do - Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf &amp; Kristian Kersting (2022)
</a></li><li><a title="On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - Emily M. Bender  ,  Timnit Gebru  ,  Angelina McMillan-Major  ,  Shmargaret Shmitchell (2021)  " rel="nofollow" href="https://dl.acm.org/doi/10.1145/3442188.3445922">On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - Emily M. Bender  ,  Timnit Gebru  ,  Angelina McMillan-Major  ,  Shmargaret Shmitchell (2021)  
</a></li><li><a title="In Two Moves, AlphaGo and Lee Sedol Redefined the Future | Wired Magazine (2016)" rel="nofollow" href="https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/">In Two Moves, AlphaGo and Lee Sedol Redefined the Future | Wired Magazine (2016)
</a></li></ul>]]>
  </itunes:summary>
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