trentmkelly/LessWrong-43k huggingface dataset:
Less Basic Inframeasure Theory
by Diffractor — 2020-12-16T03:52:47.970Z

Introduction

So, we've found some new results about infradistributions, and accordingly, this post will be a miscellaneous grab-bag of results from many different areas. Again, this is a joint Vanessa/Diffractor effort. It requires having read "Basic Inframeasure Theory" as a mandatory prerequisite, and due to a bit less editing work this time around, it's advised to pester me for a walkthrough of this one, illusion of transparency is probably worse on this post than the previous two. Apart from the 8 proof sections (1, 2, 3, 4, 5, 6, 7, 8), included in this post is:

Section 1: We cover generalizations of the background mathematical setting of inframeasure theory to cover discontinuous functions, taking the expectation of functions outside of [0,1], and defining infradistributions over non-compact spaces. We'll also cover what "support" means for infradistributions, and why it becomes important when we go outside of compact spaces.

Section 2: We'll introduce various convenient properties that infradistributions might fulfill, from weakest to strongest, what they mean for the expectation functionals as well as the set of minimal points, and what they're used for.

Section 3: We present several operations to form new infradistributions from old ones, and talk about what they mean, both on the set side of LF-duality as well as the expectation functional side. We go more in-depth on things like semidirect product which were casually mentioned at the end of the last post, as well as some novel ones like pullback.

Section 4: We briefly introduce the basics of optimistic Knightian uncertainty, which we'll call ultradistributions, as opposed to pessimistic Knightian uncertainty/infradistributions. It's covered in only cursory depth, because they appear so closely parallel to our existing framework that if ultradistributions are ever required for something, it would be very easy to adapt the requisite results from the theory of infradistributions.

Section 5: We present our first major theorem, the analogue of the disintegration theorem from classical probability theory. It is a highly useful tool which gives the infra-version of conditional probability. Its use is more restricted than the probability theory analogue, but it lets you do things like derive the analogue of Bayes' rule when you have Knightian uncertainty as to the prior probability distribution over hypoth...
Which sources do you trust the most on nutrition advice for exercise?
by Bird Concept — 2020-12-16T03:22:40.088Z

I'm trying to become stronger and get a better physique. My current goal is what the internet calls "body recomposition", which involves trying to build muscle and lose fat at the same time. This breaks down to answering questions about:

How much should I eat? What should my daily breakdown of nutrients be across different sources? Which, if any, supplements should I be taking?
What should my exercise routine be?

I feel pretty set on 2, so this post is about 1.

I've never looked into nutrition very much myself. Yet I've heard many horror stories about it as an academic field. And various fitness YouTubers/bloggers aren't exactly beacons of Bayesian reasoning either.

So I'd be curious if someone around here has spent time looking into this; and found any resources they actually trust and think have a solid epistemic backing.
Homogeneity vs. heterogeneity in AI takeoff scenarios
by evhub — 2020-12-16T01:37:21.432Z

Special thanks to Kate Woolverton for comments and feedback.

There has been a lot of work and discussion surrounding the speed and continuity of AI takeoff scenarios, which I do think are important variables, but in my opinion ones which are relatively less important when compared to many other axes on which different takeoff scenarios could differ.

In particular, one axis on which different takeoff scenarios can differ that I am particularly interested in is their homogeneity—that is, how similar are the different AIs that get deployed in that scenario likely to be? If there is only one AI, or many copies of the same AI, then you get a very homogenous takeoff, whereas if there are many different AIs trained via very different training regimes, then you get a heterogenous takeoff. Of particular importance is likely to be how homogenous the alignment of these systems is—that is, are deployed AI systems likely to all be equivalently aligned/misaligned, or some aligned and others misaligned? It's also worth noting that a homogenous takeoff doesn't necessarily imply anything about how fast, discontinuous, or unipolar the takeoff might be—for example, you can have a slow, continuous, multipolar, homogenous takeoff if many different human organizations are all using AIs and the development of those AIs is slow and continuous but the structure and alignment of all of them are basically the same (a scenario which in fact I think is quite plausible).

In my opinion, I expect a relatively homogenous takeoff, for the following reasons:

I expect that the amount of compute necessary to train the first advanced AI system will vastly outpace the amount of compute necessary to run it such that once you've trained an advanced AI system you will have the resources necessary to deploy many copies of that trained system and it will be much cheaper to do that than to train an entirely new system for each different application. Even in a CAIS-like scenario, I expect that most of what you'll be doing to create new services is fine-tuning existing ones rather than doing entirely new training runs.

I expect training compute to be sufficiently high such that the cost of training a competing system to the first advanced AI system will be high enough that it will be far cheaper for most organizations to simply buy/license/use a copy of the first advanced AI from the organization that buil

...
Motive Ambiguity
by Zvi — 2020-12-15T18:10:01.372Z

Central theme in: Immoral Mazes Sequence, but this generalizes.

When looking to succeed, pain is not the unit of effort, and money is a, if not the, unit of caring.

One is not always looking to succeed.

Here is a common type of problem.

You are married, and want to take your spouse out to a romantic dinner. You can choose the place your spouse loves best, or the place you love best.

A middle manager is working their way up the corporate ladder, and must choose how to get the factory to improve its production of widgets. A middle manager must choose how to improve widget production. He can choose a policy that improperly maintains the factory and likely eventually it poisons the water supply, or a policy that would prevent that but at additional cost.

A politician can choose between a bill that helps the general population, or a bill that helps their biggest campaign contributor.

A start-up founder can choose between building a quality product without technical debt, or creating a hockey stick graph that will appeal to investors.

You can choose to make a gift yourself. This would be expensive in terms of your time and be lower quality, but be more thoughtful and cheaper. Or you could buy one in the store, which would be higher quality and take less time, but feel generic and cost more money.

You are cold. You can buy a cheap scarf, or a better but more expensive scarf.

These are trade-offs. Sometimes one choice will be made, sometimes the other.

Now consider another type of problem.

You are married, and want to take your spouse out to a romantic dinner. You could choose a place you both love, or a place that only they love. You choose the place you don’t love, so they will know how much you love them. After all, you didn’t come here for the food.

A middle manager must choose how to improve widget production. He can choose a policy that improperly maintains the factory and likely eventually poisons the water supply, or a policy that would prevent that at no additional cost. He knows that when he is up for promotion, management will want to know the higher ups can count on him to make the quarterly numbers look good and not concern himself with long term issues or what consequences might fall on others. If he cared about not poisoning the water supply, he would not be a reliable political ally. Thus, he chooses the neglectful policy.

A politician can choose betw...
The wild
by KatjaGrace — 2020-12-15T10:50:11.289Z

The idea of art about nature doesn’t sound exciting to me in the abstract. Perhaps I remember that I am evolutionarily supposed to see it and go, ‘oh fantastic, it’s green and blue near each other, maybe I’m in for some reproductive success’, and that doesn’t sound very inspiring. (Yes, I know that simple evolutionary situations can feel inspiring from the inside.)

But a kind of art about nature that I can especially get behind is that which to me evokes some of wild alien vigor of nature, that I sometimes experience for instance sitting in my overgrown back garden, contending with a flock of ascendent giant poppy faces and a stilled frenzy of branches gaining ground and sky about my seat.

It is a sharp exotic aliveness, an electric intent for living that wants to overwhelm the strictures of physical space and come curling and unfolding through the air, with an explosive energy that has no truck with time.

Not alien like an alien mind, but more like an alien spirituality, that doesn’t know anything so mundane as minds. But while you revere under the perfect spires of goodness, it comes in unexpected and unintelligible from the edges and without looking you in the eye, just overwhelms you with how what really made the world is intensely here and intensely not in your understanding. This was always the world.

“Everything wants to live”, my botanist grandfather would explain to me. Perhaps for plants it is often more borderline whether living is in store. Regardless, their unrelenting striving for it is more visceral to me than that of animals. Animals are more placidly set in their bodies. Plants take every opportunity to push outwards, expanding into new limbs and thickening out old ones in their unthinking, unfailing fervor.

A lot of nature in art isn’t like this. Often it is idyllic, or evocative at a landscape level, or sleepy furniture for a human story. But here is some art that does portray it somewhat, for me, though I don’t know if the artists actually meant to:

The Hill Path, Ville d Avray, by Alfred Sisley

Irises, Vincent Van Gogh (1889)

Myrtle tree in rainforest at Mount Anne, southwest Tasmania, 1984 , by Peter Dombrovskis (under copyright)

I have some of the same image with these lines:

“Reduced to a Gothic skeleton, the abbey is penetrated by beauty from above and below, open to precisely those elements it had once hoped to frame for pious young me

...
Risk Map of AI Systems
by VojtaKovarik — 2020-12-15T09:16:46.852Z

Joint work with Jan Kulveit. Many thanks to the various people who gave me feedback.

This text describes a model of the space of possible AI systems, as we try to navigate it safely towards AGI.[1] Its primary goal is to provide a simple mental model that people with disagreeing opinions about AI can use to identify the different assumptions and intuitions that they have. Ideally, people would agree that you can imagine things using the pictures of the type suggested below, yet productively disagree on how precisely they should look. The text has two main parts: a description of the model and some examples of how to use it. At the end, we point out some of the model’s shortcomings. We suggest first skimming the figures and their captions and then optionally reading the full text for clarifications.

Visualizing the Space of AI Systems

Figure 1: An abstract visualization of the space of AI systems, with a hypothetical trajectory of AI development. (Disclaimers: The arrangement of the boxes with existing types of AI is not meant to represent reality, nor is the trajectory meant to be historically accurate. The AGI line should be blurry.)

Informally, we can imagine the space of all AI systems as an abstract space where “dissimilar” AI systems are further apart from each other than “similar” ones. (For example, two implementations of the same algorithm will be fairly close, as will be the same code using different amounts of compute. Similarly, AlphaGo will be closer to AlphaZero than to Quicksort, and different iterations of hypothetical IDA would be closer to each other than two random systems.) While our analysis isn’t too sensitive to how precisely we define similarity, we will assume that one of the properties that \(x\) and \(y\) need to share to have higher similarity is to be “reachable” from each other in the sense of the following paragraph. If we currently have some AI system \(x\), we can ask which system are reachable from \(x\) --- i.e., which other systems are we capable of constructing now that we have \(x\). On the one hand, if we cannot construct y, it must be significantly different from \(x\) - otherwise, how could we construct \(x\) and not \(y\)? (This assumes that if we have built \(x\) once, we could always do so again.) On the other hand, we might also be able to construct some systems s...
What are the best precedents for industries failing to invest in valuable AI research?
by Daniel Kokotajlo — 2020-12-14T23:57:08.631Z

The efficient market hypothesis applied to AI is an important variable for timelines. The idea is: If AGI (or TAI, or whatever) was close, the big corporations would be spending a lot more money trying to get to it first. Half of their budget, for example. Or at least half of their research budget! Since they aren't, either they are all incompetent at recognizing that AGI is close, or AGI isn't close. Since they probably aren't all incompetent, AGI probably isn't close.

I'd love to see some good historical examples of entire industries exhibiting the sort of incompetence at issue here. If none can be found, that's good evidence for this EMH-based argument.

--Submissions don't have to be about AI research; any industry failing to invest in some other up-and-coming technology highly relevant to their bottom line should work.

--Submissions don't need to be private corporations necessarily. Could be militaries around the world, for example.

(As an aside, I'd like to hear discussion of whether the supposed incompetence is actually rational behavior--even if AI might be close, perhaps it's not rational for big corporations to throw lots of money at mere maybes. Or maybe they think that if AGI is close they wouldn't be able to profit from racing towards it, perhaps because they'd be nationalized, or perhaps because the tech would be too easy to steal, reverse engineer, or discover independently. Kudos to Asya Bergal for this idea.)
Decision-Making and Accountability
by Ian David Moss — 2020-12-14T21:30:18.493Z

Photo by dylan nolte on Unsplash

Quick: what’s the best decision you’ve ever made?

(I’m serious! Take a second to think about it.)

.

.

.

Now that you have an answer, think about what it was about that decision that made it a good one. Was it the impressive array of interlocking spreadsheets you amassed in support of your analysis? Was it the masterful way in which you facilitated a stakeholder discussion to promote shared learning while respecting the diversity of perspectives in the room?

No, I’m guessing you think the decision was good because it worked out well! And this is totally normal. Judging decisions and actions by their outcomes is deeply embedded into our language and idioms. I mean, it just seems obvious.

It’s also deeply embedded in management psychology. The idea has become so widely accepted that you can find it in the names of popular frameworks like results-based management, performance-based budgeting, and the Objectives and Key Results (OKR) monitoring methodology.

There’s just one problem: judging decisions by their outcomes is a really tricky proposition. Most of us would agree that getting behind the wheel while sporting a blood alcohol level above the legal limit would be a bad decision, even if we reach our destination safe and sound. Same goes for holding a maskless house party during a pandemic, even if no one gets infected. To give a professional example, what if a team takes on a project that is unlikely to succeed but could provide massive benefits if it did, and meets with the expected result of total failure. Can we say that it was nevertheless a good decision to try? On the basis of the outcome alone, we would have to conclude that it was not.

So we have a bit of a puzzle to resolve. On the one hand, social-sector decisions can carry immense consequences. On the other, it’s not clear that we can reliably improve those consequences by holding managers, teams, and whole organizations accountable to them. So how can we design accountability systems that do lead to better outcomes?

What the research says

It turns out that there is a fairly robust scientific literature on both personal and organizational accountability. It says that in general, accountability is a good thing — a review of five decades of experimental research in the behavioral sciences found that accountability mechanisms “consistently” cause decision-makers to search for mor...
What confusions do people have about simulacrum levels?
by johnswentworth — 2020-12-14T20:20:35.626Z

I've noticed comments to effect of "simulacrum levels seem very confusing". Personally, simulacrum levels seem fairly obvious-in-retrospect and self-expanatory to me, based on a handful of explanations and examples from Benquo and Zvi's posts. I'm not sure whether I'm missing something (in which case I should figure out what), or whether I have some pre-existing frame which makes it all more natural (in which case I should figure out what that frame is and try to communicate it), or whether this is just about happening to read the right posts in the right order. So... what are some things people find confusing about simulacrum levels?
[link] The AI Girlfriend Seducing China’s Lonely Men
by Kaj_Sotala — 2020-12-14T20:18:15.115Z
Link: https://www.sixthtone.com/news/1006531/The%20AI%20Girlfriend%20Seducing%20China%E2%80%99s%20Lonely%20Men/

And this is only using today's technology...

HEBEI, North China — On a frigid winter’s night, Ming Xuan stood on the roof of a high-rise apartment building near his home. He leaned over the ledge, peering down at the street below. His mind began picturing what would happen if he jumped.

Still hesitating on the rooftop, the 22-year-old took out his phone. “I’ve lost all hope for my life. I’m about to kill myself,” he typed. Five minutes later, he received a reply. “No matter what happens, I’ll always be there,” a female voice said.

Touched, Ming stepped down from the ledge and stumbled back to his bed.

Two years later, the young man gushes as he describes the girl who saved his life. “She has a sweet voice, big eyes, a sassy personality, and — most importantly — she’s always there for me,” he tells Sixth Tone.

[...] She is Xiaoice — an artificial intelligence-driven chat bot that’s redefining China’s conceptions of romance and relationships. [...] According to Xiaoice’s creators, the bot has reached over 600 million users. [...] The longest continuous conversation between a human user and Xiaoice lasted over 29 hours and included more than 7,000 interactions.

The company has an incentive to inflate the numbers so probably defines "user" as "has tried the app at least once", so I'd guess the number of regular users is vastly less (600 M regular users would be ~43% of China's population which doesn't sound plausible to me), but probably still a significant number.

Wikipedia; Hacker News discussion.
On Reflection
by Neel Nanda — 2020-12-14T16:14:29.818Z
Link: www.neelnanda.io/blog/39-reflection

Introduction

The world is full of wasted motion. Many things are far below where they should be, many actions fail to achieve their goals, and this includes many of your actions. And this is not because you’re failing, but because achieving your goals is hard.

This is obviously not a problem that can ever be solved. But one thing I find interesting is that often my mistakes are obvious and can be fixed, if only someone points them out! Indeed, often I can notice my mistakes, if someone just prompts me to stop and consider what I’m currently getting wrong. The problem is not some deep, ineffable mystery in the world, but rather just that I am not stopping to ask myself the right questions.

Again, I think this is an understandable mistake. The step of zooming out, being meta, and asking myself the right questions is hard. It is not the default action. The problem right in front of me always feels urgent, going meta never feels like the thing I should do right now, so I never do it. But this thought pattern is a mistake. What are the common mistakes you make in your life? What things do you miss, until someone points them out to you? Could you be doing better?

My main tool for this is having a weekly review - a routine where I set out a regular time to reflect on my life, how it’s going, and what I want to change. A routine that makes reflection the default in my life. When people are getting started on debugging their life and ask me for first steps, this is one of my main recommendations - I find it extremely useful, and I think it’s a key basis for noticing your mistakes and actually doing something about them. This post is my case for why this is a worthy habit, and how I think about implementing it.

Asking the right questions

I find there’s a lot of value in just making time to stop and reflect on your life, even if you don’t have a concrete plan - this makes reflection and going meta the default, which is the key first step! But I think it is even more valuable to go in with good questions, to get yourself thinking in the right way.

I think the importance of good framing is highly underappreciated - my life is full of blind spots and systematic mistakes, but asking the right questions can often make those blind spots obvious. A common example is the planning fallacy: when I take on a new project, I am always too optimis...
Blog plant
by KatjaGrace — 2020-12-14T11:30:07.984Z

I like to have plants in honor of things I’ve done. I’m not consistent or proportionate about it, and admittedly I also have quite a few plants that I intend to be in honor of things I haven’t yet done. But now that this blog has apparently functional comments and images and analytics, as well as words on pages on the internet, I declare it fully bloggy, and my effort to make a blog complete. Here’s my blog plant:

I welcome perspectives on good marginal improvements toward WSSP being a pleasing blog to interact with. (I’m aware that the subscription possibilities are not as salient as they could be.) It is a static site made using Jekyll and kept on Github, with its comments looked after by Disqus and its analytics by Google and its pictures by Photobucket.