Michaël Trazzi

Comments

Excusing a Failure to Adjust

More generally, there's a difference between things being true and being useful. Believing that sometimes you should not update isn't a really useful habit as it forces the rationalizations you mentioned.

Another example is believing "willpower is a limited quantity" vs. "it's a muscle and the more I use it the stronger I get". The first belief will push you towards not doing anything, which is similar to the default mode of not updating in your story.

Predictions for Neuralink's Friday Announcement

Note: I also know very little about this. Few thoughts on your guesses (and my corresponding credences):

--It seems pretty likely that it will be for humans (something that works for mices wouldn't be impressive enough for an announcement). In last year's white paper they were already inserting electrode arrays in the brain. But maybe you mean something that lives inside the brain independently? (90%)

--If by "significative damage" you mean "not altering basic human capabilities" then it sounds plausible. From the white paper they seem to focus on damage to "the blood-brain barrier" and the "brain’s inflammatory response to foreign objects". My intuition is that the brain would react pretty strongly to something inside it for 10 years though. (20%)

--Other BCI companies have done similar demo-s, so given presentation is long this might happen at some point. But Neuralink might also want to show they're different from mainstream companies. (35%)

--Seems plausible. Assigning lower credence because really specific. (15%)

Matt Botvinick on the spontaneous emergence of learning algorithms

Funnily enough, I wrote a blog distilling what I learned from reproducing experiments of that 2018 Nature paper, adding some animations and diagrams. I especially look at the two-step task, the Harlow task (the one with monkeys looking at a screen), and also try to explain some brain things (e.g. how DA interacts with the PFN) at the end.

OpenAI announces GPT-3

HN comment unsure about the meta-learning generalization claims that OpenAI has a "serious duty [...] to frame their results more carefully"

Raemon's Shortform

re working memory: never thought of it during conversations, interesting. it seems that we sometime hold the nodes of the conversation tree to go back to them afterward. and maybe if you're introducing new concepts while you're talking people need to hold those definitions in working memory as well.

What would flourishing look like in Conway's Game of Life?

Some friends tried (inconclusively) to apply AlphaZero to a two-player GoL. I can put you in touch if you want their feedback.

Michaël Trazzi's Shortform

Thanks for the tutorial to download documentation, I've never done that myself so will check it out next time I go offline for a while!

I usually just run python to look at docs, importing the library, and then do help(lib.module.function). If I don't really know what the class can do, I usually do dir(class_instance) to find the available methods/attributes, and do the help thing on them.

This only works if you know reasonably well where to look at. If I were you I would try loading the "read the docs" html build offline in your browser (might be searchable this way), but then you still have a browser open (so you would really need to turn down wifi).

How to do remote co-working

Thanks for writing this up!

I've personally tried Complice coworking rooms where people synchronize on pomodoros and chat during breaks, especially EA France's study room (+discord to voice chat during breaks) but there's also a LW study hall: https://complice.co/rooms

Michaël Trazzi's Shortform

I've been experimenting with offline coding recently, sharing some of my conclusions.

Why I started 1) Most of the programming I do at the moment only needs a terminal and a text editor. I'm implementing things from scratch without needing libraries and I noticed I could just read the docs offline. 2) I came to the conclusion that googling things wasn't worth the cost of having a web browser open--using the outside view, when I look back at all the instances of coding while having the internet in easy-access, I always end up being distracted, and even if i code my mind thinks about what I could be doing.

How to go offline (Computer) 1) turn off wi-fi 2) forget network (Phone) if you're at home, put it out of reach. I turn it off then throw it on top of a closet, so far that i need to grab a chair in the living room to catch it. If you have an office, then do the same thing and go to your office without your phone.

When My general rule in January was that I could only check the internet between 11pm and 12am. The rest of the "no work + no internet" time was for deep relaxation, meditation, journaling, eating, etc. In April I went without any internet connection for a week. I was amazed at how much free time I had, but the lack of social interactions was a bit counter-productive. Currently, I'm going offline from the moment I wake up to 7pm. This seems like a good balance where I'm not too tired but still productive throughout the day.

Let me know if you have any question about the process or similar experience to share.

The Epistemology of AI risk

Thanks for all the references! I don't currently have much time to read all of it right now so I can't really engage with the specific arguments for the rejection of using utility functions/studying recursive self-improvement.

I essentially agree with most of what you wrote. There is maybe a slight disagreement in how you framed (not what you meant) how research focus shifted since 2014.

I see Superintelligence as essentially saying "hey, there is pb A. And even if we solve A, then we might also have B. And given C and D, there might be E." Now that the field is more mature and we have many more researchers getting paid to work on these problems, the arguments became much more goal focused. Now people are saying "I'm going to make progress on sub-problem X, by publishing a paper on Y. And working on Z is not cost-effective given so I'm not going to work on it given humanity's current time constraints."

These approaches are often grouped as "long-term problems-focused" and "making tractable progress now focused". In the first group you have Yudkowsky 2010, Bostrom 2014, MIRI's current research and maybe CAIS. In the second one you have current CHAI/FHI/OpenAI/DeepMind/Ought papers.

Your original framing can be interpreted as "after proving some mathematical theorems, people rejected the main arguments of Superintelligence and now most of the community agrees that working on X, Y and Z are tractable but A, B and C are more controversials".

I think a more nuanced and precise framing would be: "In Superintelligence Bostrom exposes exhaustively the risks associated with advanced AI. A short portion of the book is dedicated to the problems are working on right now. Indeed, people stopped working on the other problems (largest portion of the book) because 1) there hasn't been really productive working on them 2) some rebuttals have been written online giving convincing arguments that those pbs are not tractable anyway 3) there are now well-funded research organizations with incentives to make tangible progress on those pbs."

In your last framing, you presented precise papers/rebuttals (thanks again!) for 2), and I think rebuttals are a great reason to stop working on a pb, but I think they're not the only reason and not the real reason people stopped working on those pb. To be fair, I think 1) can be explained by many more factors than "it's theoretically impossible to make progress on those pbs". It can be that the research mindset required to work on these pbs is less socially/intellectually validating or requires much more theoretical approaches, so will be off-putting/tiresome to most recent grads that enter the field. I also think that AI Safety is now much more intertwined with evidence-based approaches such as Effective Altruism than it was in 2014, which explains 3), so people start presenting their research as "partial solutions to the pb. of AI Safety" or "research agenda".

To be clear, I'm not criticizing the current shift in research. I think it's productive for the field, both in the short term and long term. To give a bit more personal context, I started getting interested in AI Safety after reading Bostrom and have always been more interested in the "finding problems" approach. I went to FHI to work on AI Safety because I was super interested in finding new pbs related to the treacherous turn. It's now almost taboo to say that we're working on pbs that are sub-optimally minimizing AI risk, but the real reason that pushed me to think about those pbs was because they were both important and interesting. The pb. with the current "shift in framing" is that it's making it socially unacceptable for people to think/work on more long-term pbs where there is more variance in research productivity.

I don't quite understand the question?

Sorry about that. I thought there was some link to our discussion about utility functions but I misunderstood.

EDIT: I also wanted to mention that the number of pages in a book doesn't account for how important the author think the pb. is (Bostrom even comments on this in the postface of its book). Again, the book is mostly about saying "here are all the pbs", not "these are the tractable pbs we should start working on, and we should dedicate research ressources proportionally to the amount of pages I talk about it in the book".

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