The Evolution of Machine Learning

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I have updated towards thinking we should spend more of our time trying to get really fast at executing on the basic tasks of machine learning. And I've also updated towards thinking that it's easier to get value out of hiring less experienced people as long as those people are very good at programming. So does that mean you're putting more time into tool building? Well if you model yourself in the first order as a rational agent, then you’re putting moretime into tool building. Yeah, for sure.

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How hard is it to arrive at true beliefs about the world? How can you find enjoyment in being wrong? When presenting claims that will be scrutinized by others, is it better to hedge and pad the claims in lots of caveats and uncertainty, or to strive for a tone that matches (or perhaps even exaggerates) the intensity with which you hold your beliefs? Why should you maybe focus on drilling small skills when learning a new skill set? What counts as a "simple" question? How can you tell when you actually understand something and when you don't? What is "cargo culting"? Which features of AI are likely in the future to become existential threats? What are the hardest parts of AI research? What skills will we probably really wish we had on the eve of deploying superintelligent AIs?

Buck Shlegeris is the CTO of Redwood Research, an independent AI alignment research organization. He currently leads their interpretability research. He previously worked on research and outreach at the Machine Intelligence Research Institute. His website is shlegeris.com.

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