{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/68470ba8d911dedd6501609c/69f01d751c25ec341e9fa57f?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Episode 17 - What happens now that AI is good at math?","description":"<p>Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean for the future of research. They reflect on how Ernest used ChatGPT to help solve a 42-year-old open problem, the difference between deep literature search and original mathematical discovery, and what changes when AI can work over longer timelines.&nbsp;</p><p><br></p><p><strong>Chapters</strong></p><p><br></p><p>01:27 The surprising progress of AI’s math capabilities&nbsp;</p><p>03:01 Solving an open problem with ChatGPT</p><p>06:57 How models went from basic math to research level</p><p>11:32 Why math matters for AGI</p><p>14:26 AI and the Erdős problems</p><p>21:26 Building an automated researcher</p><p>28:19 The role of humans as models improve</p><p>33:52 Verifying proofs with AI</p><p>36:00 The risk of shallow understanding</p><p>41:19 Advice for learning math with ChatGPT</p><p><br></p><p><br></p>","author_name":"OpenAI"}