• #8: Bing Chat, AI labs on safety, and pausing Future Matters

  • 2023/03/21
  • 再生時間: 42 分
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#8: Bing Chat, AI labs on safety, and pausing Future Matters

  • サマリー

  • Future Matters is a newsletter about longtermism and existential risk by Matthew van der Merwe and Pablo Stafforini. Each month we curate and summarize relevant research and news from the community, and feature a conversation with a prominent researcher. You can also subscribe on Substack, read on the EA Forum and follow on Twitter. Future Matters is also available in Spanish.

    00:00 Welcome to Future Matters. 00:44 A message to our readers. 01:09 All things Bing. 05:27 Summaries. 14:20 News. 16:10 Opportunities. 17:19 Audio & video. 18:16 Newsletters. 18:50 Conversation with Tom Davidson. 19:13 The importance of understanding and forecasting AI takeoff dynamics. 21:55 Start and end points of AI takeoff. 24:25 Distinction between capabilities takeoff and impact takeoff. 25:47 The ‘compute-centric framework’ for AI forecasting. 27:12 How the compute centric assumption could be wrong. 29:26 The main lines of evidence informing estimates of the effective FLOP gap. 34:23 The main drivers of the shortened timelines in this analysis. 36:52 The idea that we'll be "swimming in runtime compute" by the time we’re training human-level AI systems. 37:28 Is the ratio between the compute required for model training vs. model inference relatively stable? 40:37 Improving estimates of AI takeoffs.

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あらすじ・解説

Future Matters is a newsletter about longtermism and existential risk by Matthew van der Merwe and Pablo Stafforini. Each month we curate and summarize relevant research and news from the community, and feature a conversation with a prominent researcher. You can also subscribe on Substack, read on the EA Forum and follow on Twitter. Future Matters is also available in Spanish.

00:00 Welcome to Future Matters. 00:44 A message to our readers. 01:09 All things Bing. 05:27 Summaries. 14:20 News. 16:10 Opportunities. 17:19 Audio & video. 18:16 Newsletters. 18:50 Conversation with Tom Davidson. 19:13 The importance of understanding and forecasting AI takeoff dynamics. 21:55 Start and end points of AI takeoff. 24:25 Distinction between capabilities takeoff and impact takeoff. 25:47 The ‘compute-centric framework’ for AI forecasting. 27:12 How the compute centric assumption could be wrong. 29:26 The main lines of evidence informing estimates of the effective FLOP gap. 34:23 The main drivers of the shortened timelines in this analysis. 36:52 The idea that we'll be "swimming in runtime compute" by the time we’re training human-level AI systems. 37:28 Is the ratio between the compute required for model training vs. model inference relatively stable? 40:37 Improving estimates of AI takeoffs.

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