LessWrong 2.0 Reader

I did a bunch of research on this a while ago, here are my high-level conclusions:

• This video is worth watching: Ali Abdaal’s “My evidence-based skincare routine”
• Sunscreen and retinoids get you almost all the way there.
• Sunscreen:
  • Wear it on your face everyday. 
  • Most sunscreen feels horrible and slimy (especially in the US where the FDA has not yet approved the superior formulas available in Europe and Asia). 
  • The best is Elta MD (UV Clear Tinted Face Sunscreen, SPF 46 Tinted Sunscreen with Zinc Oxide). It’s expensive, but it feels fine on your face, and so you’re more likely to actually use it. And since it’s only going on your face you only need a little bit so it lasts a while.
• Retinoids (vitamin A):
  • Retinols are the diluted form of retinoids that you can buy over the counter. They are basically too weak to do much, probably not worth your money.
  • The exception to this is adapalene, a new-ish synthetic retinoid you can buy over the counter such as in the form of Differin gel. Adapalene seems to be effective and less irritating than non-synthetic retinoids, but has mostly been studied for acne rather than anti-aging, so there’s just less evidence at the moment. Definitely a good place to start though.
  • Tretinoin is the form of retinoid that is best known for anti-aging. It’s only available with a prescription, but it’s easy to get one. You can either ask your doctor for a referral for a dermatologist, or you can use an online service like Curology or Dermatica, where you send photos, then they create a custom skincare formula that they send you every month - make sure to say you want it to include tretinoin. I use Dermatica and am very happy with it.
  • You have to ramp up slowly with tretinoin to avoid skin irritation. If you use Curology or Dermatica they will gradually increase your concentration over time so you don’t need to worry about it. The max they go to by default is 0.05%, but I asked to go up to 0.1% when I was ready. 
  • Only use retinoids at night since they don’t play well with sunlight.
  • A bit of irritation at first is normal (in fact, some say if you don’t experience *any* irritation your retinoid is not strong enough). You can manage this by using it only every other day, or layer it with moisturizers.
  • It takes about 6-8 weeks of regular use to start seeing benefits, but I cannot believe how much better my skin is now compared to before I started. 
• Other stuff that may be worth trying (but I am less confident in, and is probably dwarfed by the effects of sunscreen + retinoid):
  • Niacinamide: A form of vitamin B3, benefits include minimizing pores, evening skin tone, and reducing signs of aging.
  • Ceramides: Lipids that occur naturally in your skin and help keep the skin's barrier healthy. Adding more can help restore moisture and prevent irritation.
  • Vitamin C: An antioxidant that can protect the skin from damage, brighten it, and stimulate collagen production. Works best at a lower PH so good to use alongside an acid.
  • Salicylic acid: A chemical exfoliator that unclogs pores, reduces inflammation, and is antimicrobial (avoid physical exfoliators (e.g. with microbeads), they can damage the skin).
  • Hyaluronic acid: Retains moisture, keeps skin plump
• Moisturizers are worth using, but not the fancy ones. 
  • CeraVe Moisturizing Cream is cheap and great, you can use it on your face and body. It contains hyaluronic acid and ceramides, it’s fragrance free, and non-comedogenic, meaning it won’t clog your pores.
  • As far as I can tell there’s no need to use a separate eye cream, it’s a marketing gimmick. A gentle, rich moisturizer like the CeraVe one is perfectly fine. Just wear sunscreen and sunglasses to help reduce eye wrinkles.

Mathematical descriptions are powerful because they can be very terse. You can only specify the properties of a system and still get a well-defined system.

This is in contrast to writing algorithms and data structures where you need to get concrete implementations of the algorithms and data structures to get a full description.

So I agree with you that the new/surprising thing is the linearity of the probe. Also I agree that not entirely clear how surprising & new linearity of the probe is.

If you understand how the causal states construction & the MSP works in computational mechanics the experimental results isn't surprising. Indeed, it can't be any other way! That's exactly the magic of the definition of causal states.

What one person might find surprising or new another thinks trivial. The subtle magic of the right theoretical framework is that it makes the complex simple, surprising phenomena apparent, the trivial trivially trivial. I think before learning about causal states I would have not even considered that there is a unique (!) optimal minimal predictor canonical constructible from the data. Nor that the geometry of synchronizing belief states is generically a fractal. Of course, once one has properly internalized the definitions this is almost immediate. Pretty pictures can be helpful in building that intuition !

Adam and I (and many others) have been preaching the gospel of computational mechanics for a while now. Most of it has fallen on deaf ears before. Like you I have been (positively!) surprised and amused by the sudden outpouring of interest. No doubt it's in part a the testimony to the Power of the Visual! Anyway, never look a gift horse in the mouth ! ^_

I would say the parts of computational mechanics I am really excited are a little deeper - downstream of causal states & the MSP. This is just a taster !

I'm confused & intrigued by your insistence that this is follows from the good regulator theorem. Like Adam I don't understand it. It is my understanding is that the original 'theorem' was wordcelled nonsense but that John has been able to formulate a nontrivial version of the theorem. My experience is that it the theorem is often invoked in a handwavey way that leaves me no less confused than before. No doubt that's my own ignorance !

I would be curious to hear a *precise * statement why the result here follows from the Good Regular Theorem.

The Model-View-Controller architecture is very powerful. It allows us to separate concerns.

For example, if we want to implement an algorithm, we can write down only the data structures and algorithms that are used.

We might want to visualize the steps that the algorithm is performing, but this can be separated from the actual running of the algorithm.

If the algorithm is interactive, then instead of putting the interaction logic in the algorithm, which could be thought of as the rules of the world, we instead implement functionality that directly changes the underlying data that the original algorithm is working on. These could be parameters to the original algorithm, which would modify the runtime behavior (e.g. we could change the maximum search depth for BFS). It could also change the current data the algorithm is working on (e.g. in quicksort we could change the pivot, or smaller_than_list just before they are set). The distinction is somewhat arbitrary. If we were to step through some Python code with a debugger, we could just set any variables in the program.

Usually, people think of something much "narrower" when they think about the Model-View-Controller-Architecture.

We could also do the same for a mathematical description. We can write down some mathematically well-defined thing and then separately think about how we can visualize this thing. And then again, separately, we can think about how would we interact with this thing.

Galaxybrained take: the trouble is AI systems that aim to create powerful systems with low fidelity of control. Therefore, the real danger is MIRI-style stuff where you work on making utility maximizers more broadly applicable. Infrabayesianism would be a good example of a research program that one could argue should be avoided/suppressed for safety reasons. This might also apply to the QACI agenda.

Conflict notice: I'm totally singling out infrabayesianism and QACI because adherents of these agendas have been arguing that others including myself shouldn't be publishing their research. I mean, I believe in the argument I'm making, sort of, but I also think it's undignified, and e.g. logical inductors helped me understand some stuff despite them IMO falling under the problematic umbrella.

It seems underrated for LessWrong to have cached high quality answers to questions like this. Also stuff on exercise, nutrition, parenting and schooling. That we don't really have a clear set seems to point towards this being difficult or us being less competent than we'd like.

I would certainly agree with part of what you are saying. Especially the point that many important lessons are taught by pain [correct me if this is misinterpreting your comment]. Indeed, as a parent for example, if your goal is for your child to gain the capacity for self sufficiency, a certain amount of painful lessons that reflect the inherent properties of the world are necessary to achieve such a goal.

On the other hand, I do not agree with your framing of pain as being the main motivator [again, correct me if required]. In fact, a wide variety of systems in the brain are concerned with calculating and granting rewards. Perhaps pain and pleasure are the two sides of the same coin, and reward maximisation and regret minimisation are identical. In practice however, I think they often lead to different solutions.

I also do not agree with your interpretation that chronic pain does not reduce agency. For family members of mine suffering from arthritis, their chronic pain renders them unable to do many basic activities, for example, access areas for which you would need to climb stairs. I would like to emphasise that it is not the disease which limits their "degrees of freedom" [at least in the short term], and were they to take a large amount of painkillers, they could temporarily climb stairs again. 

Finally, I would suggest that your framing as a "contrast between the current state and the goal state" is basically an alternative way of talking about the transition probability from the current state to the goal state. In my opinion, this suggests that our conceptualisations of pain are overwhelmingly similar.

A few things stood out to me here; namely, a steelman and a strawman.

You make the sweeping claim that "AI can bring - already brings - lots of value, and general improvements to human lives", but don't substantiate that claim at all. (Maybe you think it's obvious, as a daily user. I think there's lots of room to challenge AI utility to human beings.) Much of the "benefits of AI" talk boils down to advertising and hopeful hype from invested industries. I would understand claiming, as an example, that "AI increases the productivity or speed of certain tasks, like writing cover letters". That might be an improvement in human lives, though it depends on things like whether the quality also decreases, and who or what is harmed as part of the cost of doing it. 

But this should be explored and supported; it is not at all obvious. Claiming that there is "lots of value" isn't very persuasive by itself -- especially since you include "improvements to human lives" in your statement. I'd be very curious to know which improvements to human lives AI has brought, and whether they actually stand up against, not just the dangers, but the actual already-existing downsides of AI, as well.

Those are, I feel, the strawmen in this argument. And I apologize if I make any inadvertently heated word-choices here; the tactic you're using is a theme I've been seeing that's getting under my skin: To weigh AI's "possible gains" against its "potential dangers" in a very sci-fi, what-might-happen-if-it-wakes-up way, while failing to weigh its actual, demonstrated harms and downsides as part of the equasion at all. This irks me. I think it particularly irks me when an argument (such as this one) claims all the potential upsides for AI as benefits for humans in one sweeping sentence, but then passes over the real harms to humans that even the fledgling version of AI we have has already caused, or set in motion. 

I understand that the "threat" of sentient supercomputer is sexier to think about -- and it serves as a great humblebrag for the industry, too. They get to say "Yes yes, we understand that the little people are worried our computers are TOO smart, hahaha, yes let's focus on that" -- but it's disingenuous to call the other problems "boring dangers", even though I'm sure there's no interest in discussing them at AI tech conventions. But many of these issues aren't dangers; they're already-present, active problems that function as distinct downsides to allowing AI (with agency or not) unfettered access to our marketplaces. 

Three of many possible examples, open to argument, but worthy of consideration, of already-a-downside "dangers" are: Damage to the environment / wasting tons of resources in an era where we should definitely be improving on efficiency (and you know, feeding the poor and stuff, maybe rather than "giving" Altman 7 trillion dollars). Mass-scale theft from artists and craftspeople that could harm or even destroy entire industries or areas of high human value. (And yes, that's an example of "the tech industry being bad guys" and not inherent to AI as a concept, but it is also how the real AI is built and used by the people actually doing it, and currently no-one seems able to stop them. So it's the same type of problem as having an AI that was designed poorly with regards to safety: Some rich dudes could absolutely decide to release it in spite of technicalities like human welfare. I mention this to point out that the mechanism for greedy corporations to ignore safety and human lives is already active in this space, so maybe "we're stopping all sorts of bad guys already" isn't such an ironclad reason to ignore those dangers. Or any dangers, because um, that's just kind of a terrible argument for anything; sorry.) And a third one, the scrambling of fact and fiction[1] to the point where search-engines are losing utility and people who need to know the difference for critical reasons -- like courts, scientists and teachers -- are struggling to do their work. 

All of which is a bit of a long way to say that I see a steelman and a strawman here, making this argument pretty weak overall. I also see ways you could improve both of those, by looking into the details (if they exist) of your steelman, and broadening your strawmen to include not just theoretical downsides but real ones. 

But you made me think, and elucidate something that's been irritating me for many months; thank you for that!

[1] I object to the term "hallucination"; it's inaccurate and offensive. I don't love "fiction" either, but at least it's accurate. 
 

It works only if it fly above your territory - and similar systems are used for drones detection now. Actually, they use people eyes and smartphones and instant messaging. But during recent attack on Iran a single F35 flied over Iraq and fired a missile from like 200 km distance on a target in Iran. 

You're intuition is correct when the jet has already passed ahead - those are very hard to catch and shoot down. But usually you detect an aircraft when it's heading towards you, and all the missile has to do is intercept. It doesn't even have to be faster than the jet (unless the jet detected it in time and does a 180).