trentmkelly/LessWrong-43k huggingface dataset:
Air Quality and Cognition
by chw — 2021-04-08T21:42:54.870Z

Overview

Air pollution and concerns about its effects continue to rise globally. However, policymakers and environmental regulators have neglected the effects of air pollution on cognitive functioning. A growing body of research points to the risk of exposure to high levels of pollution. It’s unclear how well all of the research will hold up under replication, but an abundance of studies point to clear detrimental effects of air pollution on cognition and decision-making.

Several individuals and organizations are concerned about the lesser-known effects of air pollution. For example, Matt Yglesias wrote an

article

in 2019 highlighting recent research on air quality’s effects on cognitive ability, productivity, decision-making, and dementia and Alzeihmer’s. Patrick Collison’s

blog post

on the issue appeared to inspire Ygelsias’s article.

Evidence suggests that air pollution has significant effects on both

short-term

and

long-term

cognition. While some studies used

natural

environmental

variations

to test the longer-term correlation between air pollution and cognition, other studies created

isolated

,

laboratory

experiments to test the short-term effects of air pollution on cognition. Both have yielded statistically significant results pointing to the negative effects of air pollution on cognition.

While air pollution clearly negatively affects cognition, its effects are nuanced and uneven. Cognition is an umbrella term encompassing many different domains of cognition. Generally, the six main domains of cognition are visuospatial/motor function, attention/concentration, learning/memory, executive functioning, social cognition/emotions, and language/verbal skills. Air pollution affects all domains of cognition, but the severity of the effect depends on brain matter, gender, age, and the affected domain of cognition.

In particular, multiple studies find stronger negative correlations between air quality and verbal test scores than math test scores. “Gray matter represents information processing centers, and white matter represents the networking of – or connections between – these processing centers. Mathematics abilities, which require more local processing, mainly depend on gray matter. While language skills, which require integrating and assimilating information from distributed gray-matter regions in the brain, mainly rely on white matter … A lar...
A System For Evolving Increasingly General Artificial Intelligence From Current Technologies
by Tsang Chung Shu — 2021-04-08T21:37:32.837Z

The Problem

The development of general artificial intelligence is hampered by engineers’ inability to create a system capable of assessing its own performance, and thereby of improving itself. A machine capable of these two tasks would grow more intelligent at an exponential rate — the “intelligence explosion” that is often described as a precursor to a technological singularity.

The impossibility of self-referential improvement is not a reflection of present limits on technology, but of the fundamental laws of mathematics. As Alfred Tarski proved in the 1930s, it is “impossible to construct a correct deﬁnition of truth if only such categories are used which appear in the language under consideration.”[1] In other words, it is impossible to accurately assess a system from within that system. To evaluate its own performance, a program would need to be more advanced than itself — an obvious paradox.

This raises an important question: if systems capable of self-improvement are apparently impossible to build, why are they so prevalent? You are reading this using two of them - the internet and your brain — and other examples abound in nature. The answer is that these systems are not purely self-referential. In every case they rely upon some external and incontrovertible measure by which they can objectively evaluate and improve their performance.

In this paper we argue that while it is indeed impossible to construct an accurate self-referential evaluation system in an electronic context, it is possible to establish a universal and objective measure of performance that would render such a system unnecessary. This would open the way for the development of increasingly general forms of artificial intelligence.

Evaluating Intelligence

Current methods for measuring intelligence are based on a correctness heuristic: intelligence is the ability to solve problems correctly. We propose replacing this with a survival heuristic. In our system, intelligence is the ability to survive.

As a yardstick, survival is subject to none of the paradoxes and ambiguities that bedevil correctness. If I make a decision and survive as a result, while you make a different decision and die, the fact can be measured accurately, easily and with no possibility of contestation.

In fact, the main problem with using survival as a measurement is a practical one. Survival is a single-round game and at least...
AXRP Episode 6 - Debate and Imitative Generalization with Beth Barnes
by DanielFilan — 2021-04-08T21:20:12.891Z

YouTube link

This podcast is called AXRP, pronounced axe-urp and short for the AI X-risk Research Podcast. Here, I (Daniel Filan) have conversations with researchers about their papers. We discuss the paper and hopefully get a sense of why it’s been written and how it might reduce the risk of artificial intelligence causing an existential catastrophe: that is, permanently and drastically curtailing humanity’s future potential.

One proposal to train AIs that can be useful is to have ML models debate each other about the answer to a human-provided question, where the human judges which side has won. In this episode, I talk with Beth Barnes about her thoughts on the pros and cons of this strategy, what she learned from seeing how humans behaved in debate protocols, and how a technique called imitative generalization can augment debate. Those who are already quite familiar with the basic proposal might want to skip past the explanation of debate to 13:00, “what problems does it solve and does it not solve”.

Daniel Filan:
Hello everybody, today I’m going to be talking to Beth Barnes. She’s currently a researcher at OpenAI and before that she was the research assistant to the chief scientist at DeepMind, so that’s Shane Legg. Today we’ll be talking about her work related to the topic of AI alignment via debate. Beth, welcome to AXRP.

Beth Barnes:
Thanks for having me.

Daniel Filan:
All right. So I guess my first question is what is AI safety or AI alignment via debate? What’s the idea?

Beth Barnes:
So debate is pretty closely related to IDA or Iterated Distillation and Amplification, Paul’s idea. So I guess there’s several different ways to explain it. One is, what we want from sort of an alignment technique is something that for everything the model knows, we can extract that or create an overseer that knows all the things the model knows and therefore can oversee it adequately.

Beth Barnes:
And debate is one way to do that and to do that kind of efficiently. So the structure that’s going on there, so in IDA you have this implicit tree that’s sort of analogous to the tree in humans consulting HCH, which is like an implicit tree, which could be like, questions and answers to those questions, and sub-questions, and answers to those sub-questions, and questions to help with answering those, and so on.

Beth Barnes:
Debate you can think of as a different way to interact with ...
Homeostatic Bruce
by matejsuchy — 2021-04-08T19:13:05.723Z
Link: https://matejsuchy.substack.com/p/homeostatic-bruce

Epistemic Status: Speculative. CW: trauma

Humans are

Adaptation Executors, Not Fitness Maximizers

.

— some guy

0 Summary

Bruce is a weird guy, and I’m not the first to hypothesize that he serves an adaptive purpose. What I hope to add to the discussion is to distinguish between optimistic and pessimistic flavors of the adaptive explanation, and introduce possible mechanisms by which to optimally respond to whichever mix of the ancestral flavors best corresponds to our modern gene pool.

I. Intro

For as long as I can remember, I’ve been astounded at the ability of humans to underperform their potential. While surely there is a real, cognitive processing “speed-limit” for everyone — not everyone can pull a von Neumann, as it were — the vast majority of humans seem to be driving 15 in an 85 in terms of life success, however they define it. My sense is that this claim is not so controversial, so I won’t dwell on it. Suffice it to say I recently read a popular and successful book recommended by a teenage family member which was written by someone who, according to the anecdotes in the book itself, likely would score at around 70 on an IQ test. While the book was no

Godel, Escher, Bach

, it was not undeserving of its success; in any case, its author certainly was not. One could say that he was driving 45 in a 50 — and crushing the

game of talents

. There are people five to six standard deviations above this individual in intelligence who will massively underperform him, even according to their own personal value paradigm. I probably know a few at university. And, while this case might be especially prototypical, I don’t believe it’s exceptional.

What’s more, it seems to me that the achievement gap increases with intelligence, to a degree exceeding what one would expect using naive economic adjustments. That is to say, yes, using simple Econ 101 principles we’d expect highly intelligent people to work somewhat less than less intelligent people in order to “buy” more leisure and to see diminishing returns to intelligence otherwise, as they likely were acculturated to a life/work strategy that is optimal for the “average” individual and increasingly so not for them. But I think the real gap far outstrips these two first-order effects, to such a degree that it’s conceivable that the success returns to intelligence might become negative in the average case at the tails.

There...
A possible preference algorithm
by Stuart_Armstrong — 2021-04-08T18:25:25.855Z

I have previously discussed the constructive process of generating a reward function from the mess of human preferences.

But, let's be honest, that process was somewhat messy and ad hoc. It used things like "partial preferences", "identity preferences", "one step hypotheticals", "partial meta-preferences about the synthesis process", and so on.

The main reasons for this messiness are that human preferences and meta-preferences have so many features and requirements, and many of the intuitive terms and definitions in the area are ambiguous. So I had to chunk different aspects of human preferences into simple formal categories that were imperfect fits, and deal with each category in a somewhat different way.

Here I'll try and present a much simpler system that accomplishes the same goal. To do so, I'll rely on two main ideas: model splintering and delegation to future selves - via present selves.

Model splintering allows us to defer most of the issues with underdefined concepts. So we can use those concepts to define key parts of the preference-construction process - and punt to a future 'general solution to the model splintering problem' to make that rigorous.

Delegation to future selves allows us to encode a lot of the more complicated aspects of the process to our future selves, as long as we are happy with the properties of these future selves. Again, model splintering is essential to allowing this to function well, since the relevant 'properties' of our future selves are themselves underdefined.

Then constructing the human preference function becomes an issue of energy minimisation.

The reward function algorithm

The setup

Let hi be a human at time t=0. An AI will attempt to construct Ri, a suitable reward function for that human; this process will be finished at time t=τ (when we presume the human is still around). From that moment on, the AI will maximise Ri.

The building blocks: partial preferences

Defining partial preferences has been tricky. These are supposed to capture our internal mental models - what happens when we compare images of the world in our minds and select which one is superior.

In the feature language of model splintering, define partial preferences as follows. Let f be a feature, and F a set of 'background' features. Let ρF be the set of values that F can take - a possible 'range' of the features F.

Then define the real number

ri(f,F,x,y,ρF)

as...
If you don't design for extrapolation, you'll extrapolate poorly - possibly fatally
by Stuart_Armstrong — 2021-04-08T18:10:52.420Z

Given an AI A, let E be the set of typical environments in which we know its behaviour - or know something about its behaviour. A sort of general training environment, if you will.

Let E+ be the set of all environments that A might be deployed on, or might generate itself.

There once was/is/will be an algorithm A. We have tested it on every environment in the set E, and it works as desired. Or maybe we just have strong argument or intuitions that it works on all of E. In any case, its performance on E is not in serious question.

Now, we want to use it on the much larger set of environments, E+. This includes environments very different from any in E, and may also include environments that A will create itself. How can we know that A will still perform well?

This is the standard training-testing set problem. Or the standard out-of-distribution. Or the model splintering issue I described in a previous post.

Or, at a more meta level, the whole of the AI safety problem itself. After all, an aligned AI will be designed and tested in a limited environment, and then allowed to influence all of human history. Successfully navigating that extension will be crucial.

Common design types to date

Various algorithm designs have been proposed to ensure success when extrapolating from E to E+. I'll group them into five loose categories:

Well-grounded expected utility maximiser.

Narrow low-impact and tool AI designs.

Mild optimisation AI designs.

Extrapolation-focused designs.

Non-optimiser partially constrained designs ("the rest").

Well-grounded expected utility maximiser

This is the simplest and in many ways clearest design. If we design an AI CEO such that its utility function increases with money, and if that concept of money is well-grounded, then we are confident the AI CEO will continue maximising money across all the potential weirdness of the future it will create.

The failure modes of this design are well-understood. If the concept of money is not well-grounded, then the AI will end up wireheading itself to some 'money variable'. If other things of value are not included in the utility function, then the world, humanity, and everything else in the universe may end up sacrificed to piles of cash[1].

Narrow low-impact and tool AI designs

In this category I include my low-impact designs, most oracle AIs, tool AIs, and the AI services approach.

Here, the main focus is on ...
Insights from "All of Statistics": Statistical Inference
by Rafael Harth — 2021-04-08T17:49:16.270Z

(This is the second of two posts on the textbook All of Statistics. Click here for post I.)

4. Fundamentals of Statistical Inference

Probability theory is about using distributions to derive information about outcomes. Given the assumption that X∼Poisson(3), we can compute probabilities of outcomes the form Pr(X=k). Statistics is about the opposite: using outcomes to derive information about distributions.

The book exclusively considers the setting where we observe independently sampled data points x1,...,xn∼X where the distribution of X is unknown. It is often convenient to talk about the RV that generated the k-th sample point, the logical notation for which is Xk. The book uses upper-case letters for both (e.g., 'we observe data X1,...,Xn"), not differentiating between the RV that generates the k-th point (i.e., Xk) and the k-th point itself (xk).

On the highest level, one can divide all inference problems into two categories: parametric inference and non-parametric inference. In parametric Inference, we assume that we know the family of distributions that X belongs to (Binomial, Poission, Normal, etc.). In this case, the problem reduces to inferring the parameters that characterize distributions in that family. For example, if we observe data on traffic accidents, we may assume that X∼Poisson(λ), and all that's left to estimate is λ. Conversely, in the context of non-parametric inference, one does not assume that X belongs to any one family and thus has to estimate the distribution directly.

5. Bayesian Parametric Inference

5.1. The concept

Let Θ be the parameter we wish to learn about. Since we don't know its value, we treat it as a RV that ranges over several possible values. Furthermore, let x=(x1,...,xn) be our observed data, and let X=(X1,...,Xn) be the RVs that generate this data.

Bayes' theorem is an equation relating PrB(A) to PrA(A). (With PY(X):=P(X|Y).) In our case, we are interested in terms of the form P[X=x](Θ=θ), and we know how to compute terms of the form P[Θ=θ](X=x) because once we fix a value of Θ, the distribution is known and we can compute the required probabilities. Thus, Bayes' theorem gives us a way to reduce a problem of statistical inference to a problem of probability theory.

Alas, we have

P[X=x](Θ=θ)=P[Θ=θ](X=x)P(Θ=θ)P(X=x)

Our prior on θ will virtually always be continuous. Let h be the pdf of this prior. The Xi may be continuou...
Insights from "All of Statistics": Probability
by Rafael Harth — 2021-04-08T17:48:10.972Z

All of Statistics is yet another book on Miri's research guide. This book has also been reviewed by Alex Turner.

This post is something like an abridged summary. It's definitely not a self-contained introduction to the field; I've included all important subjects as sections mostly to remind myself, but I skip over many of them and make no effort to introduce every concept I write about. The primary purpose is learning by writing; beyond that, it may be useful for people already familiar with statistics to refresh their knowledge.

I'm also including more of my own takes than I've done in my previous posts. For example, if I sound dismissive of Frequentism, this is not the book's fault.

My verdict on the book's quality that it's one of the weakest ones Miri's guide (but the competition is tough). This is mostly based on my recurring feeling that 'this could have been explained better'. Nonetheless, I'm not familiar with a better book on statistics.

The book is structured in three parts: (1) Probability, (2) Statistical Inference, and (3) Statistical Models and Methods. Since this post was getting too long, I've decided to structure my summary in the same way: this post covers probability, the other post covers Statistical Inference, and there may be a third post in the future covering Statistical Models and Methods.

1. Probability spaces and distributions

The general notion of a probability space includes σ-algebras, but the book gets by without them. Thus, a probability space is either discrete with a probability mass function (pmf) or continuous with a probability density function (pdf). I denote both by writing f. In both cases, f determines a distribution P that assigns probability mass to subsets of the sample space Ω.

1.1. Events

1.2. Independence

1.3. Finite Sample Spaces

1.4. Conditional Probability

Given two events A,B⊂Ω, the formula everyone learns in high school is

P(A|B)=P(A∩B)P(B)

In university courses, it may also be mentioned that P(⋅|B) is a probability space for any nonempty set B⊂Ω.

I prefer to take that as the starting point, i.e., "evaluate event A in the probability distribution over B" rather than "plug A into the formula above". This seems to be what people do when they're not trying to do math. If I hear that a die came up either 2 or 3 or 5 and consider the probability that it's at least a 5, I don't think "let's take the intersection of {5,6...
The Japanese Quiz: a Thought Experiment of Statistical Epistemology
by DanB — 2021-04-08T17:37:14.653Z

This post is an excerpt from my book,
"Notes on a New Philosophy of Empirical Science".
I've found it hard to publish small pieces from the book,
because each concept is inextricably linked in my mind to all the other concepts.
But I think this section and the related discussion can stand on its own.
It starts with a story about a physicist named Sophie,
who encounters an odd group of language activists while on holiday.

The Society for the Promotion of the Japanese Language

After spending many months developing her theory,
building the software to test the theory,
and writing up the results for publication,
Sophie decided to take a long vacation on the West Coast.
She visited the cafes and bookstores of Vancouver and Seattle,
and took long hikes in the beautiful parks of the Pacific Northwest.
Then she took the train down through Oregon and Northern California to San Francisco,
where she planned to enjoy the mild weather and visit some old friends who worked in the tech sector.

During her stay in San Francisco,
Sophie decided to visit the city's Japantown,
because she had a special appreciation for artisanal Japanese kitchenware,
which she found to be useful not just for cooking but sometimes also
for ad hoc repair work in the lab.
As she was window shopping in the neighborhood,
a strange sight caught her attention.
It was a booth, manned by well-dressed, older Asian people,
who seemed to be distributing pamphlets.
At first she thought it was an social activist group,
but then she noticed that a poster with the words
"Society for the Promotion of the Japanese Language" at the top.
Sophie had always been interested in Japanese,
so she approached the table.

After a comically polite interaction with the one of the fashionable men,
she came to learn that the organization was working to encourage
people across the globe to learn Japanese.
However, they did not provide courses or educational material themselves.
Instead, their strategy was to create an incentive for people to learn the language,
by offering a prize of ten thousand dollars to anyone who reached a
certain level of proficiency.
They measured this proficiency by proctoring a short quiz.

Sophie did not know any Japanese,
but she had long believed that she could pass any standardized test
simply by exploiting hints that were implicit in the phrasing of the questions
(it's usually "all of the above").
So s...