I'm not sure I entirely agree with the overall recommendation for researchers working on internals-based techniques. I do agree that findings will need to be behavioral initially in order to be legible and something that decision-makers find worth acting on.

My expectation is that internals-based techniques (including mech interp) and techniques that detect specific highly legible behaviors will ultimately converge. That is:

1. Internals/mech interp researchers will, as they have been so far at least in model organisms, find examples of concerning cognition that will be largely ignored or not acted on fully
2. Eventually, legible examples of misbehavior will be found, resulting in action or increased scrutiny
3. This scrutiny will then propagate backwards to finding causes or indicators of that misbehavior, and provided interp tools are indeed predictive, this path that has been developed in parallel will suddenly be much more worth paying attention to

Thus, I think it's worth progressing on these internals-based techniques even if their use isn't immediately apparent. When legible misbehaviors arrive, I expect internals-based detection or analysis to be more directly applicable.

I use Freedom and Limit on my computer and Stay Focused on my Android phone. The former two allow for a combination of complete blocking during certain time windows and time limits (for any website, even across browsers and even if you open an incognito window). The latter does both for my phone.

I block all social media and content during prime working hours and implement a 30-minute limit outside of that. It works pretty well. I may make it more strict because I sometimes find myself looking at Twitter, etc. occasionally when watching a TV show in the evenings.

I also use BlockTube to get rid of YouTube Shorts entirely from my web browser. They no longer show up in search results or in the menu.

Finally, I recommend the tools here, though I haven't tried all of them: https://liamrosen.com/2023/04/18/modding-social-media-to-win-the-attention-war/

I find this argument quite compelling, and this is also why I find the idea of "AI girl/boyfriends" largely uninteresting. Without actual connection to another mind (that has experiences and phenomenal consciousness), any of these things--art, deep conversations about thoughts/feelings, what have you--eventually falls flat. (That includes one-way connection through art).

I quite enjoyed reading this. Very evocative.

Welcome to San Francisco.

Domain: Software engineering, mech interp

Bryce Meyer (primary maintainer of TransformerLens, and software engineer with many years of experience) has a weekly coding stream event where he does live coding on TransformerLens--resolving bugs, adding features and tests, etc. I've found it to be useful!

You can find it in the Open Source Mechanistic Interpretability Slack, under the "code-sessions" channel (feel free to DM for an invite).

Great post, but there is one part I'd like to push back on:

Iterators are also easier to identify, both by their resumes and demonstrated skills. If you compare two CVs of postdocs that have spent the same amount of time in academia, and one of them has substantially more papers (or GitHub commits) to their name than the other (controlling for quality), you’ve found the better Iterator. Similarly, if you compare two CodeSignal tests with the same score but different completion times, the one completed more quickly belongs to the stronger Iterator.

This seems like a bit of an over-claim. I would endorse a weaker claim, like "in the presence of a high volume of applicants, CodeSignal tests, GitHub commits, and paper count statistically provide some signal," but the reality of work in the fields of research and software development is often such that there isn't a clean correspondence between these measures and someone's performance. In addition, all three of these measures are quite easy to game (or Goodhart).

For example, in research alone, not every paper entails the same-sized project; two high-quality papers could have an order of magnitude difference in the amount of work required to produce them. Not every research bet pays off, too--some projects don't result in papers, and research management often plays a role in what directions get pursued (and dropped or not if they are unproductive). There are also many researchers who have made a career out of getting their names on as many papers as possible; there is an entire science to doing this that is completely independent of your actual research abilities.

In the case of CodeSignal evaluations, signal is likewise relatively low-dimensional and primarily conveys one thing: enough experience with a relatively small set of patterns that one can do the assessment very quickly. I've taken enough of these and seen enough reviews from senior engineers on CodeSignal tests to know that they capture only a small, specific part of what it takes to be a good engineer, and overemphasize speed (which is not the main thing you want from an actual senior engineer; you want quality as well as maintainability and readability, which often are at odds with speed. Senior engineers' first instinct is not generally to jump in and start spitting out lines of code like their lives depend on it). Then there's the issue of how hackable/gameable the assessments are; senior engineer Yanir Seroussi has a good blog post on CodeSignal specifically:  https://yanirseroussi.com/2023/05/26/how-hackable-are-automated-coding-assessments/

I'm definitely not arguing that these metrics are useless, however. They do provide some signal (especially if the volume of applicants is high), but I'd suggest that we see them as imperfect proxies that we're forced to use due to insufficient manpower for comprehensive candidate evaluations, rather than actually capturing some kind of ground truth.

Perhaps more important than these details: How do you curate input to take notes on, and what is the purpose you take the notes for? How do you use the notes once written? (This latter point seems to be one of the biggest reason many people have dropped PKM systems.)

Very kind of you to say. :) I think for me, though, the source of the emotion I felt when reading this series was something like: "Ah, so in addition to ensuring we are dateable ourselves, we must fix society, capitalism (at least the dating part of it), culture, etc. in order to have a Good Dating Universe." Which in retrospect was a bit overblown of me, so I think I no longer endorse the strong version of what I said in that comment.

I think this list may successfully convince some to stay off the dating market indefinitely. Who in the world has time to work on all of this? At best, this is just a massive set of to-dos; at worst, it's an enormous list of all the ways the dating world sucks and reasons why you'll fail. 

Upon reflection: This is a good collection of information, even if it is rather discouraging to read. May we all find exceptions to the unfortunate trends that seem to characterize the modern dating landscape.