trentmkelly/LessWrong-43k huggingface dataset:
Will you let your kid play football?
by 5hout — 2022-10-04T17:48:25.809Z

Sparked by a somewhat vitriolic discussion on a dadgroup I'm in. Would you let your kid play football, if so what restrictions? If not, what other sports are allowed, and with what restrictions?

https://publications.aap.org/pediatrics/article/144/5/e20192180/38225/Concussion-Incidence-and-Trends-in-20-High-School?fbclid=IwAR0EFuaf5EsV5OafmtAAXq3zRGI32dlWadaBM7U18SatYh6txO2v6oaJu-s?autologincheck=redirected?nfToken=00000000-0000-0000-0000-000000000000

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2140075/?fbclid=IwAR18lhXJN3IjhzNwwdd-qfo3g9RBMUtYIDniLxSuH25yFxWZLVGGK_dyLVY

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384815/?fbclid=IwAR1tPWAGlN0NSupBRQMFokLAqlThBt5b0jEE-A_YEem3lWcWm3eKCBIqWXc

https://concussionfoundation.org/news/press-release/breakthrough-study-reveals-repetitive-head-impacts-definitive-cause-CTE

The "dadgroup consensus" seemed to be that football was right out, but that all other mainstream HS sports were fine. My read of the above links is that football is the cause of the week, but that playing MS/HS football is not actually outrageously more dangerous than other mainstream HS sports. A surprisingly high # of people seemed to attack the idea of HS sport as valuable at all, and that kids should only play non-contact sports or no sports b/c the risk is too great.

Sports (specifically) and competitive activities (more generally) are a great way to teach instant and lasting life lessons. How to deal with defeat, the value of hard work, getting along/working with people you like (and those you don't), being a gracious winner, how good it feels to win, building confidence through growing competence and many more. Essentially, sports are life writ small and allow kids to experience previews and learn from those previews in a lower cost environment. Certainly other competitive activities provide many of these lessons (in a saner world people would include progression raiding experience from MMOs on their resumes), but MS/HS sports provide them + the physical activity that growing bodies thrive on. Additionally, many benefits to regular physical activity are well established (and, with the difficulty of becoming physically active later in life, starting young seems a good idea). (To be completely honest, I find the benefits of sports so obvious that the need to include this gasts my flabbers, but given the previous discussion I felt compelled to).

The ri...
Quick notes on “mirror neurons”
by Steven Byrnes — 2022-10-04T17:39:53.144Z

1. Two definitions of “mirror neurons”

I have noticed the term “mirror neurons” being used in two ways:

(A) Any set of neurons that activates both when I am doing / feeling / relating to X and when I am watching (or imagining) someone else doing / feeling / relating to that same X.
(B) Same as (A), but where X is more specifically an action concept (e.g. “grasp”), and where the neurons in question are more specifically in certain specific frontal-lobe brain areas including (parts of) premotor cortex, primary motor cortex, and Broca’s area.

The (B) usage is the original one, and much more common in the technical neuroscience literature. By contrast, (A) seems quite common among non-neuroscientists, and shows up at least occasionally even in the technical neuroscientist literature.[1]

Anyway, regardless of what we call it, I strongly believe that (A) is a real neuroscience phenomenon. If I watch you juggle, versus if I juggle myself, some of the same juggling-related neural circuits are active in both cases. That seems perfectly obvious to me. After all, how else could I possibly generalize from one to the other? How could I even know that they have anything to do with each other??

By contrast, my sense is that (B) is a giant dumpster fire of nonsense and badly-interpreted experiments. I recommend the excellent book The Myth of Mirror Neurons by Gregory Hickok for that whole story.

2. Mirror neurons and kindness

There’s a pop science trope that mirror neurons explain love and kindness and so on. I don’t think that (A) in itself explains human prosocial behavior. I do think that (A) is probably an ingredient in the explanation of human prosocial behavior. And I also think that (A) is probably an ingredient in the explanation of human antisocial behavior! For more on that, see Symbol grounding & human social instincts, Section 13.5 on empathetic simulation.

3. Why I myself don’t use the term “mirror neurons”

Why don’t I personally use the term “mirror neurons” for (A)? First, because it would be arguably an incorrect usage, or at least ambiguous—see above. Second, because I find “mirror neuron” a somewhat misleading term anyway.

For example, as above, take some set of temporal-lobe neurons which collectively represent the concept “juggle”. These neurons are (I claim) active both when I’m juggling and when I’m watching you juggle. Should we call those neurons “mirror ne...
Feature request: Filter by read/ upvoted
by Nathan Young — 2022-10-04T17:17:56.649Z

I am looking for a post that I read and probably upvoted. I can't find it. I am 90% sure you know which posts I've read and 99.9% sure you know which posts I've upvoted. Can I be able to filter by them.
That's it. That's the post.

It's that post about not having to have perfect views but just saying why you have the views you have so you are easy to correct.
Layers Of Mind
by PeteG — 2022-10-04T16:52:26.209Z

I did not intend to post this anywhere. I just wrote this for myself. But then I thought that I should maybe post it on Less Wrong — a blog I have not read since its golden years. I almost never post anything I write anywhere, but this time I thought of the virtue of Relinquishment — that I should submit myself to ordeals and test myself in fire. So here is my post:

Lies permeate themselves within the structures of our minds. Consciously, they control us more than we control them. But unconsciously, we all dance to the rhythm of the truth. I conceived of four layers of the mind and their relation to truth, lies, and understanding.

First Layer
The things you could consciously express by yourself without anyone telling you anything. You can also state any number of lies here. You are the navigating captain.

Second Layer
The things you need some help consciously expressing. If you are by yourself thinking about them, you could stop thinking after a time that is not too long. Since you are not as consciously aware, more factual data about the world gets stored here. And having less control means lying here becomes weaker. You navigate here, but the waters could be intimidating.

Third Layer
The things you need a lot of help to consciously express. The things expressed at this layer are usually done so unconsciously. You have to be very attentive to your mind to notice what you are doing here. If you are an analytic person, this means laying out the data and working with it to a sound conclusion. If you are artistic, this means your artwork will come out very abstract. Since this is a deep layer of the mind that you have to dig into to take something out, it is going to be very hard to place lies here. There is a lot of inaccessible order here, and placing a lie anywhere will have it surrounded and crushed to a minimum by many truths. You no longer know how to control your boat, and it could be easily flipped over.

Fourth Layer
This is the unreachable layer. You cannot take anything out of here. It comes out when it needs to come out. It cannot even be said to really belong to you. It is the God spot of your mind — the layer that does not accept any lies. This layer holds all data that entered you in its unfiltered form. When you try to create a sophisticated lie to believe something that you really want to believe, this is the place where it cannot go. When enough truths ...
Does Google still hire people via their foobar challenge?
by Algon — 2022-10-04T15:39:35.260Z

In years since past, the foobar challenge first appeared. 5 levels of ever harder coding challenges, and if you bested the first three you'd be given the option of providing your details to Google. There was a decent chance you'd be sent an interview request sometime later or , if you applied to them yourself, you're application would be given extra weight. Recently, I got the challenge from Google and after getting to the last problem of level three, I wondered "do they even hire people using this thing anymore"? Some quick searches on medium blogs, quora and hackernews later, I was left with the impression that they don't but no one could cite a source for this.

So does anyone have any info, definitive or otherwise, that would tell me and other lost souls whether Google still hires using this thing?

Edit: changed the name
Russia will do a nuclear test
by sanxiyn — 2022-10-04T14:59:20.288Z

A thought occurred to me, and it's so logical, I concluded that it must be true.

Russia will not detonate a nuclear weapon in Ukraine, fearing repercussion.

Russia will detonate a nuclear weapon in Russia. In other words, Russia will do a nuclear test. Like North Korea did.

As far as I know, there is no international law against Russia doing a nuclear test. Russia is a recognized nuclear weapon state of NPT, unlike North Korea. The test will give weight to Putin's words that he is not bluffing.
Paper+Summary: OMNIGROK: GROKKING BEYOND ALGORITHMIC DATA
by Marius Hobbhahn — 2022-10-04T07:22:14.975Z
Link: https://arxiv.org/abs/2210.01117

There is a new paper by Liu et al. that claims to have understood the key mechanism underlying grokking (potentially even generalization more broadly).

They argue:
1. Grokking can be explained via the norm of the weights. They claim that there is a constant level of the weight norm that is optimal for generalization.
2. If there is an optimal level of the weight norm, the weight norm of your model after initialization can be either too low, too high or optimal. They claim that grokking is a phenomenon where we initialize the model with a large weight norm and it then slowly walks toward the optimal weight norm and then generalizes.

3. They also claim that you can get the same results as grokking but much faster if you set the weight norm correctly at every step.
4. They set the norm "correctly" by rescaling the weights after each unconstrained optimization step (so after every weight update loss.backward()?!)
Implications:

I think they have found a very important insight of grokking and finding generalizing circuits more broadly.
I'm still a bit skeptical of some of the claims and results. On some level "just fix your weight norm and the model generalizes" sounds too simple to be true for all tasks.
I think this result could have big implications but I'm not yet sure whether they are positive or negative. On the one hand, finding generalizing circuits seems to solve some of the problems associated with bad out-of-distribution generalization. On the other hand, it likely speeds up capabilities.

I'm very unsure about this paper but intuitively it feels important. Thoughts?
Frontline of AGI Alignment 
by SD Marlow — 2022-10-04T03:47:43.944Z
Link: https://robothouse.substack.com/p/agi-xrisk-competition?r=av4g9&utm_campaign=post&utm_medium=web

I created a post for the Future Fund AI World Prize (AGI xRisk Competition) only days after seeing it mentioned on twitter, but I don’t really follow people on the alignment-side of the isle, so of course that post fell flat. As I’ve finally decided to make the formal post here according to the challenge rules, it was yet another knock against me seeing that I may have already poked the bear when I was participating in the MIRI/Visible Thoughts Project. It’s the reason I already had an account on LessWrong, but my post covering the experience called into question the reasoning. Short version: it was aimed at collecting data that explicitly formats data to cover for training model failures vs being geared toward future architectures. The long version is here: Slaying the ML Dragon

While I am slightly critical of in-group vs open debate, in terms of being able to even accept “outside ideas,” recent events have highlighted how that happens within the AI community. Industry insiders are talking down or sniping at one another. The actual commentary, and I would argue, the actual dialog on ideas, happens on the fringe, below the attention of those that have already established themselves (thru action or pedigree). The idea of training a new generation of researchers on safety and ethics is all for naught when those attempting to engage are derided or chastised.

*not without irony that Alignment forum has “participation in the Forum is limited to deeply established researchers” as part of the Welcome page, and later states that there are only about 100 people that can post there!

If “urgency” is based on the assumption that AGI could arrive within a decade, then any effort directed at “the entry point” is already moot. The snowball has already started its descent down the hillside, as one might say.

While mentioned in my original post, I must emphasize the importance of heads up and eyes open. Understand what the real milestones are by understanding the real challenges involved. Then, instead of sitting-back and waiting for bold announcements by tech giants or presentations at large conferences, look for the people “on the fringe” that are at least saying the right things.
Adversarial vs Collaborative Contexts
by jefftk — 2022-10-04T02:40:04.929Z

You can often make pretty clear predictions about what sort of effect
something would have in the world as it exists today, but when you
start to take into account how the world might change in response it
gets very difficult. One tool here is thinking about whether you
mostly expect collaborative or adversarial effects.

In a collaborative context other people respond to your change by
building on it, and it typically has a larger effect than you'd
naively expect. For example, say you create an icon to communicate an
idea in your program, other people start using it, and it becomes the
standard representation for the concept. While an A/B test might have
been only mildly positive, or even negative, after the world adapted
to the new icon it would have become very clearly the right symbol to
use.

In an adversarial context other people respond to your change by
countering it, and it typically has a smaller effect. You figure out
a way to detect bot
traffic, but the bot operators are very motivated to shift their
behavior to undo your work.

Some contexts are also neutral, where both collaborative and
adversarial effects are minimal or nonexistent. A baby toy design
doesn't get better or worse with the generations.

Some examples:

Diseases are adversarial: antibiotics lose
resistance, new variants evade immunity. While something looks good
in initial testing, that advantage will erode over time. An
interesting exception here is
cancer, which (almost
always) has to evolve anew in each patient, and so cancer
treatments don't become less effective over time (in new patients).

Adopting standards is collaborative: each computer with USB
increased demand for devices that spoke USB and vice-versa. Someone
thinking "let's not bother with USB, it's not that widely supported
yet" would have been ignoring collaborative effects.

Same with building on a platform: the more iPhones sold the
more people wanted apps, and the more apps there were the more people
wanted iPhones.

Interaction with nature is generally adversarial: cockatoos
figure out how to get
into trash, weeds evolve herbicide resistance.

A new kind of ad has both kinds of effects: adversarial in that
people will notice novel ads more, but cooperative in that advertisers
haven't yet learned how to make the most of the format.

This sort of effect is especially important...
Humans aren't fitness maximizers
by So8res — 2022-10-04T01:31:47.566Z

Recently I've gotten a bunch of pushback when I claim that humans are not maximizers of inclusive genetic fitness (IGF).

I think that part of what's going on here is a conflation of a few claims.

One claim that is hopefully uncontroversial (but that I'll expand upon below anyway) is:

Humans are not literally optimizing for IGF, and regularly trade other values off against IGF.

Separately, we have a stronger and more controversial claim:

If an AI's objectives included goodness in the same way that our values include IGF, then the future would not be particularly good.

I think there's more room for argument here, and will provide some arguments.

A semi-related third claim that seems to come up when I have discussed this in person is:

Niceness is not particularly canonical; AIs will not by default give humanity any significant fraction of the universe in the spirit of cooperation.

I endorse that point as well. It takes us somewhat further afield, and I don't plan to argue it here, but I might argue it later.

On the subject of whether humans are literally IGF optimizers, I observe the following:

We profess to enjoy many other things, such as art and fine foods.

Suppose someone came to you and said: "I see that you've got a whole complex sensorium centered around visual stimuli. That sure is an inefficient way to optimize for fitness! Please sit still while I remove your enjoyment of beautiful scenery and moving art pieces, and replace it with a module that does all the same work your enjoyment was originally intended to do (such as causing you to settle down in safe locations with abundant food), but using mechanical reasoning that can see farther than your evolved heuristics." Would you sit still? I sure wouldn't.

And if you're like "maybe mates would be less likely to sleep with me if I didn't enjoy fine art", suppose that we tune your desirability-to-mates upwards exactly as much as needed to cancel out this second-order effect. Would you give up your enjoyment of visual stimuli then, like an actual IGF optimizer would?

And when you search in yourself for protests, are you actually weighing the proposal based on how many more offspring and kin's-offspring you'll have in the next generation? Or do you have some other sort of attachment to your enjoyment of visual stimuli, some unease about giving it up, that you're trying to defend?

Now, there's a reasonable counterargument ...
Self-defeating conspiracy theorists and their theories
by M. Y. Zuo — 2022-10-04T00:48:53.998Z

Everyone who's been on the internet for sometime has likely encountered odd theories regarding every sphere of human activity. The ones that attract the most adherents are characteristically concentrated in the political and economic spheres.

Some of these theories admittedly have proven to be grounded in real events, the most notable of those proven to be largely true are likely those regarding the affairs of clandestine agencies such as the CIA, NSA, KGB, and so on.

Some of the remaining theories may have a grain of truth to them, but mixed in with so much confabulation that it's not worth the effort to separate out the signal from noise.

Some types however, such as the ones requiring some type of global control, can be recognized as absurd in themselves due to the paradoxes that arise between their premises and alleged existence.

-

For example, the idea that X secretly rules the world. Where X can be the Illuminati, Jews, Aliens, etc...

There are elaborate ways of questioning the likelihood of these claims but thankfully there are straightforward questions, that when asked in the socratic style, usually lead to obvious absurdities if the claims are taken seriously. Thus forcing the claimant to accept absurd or unattractive premises, which they almost never do by-the-way, if they wish to continue free of contradiction.

Here's one possible chain of questioning:

If X is so organized, so strong, etc., as to currently rule the entire world, why wouldn't they have brought conspiracy theorists under their heel already?

Well they're not strong enough to suppress everyone!

If they don't have the capacity to control everyone, how can they be said to rule?

They're hiding so they don't want to reveal too much.

If they have the ability to control selectively but decide not to spend any effort for this specific case, doesn't that imply that these claims are irrelevant to X or their goals?

Well they may think that, but trust me, this is the real deal.

Why should I trust anyone? Wasn't the claim that X, a supposedly super secret organization, are puppet masters of mankind or something to that effect?

I'm right here, I'm obviously trustworthy, I got nothing to hide.

How can we establish that neither one of us are puppets of X if they're allegedly so much smarter and more capable then us?

Well they're not, we're both smart cookies, not like the sheeple!

So X go

...
No free lunch theorem is irrelevant
by Catnee — 2022-10-04T00:21:54.790Z

I often see people cite the "no free lunch theorem" as a counterargument to the existence of an AGI. I think this is a very bad argument. This theorem is formulated as a problem of predicting random and uniformly distributed data. Just open the Wikipedia!. In my opinion, this is another example of how people use results from mathematics simply because they sound relevant without understanding their original meaning and limitations. What makes them think it's relevant to AGI?
It sounds something like “well, since there is such a theorem, it turns out that we will not be able to create an algorithm that can do many tasks.” This is complete nonsense. In order to somehow bind it to real systems, you need to make a reservation and add a restriction on some kind of physical size of the model: the number of parameters, disk space, total equipment weight, training time, total budget whatever. In this case, well, yes. If we have a general algorithm for solving many problems, then for any problem we can probably use the same resources to make an algorithm that performs better. The problem is that we don't say "how much better". Any improvement will do.
It will not be the original theorem, but I think this is close to "what people think when they say no free lunch theorem".
If our algorithm for solving many problems shows itself “kind of meh” compared to specialized algorithms, then what prevents us from increasing the resource budget and collecting all our specialized algorithms into one big one? Yes, the new one can also be reassembled into specialized algorithms, but sooner or later we will hit diminishing returns. It is impossible to calculate arithmetic more accurately than in 100% of cases, it is impossible to predict the structure of a protein more accurately than it will turn out in reality, and so on. By increasing the available resources, distilling and amplifying, we can make a general-purpose algorithm as good as we like in any task. As close to maximum performance for the metrics as we need.