{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"80,000 Hours Podcast","title":"#61 - Helen Toner on emerging technology, national security, and China","html":"","width":"100%","height":180,"duration":6897,"description":"From 1870 to 1950, the introduction of electricity transformed life in the US and UK, as people gained access to lighting, radio and a wide range of household appliances for the first time. Electricity turned out to be a general purpose technology that could help with almost everything people did.\n\nSome think this is the best historical analogy we have for how machine learning could alter life in the 21st century.\n\nIn addition to massively changing everyday life, past general purpose technologies have also changed the nature of war. For example, when electricity was introduced to the battlefield, commanders gained the ability to communicate quickly with units in the field over great distances.\n\nHow might international security be altered if the impact of machine learning reaches a similar scope to that of electricity? Today's guest — Helen Toner — recently helped found the Center for Security and Emerging Technology at Georgetown University to help policymakers prepare for such disruptive technical changes that might threaten international peace.\n\n• Links to learn more, summary and full transcript\n• Philosophy is one of the hardest grad programs. Is it worth it, if you want to use ideas to change the world? by Arden Koehler and Will MacAskill\n• The case for building expertise to work on US AI policy, and how to do it by Niel Bowerman\n• AI strategy and governance roles on the job board\n\nTheir first focus is machine learning (ML), a technology which allows computers to recognise patterns, learn from them, and develop 'intuitions' that inform their judgement about future cases. This is something humans do constantly, whether we're playing tennis, reading someone's face, diagnosing a patient, or figuring out which business ideas are likely to succeed.\n\nSometimes these ML algorithms can seem uncannily insightful, and they're only getting better over time. Ultimately a wide range of different ML algorithms could end up helping us with all kinds of decisions, just as...","thumbnail_url":"https://img.transistorcdn.com/VO1STE7hN95RRg9QdLo4soV2VhhbR9PF5ZZlRhDYcwE/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDAyLzE2ODM1/NDQ1NDAtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}