Posts

What was your behavioral response to covid-19 ? 2020-10-08T19:27:07.460Z · score: 7 (6 votes)
The ethics of breeding to kill 2020-09-06T20:12:00.519Z · score: -3 (13 votes)
Longevity interventions when young 2020-07-24T11:25:35.249Z · score: 27 (14 votes)
Divergence causes isolated demands for rigor 2020-07-15T18:59:57.606Z · score: 13 (7 votes)
Science eats its young 2020-07-12T12:32:39.066Z · score: 14 (6 votes)
Causality and its harms 2020-07-04T14:42:56.418Z · score: 16 (9 votes)
Training our humans on the wrong dataset 2020-06-21T17:17:07.267Z · score: 4 (3 votes)
Your abstraction isn't wrong, it's just really bad 2020-05-26T20:14:04.534Z · score: 32 (12 votes)
What is your internet search methodology ? 2020-05-23T20:33:53.668Z · score: 15 (9 votes)
Named Distributions as Artifacts 2020-05-04T08:54:13.616Z · score: 23 (10 votes)
Prolonging life is about the optionality, not about the immortality 2020-05-01T07:41:16.559Z · score: 7 (4 votes)
Should theories have a control group 2020-04-24T14:45:33.302Z · score: 5 (2 votes)
Is ethics a memetic trap ? 2020-04-23T10:49:29.874Z · score: 6 (3 votes)
Truth value as magnitude of predictions 2020-04-05T21:57:01.128Z · score: 3 (1 votes)
When to assume neural networks can solve a problem 2020-03-27T17:52:45.208Z · score: 13 (4 votes)
SARS-CoV-2, 19 times less likely to infect people under 15 2020-03-24T18:10:58.113Z · score: 2 (4 votes)
The questions one needs not address 2020-03-21T19:51:01.764Z · score: 15 (9 votes)
Does donating to EA make sense in light of the mere addition paradox ? 2020-02-19T14:14:51.569Z · score: 6 (3 votes)
How to actually switch to an artificial body – Gradual remapping 2020-02-18T13:19:07.076Z · score: 9 (5 votes)
Why Science is slowing down, Universities and Maslow's hierarchy of needs 2020-02-15T20:39:36.559Z · score: 19 (16 votes)
If Van der Waals was a neural network 2020-01-28T18:38:31.561Z · score: 19 (7 votes)
Neural networks as non-leaky mathematical abstraction 2019-12-19T12:23:17.683Z · score: 17 (7 votes)
George's Shortform 2019-10-25T09:21:21.960Z · score: 3 (1 votes)
Artificial general intelligence is here, and it's useless 2019-10-23T19:01:26.584Z · score: 0 (16 votes)

Comments

Comment by george3d6 on Is Stupidity Expanding? Some Hypotheses. · 2020-10-17T11:19:05.006Z · score: 2 (2 votes) · LW · GW

I guess it depends on what you classify as stupidity, I'd wager the reason is a mix of:

People use intelligence for different things in different eras. Just as language, music, art changes over time, so does thinking. I’m just not keeping up, and assuming because kids these days can’t dance the mental Charleston that they can’t dance at all.

and

What I’m interpreting as rising stupidity has been the collapse in power and status of that clique and the political obsolescence of the variety of “truth” and “rationality” I internalized as a child. Those pomo philosophers were right all along.

The arguments here are many and long, so let me point of a few:

  1. "Intelligence", as was viewed "back in the day", is associated with a corrupt meritocratic ssystem and thus people don't want to signal it. See "The Tyranny of Merit", I believe it explains this point much better, or for a quicker listen the PEL disucssion with the author.
  2. You are not looking for intelligence, you are looking for "signals" of intelligence that have changed. You'r definition of an "intelligent" person probably requires,  at minimum, the ability to do reasonably complex mental calculations, the ability to write in gramatically correct <their native language>, the ability to write (using a pen), and a college degree (or at leas the ability to sit still and learn in a college style education). But all those 4 skills are made redundant and thus potentially harmful for those who still hang on to them instead of, .e.g: Using a computer which include a spellchecker, using a programing language for complex computational problems, learning in short and efficient bursts from varried sources depending on your immediate interests.  An 18th century puritan would think you are somehwat dumb for not knowing a bit of Greek or Latin and having not read at least one version of the bible in both those language.

As well as:

People ordinarily use different modes of thinking in different communications contexts. In some, finding the truth is important and so they use rational intelligence. In others, decorative display, ritual, asserting dominance or submission, displaying tribal allegiances, etc. are more important and so they use modes more appropriate to those things. It’s not that people are getting stupider, but that these non-intelligent forms of communication (a) are more amplified than they used to be, (b) more commonly practiced than they used to be, or (c) are more prominent where I happen to be training my attention.

E.g. you and I might think a famous yogi guru is stupid, but the yogi guru is healthy, well loved, makes loads of money, seems genuinely happy, works relatively little and enjoys his work. So is the yogi guru stupid or not understanding modern science ? No, he's just manifesting his intelligence towards another fascet of the world that requires a different metaphysical grounding and different epistemology to understand.

It is possible that a set of social incentives that promoted "kosher 20th century western intelligence" as a core value made the market for "kosher 20th-century20th century western intelligence" oversaturated, so what you are observing now is just people branching towards other areas of using their intellect.

Comment by george3d6 on The Colliding Exponentials of AI · 2020-10-16T12:36:46.758Z · score: 3 (2 votes) · LW · GW

At the end of the day, the best thing to do is to actually try and apply the advances to real-world problems.

I work on open source stuff that anyone can use, and there's plenty of companies willing to pay 6 figures a year if we can do some custom development to give them a 1-2% boost in performance. So the market is certainly there and waiting.

Even a minimal increase in accuracy can be worth millions or billions to the right people. In some industries (advertising, trading) you can even go at it alone, you don't need customers.

But there's plenty of domain-specific competitions that pay in the dozens or hundreds of thousands for relatively small improvements. Look past Kaggle at things that are domain-specific (e.g. https://unearthed.solutions/) and you'll find plenty.

That way you'll probably get a better understanding of what happens when you take a technique that's good on paper and try to generalize. And I don't mean this as a "you will fail", you might well succede but it will probably make you see how minimal of an improvement "success" actually is and how hard you must work for that improvement. So I think it's a win-win.

The problem with companies like OpenAI (and even more so with "AI experts" on LW/Alignment) is that they don't have a stake by which to measure success or failure. If waxing lyrically and picking the evidence that suits your narrative is your benchmark for how well you are doing, you can make anything from horoscopes to homeopathy sound ground-breaking.

When you measure your ideas about "what works" against the real world that's when the story changes. After all, one shouldn't forget that since OpenAI was created it got its funding via optimizing the "Impress Paul Graham and Elon Musk", rather than via the "Create an algorithm that can do something better than a human than sell it to humans that want that thing done better" strategy... which is an incentive 101 kinda problem and what makes me wary of many of their claims.

Again, not trying to disparage here, I also get my funding via the "Impress Paul Graham" route, I'm just saying that people in AI startups are not the best to listen to in terms of AI progress, none of them are going to say "Actually, it's kinda stagnating". Not because they are dishonest, but because the kind of people that work in and get funding for AI startups genuinely believe that... otherwise they'd be doing something else. However, as has been well pointed about by many here, confirmation bias is often much more insidious and credible than outright lies. Even I fall on the side of "exponential improvement" at the end of the day, but all my incentives are working towards biasing me in that direction, so thinking about it rationally, I'm likely wrong.

Comment by george3d6 on The Colliding Exponentials of AI · 2020-10-15T21:36:44.081Z · score: 6 (2 votes) · LW · GW

Could you clarify, you mean the primary cause of efficiency increase wasn’t algorithmic or architectural developments, but researchers just fine-tuning weight transferred models?

 

Algorithm/Architecture are fundamentally hyperparameters, so when I say "fine-tuning hyperparameters" (i.e. the ones that aren't tuned by the learning process itself), those are included.

Granted, you have jumped from e.g. LSTM to attention, where you can't think of it as "hyperparameter" tuning, since it's basically a shift in mentality in many ways.

But in computer vision, at least to my knowledge, most of the improvements would boil down to tuning optimization methods. E.g here's an analysis of the subject (https://www.fast.ai/2018/07/02/adam-weight-decay/) describing some now-common method, mainly around CV.

However, the problem is that the optimization is happening around the exact same datasets Alexnet was built around. Even if you don't transfer weight, "knowing" a very good solution helps you fine-tune much quicker around a problem ala ImagNet, or cifrar, or mnist or various other datasets that fall into the category of "classifying things which are obviously distinct to humans from square images of roughly 50 to 255px width/height"

But that domain is fairly niche if we were to look at, e.g., almost any time-series prediction datasets... not much progress has been made since the mid 20s. And maybe that's because no more progress can be made, but the problem is that until we know the limits of how "solvable" a problem is, the problem is hard. Once we know how to solve the problem in one way, achieving similar results, but faster, is a question of human ingenuity we've been good at since at least the industrial revolution.

I mean, you could build an Alexnet-specific circuit, not now, but back when it was invented, and get 100x or 1000x performance, but nobody is doing that because our focus is not (or, at least, should not) fall under optimizing very specific problems. Rather, the important thing is finding techniques that can generalize.

**Note: Not a hardware engineer, not sure how easily one can come up with auto diff circuits, might be harder than I'd expect for that specific case, just trying to illustrate the general point**

Are you saying that the evidence for exponential algorithmic efficiency, not just in image processing, is entirely cherry picked? 

Ahm, yes.

https://paperswithcode.com/

if you want a simple overview of how speed and accuracy has evolved on a broader range of problems. And even those problems are cherry picked, in that they are very specific competition/research problems that hundreds of people are working on.

I googled that and there were no results, and I couldn’t find an "academica/internet flamewar library" either.

Some examples:

Paper with good arguments that impressive results achieved by transformer architectures are just test data contamination: https://arxiv.org/pdf/1907.07355.pdf

A simpler article: https://hackingsemantics.xyz/2019/leaderboards/ (which makes the same point as the above paper)

Then there's the problem of how one actually "evaluates" how good an NLP model is.

As in, think of the problem for a second, I ask you:

"How good is this translation from English to French, on a scale from 1 to 10" ?

For anything beyond simple phrases that question is very hard, almost impossible. And even if it iisn'tsnt', i.e. if we can use the aggregate perceptions of many humans to determine "truth" in that regard, you can't capture that in a simple accuracy function that evaluates the model.

Granted, I think my definition of "flamewar" is superfluous, I mean more so passive-aggressive snarky questions with a genuine interest in improving behind them posted on forums ala: https://www.reddit.com/r/LanguageTechnology/comments/bcehbv/why_do_all_the_new_nlp_models_preform_poor_on_the/

More on the idea of how NLP models are overfitting on very poor accuracy functions that won't allow them to progress much further:

https://arxiv.org/pdf/1902.01007.pdf

And a more recent one (202) with similar ideas that proposes solutions: https://www.aclweb.org/anthology/2020.acl-main.408.pdf

If you want to generalize this idea outside of NLP, see, for example, this: https://arxiv.org/pdf/1803.05252.pdf

And if you want anecdotes from another field I'm more familiar with, the whole "field" of neural architecture search (building algorithms to build algorithms), has arguably overfit on specific problems for the last 5 years to the point that all state of the art solutions are:

Basically no better than random and often worst: https://arxiv.org/pdf/1902.08142.pdf

And the results are often unreliable/unreplicable: https://arxiv.org/pdf/1902.07638.pdf

*****

But honestly, probably not the best reference, you know why?

Because I don't bookmark negative findings, and neither does anyone. We laugh at them and then move on with life. The field is 99% "research" that's usually spending months or years optimizing a toy problem and then having a 2 paragraph discussion section about "This should generalize to other problems"... and then nobody bothers to replicate the original study or to work on the "generalize" part. Because where's the money in an ML researcher saying "actually, guys, the field has a lot of limitations and a lot of research directions are artificial, pun not intended, and can't be applied to relevant problems outside of generating on-demand furry porn or some other pointless nonsense".

But as is the case over and over again, when people try to replicate techniques that "work" in papers in slightly different conditions they return to baseline. Probably the prime example of this is a paper that made it into **** nature about how to predict earthquake aftershocks with neural networks and then somebody tried to apply a linear regression to the same data instead and we got this gem

One neuron is more informative than a deep neural network for aftershock pattern forecasting

(In case the pun is not obvious, a one neuron network is  a linear regression)

And while improvements certainly exist, we have observed exponential improvements in the real world. On the whole, we don't have much more "AI powered" technology now than in the 80s.

I'm the first to argue that this is in part because of over-regulation, I've written a lot on that subject and I do agree that it's part of the issue. But part of the issue is that there are not so many things with real-world applications. Because at the end of the day all you are seeing in numbers like the ones above is a generalization on a few niche problems.

Anyway, I should probably stop ranting about this subject on LW, it's head-against-wall banging.

Comment by george3d6 on The Colliding Exponentials of AI · 2020-10-15T08:58:46.317Z · score: 4 (1 votes) · LW · GW

It seems to me like you are miss-interpreting the numbers and/or taking them out of context.

This resulted in a 44x decrease in compute required to reach Alexnet level performance after 7 years, as Figure 1 shows.  

You can achieve infinitely (literally) faster than Alexnet training time if you just take the weight of Alexnet.

You can also achieve much faster performance if you rely on weight transfer and or hyperparameter optimization based on looking at the behavior of an already trained Alexnet. Or, mind you, some other image-classification model based on that.

Once a given task is "solved" it become trivial to compute models that can train on said task exponentially faster, since you're already working down from a solution.

On the other hand, improvements on ImageNet (the datasets alexnet excelled on at the time) itself are logarithmic rather than exponential and at this point seem to have reached a cap at around human level ability or a bit less (maybe people got bored of it?)

To get back to my point, however, the problem with solved tasks is that whatever speed improvements you have on them don't generalized, since the solution is only obvious in hindsight.

***

Other developments that help with training time (e.g. the kind of schedulers fastAI is doing) are, interesting, but not applicable for "hard" problems where one has to squeeze a lot of accuracy and not widely used in RL (why, I don't know)

However, if you want to look for exp improvement you can always find it and if you want to look for log improvement you always will.

The OpenAI paper is disingenuous in not mentioning this, or at least disingenuous in marketing itself to a public that doesn't understand this.

***

In regards to training text models "x time faster",  go into the "how do we actually benchmark text models" section the academica/internet flamewar library. In that case my bet is usually on someone hyperoptimizing for a narrow error function (not that there's an alternative). But also, above reasoning about solved >> easier than unsolved still applies.

Comment by george3d6 on What was your behavioral response to covid-19 ? · 2020-10-14T13:32:50.564Z · score: 2 (2 votes) · LW · GW

However, I'm still being really cautious because of the not-well-understood long-term effects. SARS was really nasty on that front. What evidence convinced you that's not a big deal? If you don't already have evidence for that, then rationality isn't the reason you changed your behavior.

Not sure this is directed at me or just a question for poetic reasons, but I'm going to answer it anyway:

  1. The "bradykinin hypothesis" is the only one that has a reasonable model of long term damage, basically attributing it to ACE2 expression in tissues where it would be normally close-to-absent and bradykinin overproduction being triggered in part by that an synergizing badly with it.
  2. This is "hopeful" in that it predicts side effects are non-random and instead associated with a poor immune response. That is to say, youth's protective role against death also protects against side effects.
  3. I found no quantifiable studies of side effects after the infection, the ones that exist are case studies and/or very small n and in older demographics (i.e. the kind that needs to attend the hospital in the first place and is then monitored long term after the infection passed)
  4. Absence of evidence is not evidence of absence and a model of infection is just a useful tool not a predictor of reality, plus my understanding of it is likely simplistic. But that same statement I could make about a lot of coronavrisues and influenza viruses I expose myself to every year.
Comment by George3d6 on [deleted post] 2020-10-12T20:47:05.278Z

Point, I'm not sure the analogy is correct here. Too many mistakes, moving this to draft, probably not worth debating in favor of.

Comment by george3d6 on The Treacherous Path to Rationality · 2020-10-11T23:14:14.389Z · score: 12 (3 votes) · LW · GW

I'd stress the idea here that finding a "solution" to the pandemic is easy and preventing it early on based on evidence also is.

Most people could implement a solution better than those currently affecting the US and Europe, if they were a global tsar with infinite power.

But solving the coordination problems involved in implementing that solution is hard, that's the part that nerds solving and nobody is closer to a solution there.

Comment by george3d6 on Against Victimhood · 2020-09-20T10:53:51.196Z · score: 4 (3 votes) · LW · GW

I agree that victim mentality is useless, but reminding oneself that you were a victim of certain things isn't.

Outside of, maybe, a pure objectivist, reminding yourself that a certain system or group is against you can serve as a good driver of rational actions, i.e. you can use it to tone down your empathy and act in a more self-interested way towards that group.

Of course, the key word here is "self-interest", the problems you rightfully point out with victim mentality is that people often act upon it in ways that aren't self-interested, where they go into depressive or aggressive spirals that are of not help to themselves and at most (though seldom) just serve to hurt their victimizer, though often at greater personal cost.

Comment by george3d6 on The ethics of breeding to kill · 2020-09-11T09:09:24.360Z · score: 0 (2 votes) · LW · GW

You bring up good points, I don't have time to answer in full, but notes on a few of them to which I can properly retort:

I don't think I agree that suicide is a sufficient proxy for whether an entity enjoys life more than it dislikes life because I can imagine too many plausible, yet currently unknown mechanisms wherein there are mitigating factors. For example:
I imagine that there are mental processes and instincts in most evolved entities that adds a significant extra prohibition against making the active choice to end their own life and thus that mental ability has a much smaller role in suicide "decisions".
In the world where there is no built-in prohibition against ending your own life, if the "enjoys life" indicator is at level 10 and the "hates life" indicator is at level 11, then suicide is on the table.
In, what I think is probably our world, when the "enjoys life" indicator is at level 10 the "hates life" indicator has to be at level 50.
What's more, it seems plausible to me that the value of this own-life-valuing indicator addon varies from species to species and individual to individual.

But, if we applied this model, what would make it unique to suicide and not to any other preference ?

And if you apply this model to any other preference and extent it to humans, things get really dystopian really fast.

This seems to me similar to the arguments made akin to "why waste money on space telescopes (or whatever) when people are going hungry right here on earth?".

This is not really analogous, in that my example is "potential to reduce suffering" vs "obviously reducing suffering". A telescope is neither of those, it's working towards what I'd argue is more of a transcedent goal.

It's more like arguing "Let's give homeless people a place to sleep now, rather than focusing on market policies that have potential for reducing housing costs later down the line" (which I still think is a good counter-example).

In summary, I think the main critique I have of the line of argument presented in this post is that it hangs on suicide being a proxy for life-worth-living and that it's equivalent to not having existed in the first place.
I don't think you've made a strong enough case that suicide is a sufficient measure of suffering-has-exceeded-the-cost-of-continuing-to-live. There are too many potential and plausible confounding factors. I think that the case needs to be really strong to outweigh the costs of being wrong.

I don't think what I was trying is to make a definitive case for "suicide is a sufficient measure of suffering-has-exceeded-the-cost-of-continuing-to-live" I was making a case for something close to "suicide is better than any other measure of suffering-has-exceeded-the-cost-of-continuing-to-live if we want to keep living in a society where we treat humans as free conscious agents and give them rights based on that assumption, and while it is still imperfect, any other arbitrary measure will also be so, but worst" (which is still a case I don't make perfectly, but at least one I could argue I'm creeping towards).

My base assumption here is that in a society of animal-killers, the ball is in the court of the animal-antinatalists to come up with a sufficient argument to justify the (human-pleasure-reducing) change. But it seems to me like the intuitions based on which we breed&kill animals are almost never spelled out, so I tried to give words to what I hoped might be a common intuition as to why we are fine with breeding&killing animals but not humans.

Here you're seemingly willing to acknowledge that it's at least *possible* that animals dislike life more than they enjoy it. If I read you correctly and that is what you're acknowledging, then you would really need to compare the cost of that possibility being correct vs the cost of not eating meat before making any conclusion about the ethical state of eating animals.

I am also willing to acknowledge that it is at least *possible* some humans might benefit from actions that they don't consent to, but still I don't engage in those actions because I think it's preferable to treat them as agentic beings that can make their own choices about what makes them happy.

If I give that same "agentic being" treatment to animals, then the suicide argument kind-of-hold. If I don't give that same "agentic being" treatment to animals, then what is to say suffering as a concept even applies to them ? After all a mycelia or an ecosystem is also a very complex "reasoning" machine but I don't feel any moral guilt when plucking a leaf or a mushroom.

Comment by george3d6 on The ethics of breeding to kill · 2020-09-08T12:31:19.442Z · score: 1 (1 votes) · LW · GW
I’m taking it as granted that every human not in a coma can suffer, which I hope is uncontroversial.

I don't think it's that uncontroversial

https://en.wikipedia.org/wiki/Abortion#Gestational_age_and_method

Similarly, in England and Wales in 2006, 89% of terminations occurred at or under 12 weeks, 9% between 13 and 19 weeks, and 2% at or over 20 weeks.

CNS starts developing at ~4 weeks, but the cerebral hemispheres start differentiating around week 8. Given 200,000 abortions a year in the UK alone, which the people doing and most (all?) of us don't see as an immoral act, that's at least 12,000 human children with a functioning brain killed a year in the UK, a number that is probably 10x in the US and hundreds of times higher if you account for all the world.

When you reach 20 weeks, where abortions still happens, well, one could argue the brain could be more developed than that of living human being, unless you want to assume it's not a question of synaptic activity, nr of neurons & axons but instead of divine transubstantiation ( in which case the whole debate is moot).

So I would indeed say many humans agree that suffering is not a universal experience for every single being that shares our genetic code and exception such as human still in a mother's womb are made. Whether that is true or not is another question entirely.

Many of us might claim this is not the case, but as I made it clear in this article, I'm a fan of looking at our actions rather than the moral stances we echo from soapboxes.

Comment by george3d6 on The ethics of breeding to kill · 2020-09-08T12:18:30.714Z · score: 1 (1 votes) · LW · GW
I'd expect the same to apply to typically developing toddlers

Very quick search reveals suicide as young as 6:

https://ewn.co.za/2019/10/10/youngest-person-to-ever-commit-suicide-in-sa-was-a-six-year-old-sadag

Murder as young as 4:

https://en.wikipedia.org/wiki/List_of_youngest_killers

Presumably cloud happen earlier in kids with a better developmental environment, but suicide and murder at an age this young is going to come from outliers that lived in a hellish developmental environment.

Not sure about ages < 1 or 2 years of age, but:

1. We think that beyond a certain point of brain development abortion is acceptable since the kid is not in any way "human". So why not start you argument there ? and if you do, well, you reach a very tricky gray line

2. Surgeons did use to think toddlers couldn't feel "pain" the way we do and operate on them without anesthesia. This was stopped due to concerns/proof of PTSD, not due to anyone remembering the exact experience, after all there's a lot of traumatic pain one goes through before the age of 1 that none will remember. Conscious experience might be present at that age but... this is really arguable. People don't have memories at ages bellow 1 or 2 and certainly no memories indicative of conscious experience. It might exist, but I think this falls in the same realms as "monkeys" rather than fully fledged humans in terms of certainty.

and it's plausible to me that you could in principle shelter normally developing humans from understanding of death and suicide into adulthood, and torture them, and they too would not attempt suicide.

This I find, harder to believe, but it could be a good thought experiment to counter my intuition if I ever have the time to mold it into a form that fits my own conception of the world and of people.

We (humans and other animals) also have instincts (especially fear) that deter us from committing suicide or harming ourselves regardless of our quality of life, and nonhuman animals rely on instinct more, so I'd expect suicide rates to underestimate the prevalence of bad lives.

I don't see how this undermines the point, unless you want to argue the "fear" of death can be so powerful one can lead what is essentially a negative value life because an instinct to not die (similarly to, say, how one would be able to feel pain from a certain muscle twitch yet be unable to stop in until it becomes unbearable).

I don't necessarily disagree with this perspective, but from this angle you reach a antinatalist utilitarian view of "Kill every single form of potentially conscious life in a painless way as quickly as possible, and most humans for good measure, and either have a planet with no life, or with very few forms of conscious life that have nothing to cause them harm". No matter how valid this perspective is, almost by definition it will never make it into the zeitgeist and it's fairly pointless to think about since it's impossible to act upon and the moral downside of being wrong would be gigantic.

Comment by george3d6 on The ethics of breeding to kill · 2020-09-07T05:54:48.375Z · score: 1 (3 votes) · LW · GW

The problem with that research is that it's shabby, I encountered this problem when dealing with the research on animal suicide and the one on animal emotions expands that trend.

Fundamentally, it's a problem that can't be studied unless you are able to metaphorically see as a bat, which you can't, so I chose to think the closest thing we can do is treat it much like we do with other humans, assume their mental state based on their actions and act accordingly.

Comment by george3d6 on The ethics of breeding to kill · 2020-09-07T05:52:09.653Z · score: 1 (1 votes) · LW · GW
The first point seems fallacious, since most factory farmed animals don't have the physical ability to commit suicide.

Does the argument require for that to be the case ? In the ideal scenario yes, but in the pragamatic scenario one can just look for such behavior in conditions where it can be expressed. Since, much like humans vary enough that some "suffer" under the best of conditions enough to commit suicide, presumably so would animals.

There are many humans who don't have the ability to reason about suicide but undoubtedly suffer

Wait, what ? Ahm, can I ask for source on that ?

Comment by george3d6 on On Systems - Living a life of zero willpower · 2020-08-17T21:44:26.946Z · score: 2 (2 votes) · LW · GW

The main issue with these kind of routines, in my experience, is that they are very rigid and breaking them is hard.

A lot of things (hard and difficult things that make life worth living) involve breaking routines, be it starting a company/ngo, having kids, doing ground-breaking research or even just traveling (including e.g. difficult hikes to remote places or visiting weird cities, towns and villages half a world away).

So to some extent these kind of routines work if you want to get to an "ok" place and have an overall stable life outside of e.g. health issues, but seem to put you in a bad spot if you want to do anything else.

Of course, not everything here is routine-focused advice, but a lot of it seems to be, so I just wanted to give this perspective on that particular topic.

Comment by george3d6 on Longevity interventions when young · 2020-07-26T19:32:58.299Z · score: 1 (1 votes) · LW · GW

No... and searching for it I can only find things like: https://selfhacked.com/blog/nicotinamide-riboside/

Which are referring to other forms of B3 being found in whey protein.

The things with NR is that it's considered a form of B3 (which is the **** way "vitamins" work in that for some of them the "vitamin" is actually any substance that after some point turns by some % into a specific metabolite) and some other forms of B3 are found in whey protein.

I haven't seen claims of NR specifically being found in whey protein, so I have no idea and a quick google doesn't reveal much for me other than stuff like the above.

Comment by george3d6 on Longevity interventions when young · 2020-07-26T07:54:39.097Z · score: 1 (1 votes) · LW · GW
What do you mean by the advice "test your drugs"?

A joke

Which blood biomarkers do you measure for assessing the effectiveness of the supplements?

Would be an article on it's own, ask your doctor, see my response above about vitamin D3 for an example.

You can just look at the studies done on the supplements and measure what they measure. If experts say: "This supplement is good because it increases/decreases X,Y,Z as per studies done on it", if you take it and your X/Y/Z decrease/increase it's also good for you.

What's your intuition on the expected life added by researching this stuff personally and in-depth?

No idea, long discussion.

With a protocol like this I'm hopeful one could get 20, maybe 30% added years in the 20-35 "pocket" where you're "at your prime", but I'm pulling those out of my arse.

Comment by george3d6 on Longevity interventions when young · 2020-07-26T07:52:05.229Z · score: 1 (1 votes) · LW · GW

I should have specified different IQ tests meant to give similar results, of you can't take the same one twice.

Long-term I would expect that you can mine existing data like Anki for a measurement of cognitive ability. 

Personally that's what I'm doing, simpler cognitive tests + other metrics such as my WPM while doing various things and getting the data from that. But that's simply because I have a silly point of pride for never getting an IQ test, and I thought the IQ test is the safer thing to recommend for "long term" measurement.

Short term, obviously, things like short cognitive tests work best.

Comment by george3d6 on Longevity interventions when young · 2020-07-26T07:49:56.095Z · score: 1 (1 votes) · LW · GW

Please see my whole point there, I'm giving these as an example, you should figure out your own dosages. Hence why I'm not specifying a given amount. If you don't know how much of a supplement to dose and have no marker you want to improve that you can look at (+no idea of potential side effects) I'd go towards taking 0IU.

With D3 I'd monitor energy/focus levels 1h after taking it in the morning (e.g. via a click speed test or seeing how many pushups you can do or 2-3 of these) + 25(oH)D + calcium + inonized calcium (+PTH and kidney function if calcium is super low/high or you are feeling icky... for kidney function and what to test the story is rather long).

But INAD and that's what I'd look at for my own self and maybe you have a whole different reason to take it.

If you can't monitor stuff, just don't take them, I'm serious, here's a simple video that maybe will get the point across better. Supplements are high-risk low-reward and will be all but useless if you have a healthy diet, if anything a healthy diet nowadays risks going way over the RDA for most micronutrients, not under.

Comment by george3d6 on Longevity interventions when young · 2020-07-25T07:15:07.223Z · score: 1 (1 votes) · LW · GW

I want to look into the FMD at some point but I haven't until now,tbh I'm kind of doubtful about it but I don't want to speak until have time to review it.

So,ahm,no idea.

Comment by george3d6 on Self-sacrifice is a scarce resource · 2020-07-21T09:47:01.754Z · score: 4 (1 votes) · LW · GW

I think there is one important negative of of self-sacrifice that you are missing here, or at least of self-sacrifice that is apparent to anyone but yourself.

Even though it's a cliche quote, Zarathustra puts it best:

What should I have to give thee! Let me rather hurry hence lest I take aught away from thee!

It is extremely hard to criticize the choices of someone that seems to be sacrificing a lot, or at least who seems to have that impression of themselves and whom others have that impression of. For you are afraid of disturbing whatever "holiness" lead the them there, and even if not, you are afraid of other people seeing it that way and thus shunning you for the criticism given.

Comment by george3d6 on Divergence causes isolated demands for rigor · 2020-07-15T21:42:05.785Z · score: 1 (1 votes) · LW · GW
Related to https://wiki.lesswrong.com/wiki/Arguments_as_soldiers - these are mostly examples of non-truth-seeking discussions, looking for advantage or status, rather than reframing the questions into verifiable propositions. See also https://wiki.lesswrong.com/wiki/Politics_is_the_Mind-Killer - these topics mostly can't be usefully resolved without a pretty serious commitment by all participants.

In hindsight I think I'm repeating a lot of the points made here, but maybe with more of an emphasis on how "not" to discredit a bad idea rather than on ideas competing on "equal" grounds.

Alternative medicine proponents (as far as I've seen) nearly universally make amazingly strong claims that their methods should be embraced with near-zero theoretical nor statistical backup. If they just said "the standard model misses a lot of individual variance, and this thing has the following risk/benefit likelihoods", I'd listen more.

Yes, but generally speaking I think these kind of people are selected exactly because the "the standard model misses a lot of individual variance, and this thing has the following risk/benefit likelihoods" kind of people are treated with equally inadequate standards.

To take one example here, on the "gluten" debate one can detect 3 camps:

1. Standard position (should only be cut in the case of celiac disease)

2. Nuanced alternative (celiac disease is not clearly defined, we should look at various antibodiy levels after a gluten challenge + various HLA genes and recommend a gluten-free diet if we notice values 0.5std above the mean... or something like that)

3. Gluten is bad and literally the source of the primordial decline of man, nobody should eat it.


Arguably, position 2 is probably too extreme and there's still lacking evidence for it, but given that a significant amount of the population seems to do better without gluten, you either decide to cut position to some epistemtic slack and merge it into the mainstream (or at least propose it as an option, much like a orthopedist might suggest yoga as an alternative to standard physiotherapy), or you get people flocking to 3. Since 2 and 3 are seen as equally bad, and 3 is simpler plus is packed with an explanation as to why the establishment rejects it (the establishment are blind and/or evil)


Finally, this often comes up on topics where one or more participants isn't motivated to seek the truth. If you're arguing for entertainment, rather than putting work into understanding things, all bets are off. And if you're trying to explore the truth, but your partners are just enjoying the attention, you're likely to find yourself frustrated. Probably best to find new partners for that part of your investigation.

Easy to say, but hard to detect. It's easy to detect in e.g. politics, but maybe not so much in a more rigorous subject where the isolated demands for rigor being thrown against the divergent position might be very similar to those "common knowledge" is held up to.

Comment by george3d6 on Science eats its young · 2020-07-15T18:55:51.586Z · score: 1 (1 votes) · LW · GW

I meant to say the same speed, but yes, point taken.

Comment by george3d6 on Science eats its young · 2020-07-12T17:14:25.479Z · score: 1 (1 votes) · LW · GW

I kinda of agree with this approach, I actually propose it (thought a different program related to biology) in my previous article I posted here.

The reason I haven't gotten into describing it that much is because it's not like this is an area where I have a lot of power to influence stuff, my only goal here is to figure out why the failure modes happen to better avoid them myself.

Comment by george3d6 on Science eats its young · 2020-07-12T14:19:12.322Z · score: 3 (2 votes) · LW · GW

I don't think I ever said "running experiments", I said looking at data was relevant (i.e. the data other people had collected about the movement of the planets, Earth's moon and objects here on earth)

If I implied otherwise my bad, please point it out, I will correct it.

Comment by george3d6 on Causality and its harms · 2020-07-09T22:50:15.184Z · score: 1 (1 votes) · LW · GW

I believe the thing we differ on might just be a semantic, at least as far as redefinition goes. My final conclusion is around the fact that the term is bad because it's ill-defined, but with a stronger definitions (or ideally multiple definitions for different cases) it would be useful, it would also, however, be very foreign to a lot of people.

Comment by george3d6 on Causality and its harms · 2020-07-06T18:38:15.256Z · score: 1 (1 votes) · LW · GW

Corrected the wording to be a bit "weaker" on that claim, but also, it's just a starting point and the final definition I dispute against doesn't rest on it.

Comment by george3d6 on How far is AGI? · 2020-07-06T04:26:30.857Z · score: -2 (2 votes) · LW · GW

1. The problem with theories along the vein of AIXI is that they assume exploration is simple (as it is, in RL), but exploration is very expensive IRL

So if you want to think based on that framework, well, then AGI is as far away as it takes to build a robust simulation of the world in which we want it to operate (very far away)

2. In the world of mortals, I would say AGI is basically already here, but it's not obvious because it's impact is not that great.

We have ML-based systems that could in theory do almost any job, the real problem lies in the fact that they are much more expensive than humans to "get right" and in some cases (e.g. self driving) there are regulatory hurdles to cross.

The main problem with a physical human-like platform running an AGI is not that designing the algorithms for it to perform useful tasks is hard, the problem is that designing a human like platform is impossible with current technology and the closest alternatives we've got are still more expensive to build and maintain than just hiring a human.

Hence why companies are buying checkout machines to replace employees rather than buying checkout robots.

3. If you're referring to "superintelligence" style AGI, i.e. something that is much more intelligent than a human, I'd argue we can't tell how far away this is or if it can even exists (i.e. I think it's non obvious that the bottleneck at the moment is intelligence and not physical limitations, see 1 + corrupt incentives structures, aka why smart humans are still not always used to their full potential).

Comment by george3d6 on Have general decomposers been formalized? · 2020-07-02T00:25:51.768Z · score: 3 (2 votes) · LW · GW

I was asking why because I wanted to understand what you mean by "decomposition".

a system is a decomposer if it can take a thing and break it down into sub-things with a specific vision about how the sub-things recombin

Defines many things.

Usually the goal is feature extraction (think Bert) or reducing the size of a representation (think autoencoders or simpler , PCA)

You need to narrow down your definition, I think, to get a meaningful answers.

Comment by george3d6 on Have general decomposers been formalized? · 2020-06-27T21:26:12.152Z · score: 1 (1 votes) · LW · GW

Why is the literature into reversible encoders/autoencoders/embedding generators not relevant for your specific usecase ?

Give an answer to that it might be easier to recommend stuff.

Comment by george3d6 on Do Women Like Assholes? · 2020-06-23T00:32:06.842Z · score: 8 (6 votes) · LW · GW

I don't want to get into the whole CW thing around this topic, *but*:

1. Since you so off handedly decided not to use p values, why do you:

a) Use linear models for the analysis provided such low r2 scores

b) Why use r2 at all ? Does it seem meaningful for this case, otherwise, if your whole shtik is being intuitive why not use mae or even some pct based error ?

c) Are you overfitting those regression models instead of doing cross validation ?

d) If the answer to c is no, then: provide nr of folds and variation of the coefficients given the folds, this is an amazing messure to determine a confidence value regarding the coeficient associated not being spurious (i.e of the variation is 0.001-0.1 then that means said coeficient is just overfitting on noise).

f) If the answer is no, why ? I mean, cross validation is basically required for this kind of analysis, if you're just overfitting your whole dataset that basically makes the rest of your analysis invalid, you're just finding noise that can be approximated using a linear function summation.

Also, provided the small effect sizes you found, why consider the data relevant at all ?

If anything this analysis shows that all the metrics you care about depends mostly on some hidden variable neither you nor the pseudoscientists you are responding to have found.

Maybe missing something here though, it's 3:30am here, so do let me know if I'm being uncharitable here or underspecfying some of my questions/contention-points.

Comment by george3d6 on Training our humans on the wrong dataset · 2020-06-22T11:28:42.931Z · score: 1 (1 votes) · LW · GW

The more specific case I was hinting at was figuring out the loss <--> gradient landscape relationship.

Which yes, a highschooler can do for a 5 cell network, but for any real network it seems like it's fairly hard to say anything about it... I.e. I've read a few paper delving into the subject and they seem complex to me.

Maybe not PhD level ? I don't know. But hard enough that most people usually choose to stick with a loss that makes sense for the task rather than optimize it such that the resulting gradient is "easy to solve" (aka yields faster training and/or converges on a "more" optimal solution).

But I'm not 100% sure I'm correct here and maybe learning the correct 5 primitives makes the whole thing seem like childplay... though based on people's behavior around the subject I kinda doubt it.

Comment by george3d6 on Training our humans on the wrong dataset · 2020-06-21T22:22:14.679Z · score: 3 (2 votes) · LW · GW

TL;DR Please provide references in order for me to give a more cohesive reply, see papers bellow + my reasoning & explanation as to why you are basically wrong and/or confusing things that work in RL with things that work in SL and/or confusing techniques being used to train with scarce data for ones that would work even when the data is large enough that compute is a bottleneck (which is the case I'm arguing for, i.e. that compute should first be thrown at the most relevant data)

Maybe you do this, but me, and many people in ML, do our best to avoid ever doing that. Transfer learning powers the best and highest-performing models. Even in pure supervised learning, you train on the largest dataset possible, and then finetune. And that works much better than training on just the target task. You cannot throw a stick in ML today without observing this basic paradigm.

I would ask for a citation on that.

Never in any ML literature have I ever heard of people training models on datasets other than those they wanted to solve as a more efficient alternative to training on the dataset itself. Of course, provided more time once you converge on your data training on related data can be helpful, but my point is just that training on the actual data is the first approach one takes (obviously, depending on the size of the problem you might start with weight transfer directly)

People transfer weights all the time, but that's because it shortens training time.

New examples of unrelated data (or less-related data) does not make a model converge faster on validation data assuming you could instead create a new example of problem-specific data.

In theory it could make the model generalize better, but when I say "in theory" I mean in layman's terms since doing research on this topic is hard and there's scarce little in supervised learning.

Most rigorous research on this topic seems to be in RL, e.g.: https://arxiv.org/pdf/1909.01331.pdf and it's nowhere near clear cut.

Out of the research that seems to apply better to SL I find this theory/paper to be most rigorous and up to date: https://openreview.net/pdf?id=ryfMLoCqtQ ... and the findings here as in literally any other paper by a respected team or university you will find on the subject can be boiled down to:

"Sometime it helps with generalization on the kind of data not present in the training set and sometime it just results in a shittier models and it depends a lot on the SNR of the data the model was trained on relative to the data you are training for now"

There are GAN papers, among others, which do pretty much this for inferring models & depth maps.

Again, links to papers please. My bet is that the GAN papers do this:

a) Because they lack 3d rendering of the objects they want to create.

b) Because they lack 3d renderings of most of the objects they want to create.

c) Because they are trying to showcase an approach that generalizes to different classes of data that aren't available at training time (I.e. showing that a car 3d rendering model can generalize to do 3d renderings of glasses, not that it can perform better than one that's been specifically trained to generate 3d renderings of glasses).

If one can achieve better results with unrelated data than with related data in similar compute time (i.e. up until either of the models has converged on a validation dataset/runs or in a predefined period of time), or even if one can achieve better results by training on unrelated data *first* and then on related data rather than vice versa... I will eat my metaphorical hat and retract this whole article. (Provided both models use appropriate regularization or that at least the relevant-data model uses it, otherwise I can see a hypothetical where a bit of high-noise data can serve as a form of regularization, but even this I would think to be highly unlikely)

No. You don't do it 'just' to save computation. You do it because it learns superior representations and generalizes better on less data. That finetuning is a lot cheaper is merely convenient.

Again see my answers above and please provide relevant citations if you wish to claim the contrary, it seems to me that what you are saying here goes both against common sense. i.e. given a choice between problem-specific data and less-related data your claim is that at some point using less-related data is superior.

A charitable reading of this is that introducing noise in the training data helps generalize (see e.g. techniques involving introducing noise in the training data, l2 regularization and dropout), which seems kind of true but far from true on that many tasks and I invite you to experiment with it an realize it actually doesn't really apply to everything nor are the effect sizes large unless you are specifically focusing on adversarial examples or datasets where the train set covers only a minute portion of potential data.

Comment by george3d6 on Should we stop using the term 'Rationalist'? · 2020-06-01T00:34:19.731Z · score: 1 (1 votes) · LW · GW

To address 2) specifically, I would say that philosophical "Rationalists" are a wider group but they would generally include the kind of philosophical views that most people on e.g. LW hold, or at least they include a pathway to reaching those view.


See the philsophers listed in the wikipedia article for example:


Pythagoras -- foundation for mathematical inquiry into the world and mathematical formalism creating in general

Plato -- foundation for "modern" reasoning and logic in general, with a lot of ***s

Aristotle -- (outdated) foundation for observing the world and creating theories and taxonomies. The fact that he's mostly "wrong" about everything and the "wrongness" is obvious also gets you 1/2 of the way to understand Kuhn

René Descartes -- "questioning" more fundamental assumptions that e.g. Socrates would have had problems seeing as assumptions. Also foundational for modern mathematics.

Baruch Spinoza -- I don't feel like I can summarize why reading "Spinoza" leads one to the LW-brand of rationalism. I think it boils down to this obsession with internal consistency and his obsession to burn any bridge for the sake of reaching a "correct" conclusion.

Gottfried Leibniz -- I mean, personally, I hate this guys. But it seems to me that the interpretations of physics that I've seen around here, and also those that important people in the community (e.g. Eliezer and Scott) use are heavily influenced by this work. Also arguably one of the earliest people to build computers and think about them so there's that.

Immanuel Kant -- Arguably introduced the Game Theoretical view to the world. Also helped correcting/disproving a lot of biased reasoning in philosophy that leads to e.g. arguments for the existence of good based on linguistic quirks.


I think, at least in regards to philosophy until Kant, if one were to read philosophy following this exact chain of philosopher, they would basically have a very strong base from which to approach/develop rationalist thought as seemingly espoused by LW.

So in that sense, the term "Rationalist" seems well fitting if wanting to describe "The general philosophical direction" most people here are coming from.

Comment by george3d6 on Obsidian: A Mind Mapping Markdown Editor · 2020-05-27T17:11:13.269Z · score: 1 (1 votes) · LW · GW

But is there some functionality that this would provide that a wiki doesn't ? (or some nice interface for that functionality that a wiki doesn't).

Or is just the simplicity of installation and/or the simplicity of the data format ?

Comment by george3d6 on Obsidian: A Mind Mapping Markdown Editor · 2020-05-27T16:41:52.959Z · score: 1 (1 votes) · LW · GW

Do you think this is better than having e.g. a personal wiki ?

Comment by george3d6 on Your abstraction isn't wrong, it's just really bad · 2020-05-27T12:18:28.770Z · score: 3 (2 votes) · LW · GW

I mean, I basically agree with this criticism.

However, my problem isn't that in the literal sense new theories don't exist, my issue is that old theories are so calcified that one can't really do without knowing them.

E.g. if I as a programmer said "Fuck this C nonsense, it's useless in the modern world, maybe some hermits in an Intel lab need to know it, but I can do just fine by using PHP" then they can become Mark Zuckerberg. I don't mean that in the "become rich as *** sense" but in the "become the technical lead of a team developing one of the most complex software products in the world" sense.

Or, if someone doesn't say "fuck C" but says "C seems to complex, I'm going to start with something else" then they can do that and after 5 years of coding in high level languages they have acquired a set of skills that allowed them to dig back down and learn C very quickly.

And you can replace C with any "old" abstraction that people still consider to be useful and PHP with any new abstraction that makes things easier but is arguably more limited in various key areas (Also, I wouldn't even claim PHP is easier than C, PHP is a horrible mess and C is beautiful by comparison, but I think the general consensus is against me here, so I'm giving it as an example).

In mathematics this does not seem to be an option, there's no 2nd year psychology major that decided to take a very simple mathematical abstraction to it's limits and became the technical leader of one of the most elite teams of mathematicians in the world. Even the mere idea of that happening seems silly.

I don't know why that is, maybe it's because, again, math is just harder and there's not 3-month crash course that will basically give you mastery of a huge area of mathematics the same way a 3-month crash course in PHP will give you the tools needed to build proto-facebook (or any other piece of software that defines a communication and information interpretation & rendering protocol between multiple computers).

Mathematics doesn't have useful abstractions that allow the user to be blind to the lower level abstractions, nonstandard analysis exists but good luck trying to learn it if you don't know a more kosher version of analysis already, you can't start at nonstandard analysis... or maybe you can ? But then that means this is a very under-exploited idea and it gets back to the point I was making.

I'm using programming as the bar here since it seems that, from the 40s onward, the requirements to be a good programmer has been severely lowered due to the new abstraction we introduce. In the 40s you had to be a genius to even understand the idea of computer. In modern times you can be a kinda smart but otherwise unimpressive person and create revolutionary software or write an amazing language of library. Somehow, even though the field got more complex, the entry cost went from 20+ years including the study of mathematics, electrical engineering and formal logic to a 3-month bootcamp or like... reading 3 books online. In mathematics it seems that the entry cost gets higher as time progresses and any attempts to lower that are just tiny corrections or simplifications of existing theory.

And lastly, I don't know if there's a process "harming" math's complexity that could easily be stopped, but there are obvious processes harming programming's complexity that seems, at least in principle, stopable. E.g. if you look at things like coroutines vs threads vs processes, which get thought as separate abstractions, yet are basically the same **** thing if you move to all but a few kernels that have some niche ideas about asyncio and memory sharing.

That is to say, I can see a language that says "Screw coroutines vs threads vs processes nonsense, we'll try to auto-detect the best abstraction that the kernel+CPU combination you have supports for this, maybe with some input from the user, and go from there" (I think, at least in part, Go has tried this, but in a very bad fashion, and at least in principle you could write a JVM + JVM language that does this, but the current JVM languages and implementations wouldn't allow for this).

But if that language never comes, and every single programmers learn to think in terms of those 3 different parallelism abstractions and their off-shots, then we've just added some arguably-pointless complexity, that makes sense for our day and age but could well become pointless in a better-designed future.

And at some point you're bound to be stuck with things like that and increase the entry cost, though hopefully other abstractions are simplified to lower it and the equilibrium keeps staying at a pretty low number of hours.

Comment by george3d6 on Why aren’t we testing general intelligence distribution? · 2020-05-27T10:22:56.080Z · score: 2 (2 votes) · LW · GW

Basically, the way I would explain it, you are right, using a bell curve and using various techniques to make your data fit it is stupid.

This derives from two reasons, one is am artifact, the fact that distributions were computation-simplyfing mechanisms in the past, even though this is no longer true. More on this here: https://www.lesswrong.com/posts/gea4TBueYq7ZqXyAk/named-distributions-as-artifacts

This is the same mistake, broadly speaking, as using something like pearsonr instead of an arbitrary estimator (or even better, 20 of them) and a k-fold-crossvalidation in order to determine "correlation" as a factor of the predictive power of the best models.

Second, and see an SSC post on this that does the subject better justice (completely missing the point), we love drawing metaphorical straight line, we believe and give social status to people that do this.

If you were to study intelligence with an endpoint/goal in mind, or with the goal of explaining the world, the standard dist would be useless. Except for one goal, that of making your "findings" seem appealing, of giving them extra generalizability/authorizativeness that they lack, normalizing tests and results to fit the bell curve does exactly that.

Comment by george3d6 on Your abstraction isn't wrong, it's just really bad · 2020-05-27T10:10:40.811Z · score: 1 (1 votes) · LW · GW

When you use them to mentally sort things for general knowledge of what's out there and memory storage like in biology, if it works it works. Kingdoms seem to work for this.

Could you expand this a bit ?

Comment by george3d6 on Your abstraction isn't wrong, it's just really bad · 2020-05-27T10:07:54.372Z · score: 3 (2 votes) · LW · GW

3000 is a bit of an exaggeration, seeing as the vast majority of mathematics was invented from the 17th century onwards, it's more fair to call it 400 years vs programming's 70-something.

Though, if we consider analogue calculators, e.g. the on leibniz made, then you argue programming is about as old as modern math...but I think that's cheating.

But, well, that's kind of my point. It may be that 400 years calcifies a field, be that math or programming or anything else.

Now, the question remains as to whether this is good or not, intuitively it seems like something bad.

Comment by george3d6 on Movable Housing for Scalable Cities · 2020-05-27T00:45:28.368Z · score: 7 (3 votes) · LW · GW

I don't really understand how this helps outside of a world consisting of an idealized plane.

The main issue with housing is that it has to conform to the environment:

  • Rainfall , both maxima over a few seconds and average of days.
  • Earthquakes
  • Flooding
  • Torandoes
  • Temperature
  • Air composition, wind patterns
  • Things like humidity that are mainly a combination of the above

But also things like:

  • State/country specific regulations (e.g. fire hazard rulings, environmental rulings deciding what kind of air conditioning you can use and how many solar panel you'd roof needs)
  • Accounting mess because property taxes might get weird and when things get weird the IRS policy states that it's the taxpayer's job to navigate the complexity.

This idea sounds like something extremely hard to implement and extremely fragile. It only brings marginal benefits, since at the eod the foundation is still immutable.

Also, irrelevant while the pop of almost all US cities is growing, since this becomes efficient compared to rent only when assuming loads of vacant housing.

Something, something, Uber for puppies.

Comment by george3d6 on Baking is Not a Ritual · 2020-05-26T23:58:00.583Z · score: 1 (1 votes) · LW · GW

To me this doesn't seem too far off from the mentality/approach one should take when cooking or when making metaphorical bathtub drugs. Though it's probably in between the two regarding complexity.

The on thing that annoys me about baking as opposed to cooking is that for most of the process you can't taste things and adjust based on that, whereas with cooking there's usually more feedback you can get via constant tasting , which goes a long way especially when your only making the dish for yourself or yourself + people which have culinary preferences well known to you.

On the other hand, isn't it very easy for baking to fall into a taste/health trade-off where the better your pastries the more likely you are to regret eating them 10 years from now ?

Comment by george3d6 on Your abstraction isn't wrong, it's just really bad · 2020-05-26T23:40:51.633Z · score: 4 (1 votes) · LW · GW

Alright, I think what you're saying make more sense, and I think in principle I agree if you don't claim the existence of a clear division between , let's call them design problems and descriptive problems.

However it seems to me that you are partially basing this hypothesis on science being more unified than it seems to me.

I.e. if the task of physicists was to design an abstraction that fully explained the world, then I would indeed understand how that's different from designing an abstraction that is meant to work very well for a niche set of problems such as parsing ASTs or creating encryption algorithms (aka things for which there exists specialized language and libraries).

However, it seems to me like, in practice, scientific theory is not at all unified and the few parts of it that are unified are the ones that tend to be "wrong" at a closer look and just serve as an entry point into the more "correct" and complex theories that can be used to solve relevant problems.

So if e.g. there was one theory to explain interactions in the nucleus and it was consistent with the rest of physics I would agree that maybe it's hard to come up with another one. If there's 5 different theories and all of them are designed for explaining specific cases and have fuzzy boundaries where they break and they kinda make sense in the wider context if you squint a bit but not that much... then that feels much closer to the way programming tools are. To me it seems like physics is much closer to the second scenario, but I'm not a physicist, so I don't know.

Even more so, it seems that scientific theory, much like programming abstraction, is often constrained by things such as speed. I.e. a theory can be "correct" but if the computations are too complex to make (e.g. trying to simulate macromolecules using elementary-particle based simulations) than the theory is not considered for a certain set of problems. This is very similar to e.g. not using Haskell for a certain library (e.g. one that is meant to simulate elementary-particle based physics and thus requires very fast computations), even though in theory Haskell could produce simpler and easier to validate (read: with fewer bugs) code than using Fortran or C.

Comment by george3d6 on Your abstraction isn't wrong, it's just really bad · 2020-05-26T22:30:09.902Z · score: 4 (1 votes) · LW · GW
There is a major difference between programming and math/science with respect to abstraction: in programming, we don't just get to choose the abstraction, we get to design the system to match that abstraction. In math and the sciences, we don't get to choose the structure of the underlying system; the only choice we have is in how to model it.

The way I'd choose to think about it is more like:

1. Language, libraries ...etc are abstractions under an underlying system (some sort of imperfect Turing machine), that programmers don't have much control over

2. Code is an abstraction over a real world problem meant to regorize-it to the point where it can be executed by a computer (much like math in e.g. physics is an abstraction meant to do... exactly the same thing, nowadays)

Granted, what the "immutable reality" and the "abstraction" are depends on who's view you take.

The main issue is that reality has structure (especially causal structure), and we don't get to choose that structure.

Again, I think we do get to chose structure. If your requirement is e.g. building a search engine and one of the abstractions you chose is "the bit that stores all the data for fast querying", because that more or less interacts with the rest only through a few well defined channels, then that is exactly like your cell biology analogy, for example.

To draw a proper analogy between abstraction-choice in biology and programming: imagine that you were performing reverse compilation. You take in assembly code, and attempt to provide equivalent, maximally-human-readable code in some other language. That's basically the right analogy for abstraction-choice in biology.

Ok, granted, but programmers literally write abstractions to do just that when they write code for reverse engineering... and as far as I'm aware the abstractions we have work quite well for it and people doing reverse engineering have the same abstraction-choosing and creating rules every other programmer has.

Picture that, and hopefully it's clear that there are far fewer degrees of freedom in the choice of abstraction, compared to normal programming problems. That's why people in math/science don't experiment with alternative abstractions very often compared to programming: there just aren't that many options which make any sense at all. That's not to say that progress isn't made from time to time; Feynman's formulation of quantum mechanics was a big step forward. But there's not a whole continuum of similarly-decent formulations of quantum mechanics like there is a continuum of similarly-decent programming languages; the abstraction choice is much more constrained

I mean, this is what the problem boils down to at the end of the day, nr of degrees of freedom you have to work with, but the fact that sciences have few of them seems non obvious to me.

Again, keep in mind that programmers also work within constraints, sometimes very very very tight constraints, e.g. a banking software's requirements are much stricter (if simpler) than those of a theory that explains RNA Polymerase binding affinity to various sites.

It seems that you are trying to imply there's something fundamentally different between the degrees of freedom in programming and those in science, but I'm not sure I can quite make it out from your comment.

Comment by george3d6 on What is your internet search methodology ? · 2020-05-25T10:25:07.391Z · score: 1 (1 votes) · LW · GW

The Gwern article I was unaware of, I will check it out.

In addition, I wouldn't bother trying to search sci-hub directly from Google. Instead, find the actual journal article you're looking for, copy its DOI number, and paste that into sci-hub.

I was speaking of a sci-hub addon, which auto detects the DOI in a page you are reading and opens the article in scihub (i.e. to make find DOI -> open sci hub -> past DOI and search a single step of "click addon button")

Comment by george3d6 on What is your internet search methodology ? · 2020-05-24T20:49:35.126Z · score: 2 (2 votes) · LW · GW

Can't you simply e.g. donate 200$ each year to offset this ? E.g. google charge (I think) ~1$/click for a US demographic (some exceptions, blah blah) and how many search engine ads do you click ? For me it's ~0, but let's say... 100 a year ? add to that like 1$ hundred impressions + 10,000 searches a year. Granted, this is a very rough number, but I'm being rather charitable with the profit here, I think, considering a large part of that is actually operational costs.

It seems like your search data is hardly worth more than that, and the advantages of using google are many in terms of time saving. Enough to be e.g. worth 200$.

I get why one wouldn't want to use google for ethical reasons, but at the eod all the search engines which use a centralized structure are equally bad, they just happen not to hold a monopoly (however, in that case, if you're just anti-monopoly, you might as well use e.g. Bing which seems closest to google in terms of quality)

Comment by george3d6 on Making a Crowdaction platform · 2020-05-18T00:18:15.590Z · score: 1 (1 votes) · LW · GW

You can use e.g. WordPress + some poll plugins to build this yourself.

The problem is:

  • If it's centralized it will be fundamentally unsafe since the people controlling it can use it as a way to get free labour behind a thing they benefit from (see democratic governments)
  • If it's decentralized it's either expensive to vote and/or start an issue (see captialist economies) or your back to problem one.
  • Getting people to use it is a coordination problem in of itself.

The closest you can get to something work-able is to look at various block chain project for implementing democratic voting and put some pretty trappings around them. But it doesn't quite solve issue 1 and 2, might add the issue of registration being hard (e.g. id checking smart contract) and doesn't solve 3.

Comment by george3d6 on That Alien Message · 2020-05-16T19:27:34.263Z · score: 1 (1 votes) · LW · GW

It seems to me that the stipulations made here about the inferential potential or little information is made from the naive viewpoint that piece of information are independent.

The idea of the plenitude of information with inferential ability that is readily accessible to a smart enough agent doesn't hold if that information consists of things which are mostly dependent on each other.

A <try to taboo this word whenever you see it> hooked up to a webcam, would invent General Relativity as a hypothesis—perhaps not the dominant hypothesis, compared to Newtonian mechanics, but still a hypothesis under direct consideration—by the time it had seen the third frame of a falling apple.  It might guess it from the first frame, if it saw the statics of a bent blade of grass.

This statement could be true, however, this doesn't mean that upon seeing a second blade of grass it could generate a new hypothesis, or upon seeing all that is on earth on a macroscopic or even on a microscopic (up to limit of current instruments).

Heck, if you see a single bit, as long as you have the ideas of causality, you can generate infinite hypothesis for why that bit was caused to be zero or one... you can even assign probabilities to them based on their complexity. A single bit is enough to generate all hypothesis about how the universe might work ,but you're just left with an infinite and very flat search space.

So, this view of the world boils down to:

  • Most properties of the world can be inferred with a very small probability from a very small amount of information. This is literally an inversion of the basic scientific assumption that observations about properties of the world carry over into other systems. If one can find properties that are generalizable, one can at least speculate as to what they are even by observing a single one of the things they generalize to.
  • However, new information serves to shrink the search space and increase our probability for a hypothesis being true

Which is... true, but it's such an obvious thing that I don't think anyone would disagree with it. It's just formulated in a very awkward way in this article to make it seem "new". Or at least, I've got no additional insight from this other than the above.

Comment by george3d6 on Named Distributions as Artifacts · 2020-05-04T21:18:51.598Z · score: 3 (2 votes) · LW · GW
And the latter is usually what we actually use in basic analysis of experimental data - e.g. to decide whether there's a significant different between the champagne-drinking group and the non-champagne-drinking group

I never bought up null-hypothesis testing in the liver weight example and it was not meant to illustrate that... hence why I never bought up the idea of signfiance.

Mind you, I disagree that signficance testing is done correctly, but this is not the argument against it nor is it related to it.

(The OP also complains that "We can't determine the interval for which most processes will yield values". This is not necessarily a problem; there's like a gazillion versions of the CLT, and not all of them depend on bounding possible values. CLT for e.g. the Cauchy distribution even works for infinite variance.)

My argument is not that you can't come up with a distribution for every little edge case imaginable, my argument is exactly that you CAN and you SHOULD but this process should be done automatically, because every single problem is different and we have the means to dynamically see the model that best suits every problem rather than stick to choosing between e.g. 60 names distributions.

Even here, we can apply a linearity -> normality argument as long as the errors are small relative to curvature.

I fail to see your argument here, as in, I fail to see how it deals with the interconnected bit of my argument and I fail to see how noise being small is something that ever happens in a real system, in the sense you use it here, as in, noise being everything that's not inference we are looking for.

There absolutely is a property of mathematics that tells us what a slightly-off right-angled triangle is: it's a triangle which satisfies Pythagoras' formula, to within some uncertainty.

But, by this definition that you use here, any arbitrary thing I want to define mathematically, even if it contains within it some amount of hand wavyness or uncertainty, can be a property of mathematics ?

I fully support quoting Wikipedia, and it is inherently bad to use complex models instead of simple ones when avoidable. The relevant ideas are in chapter 20 of Jaynes' Probability Theory: The Logic of Science, or you can read about Bayesian model comparison.

Your article seems to have some assumption that increase complexity == proneness to overfitting.

Which in itself is true if you aren't validating the model, but if you aren't validating the model it seems to me that you're not even in the correct game.

If you are validating the model, I don't see how the argument holds (will look into the book tomorrow if I have time)

Intuitively, it's the same idea as conservation of expected evidence: if one model predicts "it will definitely be sunny tomorrow" and another model predicts "it might be sunny or it might rain", and it turns out to be sunny, then we must update in favor of the first model. In general, when a complex model is consistent with more possible datasets than a simple model, if we see a dataset which is consistent with the simple model, then we must update in favor of the simple model. It's that simple. Bayesian model comparison quantifies that idea, and gives a more precise tradeoff between quality-of-fit and model complexity.

I fail to understand this argument and I did previously read the article mentioned here, but maybe it's just a function of it being 1AM here, I will try again tomorrow.

Comment by george3d6 on Prolonging life is about the optionality, not about the immortality · 2020-05-04T08:35:15.351Z · score: 3 (2 votes) · LW · GW

I mean, It's not a claim I will defend per say, it was more "Here's a list of arguments I've already heard around the issue, to give some context to where I'm placing mine".

I think I agree with this claim, but I'm not 100% sure by any stretch and I don't have the required sources to make a good case for it, other than my intuition which tells me it's right, but that's not a very good source of truth.

Comment by george3d6 on Is ethics a memetic trap ? · 2020-04-26T17:50:33.896Z · score: 0 (2 votes) · LW · GW
Even if you are not a god-emperor, you would still be required to give your stuff away until you are no more miserable than everyone else.

But that is the crazy interpretation of consequentialism which places 0 value on the ethics of care, that nobody practices, because even true psychopaths still have a sweet spot for themselves, so, is it worth bringing to the table ?

So it's not really true that all ethical systems are undemanding, more that the undemanding forms are undemanding. People might be motivated to water down their ethical systems to make them more popular, and that might lead to a degree of convergence, but that isn't very interesting.

This is actually a good point ( though I don't believe extreme U-ism is the best example here, since again, literally nobody is practicing it.

But that's only important under consequentialism, which is only one system.

See above, the vast majority of actions under normative ethics have no inherent value, they only help in that they place you in a situation where you are better positioned when taking an action that will have normative value.

Sure they do. Prayer? Eating pork?

Also true.

But are the kind of religious systems where most actions have normative values even "in the discussion" for... anyone that potentially reads LW ?

I guess I bought them up by citing new england style christianity when I should have really just said Quakers or some other light-weight, value-of-life & tolerance focused christian sect where "god" is closer to a "philosopher's god" or "prime mover", rather than an "ominpotent demanding father"