The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use dangerously creative solutions that have side-effects that could be undesirable, or downright dangerous. Even a slight misalignment between the motives of a sufficiently advanced AI and human values could be hazardous. That’s why leading AI labs like OpenAI are already investing significant resources into AI alignment research. Understanding that research is important if you want to understand where advanced AI systems might be headed, and what challenges we might encounter as AI capabilities continue to grow — and that’s what this episode of the podcast is all about. My guest today is Jan Leike, head of AI alignment at OpenAI, and an alumnus of DeepMind and the Future of Humanity Institute. As someone who works directly with some of the world’s largest AI systems (including OpenAI’s GPT-3) Jan has a unique and interesting perspective to offer both on the current challenges facing alignment researchers, and the most promising future directions the field might take. ---  Intro music: ➞ Artist: Ron Gelinas ➞ Track Title: Daybreak Chill Blend (original mix) ➞ Link to Track: https://youtu.be/d8Y2sKIgFWc ---  Chapters:   0:00 Intro 1:35 Jan’s background 7:10 Timing of scalable solutions 16:30 Recursive reward modeling 24:30 Amplification of misalignment 31:00 Community focus 32:55 Wireheading 41:30 Arguments against the democratization of AIs 49:30 Differences between capabilities and alignment 51:15 Research to focus on 1:01:45 Formalizing an understanding of personal experience 1:04:04 OpenAI hiring 1:05:02 Wrap-up