LessWrong 2.0 Reader

"Minimize excessive UV exposure" is the steelman to the pro-sunscreen arguments. The evidence against tanning beds demonstrates that excess UV is almost certainly harmful.

I think where the pro-sunscreen arguments go wrong is in assuming that sunscreen is the best or only way to minimize excess UV.

I personally don't have what it takes to use sunscreen "correctly" (apply every day, "reapply every 2 hours", tolerate the sensory experience of smearing anything on my face every day, etc) so I mitigate UV exposure in other ways:

• Pursue a career of work that can be done indoors
• Avoid doing optional outdoor activities during the parts of the day with the highest UV levels -- before and after the heat of the day is more pleasant to be out in anyway
• use sun-protective clothing like UV-proof gloves, wide-brimmed hats, UV hoodies, etc
• choose shady over sunny locations, or create shade with a large hat or parasol
• choose full-coverage swimwear for outdoor recreation
• wear dark colors on hot days, because dark clothing makes it uncomfortable to remain in the sun very long. I'm good at noticing when I'm too warm, so that's my cue to relocate to shade.

I  appreciate this response because it stirred up a lot of possible responses, in me, in lots of different directions, that all somehow seems germane to the core goal of securing a Win Conditions for the sapient metacivilization of earth! <3

(A) Physical reality is probably hyper-computational, but also probably amenable to pulling a nearly infinite stack of "big salient features" from a reductively analyzable real world situation. 

My intuition says that this STOPS being "relevant to human interests" (except for modern material engineering and material prosperity and so on) roughly below the level of "the cell".

Other physics with other biochemistry could exist, and I don't think any human would "really care"?

Suppose a Benevolent SAI had already replaced all of our cells with nanobots without our permission AND without us noticing because it wanted to have "backups" or something like that... 

(The AI in TMOPI does this much less elegantly, because everything in that story is full of hacks and stupidity. The overall fact that "everything is full of hacks and stupidity" is basically one of the themes of that novel.)

Contingent on a Benevoent SAI having thought it had good reason to do such a thing, I don't think that once we fully understand the argument in favor of doing it that we would really have much basis for objecting?

But I don't know for sure, one way or the other...

((To be clear, in this hypothetical, I think I'd volunteer to accept the extra risk to be one of the last who was "Saved" this way, and I'd volunteer to keep the secret, and help in a QA loop of grounded human perceptual feedback, to see if some subtle spark of magical-somethingness had been lost in everyone transformed this way? Like... like hypothetically "quantum consciousness" might be a real thing, and maybe people switched over to running atop "greygoo" instead of our default "pinkgoo" changes how "quantum consciousness" works and so the changeover would non-obviously involve a huge cognitive holocaust of sorts? But maybe not! Experiments might be called for... and they might need informed consent? ...and I think I'd probably consent to be in "the control group that is unblinded as part of the later stages of the testing process" but I would have a LOT of questions before I gave consent to something Big And Smart that respected "my puny human capacity to even be informed, and 'consent' in some limited and animal-like way".))

What I'm saying is: I think maybe NORMAL human values (amongst people with default mental patterns rather than weirdo autists who try to actually be philosophically coherent and ended up with utility functions that have coherently and intentionally unbounded upsides) might well be finite, and a rule for granting normal humans a perceptually indistinguishable version of "heaven" might be quite OK to approximate with "a mere a few billion well chosen if/then statements".

To be clear, the above is a response to this bit:

As such, I think the linear separability comes from the power of the "lol stack more layers" approach, not from some intrinsic simple structure of the underlying data. As such, I don't expect very much success for approaches that look like "let's try to come up with a small set of if/else statements that cleave the categories at the joints instead of inelegantly piling learned heuristics on top of each other".

And:

I don't think that such a model would succeed because it "cleaves reality at the joints" though, I expect it would succeed because you've managed to find a way that "better than chance" is good enough and you don't need to make arbitrarily good predictions.

Basically, I think "good enough" might be "good enough" for persons with finite utility functions?

(B) A completely OTHER response here is that you should probably take care to NOT aim for something that is literally mathematically impossible...

Unless this is part of some clever long term cognitive strategy, where you try to prove one crazy extreme, and then its negation, back and forth, as a sort of "personally implemented GAN research process" (and even then?!)...

...you should probably not spend much time trying to "prove that 1+1=5" nor try to "prove that the Halting Problem actually has a solution". Personally, any time I reduce a given plan to "oh, this is just the Halting Problem again" I tend to abandon that line of work.

Perfectly fine if you're a venture capitalist, not so great if you're seeking adversarial robustness.

Past a certain point, one can simply never be adversarially robust in a programmatic and symbolically expressible way.

Humans would have to have non-Turing-Complete souls, and so would any hypothetical Corrigible Robot Saint/Slaves [? · GW], in order to literally 100% prove that literally infinite computational power won't find a way to make things horrible.

There is no such thing as a finitely expressible "Halt if Evil" algorithm...

...unless (I think?) all "agents" involved are definitely not Turing Complete and have no emotional attachments to any questions whose answers partake of the challenges of working with Turing Complete systems? And maybe someone other than me is somehow smart enough to write a model of "all the physics we care about" and "human souls" and "the AI" all in some dependently typed language that will only compile if the compiler can generate and verify a "proof that each program, and ALL programs interacting with each other, halt on all possible inputs"?

My hunch is that that effort will fail, over and over, forever, but I don't have a good clean proof that it will fail.

Note that I'm pretty sure A and B are incompatible takes.

In "take A" I'm working from human subjectivity "down towards physics (through a vast stack of sociology and biology and so on)" and it just kinda seems like physics is safe to throw away because human souls and our humanistically normal concerns are probably mostly pretty "computational paltry" and merely about securing food, and safety, and having OK romantic lives?

In "take B" I'm starting with the material that mathematicians care about, and noticing that it means the project is doomed if the requirement is to have a mathematical proof about all mathematically expressible cares or concerns.

It would be... kinda funny, maybe, to end up believing "we can secure a Win Condition for the Normies (because take A is basically true), but True Mathematicians are doomed-and-blessed-at-the-same-time to eternal recursive yearning and Real Risk (because take B is also basically true)" <3

(C) Chaos is a thing! Even (and especially) in big equations, including the equations of mind that big stacks of adversarially optimized matrices represent!

This isn't a "logically deep" point. I'm just vibing with your picture where you imagine that the "turbulent looking" thing is a metaphor for reality.

In observable practice, the boundary conditions of the equations of AI also look like fractally beautiful turbulence!

I predict that you will be surprised by this empirical result. Here is the "high church papering" of the result:

TITLE: The boundary of neural network trainability is fractal

Abstract: Some fractals -- for instance those associated with the Mandelbrot and quadratic Julia sets -- are computed by iterating a function, and identifying the boundary between hyperparameters for which the resulting series diverges or remains bounded. Neural network training similarly involves iterating an update function (e.g. repeated steps of gradient descent), can result in convergent or divergent behavior, and can be extremely sensitive to small changes in hyperparameters. Motivated by these similarities, we experimentally examine the boundary between neural network hyperparameters that lead to stable and divergent training. We find that this boundary is fractal over more than ten decades of scale in all tested configurations.

Also, if you want to deep dive on some "half-assed peer review of this work" hacker news chatted with itself about this paper at length.

ah, but booby traps in coding puzzles can be deliberate... one might even say that it can feel "rewarding" when we train ourselves on these "adversarial" examples

the phenomenon of programmers introducing similar bugs in similar situations might be fascinating, but I wouldn't expect a clear answer to the question "Is this true?" without a slightly more precise definitions of:

• "same" bug
• same "bug"
• "hastily" cobbled-together programs
• hastily "cobbled-together" programs ...

It's fascinating (and a little disturbing and kind of unhelpful in understanding) how much steering and context adjustment that's very difficult in older/smaller/weaker LLMs becomes irrelevant in bigger/newer ones.  Here's ChatGPT4:

You

Please just give 100 digits of e * sqrt(3)

ChatGPT

Sure, here you go:

8.2761913499119 7879730592420 6406252514600 7593422317117 2432426801966 6316550192623 9564252000874 9569403709858

there is strong reluctance from employees to reveal that LLMs have boosted productivity and/or automated certain tasks.

The thing with "boosting productivity" is tricky, because productivity is not a linear thing. For example, in software development, using a new library can make adding new features faster (more functionality out of the box), but fixing bugs slower (more complexity involved, especially behind the scenes).

So what I would expect to happen is that there is a month or two with exceptionally few bugs, the team velocity is measured and announced as a new standard, deadlines are adjusted accordingly, then a few bugs happen and now you are under a lot more pressure than before.

Similarly, with LLMs it will be difficult to explain to non-technical management if they happen to be good at some kind of tasks, but worse at a different kind of tasks. Also, losing control... for some reasons that you do not understand, the LLM has a problem with the specific task that was assigned to you, and you are blamed for that.

When I said "AI control is easy", I meant "AI control mitigates most risk arising from human-ish-level schemers directly causing catastrophes"; I wasn't trying to comment more generally. I agree with your concern.

You're here, which tells me you have internet access.

I mentally categorize options like Fiverr and mturk as "about as scammy as DoorDash". I don't think they're a good option, but I also don't think DoorDash is a very good option either. It's probably worth looking into online gig economy options.

What skills were you renting to companies before you became a stay-at-home parent? There are probably online options to rent the same skills to others around the world.

You write fluently in English and it sounds like English is your first language. Have you considered renting your linguistic skills to people with English as a second language? You may be able to find wealthy international people who value your proof-reading skills on their college work, or conversational skills to practice their spoken English with gentle correction as needed. It won't pay competitively with the tech industry, but it'll pay more than nothing.

If you're in excellent health, the classic "super weird side gig" is stool donor programs. https://www.lesswrong.com/posts/i48nw33pW9kuXsFBw/being-a-donor-for-fecal-microbiota-transplants-fmt-do-good [LW · GW] for more.

Another weird one that depends on your age and health and bodily situation, since you've had more than 0 kids of your own, is gestational surrogacy. Maybe not a good fit, but hey, you asked for weird.

For a less weird one, try browsing Craigslist in a more affluent area to see what personal services people offer. House cleaning? Gardening? Dog walking? Browse Craigslist in your area and see which of those niches seem under-populated relative to elsewhere. Then use what you saw in the professionalism of the ads in wealthier areas to offer the missing services. This may get 0 results, but you might discover that there are local rich techies who would quite enjoy outsourcing certain household services for a rate that seems affordable to them but game-changing to you. Basically anything you imagine servants doing for a fairytale princess, someone with money probably wants to hire a person to do for them.

You mention that your kids are in the picture. This suggests a couple options:

1. Have you contacted social services to find out what options are available to support kids whose parents are in situations like yours? You probably qualify for food stamps, and there may be options for insurance, kids' clothing, etc through municipal or school programs. If your kids are in school, asking whatever school district employee you have the best personal rapport with is an excellent starting point.

2. What do childcare prices look like in your area? Do you have friends who are parents and need childcare? Can you rent your time to other parents to provide childcare for their kids at a rate lower than their other options? This may or may not be feasible depending on your living situation.

It is pretty plausible to me that AI control is quite easy

I think it depends on how you're defining an "AI control success". If success is defined as "we have an early transformative system that does not instantly kill us– we are able to get some value out of it", then I agree that this seems relatively easy under the assumptions you articulated.

If success is defined as "we have an early transformative that does not instantly kill us and we have enough time, caution, and organizational adequacy to use that system in ways that get us out of an acute risk period", then this seems much harder.

The classic race dynamic threat model seems relevant here: Suppose Lab A implements good control techniques on GPT-8, and then it's trying very hard to get good alignment techniques out of GPT-8 to align a successor GPT-9. However, Lab B was only ~2 months behind, so Lab A feels like it needs to figure all of this out within 2 months. Lab B– either because it's less cautious or because it feels like it needs to cut corners to catch up– either doesn't want to implement the control techniques or it's fine implementing the control techniques but it plans to be less cautious around when we're ready to scale up to GPT-9. 

I think it's fine to say "the control agenda is valuable even if it doesn't solve the whole problem, and yes other things will be needed to address race dynamics otherwise you will only be able to control GPT-8 for a small window of time before you are forced to scale up prematurely or hope that your competitor doesn't cause a catastrophe." But this has a different vibe than "AI control is quite easy", even if that statement is technically correct.

(Also, please do point out if there's some way in which the control agenda "solves" or circumvents this threat model– apologies if you or Ryan has written/spoken about it somewhere that I missed.)

I live and die by hyaluronic acid. It doesn’t create permanent changes AFAIK but makes a massive difference for me day to day — plus or minus 5 years depending.

"Mathematical descriptions" is a little ambiguous.  Equations and models are terse.  The mapping of such equations to human-level system expectations (anticipated conditional experiences) can require quite a bit of verbosity.  

I think that's what you're saying with the "algorithms and data structures" part, but I'm unsure if you're claiming that the property specification of the math is sufficient as a description, and comparable in fidelity to the algorithmic implementation.