All of poppingtonic's Comments + Replies

Open Thread #38

I like both of them, but I'm wondering: why wait so long? Isn't there a way some group (maybe us) could build 10% of the kind of prediction market that gets us 90% of what we actually need? I need to think about this more, but waiting for Gnosis and Augur to mature seems risky. Unless de-risking that bet means joining both projects to accelerate their advent.

Personal thoughts on careers in AI policy and strategy

Your link [2] points to a .docx file in a folder on a computer. It isn't a usable download link. Was that the purpose?

'file:///C:/Users/futureuser/Downloads/Careers%20in%20AI%20strategy%20and%20policy%201.4.docx#_ftnref2'

0carrickflynn4yI wrote this in a google doc and copy-pasted, without intending the numbers to be links to anything. I'm not really sure why it made them highlight like a hyperlink.
Open Thread #38

I think that the Good Judgment Project (founded by Philip Tetlock, the author of Superforecasting) is trying to build this with their experiments.

0WillPearson4yI'd not thought to look at it, I assumed it was/stayed an IARPA thing and so focused on world affairs. Thanks! It looks like it has become a for-profit endeavour now [http://goodjudgment.com/] with an open component [https://www.gjopen.com/]. From the looks of it there are no ways to submit questions and you can't see the models of the world used to make the predictions, so I'm not sure if charities (or people investing in charities) can gain much value from it. We would want questions of the form: if intervention Y occurs what is the expected magnitude of outcome Z. I'm not sure how best to tackle this.
An Argument for Why the Future May Be Good

A complication: Whole-brain emulation seeks to instantiate human minds, which are conscious by default, in virtual worlds. Any suffering involved in that can presumably be edited away if I go by what Robin Hanson wrote in Age of Em. Hanson also thinks that this might be a more likely first route for HLAI, which suggests that may be the "lazy solution", compared to mathematically-based AGI. However, in the S-risks talk at EAG Boston, an example of s-risk was something like this.

Analogizing like this isn't my idea of a first-principle argument, and... (read more)

Ask MIRI Anything (AMA)

Quoting Nate's supplement from OpenPhil's review of "Proof-producing reflection for HOL" (PPRHOL) :

there are basic gaps in our models of what it means to do good reasoning (especially when it comes to things like long-running computations, and doubly so when those computations are the reasoner’s source code)

How far along the way are you towards narrowing these gaps, now that "Logical Induction" is a thing people can talk about? Are there variants of it that narrow these gaps, or are there planned follow-ups to PPRHOL that might improve our models? What kinds of experiments seem valuable for this subgoal?

I endorse Tsvi's comment above. I'll add that it’s hard to say how close we are to closing basic gaps in understanding of things like “good reasoning”, because mathematical insight is notoriously difficult to predict. All I can say is that logical induction does seem like progress to me, and we're taking various different approaches on the remaining problems. Also, yeah, one of those avenues is a follow-up to PPRHOL. (One experiment we’re running now is an attempt to implement a cellular automaton in HOL that implements a reflective reasoner with access to... (read more)

Scott Garrabrant’s logical induction framework feels to me like a large step forward. It provides a model of “good reasoning” about logical facts using bounded computational resources, and that model is already producing preliminary insights into decision theory. In particular, we can now write down models of agents that use logical inductors to model the world---and in some cases these agents learn to have sane beliefs about their own actions, other agents’ actions, and how those actions affect the world. This, despite the usual obstacles to self-modeling... (read more)

Ask MIRI Anything (AMA)

Thanks for doing this AMA! Which of the points in your strategy have you seen a need to update on, based on the unexpected progress of having published the "Logical Induction" paper (which I'm currently perusing)?

9So8res5yGood question. The main effect is that I’ve increased my confidence in the vague MIRI mathematical intuitions being good, and the MIRI methodology for approaching big vague problems actually working. This doesn’t constitute a very large strategic shift, for a few reasons. One reason is that my strategy was already predicated on the idea that our mathematical intuitions and methodology are up to the task. As I said in last year’s AMA [http://effective-altruism.com/ea/ju/i_am_nate_soares_ama/413?context=1#comments/] , visible progress on problems like logical uncertainty (and four other problems) were one of the key indicators of success that I was tracking; and as I said in February [http://effective-altruism.com/ea/tx/lets_conduct_a_survey_on_the_quality_of_miris/6sd] , failure to achieve results of this caliber in a 5-year timeframe would have caused me to lose confidence in our approach. (As of last year, that seemed like a real possibility.) The logical induction result increases my confidence in our current course, but it doesn't shift it much. Another reason logical induction doesn’t affect my strategy too much is that it isn’t that big a result. It’s one step on a path, and it’s definitely mathematically exciting, and it gives answers to a bunch of longstanding philosophical problems, but it’s not a tool for aligning AI systems on the object level. We’re building towards a better understanding of “good reasoning”, and we expect this to be valuable for AI alignment, and logical induction is a step in that direction, but it's only one step. It’s not terribly useful in isolation, and so it doesn’t call for much change in course.