trentmkelly/LessWrong-43k huggingface dataset:
Andrew Huberman on How to Optimize Sleep
by Leon Lang — 2023-02-02T20:17:12.010Z

Edit: I am not as confident anymore as I have been in Andrew Huberman's general epistemics, so take this post with a grain of salt.

Introduction

In this post, I list all the advice from Andrew Huberman's podcast Sleep Toolkit: Tools for Optimizing Sleep & sleep and Sleep-Wake Timing. A large subset of that advice can also be found on his website, so feel free to read there and ignore this post.

I did not do any research to check whether the advice is actually correct and comprehensive. If you know of any important missing tools or wrong advice in the list below, please let me know. I think the main tools for improving sleep that are missing from this podcast episode are:

General CBT-I (Cognitive Behavioral Therapy for Insomnia);
General stress reduction;
Any other psychological improvement strategies (e.g., therapy against depression and anxiety);
Choice of clothes, pillow, mattress, and blanket for sleeping;
bedtime routines;
Nutrition (except for some short advice on breakfast);
Prescription Drugs.

If you know of other important factors, please let me know in the comments.

In summary, the advice is centered around using 8 "tools" in such a way as to obtain a stable, healthy circadian rhythm:

Light
Darkness
Temperature
Food
Exercise
Caffeine
Supplements
Digital Tools

Below, I mostly focus on the advice and less on the reasoning behind that advice. My impression is that you can probably ignore much of the advice if you feel like your sleep is already great, though especially light exposure in the morning and dim light in the evenings seem like clearly useful for almost everyone.

The Three "Critical Periods"

Huberman names three "critical periods": the morning, throughout the day, and the evening. He emphasizes the importance of one's behavior in the morning the most, as he claims it has a crucial effect on the circadian rhythm.

Period 1: Morning (until ~3 hours after waking)

Early in the morning, Huberman recommends getting natural light exposure for setting the circadian rhythm, and to exercise, getting a short cold shower, and having breakfast for activating the body and increasing the core body temperature. While all of this "wakes up the body", Huberman indicates that it is also useful for getting better sleep later on.

Finally, Huberman lists some advice on how to responsibly use caffeine. I am not sure if he claims that caffeine can help with s...
How can I help inflammation-based nerve damage be temporary?
by Optimization Process — 2023-02-02T19:20:19.823Z

Bounty [closed]: $50 for any suggestion that I consider promising enough to change my behavior for, conditional on my fully recovering by May 1.[1][2][3][4]

Question: I recently got shingles; I'm now completely better, except for postherpetic neuralgia (~inflammation-caused nerve damage) in/around one ear, which intermittently causes weird sometimes-painful sensations, which (a) is distracting and (b) interferes with my sleep. I'm in the market for suggestions for how to ensure it's temporary: it "usually" goes away within a couple months, but I'll be real bummed if it lasts forever for me.

Unnecessary details that might help guide your suggestions:

I'm a ~30yo Caucasian male, pretty much perfectly healthy aside from this.
My first shingles-symptoms were on Jan 14; my last non-neuralgia symptom was around Jan 22.
My default strategy is basically just (a) rest a lot because I'm tired, (b) drink a lot of water because I'm thirsty, and (c) take 325mg of aspirin 3x/day on the theory that (i) it's unlikely to hurt and (ii) it could plausibly help because it's anti-inflammatory.
Suggestions I would pay out for if I hadn't already tried them: "ask a doctor"; "Google around"; "Google Scholar around"; "take aspirin"; "offer a bounty on Less Wrong."

^

Max 10 suggestions, first come first serve.

^

I'll pay more for suggestions I consider especially great.

^

Things that make me more-likely-though-not-certain to consider a suggestion "promising": links to reputable sources (like journal papers or LW users with high karma) supporting the idea; common-sense arguments describing the mechanism by which the suggested action should help; not involving unusual drugs.

^

My evaluation criterion is super subjective, and I'm a skeptical person, so I might naturally not pay out at all; so, to demonstrate seriousness, I commit to paying at least two bounties, to the most promising suggestions, even if they wouldn't otherwise meet my bar.
More findings on maximal data dimension
by Marius Hobbhahn — 2023-02-02T18:33:53.606Z

Produced as part of the

SERI ML Alignment Theory Scholars Program

- Winter 2022 Cohort.

I’d like to thank Wes Gurnee, Aryan Bhatt, Eric Purdy and Stefan Heimersheim for discussions and Evan Hubinger, Neel Nanda, Adam Jermyn and Chris Olah for mentorship and feedback.

The post contains a lot of figures, so the suggested length is deceiving. Code can be found in

this

colab notebook.

This is the second in a series of N posts on trying to understand memorization in NNs.

Executive summary

I look at a variety of settings and experiments to better understand memorization in toy models. My primary motivation is to increase our general understanding of NNs but I also suspect that understanding memorization better might increase our ability to detect backdoors/trojans. This post specifically focuses on measuring memorization with the maximal data dimensionality metric.

In a comment to the “

Superposition, Memorization and double descent

” paper, Chris Olah introduces maximal data dimensionality D*, a metric that supposedly tells to which degree a network memorized a datapoint compared to using features that are shared between datapoints. I extend the research on this metric with the following findings

In the double descent setting, the metric describes exactly what we would predict, i.e. with few inputs the network memorizes all datapoints and with a lot of input it learns some features.
On MNIST, I can reproduce the shape of the D* curve and also the findings that memorized datapoints have high D*, datapoints that share many features are in the middle and datapoints that the network is confused about have low D*. However, I was surprised to find that the datapoints the network misclassified on the training data are evenly distributed across the D* spectrum. I would have expected them to all have low D* didn’t learn them.
When we train the network to different levels of accuracy, we find that the distribution of errors is actually slightly left-heavy instead of right-heavy. I have not yet understood why it is the case but I’d be interest in follow-up research to see whether it tells us something interesting.
Different classes are not evenly distributed across the spectrum, e.g. “8” is far more regular than “5” according to D*. This is what we would expect.
Across different hidden sizes, the shape of the D* curve stays nearly the same but the

...
Heritability, Behaviorism, and Within-Lifetime RL
by Steven Byrnes — 2023-02-02T16:34:33.182Z

I’m a firm subscriber to both:

(A) The theory that people’s personalities are significantly predictable from their genes, and mostly independent of how their parents raised them (at least within the typical distribution, i.e. leaving aside cases of flagrant abuse and neglect etc.). See e.g. popular expositions of this theory

by Judith Harris

or

by Bryan Caplan

for the fine print.
(B) The theory that we should think of people’s beliefs and goals and preferences developing via within-lifetime learning, and more specifically via within-lifetime Model-based Reinforcement Learning (details), with randomly-initialized

(“learning-from-scratch”)

world-model and value function.

I feel like there’s an idea in the air that these two beliefs are contradictory. For example, one time someone politely informed me that (A) is true and therefore obviously (B) must be false.

Needless to say, I don’t think they’re contradictory. Indeed, I think that (B) naturally implies (A).

But I admit that they sorta feel contradictory. Why do they feel that way? I think because:

(A) is sorta vaguely affiliated with cognitive science, evolutionary psychology, etc.
(B) is sorta vaguely affiliated with

B.F. Skinner-style behaviorism

,
…and those two schools-of-thought are generally considered to be bitter enemies.

In this short post I want to explain why we should put aside that baggage and see (A) & (B) as natural allies.

Two dubious steps to get from (B) to Behaviorism

Here’s the fleshed-out argument as I see it:

I’ll go through the two dubious steps in the opposite order.

Dubious step #1: “No more learning / unlearning after the kid grows up”

Here are two stories:

“RL with continuous learning” story:

The person has an internal reward function in their head, and over time they’ll settle into the patterns of thought & behavior that best tickle their internal reward function[1].

If they spend a lot of time in the presence of their parents, they’ll gradually learn patterns of thought & behavior that best tickle their innate internal reward function in the presence of their parents.
If they spend a lot of time hanging out with friends, they’ll gradually learn patterns of thought & behavior that best tickle their innate internal reward function when they’re hanging out with friends.
As adults in society, they’ll gradually learn patterns of thought &

...
Covid 2/2/23: The Emergency Ends on 5/11
by Zvi — 2023-02-02T14:00:00.572Z

We finally have a date. On May 11, the official Covid Emergency ends. The actual emergency is, of course, long over, but for technical reasons the official change will have to wait a bit.

A related question is when it stops making sense to continue writing weekly Covid posts. My basic plan for a while has been ‘when there stops being content for them’ and somehow I still end up with a bunch of content every week. That does not mean this is the right format for that content, or that this is the right content to be creating.

I will continue to think more about this, and have not made a decision yet.

The other news is that we finally have a reformulation of all shots of the Covid vaccine… to the version we approved for boosters that is already out of date. We will likely never have a properly up to date Covid vaccine again. Oh well.

Executive Summary

Covid emergency to end on May 11, 2023.

Vaccine formulations all to be updated to bivalent composition.

Other news will be covered in roundups soon.

Let’s run the numbers.

The Numbers

Predictions

Predictions from Last Week: 223k cases (-10%) and 3,300 deaths (-4%)

Results: 229k cases (-7%) and 3,437 deaths (-1%).

Predictions for Next Week: 210k cases (-8%) and 3,150 deaths (-8%).

Everything seems right back on schedule. I see no reason this shouldn’t continue.

Deaths

Cases

Physical World Modeling

Covid death rates by age, over time, on a log scale, as Covid deaths with a two-week lag divided by reported Covid cases. Cases are vastly undercounted, far more than deaths could possibly be, so the CFR here remains much higher than the true IFR.

I think some of the wonkiness here is that two weeks is a little bit short in terms of lag.

See the sharp drop with Omicron’s arrival.

For reference, here’s the linear scale.

Most Long Covid studies continue to be terrible and tell us nothing. This one was especially terrible.

Zeynep thread on how people use snippets, selective quotes and screenshots to misrepresent scientific findings and scare people, if you need another of those. She is performing a public service here, yet I grow weary. Chise thread covers the meeting.

Molnupiravir conclusively caused Covid-19 mutations in the wild. Kind of wild that we allowed people to use it. We should stop allowing that.

Bloom Labs thread on predicting future mutatio...
You are probably not a good alignment researcher, and other blatant lies
by junk heap homotopy — 2023-02-02T13:55:15.186Z

When people talk about research ability, a common meme I keep hearing goes something like this:

Someone who would become a great alignment researcher will probably not be stopped by Confusions about a Thorny Technical Problem X that's Only Obvious to Someone Who Did the Right PhD.

Someone who would become a great alignment researcher will probably not have Extremely Hairy But Also Extremely Common Productivity Issue Y.

Someone who would become...great...would probably not have Insecurity Z that Everyone in the Audience Secretly Has.

What is the point of telling anyone any of these? If I were being particularly uncharitable, I'd guess the most obvious explanation that it's some kind of barely-acceptable status play, kind of like the budget version of saying "Are you smarter than Paul Christiano? I didn't think so." Or maybe I'm feeling a bit more generous today so I'll think that it's Wittgenstein's Ruler, a convoluted call for help pointing out the insecurities that the said person cannot admit to themselves.

But this is LessWrong and it's not customary to be so suspicious of people's motivations, so let's assume that it's just an honest and pithy way of communicating the boundaries of hard-to-articulate internal models.

First of all, what model?

Most people here believe some form of biodeterminism. That we are not born tabula rasa, that our genes influence the way we are, that the conditions in our mother's womb can and do often snowball into observable differences when we grow up.

But the thing is, these facts do not constitute a useful causal model of reality. IQ, aka (a proxy for) the most important psychometric construct ever discovered and most often the single biggest predictor of outcomes in a vast number of human endeavours, is not a gears-level model.

Huh? Suppose it were, and it were the sole determinant of performance in any mentally taxing field. Take two mathematicians with the exact same IQ. Can you tell me who would go on to become a number theorist vs an algebraic topologist? Can you tell me who would over-rely on forcing when disentangling certain logic problems? Can you tell me why Terrence Tao hasn't solved the Riemann hypothesis yet?

There is so much more that goes into becoming a successful scientist than can be distilled in a single number it's not even funny. Even if said number means a 180 IQ scientist is more likely to win a Nobel than ...
Don't Judge a Tool by its Average Output
by silentbob — 2023-02-02T13:42:28.994Z

Epistemic status: this post’s main point itself is probably somewhat obvious. The concrete examples may leave some room for disagreement. No guarantees about my views on ChatGPT vs GPT3 in particular.

Summary: Many tools and platforms out there provide users with certain input-dependent outputs. These outputs can vary greatly in their quality or usefulness. People’s intuition in such cases is often that if the outputs are of low quality sufficiently often, then the tool must be useless. This conclusion can be mistaken however if it’s possible to systematically obtain high quality outputs by deliberately altering one’s inputs or filtering the outputs one consumes.

Let’s look at three examples: ChatGPT, Reddit and Elon Musk. When judging such tools (let’s just refer to all of them as “tools” for simplicity), it’s very easy to look at the typical interaction of a typical user with the tool and make one’s judgment based on that. For ChatGPT this may mean that somebody asks it some factual or math question, and gets a very confident but incorrect reply. So some people think: ChatGPT is a nice toy, but given how unreliable it is due to its many mistakes, it’s ultimately useless (at least as of January 2023). Reddit on the other hand is considered by many people to be basically a huge time sink, an endless feed of entertainment and banter that gets in the way of more important things. As for Elon Musk, who’s admittedly a somewhat far-fetched example for this whole concept, it’s not so much about actual “input” but more about how you select/filter the output he produces. E.g. when it comes to his views and opinions, people may think of some typical widely-shared tweet of his, which probably scores highly on the troll scale. Hence people may get the impression that Musk is mad and/or stupid and there’s nothing to learn from him.

Elon Musk right now, probably

These three views surely have some truth, and it’s probably the case that there are many people who would be better off not using these tools at all (i.e. not use ChatGPT, not sign up to or visit reddit, and avoid exposure to things Elon Musk said) – at least not in the way they’ve been using them before.

However, I think that this is a very incomplete view of usefulness. The main reason for that is that such tools are not inherently useful or useless, but their usefulness depends greatly on how you i...
Epoch Impact Report 2022
by Jsevillamol — 2023-02-02T13:09:00.659Z
Link: https://epochai.org/blog/epoch-impact-report-2022

Epoch is a research group forecasting the development of transformative Artificial Intelligence. We try to understand how progress in AI happens and what economic impacts we might see from advanced AI.

We want to enable better governance during this economic transition by gathering information about the timing of new developments, studying which levers can be used to influence AI progress and making current and past trends in ML more understandable.

Founded in April of 2022, Epoch currently has a staff of 13 people, corresponding to 9 FTEs. We have received $1.96M in funding through a grant from Open Philanthropy. We are fiscally sponsored and operationally supported by Rethink Priorities, whose Special Projects team has been a core part of our success as an organisation.

Epoch is fundraising a total of $6.07M over 2 years, or approximately $2.64M for October 2023 to September 2024, and $3.42M for October 2024 to September 2025. A detailed budget can be found in the full report.

With this funding, we expect to continue and expand our research capacity in understanding the future of AI. Through our research, we hope to continue informing about key drivers of AI progress and when key AI capabilities will be developed.

You can now donate to Epoch and subscribe to our newsletter.

Read the full report to learn about our key insights this year, an overview of our research and non-research activities, and our plans for the future.

Epoch is one of the coolest (and in my opinion underrated) research orgs for understanding trends in ML. Rather than speculating, they meticulously analyze empirical trends and make projections for the future. Lots of interesting findings in their data! – Jacob Steinhardt

Read now the full report
You Don't Exist, Duncan
by Duncan Sabien (Inactive) — 2023-02-02T08:37:01.049Z

This is an experimental essay, not in the typical LessWrong or Duncan Sabien style.

Depending on how this goes, I might try writing a companion piece in the typical style, laying out the model clearly and explicitly and deriving concrete and specific recommendations from it.

But it seemed worth it to try communicating at a lower and more emotional/visceral level, not least because that is the level at which I actually experience The Problem. Any clear, analytical essay would be the result of me trying to make sense of the thing that I'm going to try to directly convey, below.

It is the year 1995. I am nine years old. In front of me there is a sheet of paper, upon which are written a dozen or so lines of math. The first is:

f(x)=x2+7

I stare at it. I know that I can divide both sides of the equation by x, leaving me with:

f=x+7/x

...but this does not seem to do any good.

I raise my hand. The afterschool volunteer comes over.

"No," he says. "That's not right. X isn't a term on the left side. F is a function."

He has explained nothing.

"F is a function, so what this is saying is to take X, and square it, and add seven."

I look up at him, confused. I am nine. I have never heard the word "function" used in this way before. No one has grounded me in the activity of the day; no one has oriented me; no one has told me today you are learning what a function is, and you will learn by looking at a bunch of examples. No one has said today, parentheses don't mean the thing you're expecting them to mean. No one has said f is a thing that eats xs, and what the right side is showing you is how it eats them—what it does to them.

"So, like, if X is three, right?" he continues. "X is three? So F of X is three squared plus seven, which is sixteen."

I say the words again in my mind, more slowly. F ... of ... (of? What?) ... X. ""F of X"" (okay, whatever, that's nonsense, but whatever) is sixteen.

I look back down at the paper. If the right side of the equation is sixteen, and X is three...

"F is five-point-three-repeating," I say, trying to inject a measure of confidence I do not feel into my tone.

"What? No. F isn't anything. F is a function. It's not part of the equation."

Not part of the equation, he says. Looking back from a distance of twenty-five years, I...
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control
by Roman Leventov — 2023-02-02T06:29:21.137Z
Link: https://arxiv.org/abs/2301.00723

A preprint is published by Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrance Sejnowski, and Hava Siegelmann in December 2022.
Abstract:

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.

Conclusion:

In this work, we presented Temporally Layered Architecture (TLA), a framework for distributed, adaptive response time in reinforcement learning. The framework allows the RL agent to achieve smooth control in a real-time setting using a slow controller while a fast controller monitors and intervenes as required. Additionally, we demonstrated an alternative setting where the slow controller can gate the fast controller, activating it only when required for efficient control. We demonstrate faster convergence and more action repetition in the closed-loop approach and fewer decision and faster convergence in the partially-open loop approach. Additionally, we demonstrate in a real time setting, where processing and actuation delays ar

...