<div><img alt="AI. Where do you stand?" class="attachment-large size-large wp-post-image" height="450" src="https://hackaday.com/wp-content/uploads/2026/01/AI-where-do-you-stand-banner.jpg?w=800" style="margin: 0 auto; margin-bottom: 15px;" width="800" /></div><p>Over on YouTube you can see [Yang-Hui He] present to <em>The Royal Institution</em> about <a href="https://www.youtube.com/watch?v=oOYcPkBaotg" target="_blank">Mathematics: The rise of the machines</a>.</p>
<p>In this one hour presentation [Yang-Hui He] explains how AI is driving progress in pure mathematics. He says that right now AI is poised to change the very nature of how mathematics is done. He is part of a community of hundreds of mathematicians pursuing the use of AI for research purposes.</p>
<p>[Yang-Hui He] traces the genesis of the term “artificial intelligence” to a research proposal from J. McCarthy, M.L. Minsky, N. Rochester, and C.E. Shannon dated August 31, 1955. He says that his mantra has become: connectivism leads to emergence, and goes on to explain what he means by that, then follows with universal approximation theorems.</p>
<p>He goes on to enumerate some of the key moments in AI: <a href="https://en.wikipedia.org/wiki/Animal_machine" target="_blank">Descartes’s bête-machine</a>, 1617; <a href="https://en.wikipedia.org/wiki/Ada_Lovelace#Insight_into_potential_of_computing_devices" target="_blank">Lovelace’s speculation</a>, 1842; <a href="https://en.wikipedia.org/wiki/Turing_test" target="_blank">Turing test</a>, 1949; <a href="https://en.wikipedia.org/wiki/Dartmouth_workshop" target="_blank">Dartmouth conference</a>, 1956; <a href="https://en.wikipedia.org/wiki/Perceptron" target="_blank">Rosenblatt’s Perceptron</a>, 1957; <a href="https://en.wikipedia.org/wiki/Hopfield_network" target="_blank">Hopfield’s network</a>, 1982; <a href="https://en.wikipedia.org/wiki/Boltzmann_machine" target="_blank">Hinton’s Boltzmann machine</a>, 1984; <a href="https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)" target="_blank">IBM’s Deep Blue</a>, 1997; and <a href="https://en.wikipedia.org/wiki/AlphaGo" target="_blank">DeepMind’s AlphaGo</a>, 2012.</p>
<p>He continues with some navel-gazing about what is mathematics, and what is artificial intelligence. He considers how we do mathematics as bottom-up, top-down, or meta-mathematics. He mentions about one of his earliest papers on the subject <a href="https://openaccess.city.ac.uk/id/eprint/18685/1/Machine_learning%20the%20string%20landscape.pdf" target="_blank">Machine-learning the string landscape</a> (PDF) and his books <a href="https://www.amazon.com//dp/B09BLRXFK2" target="_blank">The Calabi–Yau Landscape: From Geometry, to Physics, to Machine Learning</a> and <a href="https://www.amazon.com/dp/B0CC5NWDMY" target="_blank">Machine Learning in Pure Mathematics and Theoretical Physics</a>.</p>
<p>He goes on to explain about <a href="https://mathlib.org/" target="_blank">Mathlib</a> and the <a href="https://www.ma.imperial.ac.uk/~buzzard/xena/" target="_blank">Xena Project</a>. He discusses <a href="https://www.ams.org/notices/202501/rnoti-p6.pdf" target="_blank">Machine-Assisted Proof by Terence Tao</a> (PDF) and goes on to talk more about the history of mathematics and particularly experimental mathematics. All in all a very interesting talk, if you can find a spare hour!</p>
<p>In conclusion: Has AI solved any major open conjecture? No. Is AI beginning to help to advance mathematical discovery? Yes. Has AI changed the speaker’s day-to-day research routine? Yes and no.</p>
<p>If you’re interested in more fun math articles be sure to check out <a href="https://hackaday.com/2025/01/30/digital-paint-mixing-has-been-greatly-improved-with-1930s-math/">Digital Paint Mixing Has Been Greatly Improved With 1930s Math</a> and <a href="https://hackaday.com/2022/01/04/painted-over-but-not-forgotten-restoring-lost-paintings-with-radiation-and-mathematics/">Painted Over But Not Forgotten: Restoring Lost Paintings With Radiation And Mathematics</a>.</p>
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Over on YouTube you can see [Yang-Hui He] present to The Royal Institution about Mathematics: The rise of the machines. In this one hour presentation [Yang-Hui He] explains how AI …read more![<div><img alt="AI. Where do you stand?" class="attachment-large size-large wp-post-image" height="450" src="https://hackaday.com/wp-content/uploads/2026/01/AI-where-do-you-stand-banner.jpg?w=800" style="margin: 0 auto; margin-bottom: 15px;" width="800" /></div><p>Over on YouTube you can see [Yang-Hui He] present to <em>The Royal Institution</em> about <a href="https://www.youtube.com/watch?v=oOYcPkBaotg" target="_blank">Mathematics: The rise of the machines</a>.</p>
<p>In this one hour presentation [Yang-Hui He] explains how AI is driving progress in pure mathematics. He says that right now AI is poised to change the very nature of how mathematics is done. He is part of a community of hundreds of mathematicians pursuing the use of AI for research purposes.</p>
<p>[Yang-Hui He] traces the genesis of the term “artificial intelligence” to a research proposal from J. McCarthy, M.L. Minsky, N. Rochester, and C.E. Shannon dated August 31, 1955. He says that his mantra has become: connectivism leads to emergence, and goes on to explain what he means by that, then follows with universal approximation theorems.</p>
<p>He goes on to enumerate some of the key moments in AI: <a href="https://en.wikipedia.org/wiki/Animal_machine" target="_blank">Descartes’s bête-machine</a>, 1617; <a href="https://en.wikipedia.org/wiki/Ada_Lovelace#Insight_into_potential_of_computing_devices" target="_blank">Lovelace’s speculation</a>, 1842; <a href="https://en.wikipedia.org/wiki/Turing_test" target="_blank">Turing test</a>, 1949; <a href="https://en.wikipedia.org/wiki/Dartmouth_workshop" target="_blank">Dartmouth conference</a>, 1956; <a href="https://en.wikipedia.org/wiki/Perceptron" target="_blank">Rosenblatt’s Perceptron</a>, 1957; <a href="https://en.wikipedia.org/wiki/Hopfield_network" target="_blank">Hopfield’s network</a>, 1982; <a href="https://en.wikipedia.org/wiki/Boltzmann_machine" target="_blank">Hinton’s Boltzmann machine</a>, 1984; <a href="https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)" target="_blank">IBM’s Deep Blue</a>, 1997; and <a href="https://en.wikipedia.org/wiki/AlphaGo" target="_blank">DeepMind’s AlphaGo</a>, 2012.</p>
<p>He continues with some navel-gazing about what is mathematics, and what is artificial intelligence. He considers how we do mathematics as bottom-up, top-down, or meta-mathematics. He mentions about one of his earliest papers on the subject <a href="https://openaccess.city.ac.uk/id/eprint/18685/1/Machine_learning%20the%20string%20landscape.pdf" target="_blank">Machine-learning the string landscape</a> (PDF) and his books <a href="https://www.amazon.com//dp/B09BLRXFK2" target="_blank">The Calabi–Yau Landscape: From Geometry, to Physics, to Machine Learning</a> and <a href="https://www.amazon.com/dp/B0CC5NWDMY" target="_blank">Machine Learning in Pure Mathematics and Theoretical Physics</a>.</p>
<p>He goes on to explain about <a href="https://mathlib.org/" target="_blank">Mathlib</a> and the <a href="https://www.ma.imperial.ac.uk/~buzzard/xena/" target="_blank">Xena Project</a>. He discusses <a href="https://www.ams.org/notices/202501/rnoti-p6.pdf" target="_blank">Machine-Assisted Proof by Terence Tao</a> (PDF) and goes on to talk more about the history of mathematics and particularly experimental mathematics. All in all a very interesting talk, if you can find a spare hour!</p>
<p>In conclusion: Has AI solved any major open conjecture? No. Is AI beginning to help to advance mathematical discovery? Yes. Has AI changed the speaker’s day-to-day research routine? Yes and no.</p>
<p>If you’re interested in more fun math articles be sure to check out <a href="https://hackaday.com/2025/01/30/digital-paint-mixing-has-been-greatly-improved-with-1930s-math/">Digital Paint Mixing Has Been Greatly Improved With 1930s Math</a> and <a href="https://hackaday.com/2022/01/04/painted-over-but-not-forgotten-restoring-lost-paintings-with-radiation-and-mathematics/">Painted Over But Not Forgotten: Restoring Lost Paintings With Radiation And Mathematics</a>.</p>
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