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One Hot Research. Лекторий Data Science

События в Кочерге - 25 июля, 2019 - 19:00
Каждую неделю публикуются сотни научных работ по машинному обучению, нейронным сетям, компьютерному зрению. Наша задача — разобраться в самых горячих! На этой неделе рассмотрим улучшенный вариант, пожалуй, самого известного метода разбиения данных на подгруппы — k-means, — который недавно представила группа учёных из пекинского Tsinghua university и университетского колледжа в Дублине. А чтобы понять, что к чему, разберём метод максимального правдоподобия, классический k-means и gaussian mixture model. Если вы можете посчитать, какова вероятность получить хотя бы одно чётное число при броске двух шестигранных игральных костей, то скорее всего поймёте и остальное.

On the purposes of decision theory research

Новости LessWrong.com - 25 июля, 2019 - 10:18
Published on July 25, 2019 7:18 AM UTC

Following the examples of Rob Bensinger and Rohin Shah, this post will try to clarify the aims of part of my research interests, and disclaim some possible misunderstandings about it. (I'm obviously only speaking for myself and not for anyone else doing decision theory research.)

I think decision theory research is useful for:

  1. Gaining information about the nature of rationality (e.g., is “realism about rationality” true?) and the nature of philosophy (e.g., is it possible to make real progress in decision theory, and if so what cognitive processes are we using to do that?), and helping to solve the problems of normativity, meta-ethics, and metaphilosophy.
  2. Better understanding potential AI safety failure modes that are due to flawed decision procedures implemented in or by AI.
  3. Making progress on various seemingly important intellectual puzzles that seem directly related to decision theory, such as free will, anthropic reasoning, logical uncertainty, Rob's examples of counterfactuals, updatelessness, and coordination, and more.
  4. Firming up the foundations of human rationality.

To me, decision theory research is not meant to:

  1. Provide a correct or normative decision theory that will be used as a specification or approximation target for programming or training a potentially superintelligent AI.
  2. Help create "safety arguments" that aim to show that a proposed or already existing AI is free from decision theoretic flaws.

To help explain 5 and 6, here's what I wrote in a previous comment (slightly edited):

One meta level above what even UDT tries to be is decision theory (as a philosophical subject) and one level above that is metaphilosophy, and my current thinking is that it seems bad (potentially dangerous or regretful) to put any significant (i.e., superhuman) amount of computation into anything except doing philosophy.

To put it another way, any decision theory that we come up with might have some kind of flaw that other agents can exploit, or just a flaw in general, such as in how well it cooperates or negotiates with or exploits other agents (which might include how quickly/cleverly it can make the necessary commitments). Wouldn’t it be better to put computation into trying to find and fix such flaws (in other words, coming up with better decision theories) than into any particular object-level decision theory, at least until the superhuman philosophical computation itself decides to start doing the latter?

Comparing my current post to Rob's post on the same general topic, my mentions of 1, 2, and 4 above seem to be new, and he didn't seem to share (or didn't choose to emphasize) my concern that decision theory research (as done by humans in the foreseeable future) can't solve decision theory in a definitive enough way that would obviate the need to make sure that any potentially superintelligent AI can find and fix decision theoretic flaws in itself.


AnnaSalamon's Shortform

Новости LessWrong.com - 25 июля, 2019 - 08:24
Published on July 25, 2019 5:24 AM UTC



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