trentmkelly/LessWrong-43k huggingface dataset:
Book Review: Spark! How exercise will improve the performance of your brain
by Caridorc Tergilti — 2021-10-01T22:43:44.292Z

Physical exercise is HUGELY important for both your physical and mental health. This books focuses mostly on the mental side and just from the table of contents of the book we see the wide variety of problems where exercise can help:

Learning
Stress
Anxiety
Depression
Addiction
Hormonal changes
Ageing

While the book focuses on the effects of exercise on the "bad" versions of this problems, it is clear that if exercise can help in the worst cases it can also help in the sub-clinical cases of feeling a bit stressed or sad in our day to day lives.

Learning

The book begins by describing the fascinating experiment "Zero Hour PE" carried out to the West of Chicago, in Naperville District 203, involving 19000 students in 2003.

Students were free to come to school one hour earlier than usual and spend that hour exercising. They performed fitness activities such as running rather than team sports, where there can be a lot of waiting around and a much more diluted exercise.

In order to motivate people with bad athleticism genetics, the grading was based on effort rather than absolute goals, by using the at the time futuristic technology of heart rate monitors. The kids where graded based on how much time they stayed in their target heart rate zone. An extremely important feature necessary to make the habit of training more likely to stick for life is to give a wide variety of possible sports to kids, in order to allow them to find something they are good at and associate it with a feeling of proficiency and mastery.

The program brought a slight improvement in the students grades with respect to the control group with 17% improvement for kids participating in the program versus a 10.7% improvement for kids not participating. Think about the massive effect of this change compounded for 10 years of education. We must also remember that the alternative was sleeping, and, considering how often teenagers are sleep-deprived, I would expect that removing an hour of sleep would bring about a substantial decline of academic performance, so, given that here we see the opposite, in my opinion the effect of exercise is quite larger than 6.3%.

But life is not all about books and learning, avoiding jail is also really good. At Woodland Elementary School in Kansas City, expanding PE class from once a week to 5 times a week with a focus on cardiovascular fitness decreased the number of violent inci...
The Simulation Hypothesis Undercuts the SIA/Great Filter Doomsday Argument
by Mark Xu — 2021-10-01T22:23:23.488Z

This post was written by Mark Xu based on interviews with Carl Shulman. It was paid for by Open Philanthropy but is not representative of their views.

Summary

The absence of evidence for extraterrestrial life suggests the existence of a Great Filter, a set of factors that in combination drive the probability of a given star producing expansionist interstellar civilization very low.

The self-indication assumption (SIA) says that you should think it more probable that you exist in worlds where there are more observers faced with your exact observations.

Several SIA Doomsday Arguments note that there can be more naturally developed observers on primitive planets if the Great Filter lies after us, e.g. civilizations like ours are incapable of space travel, self-destruct, or are suppressed by hidden aliens; thus such arguments claim SIA gives overwhelming reason to expect a civilization like ours to fail to expand.

However, these so-called SIA Doomsday Arguments don’t take into account the possibility of mature civilizations simulating previous ones.

SIA overwhelmingly favors the hypothesis of simulations because it allows far more observers in our apparent position.

The number of such simulations is maximized when colonization is frequent enough for a large share of all resources to be colonized, but is largely indifferent about precise frequencies given that; so SIA does not suggest that unsimulated civilizations that look like Earth face a late Great Filter.

Note that we are not endorsing the underlying SIA decision making framework here, only discussing whether certain conclusions follow it. In dealing with such anthropic problems we would prefer approaches closer to Armstrong’s Anthropic decision theory, which we think is better for avoiding certain sorts of self-destructive anthropic confusions.

Introduction

The search for extraterrestrial intelligence has not yet yielded fruit. Hanson (1998) argues that this implies the probability of life evolving on a planet and becoming visible must be extremely low, a so-called Great Filter. Such a filter could be located in many possible difficulties: abiogenesis, intelligent life, interstellar colonization, etc. Humanity’s future prospects may depend on whether the difficulties have already passed or lie ahead. Thus we are left with a troubling question: how far along the filter are we?

Sandberg et al. (2018) obs...
Whole Brain Emulation: No Progress on C. elegans After 10 Years
by niconiconi — 2021-10-01T21:44:37.397Z

Since the early 21st century, some transhumanist proponents and futuristic researchers claim that Whole Brain Emulation (WBE) is not merely science fiction - although still hypothetical, it's said to be a potentially viable technology in the near future. Such beliefs attracted significant fanfare in tech communities such as LessWrong.

In 2011 at LessWrong, jefftk did a literature review on the emulation of a worm, C. elegans, as an indicator of WBE research progress.

Because the human brain is so large, and we are so far from having the technical capacity to scan or emulate it, it's difficult to evaluate progress. Some other organisms, however, have much smaller brains: the nematode C. elegans has only 302 cells in its entire nervous system. It is extremely well studied and well understood, having gone through heavy use as a research animal for decades. Since at least 1986 we've known the full neural connectivity of C. elegans, something that would take decades and a huge amount of work to get for humans. At 302 neurons, simulation has been within our computational capacity for at least that long. With 25 years to work on it, shouldn't we be able to 'upload' a nematode by now?

There were three research projects from the 1990s to the 2000s, but all are dead-ends that were unable to reach the full research goals, giving a rather pessimistic vision of WBE. However, immediately after the initial publication of that post, LW readers Stephen Larson (slarson) & David Dalrymple (davidad) pointed out in the comments that they were working on it, the two ongoing new projects of their own made the future look promising again.

The first was the OpenWorm project, coordinated by slarson. Its goal is to create a complete model and simulation of C. elegans, and to release all tools and data as free and open source software. Implementing a structural model of all 302 C. elegans neurons in the NeuroML description language was an early task completed by the project.

The next was another research effort at MIT by davidad. David explained that the OpenWorm project focused on anatomical data from dead worms, but very little data exists from living animals' cells. They can't tell scientists about the relative importance of connections between neurons within the worm's neural system, only that a connection exists.

The "connectome" of C. elegans is not actually

...
Proposal: Scaling laws for RL generalization
by  — 2021-10-01T21:32:54.566Z

In this post, we (Alexander Meulemans, David Lindner, Florian Dorner) propose to study scaling laws relating the generalization capability of Reinforcement Learning (RL) agents to problem complexity, inspired by DeepMind’s recent success in training agents that perform well in a range of environments. We motivate studying such scaling laws by highlighting variable problem complexity as a key difference between language modeling and RL, and then propose a few concrete experiments that we hope readers with some knowledge of reinforcement learning might be able to perform.

Motivation

DeepMind recently trained “generally capable” agents behaving somewhat competently in an environment with procedurally generated maps and tasks called XLand, using open-ended reinforcement learning. This work has gotten a lot of attention and sparked discussions about the result’s relevance for AI timelines, including parallels to GPT being drawn. On one hand, the comparison to GPT makes sense, as the XLand results provide a proof of concept for achieving fairly general capabilities in the realm of agentic behaviour by using enough compute, just like GPT did in the realm of language modelling. On the other hand, it seems far from clear whether this advance in RL will have the same immediate economic effect language models seem to be having. As in language modelling, the training distribution for XLand will likely need to be related to the tasks the system is ultimately going to perform[1]. But unlike in language modelling, where we train a system on large amounts of text from the same language(s) upstream tasks will be in, XLand still appears to be far from any real-world environment.

Therefore, to extrapolate from results in XLand, we don’t just require the same scaling laws relating performance to compute and dataset size we have for language models, but we also need to introduce an extra dimension for problem complexity[2] and perhaps closeness to real world applications[3]. As an analogy, imagine GPT-3 would have only been trained and evaluated on abstract language, or sentences beginning with a subject, using the vocabulary of basic English. How confident would you be in extrapolating from such a model’s scaling with compute to the less restricted language models we have at this point?

We believe that a better understanding of the relationship between problem complexity and the ...
Meta learning to gradient hack
by Quintin Pope — 2021-10-01T19:25:29.595Z

Summary:

I saw some posts (here, here, here and here) discussing whether a neural net can "protect" a subnet from modification by gradient descent. I directly investigate this possibility using meta learning to find a neural network initialization that

Models the function f(x) = 1
Is very difficult to re-train with SGD to model another function (specifically f(x) = sin(x))

The meta learner is able to greatly reduce the efficacy of SGD during retraining by increasing the magnitude of the linear weight values (to over 100,000 in some cases). Edit: after closer examination (thanks evhub), it seems like the meta learner uses some other approach to prevent retraining. The increased weight magnitude was incidental. However, it can't protect the final layer's bias term from SGD, so instead the meta learner initially models f(x) = 1.68, so that it's close to modeling f(x) = 1 after the base optimizer reduces the final layer's bias term.

I've included my code and my meta trained model in this GitHub.

Background:

There are two functions: the base function and the meta function. The freshly initialized network should model the meta function. Then, the base optimizer will train the network to model the base function. My goal was to find a network initialization that continues to model the meta function, even after the base optimizer trains the network to model the base function.

It turns out that the process of gradient descent optimization is itself differentiable. Thus, we can start with a random network initialization, and repeat the following meta training cycle:

Train a freshly initialized network to model the base function.
Evaluate the trained network on the meta function.
Backpropegate from the loss on the meta function, through the training process, to the network initialization.

Methods:

I use the learn2learn framework to handle the meta learning. I train a 4 layer feed forward neural network (sizes 256, 128, 64, 64). Each cycle of training on the base function lasts between 70 and 120 training steps, with batch size 1024, a learning rate of 0.01, using the L1 loss between the output and f(x) = sin(x). The meta optimizer uses a learning rate of 0.1 and momentum of 0.92. Both base and meta optimizers use SGD. Additionally, I include the network’s initial performance on the meta objective (before any training on the base objective) in the meta loss function. ...
How does forecast quantity impact forecast quality on Metaculus?
by CharlesD — 2021-10-01T19:09:21.347Z

Cross-posted from the EA Forum

Introduction

Many people, myself included, worry about how much we can trust aggregate forecasts (e.g., the

Metaculus community median

) that are based on the predictions of only a small number of individuals. This consideration also came up in

my recent post

analysing predictions of future grants by Open Philanthropy, where having few predictors left me unsure of how much we could really trust the aggregate predictions[1]. How justified is this worry? In other words, to what extent is the number of individual predictors on a question correlated with the accuracy of the aggregate forecast on that question?

And to what extent does increasing the number of predictors on a question itself cause the aggregate forecast on that question to be more accurate?

The first question is relevant to whether and how to use the number of predictors as a proxy for how much to trust an aggregate forecast. The second question is relevant to how much question writers and others should aim to increase the number of predictors on a question, such as by creating binary rather than continuous questions (since binary questions generally receive ~twice as many predictions as continuous ones [2]), highlighting how forecasts on the question will inform decisions and thus be impactful, or increasing how many people use Metaculus.

This post mostly attempts to answer the first correlational question, but also discusses the second causal question.

Key points

Accuracy of an aggregate prediction (as measured by

Brier scores

) improves as the number of predictors rises, with the marginal improvement of the aggregate forecast given an X% increase in number of predictors (for any X) looking larger when there are lower (single digit) numbers of predictors.
The evidence suggests Brier score improvement after reaching 10 predictors is slow, and in practice I think I will now have a very similar level of confidence in a prediction if it has 10 or 30 predictors, whereas before I might have more easily dismissed the former.
Criticisms have been raised of Metaculus in particular in the past that its points system incentivises lazy predictors to “herd” around the existing median to accumulate points without needing to spend much time forecasting. I investigated this, and it doesn’t look like there is much evidence for herding among Metaculus users to any noticeable extent, or i

...
Forecasting Newsletter: September 2021.
by NunoSempere — 2021-10-01T17:06:38.771Z

Highlights

Facebook's Forecast folds
Hedgehog Markets now operational
Polygon and Augur announce a $1M liquidity rewards program

Index

Prediction Markets & Forecasting Platforms
Blog Posts
Long Content
In The News

Sign up here or browse past newsletters here.

Prediction Markets & Forecasting Platforms

Hedgehog markets

Hedgehog markets (a) launches on the Solana mainnet, meaning that their prediction markets are now open to real-money bets. A brief explanation of how their no-loss competitions work and why they're interesting can be found on the July edition of this newsletter (a). Their first competition offers $100k in prizes (a).

Hedgehog Markets’ first no-loss competitions

Hedgehog's first markets are focused on sports and on the prices of various crypto assets. But their no-loss model is uniquely suitable for more long-term markets, so I'll be watching out for that.

Metaculus

SimonM (a) kindly curated the top comments from Metaculus this past August. They are:

RyanBeck (a) points out some shortcomings in Arnold Kling's fortified essay on Two Theories of Inflation
Charles (a) looks at Macron's re-election chances in 2022
SimonM (a) calculates a base rate for US government defaults linked to the debt ceiling using Laplace's law
ugandamaximum (a) looks at Trump's electoral prospects in 2024
Comments on this question (a) wrangle with how to measure AI damage
galen (a) makes a well-informed comment on What will be the Hue (in angular degrees) of Pantone's Color of the Year for 2022? (a)

Metaculus is advertising for various job openings (a): Lead Developer, Senior NLP/Machine Learning Engineer, Effective Altruism Question Author, and “analytical storyteller” (a).

Polymarket

Polymarket writes about Why You Should Get Your News From the Blockchain (a) on one of the largest crypto newsletters. Meanwhile, Polymarket keeps experiencing outages because of troubles with The Graph—one of the services it uses to connect its webpage with its blockchain contracts.

Will inflation be 0.4% or more from July to August?, Polymarket

Last month, I lost a small amount of money following forecasts from Star Spangled Gamblers (a) and Karlstack (a), respectively on Britney Spears' conservatorship and August US inflation numbers. While I still find both sources very entertaining, I probably won't be following their lead on Polymarket in the future.

Hypermind

Hypermind launches another tournament: ...
What Selection Theorems Do We Expect/Want?
by johnswentworth — 2021-10-01T16:03:49.478Z

This post contains some of my current best guesses at aspects of agent type signatures for which I expect there are useful

Selection Theorems

, as well as properties of selection optima which I expect are key to proving these type signatures.

This post assumes you have read the

intro post on the Selection Theorem program

. The intended audience is people who might work on the program, so these blurbs are intended to be link-heavy hooks and idea generators rather than self-contained explanations.

Aspects Of Agent Type Signature

These generally reflect my own current high-level thinking (though not all of the ideas are mine). For a different (and excellent, and better-written) take on similar questions from someone else, check out Mark and Evan’s

Agents Over Cartesian World Models

.

(A -> B) -> A

Scott Garrabrant’s post

(A -> B) -> A

provides the basic starting point for a Hofstadter-esque agent type signature. Breaking it down:

A represents the agent’s “actions”.
B represents the “outcomes” of those actions.
(A -> B) is the agent’s model of what outcomes result from an action. It’s a function which takes in actions, and spits out outcomes (that’s the arrow notation).
(A -> B) -> A is the full agent: it takes in the model (A -> B), and then outputs action A as a function of the model.

In pure abstract functional terms, the overall type signature is (A -> B) -> A. Lots of other type information is missing from this - there’s nothing about the types of the actions or the outcomes, for instance. But it seems like the right basic setup at a high level.

The model (A -> B), shown diagrammatically as a cloud, causes the choice of action A. Note that there is actually no loop here! There’s just the

Hofstaderian

“strange loop”, in which node A depends on the model of later nodes, not on the later nodes themselves.

(A -> B) -> A + Abstraction

Generally speaking, low-level physical reality seems to be made of something like a

giant causal network

with symmetry

. It doesn’t have any built-in “clouds” or “strange loops”. So, how do we get (A -> B) -> A structures in the physical world?

I expect that some chunks of the world

naturally abstract

into models with an agent-like type signature. In other words, approximately-all of the information about that chunk which propagates “far away” in the low-level causal network of the universe is summarized ...
Contra Paul Christiano on Sex
by George3d6 — 2021-10-01T11:17:29.891Z
Link: https://cerebralab.com/Contra_Paul_Christiano_on_Sex

Contra Paul Christiano on Sex

I have come to think that many people don’t understand evolution. Not in the “believe that a sentient deity created the universe after a template” sense. A lot of people seem to “get” the idea of evolution in broad strokes but then fail at explaining its various oddities or making inferences based on it.

I count myself and basically everyone I know among these people because evolution is actually surprisingly hard. The reasons why its hard to get are exactly why understanding it better helps in a multitude of other domains.

Evolution is a simple principle, but when faced with the oddities of reality, it gets exponentially complex. If you were to think of evolution with causality backwards, it’s like a programmer trying to write a highly optimized piece of software that, in theory, could fit into 100 lines of lisp. But, when faced with the realities of hardware and customers, it turns into a sprawling codebase with thousands of files, hundreds of dependencies and a mixture between multiple languages.

It starts “simple”, literally, in that it can start with just RNA, a model under which “survival of the fittest” works in its most simplistic form, sometimes in a very literal lock-and-key sense of “fit”. But then it quickly (d)evolves into things like apes and cetaceans, which make absolutely no sense when viewed in this framework.

This mirrors the real-world process of engineering, and it provides a very good foil for any sort of “positive feedback” theories ala intelligence explosion.

I am in no position to explain evolution, but maybe I can enlighten some people a bit by showcasing how people get it wrong. In this case, I want to harp on something Paul Christiano (coincidentally an advocate of intelligence explosion type theories) wrote about why sex is useful. Paul Christiano is really smart. So if he can make these kinds of errors, I will assume they are fairly common, and thus this correction might be rather useful, rather than just attacking a strawman.

He writes about the role of sex, to me, his explanation sounds similar to how most biologists in the 20th century would have (wrongly) thought about it:

From the perspective of an organism trying to propagate its genes, sex is like a trade: I’ll put half of your DNA in my offspring if you put half of my DNA in yours. I still pass one copy of my genes onto the next generation per unit

...