I appreciate the arguments, and I think you’ve mostly convinced me, mostly because of the historical argument.
I do still have some remaining apprehension about using AI risk to describe every type of risk arising from AI.
I want to include philosophical failures, as long as the consequences of the failures flow through AI, because (aside from historical usage) technical problems and philosophical problems blend into each other, and I don’t see a point in drawing an arbitrary and potentially contentious border between them.
That is true. The way I see it, UDT is definitely on the technical side, even though it incorporates a large amount of philosophical background. When I say technical, I mostly mean “specific, uses math, has clear meaning within the language of computer science” rather than a more narrow meaning of “is related to machine learning” or something similar.
My issue with arguing for philosophical failure is that, as I’m sure you’re aware, there’s a well known failure mode of worrying about vague philosophical problems rather than more concrete ones. Within academic philosophy, the majority of discussion surrounding AI is centered around consciousness, intentionality, whether it’s possible to even construct a human-like machine, whether they should have rights etc.
There’s a unique thread of philosophy that arose from Lesswrong, which includes work on decision theory, that doesn’t focus on these thorny and low priority questions. While I’m comfortable with you arguing that philosophical failure is important, my impression is that the overly philosophical approach used by many people has done more harm than good for the field in the past, and continues to do so.
It is therefore sometimes nice to tell people that the problems that people work on here are concrete and specific, and don’t require doing a ton of abstract philosophy or political advocacy.
I don’t think this is a good argument, because even within “accidental technical AI risk” there are different problems that aren’t equally worthwhile to solve, so why aren’t you already worried about outsiders thinking all those problems are equally worthwhile?
This is true, but my impression is that when you tell people that a problem is “technical” it generally makes them refrain from having a strong opinion before understanding a lot about it. “Accidental” also reframes the discussion by reducing the risk of polarizing biases. This is a common theme in many fields:
Physicists sometimes get frustrated with people arguing about “the philosophy of the interpretation of quantum mechanics” because there’s a large subset of people who think that since it’s philosophical, then you don’t need to have any subject-level expertise to talk about it.
Economists try to emphasize that they use models and empirical data, because a lot of people think that their field of study is more-or-less just high status opinion + math. Emphasizing that there are real, specific models that they study helps to reduce this impression. Same with political science.
A large fraction of tech workers are frustrated about the use of Machine Learning as a buzzword right now, and part of it is that people started saying Machine Learning = AI rather than Machine Learning = Statistics, and so a lot of people thought that even if they don’t understand statistics, they can understand AI since that’s like philosophy and stuff.
But I’ve drawn much closer to the community over the last few years, because of a combination of factors: [...] The AI-risk folks started publishing some research papers that I found interesting—some with relatively approachable problems that I could see myself trying to think about if quantum computing ever got boring. This shift seems to have happened at roughly around the same time my former student, Paul Christiano, “defected” from quantum computing to AI-risk research.
My guess is that this shift in his thinking occurred because a lot of people started talking about technical risks from AI, rather than framing it as a philosophy problem, or a problem of eliminating bad actors. Eliezer has shared this viewpoint for years, writing in the CEV document,
Warning: Beware of things that are fun to argue.
reflecting the temptation to derail discussions about technical accidental risks.
I appreciate the arguments, and I think you’ve mostly convinced me, mostly because of the historical argument.
I do still have some remaining apprehension about using AI risk to describe every type of risk arising from AI.
That is true. The way I see it, UDT is definitely on the technical side, even though it incorporates a large amount of philosophical background. When I say technical, I mostly mean “specific, uses math, has clear meaning within the language of computer science” rather than a more narrow meaning of “is related to machine learning” or something similar.
My issue with arguing for philosophical failure is that, as I’m sure you’re aware, there’s a well known failure mode of worrying about vague philosophical problems rather than more concrete ones. Within academic philosophy, the majority of discussion surrounding AI is centered around consciousness, intentionality, whether it’s possible to even construct a human-like machine, whether they should have rights etc.
There’s a unique thread of philosophy that arose from Lesswrong, which includes work on decision theory, that doesn’t focus on these thorny and low priority questions. While I’m comfortable with you arguing that philosophical failure is important, my impression is that the overly philosophical approach used by many people has done more harm than good for the field in the past, and continues to do so.
It is therefore sometimes nice to tell people that the problems that people work on here are concrete and specific, and don’t require doing a ton of abstract philosophy or political advocacy.
This is true, but my impression is that when you tell people that a problem is “technical” it generally makes them refrain from having a strong opinion before understanding a lot about it. “Accidental” also reframes the discussion by reducing the risk of polarizing biases. This is a common theme in many fields:
Physicists sometimes get frustrated with people arguing about “the philosophy of the interpretation of quantum mechanics” because there’s a large subset of people who think that since it’s philosophical, then you don’t need to have any subject-level expertise to talk about it.
Economists try to emphasize that they use models and empirical data, because a lot of people think that their field of study is more-or-less just high status opinion + math. Emphasizing that there are real, specific models that they study helps to reduce this impression. Same with political science.
A large fraction of tech workers are frustrated about the use of Machine Learning as a buzzword right now, and part of it is that people started saying Machine Learning = AI rather than Machine Learning = Statistics, and so a lot of people thought that even if they don’t understand statistics, they can understand AI since that’s like philosophy and stuff.
Scott Aaronson has said
My guess is that this shift in his thinking occurred because a lot of people started talking about technical risks from AI, rather than framing it as a philosophy problem, or a problem of eliminating bad actors. Eliezer has shared this viewpoint for years, writing in the CEV document,
reflecting the temptation to derail discussions about technical accidental risks.