Curated. The wiki pages collected here, despite being written in 2015-2017 remain excellent resources on concepts and arguments for key AI alignment ideas (both still widely used and those lesser known). I found that even for concepts/arguments like the orthogonality thesis and corrigibility, I felt a gain in crispness from reading these pages. The concept of, e.g. epistemic and instrumental efficiency I didn’t have, yet feels useful in thinking about the rise of increasingly powerful AI.
Of course, there’s also non-AI content that got imported. The Bayes guide likely remains the best resource for building Bayes intuition, and same with the guide on logarithms that is extremely thorough.
Curated. The wiki pages collected here, despite being written in 2015-2017 remain excellent resources on concepts and arguments for key AI alignment ideas (both still widely used and those lesser known). I found that even for concepts/arguments like the orthogonality thesis and corrigibility, I felt a gain in crispness from reading these pages. The concept of, e.g. epistemic and instrumental efficiency I didn’t have, yet feels useful in thinking about the rise of increasingly powerful AI.
Of course, there’s also non-AI content that got imported. The Bayes guide likely remains the best resource for building Bayes intuition, and same with the guide on logarithms that is extremely thorough.