trentmkelly/LessWrong-43k huggingface dataset:
Who is available for contract work? (A la the Hacker News "Who Wants To Be Hired" threads)
by nonzerosum — 2022-05-20T23:59:55.735Z

The intention of this thread is similar to the "Hacker News Freelancer/Seeking Freelancer" threads.

Please lead with either AVAILABLE FOR PROJECTS or SEEKING FREELANCER, your location, and whether remote work is a possibility.

I was inspired to post this when a friend asked me about which rationalist adjacent people were open to contract work, though other than one researcher that I'd seen mention their rate on their website, I didn't know who to mention. Though I'm sure there are quite a few who are! So, perhaps this will solve a coordination problem.
Why does gradient descent always work on neural networks?
by MichaelDickens — 2022-05-20T21:13:28.230Z

My amateur understanding of neural networks is that they almost always train using stochastic gradient descent. The quality of a neural network comes from its size, shape, and training data, but not from the training function, which is always simple gradient descent.

This is a bit unintuitive to me because gradient descent can only find the minimum of a function if that function is convex, and I wouldn't expect typical ML problems (e.g., "find the dog in this picture" or "continue this writing prompt") to have convex cost functions. So why does gradient descent always work?

One explanation I can think of: it doesn't work if your goal is to find the optimal answer, but we hardly ever want to know the optimal answer, we just want to know a good-enough answer. For example, if a NN is trained to play Go, it doesn't have to find the best move, it just has to find a winning move. Not sure if this explanation makes sense though.
Beware boasting about non-existent forecasting track records
by Jotto999 — 2022-05-20T19:20:03.854Z

Imagine if there was a financial pundit who kept saying "Something really bad is brewing in the markets and we may be headed for a recession. But we can't know when recessions will come, nobody can predict them". And then every time there was a selloff in the market, they tell everyone "I've been saying we were headed for trouble", taking credit. This doesn't work as a forecasting track record, and it shouldn't be thought of that way.

If they want forecaster prestige, their forecasts must be:

Pre-registered,
So unambiguous that people actually agree whether the event "happened",
With probabilities and numbers so we can gauge calibration,
And include enough forecasts that it's not just a fluke or cherry-picking.

When Eliezer Yudkowsky talks about forecasting AI, he has several times claimed implied he has a great forecasting track record. But a meaningful "forecasting track record" has well-known and very specific requirements, and Eliezer doesn't show these.

Here

he dunks on the Metaculus predictors as "excruciatingly predictable" about a weak-AGI question, saying that he is a sane person with self-respect (implying the Metaculus predictors aren't):

To be a slightly better Bayesian is to spend your entire life watching others slowly update in excruciatingly predictable directions that you jumped ahead of 6 years earlier so that your remaining life could be a random epistemic walk like a sane person with self-respect.

I wonder if a Metaculus forecast of "what this forecast will look like in 3 more years" would be saner. Is Metaculus reflective, does it know what it's doing wrong?

He clearly believes he could be placing forecasts showing whether or not he is better. Yet he doesn't.

Some have argued "but he may not have time to keep up with the trends, forecasting is demanding". But he's the one making a claim about relative accuracy! And this is in the domain he says is the most important one of our era. And he seems to already be keeping up with trends -- just submit the distribution then.

And

here

he dunks on Metaculus predictors again:

What strange inputs other people require instead of the empty string, to arrive at conclusions that they could have figured out for themselves earlier; if they hadn't waited around for an obvious whack on the head that would predictably arrive later. I didn't update off this.

Bu...
How RL Agents Behave When Their Actions Are Modified? [Distillation post]
by PabloAMC — 2022-05-20T18:47:52.932Z

Summary

This is a distillation post intended to summarize the article How RL Agents Behave When Their Actions Are Modified? by Eric Langlois and Tom Everitt, published at AAAI-21. The article describes Modified Action MDPs, where the environment or another agent such as a human may override the action of an agent. Then it studies the behavior of different agents depending on the training objective. Interestingly, some standard agents may ignore the possibility of action modifications, making them corrigible.

Check also this brief summary by the authors themselves. This post was corrected and improved thanks to comments by Eric Langlois.

Introduction

Causal incentives is one research line of AI Safety, sometimes framed as closely related to embedded agency, that aims to use causality to understand and model agent instrumental incentives. In what I consider a seminal paper, Tom Everitt et al. [4] showed how one may model instrumental incentives in a simple framework that unifies previous work on AI oracles, interruptibility, and corrigibility. Indeed, while this research area makes strong assumptions about the agent or the environment it is placed, it goes straight to the heart of outer alignment and relates to embedded agents as we will see.

Since the paper, this research line has been quite productive, exploring multi-agent and multi-decision settings, its application to causal fairness, as well as more formally establishing causal definitions and diagrams that model the agent incentives (for more details check causalincentives.com). In this paper, the authors build upon the definition of Response Incentive by Ryan Carey et al. [2] to study how popular Reinforcement Learning algorithms respond to a human that corrects the agent behavior.

Technical section

Definitions

Markov Decision Process

To explain the article results, the first step is to provide the definitions we will be using. In Reinforcement Learning, the environment is almost always considered a Markov Decision Process (MDP) defined as the tuple M=(S,A,PS,R,γ), where S is the space of states, A the space of actions, PS:S×A→S a function determining the transition probabilities, R:S×A→R the reward function, and γ a temporal discount.

Modified Action Markov Decision Process

In this article, however, the MDP definition is extended by adding an additiona...
Over-digitalization: A Prelude to Analogia (Chapter 6) 
by Justin Bullock — 2022-05-20T16:39:37.686Z

The Prelude to Analogia

In what has come before, I have argued that the incorporation of machine agents into large organizations has resulted in a new form of hybrid superintelligences. And, furthermore, that these superintelligences pose a threat to human freedom. To set up this story I have pointed to two forces from the mid-20th century (over-population and over-organization) that Huxley argued posed significant threats to human freedom from his vantage point in the mid-20th century.

The mid 20th century, already transformed by innovative war machines, also gave birth to the modern computing machine. The brilliance of Alan Turing, John Von Neumann, and many others gave birth to a computing machine that was programmable. It stored memory and it processed code. It was an architecture that provided for the execution of anything that could be coded into the computer's language.

The war machines of WWI and WWII had brought destruction barely imaginable before their creation. The balance between human effort and machine capability in war broke in favor of machine destruction. This destructive power of machines was to be subsumed by the broad ranging power of computer code. The birth of the modern computer, found in the Von Neumann architecture, was a general architecture for machine intelligence. Machines that that could store knowledge, process it, and act upon it, with goal oriented behavior in its environment. It was a general structure for telling adaptable machines what to do. This machine structure, when connected into a networked environment, gave us the internet and the birth of the digital universe.

The digital universe, owing its name to the binary digit calculations being conducted at its core, has expanded at a rate that puts the trend of over-population to shame and amplifies over-organization to such a degree to become the more relevant crashing wave. It is to the growth rate of the digital universe, its sheer size, and its outsized impact on our modern lives, and both its behavior and constitution that we need to explore. These trends of over-digitalization have given us the birth of Analogia.

Before turning to a more detailed account of the rise of Analogia, I've decided that an extended prelude is first in order. A setting of the stakes, if you will. And, for the human mind, few devices appropriately set the stakes like literature....
Podcast: Spencer Greenberg talks to me about dealing with our groupstruckness and boundedness
by KatjaGrace — 2022-05-20T04:20:12.221Z

I talked to Spencer Greenberg a little while ago for his podcast Clearer Thinking. The recording is out today. According to his website we discussed:

What does it mean to be “groupstruck”? How does groupstruck-ness differ from the bystander effect, normalcy bias, and other related cognitive biases? How do we break people out of being groupstruck? What does it mean to be a “bounded” person? How can we build up better decision-making heuristics? What sorts of decisions do people usually not quantify but should (and vice versa)? How can we make rational relationship decisions without coming across as “calculating” or cold? How does anthropic reasoning affect our hypotheses about the nature of the universe and life within it (i.e., the Fermi paradox, the simulation hypothesis, etc.)?

You can listen or read the transcript here.
Formula for a Shortage
by Zvi — 2022-05-19T19:30:01.501Z

Previous coverage (not required in any way): Formula For Dying Babies

Great Formal Workups of Much of This (Scott Lincicome): America’s Infant Formula Crisis and the ‘Resiliency’ Mirage. And another from Derek Thompson at The Atlantic. If you want a shorter summary, both of those are excellent. Also, together they fight crime did a podcast.

Last week I chronicled that there is a shortage of baby formula, especially specialized baby formula, due to a combination of the same reasons that hold whenever there is a shortage of anything.

The playbook never changes. Restrict supply and subsidize demand.

Not only do we subsidize demand, we do so via the WIC in a way that forces market consolidation and inflexibility in support.

Supply is restricted to begin with in both classic ways, onerous regulatory requirements and outright protectionism via tariffs and quotas, even with those with whom we have free trade agreements. On top of that the FDA is involved.

If you’re familiar with such dynamics none of this is surprising or all that new. This is written more as a reference post for the future, and for those who are not intimately familiar with how such things work.

The memes come easily.

There are a few more I saved for later on.

I am going over this again, now that the full picture is clear and politicians have made various new insane statements, because the situation is so perfect. It’s terrible, in the sense that mothers are panicking and having trouble finding formula to keep their kids alive. I’m quite unhappy about it happening. What I mean is that this is the perfect example of a situation in which all the things our government likes to do combine to create a mysterious completely unnecessary shortage of a vital product via driving out most potential suppliers. Then those forces combine to prevent the problem from being fixed, and those responsible then blame capitalism and corporations for a problem they would have handled quite well if they’d been permitted to do so.

The short term crisis began with the Abbott plant in Michigan being shut down and staying shut down for months. It’s being reopened now, but it will take six to eight weeks to properly resume production and get it onto store shelves – the FDA expects the situation to improve over the next couple of months. That’s the good scenario.

FDA Delenda Est

So it is...
Proclamation of game-theoretic emergence
by dr_diagoras — 2022-05-19T18:09:05.218Z

Game theory studies behavior of rational agents. Physical reality has no place for rational agents. Rationality can only exist as epiphenomenon atop stochastic behavior of matter.
Partial ordering exists on the set of stochastic games, defined by IMIT (Is More Interesting Than) relation, such as G1 IMIT G2 if and only if

every optimal (in any sense) strategy of game G2 can be obtained by trivial permutation of some optimal (in any sense) strategy of game G1;
there is optimal (in any sense) strategy in game G1 that can’t be obtained by trivial permutation of any optimal (in any sense) strategies of game G2.

Some material aggregates of uniform elements that turn energy income into stochastic behavior are curious. It means that

if curious aggregate has big enough excess of internal energy for any single game, eventually some of its actors’ behavior stops being random;
if curious aggregate has big enough excess of internal energy for both games G1 IMIT G2, eventually some of its actors’ behavior stops being reductive to optimization of G2.

There is no maximally interesting games. Spontaneous unlimited complication of behavior in curious aggregates induces rationality epiphenomenon.
Liquid solutions of macromolecules are curious. Rationality epiphenomenon, emerging on such scale of conflicts, we commonly call life.
Biological neural circuits are curious. Rationality epiphenomenon, emerging on such scale of conflicts, we commonly call psyche.
Computationally universal systems are curious. Rationality epiphenomenon, that can emerge on such scale of conflicts, doesn’t have a name yet.
How has the total amount of gain-of-function research worldwide grown/shrunk over time?
by johnswentworth — 2022-05-19T15:57:55.414Z

Given the advance in underlying biological tools/technology, I would not find it surprising if gain-of-function research had increased 10x over the last decade. (This is just based on priors; getting an actual estimate is the point of this question.)

Why is this interesting? Assuming that both original covid and at least one strain were lab leaks, the rate of growth of gain-of-function research has a huge impact on how often we should expect future pandemics from lab leaks. If the amount of gain-of-function research has been roughly constant over the last 20 years, then the two covid leaks suggest a base rate of something like one or two pandemic-level leaks every 20 years, and covid was probably unusually hard to contain in a lab for some reason on top of that. On the other hand, if the field has grown 10x in the past 5 years, that suggests a base rate more like a leak every 2-3 years (and probably getting faster over time as the field continues to grow). If that's the case, then the base rate for pandemics will be way higher going forward than in the past.
14 Techniques to Accelerate Your Learning
by spencerg — 2022-05-19T13:59:54.073Z

Originally written by Belén Cobeta and

Spencer Greenberg

from

ClearerThinking.org

. Revised by Teis Rasmussen & Florence Hinder from

ThoughtSaver.com

. This is a cross-post from the Effective Altruism Forum.

TLDR: There are a number of techniques that may accelerate the speed at which you learn (14 of which we explain in this article). Additionally, embedded within this article are flashcards to provide you with the key takeaways so you can remember these ideas and more effectively put them into action.

Vasconcelos Library — photo by Diego Delso

What if you could learn more in less time? Whether you’re studying to pass your classes, aiming to improve at work, honing your personal life skills, or focusing on having the greatest impact you can, learning to learn more efficiently can be a great time investment because it accelerates the rest of your learning.

Many learning methods are inefficient

Many widespread learning practices waste a lot of time and effort, at least if we assume that the goal of those efforts is to actually learn. For example, you have probably had the experience of reading an exciting and useful piece of nonfiction, only to forget basically all of it and not take any action based on what you learned from it. And you’ve probably spent a lot of time taking classes that taught information that mostly wasn’t useful to you, and which you no longer remember either way.

It’s unfortunate that such experiences are as common as they are. The good news is that there are more efficient and powerful learning practices out there - many of us just don't adopt them.

Here we lay out 14 of our favorite techniques for improving your learning processes. They are grouped into four categories:

Learn faster
Boost understanding
Remember more
Put your learning into practice

Part 1: LEARN FASTER

This section will cover techniques that can help you absorb more information or knowledge per hour that you spend learning.

1. Listening instead of reading

Listening is the new reading: take advantage of technology by listening to books and articles. It may feel less studious than reading, but at least some

research shows that you can learn and retain just the same

. The intonation of the narrator can also help you understand the text. Use the Audible version of the book or text-to-speech software and set the reading speed to a level that feels challenging but still allows you to ...
Covid 5/19/22: The Law of Five
by Zvi — 2022-05-19T12:20:06.271Z

If you’re five, you now get a booster. If you’re four, you still get no vaccine at all (while we ‘wait for more data’) and you’re often forced to take precautions that might make sense for senior citizens.

Because no one cares. And that truly continues to be the overall message. No one cares about the physical prevention or mitigation of Covid. Congress allocates zero dollars, doctors don’t prescribe Paxlovid and life mostly continues as normal because Omicron in a mostly-already-infected-and/or-vaccinated population simply isn’t that dangerous. Cases go up, but life goes on.

It’s pretty terrible that we’re not funding pandemic prevention or anything for the current pandemic, but at this point I don’t know that the most likely alternatives are even improvements.

(And of course, if you’re under one, thanks in large part to the FDA you may have also have a problem getting food.)

Florida did not report this week. I am retroactively smoothing out Florida’s numbers by giving 50% of its reporting weeks to its no-reporting weeks, now that this has happened three times in a six-week period. All future numbers and predictions will include this adjustment (assuming I remember to do it) and I will assume up front that when Florida reports we take 50% off the top. If I find time I’ll do a smarter adjustment, since this one is slightly flawed, but it’s much better than doing nothing.

Executive Summary

Cases up.
Boosters available for ages 5-11.
No one cares and deaths are not rising yet.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 640,000 cases (+23%) and 2,100 deaths (+1%)

Results: 600,742 cases (+23% after adjusting Florida for both weeks, would have been +15% without the adjustment) and 2,004 deaths (+2% after Florida adjustment, -3% without the adjustment).

Prediction for next week:

Deaths

Deaths are rising in the Northeast and flat elsewhere, despite it being more than three weeks since cases began rising again. It seems clear that case fatality rates in this wave are going to be lower. It’s not great that thousands of Americans with Covid are dying each week, some of them from Covid as well. I wish it were not so, but I am glad we are not ruining our lives trying to stop this.

Cases

Enough weeks in a row with 20% weekly case growth and things get out of hand. We’re now triple our lo...
Should you kiss it better?
by Yair Halberstadt — 2022-05-19T03:58:40.354Z

Whenever my toddler hurts himself, I offer to kiss it better. This works like magic - he immediately stops crying and carries on playing happily. Placebos are wonderful things.

Doing this makes me happier, it makes him happier, and also serves as a useful diagnostic tool - if he carries on crying I know he's actually hurt himself badly.

Teaching children that kisses can make things better is definitely lying to them, but this seems like a good place to make a principled exception. WDYT?
Looking for AI / Application Engineer @ OpenPrinciples -- Help people align actions with principles
by ti_guo — 2022-05-19T03:23:37.139Z

I’m Ti Guo, I am into rationality and is co-founder of EA UWaterloo, and EA IBM. We are looking for remote & part-time EA-aligned Co-founding Team member, or non-paid intern AI / Application Engineer (Python), to help build our AI Life Coach product Ultrabrain under our EA-aligned startup OpenPrinciples.

What do we do:

OpenPrinciples.org is (to our knowledge) the world’s first crowdsourced database/wiki for life principles.
Ultrabrain is the AI Life Coach product under OpenPrinciples, it helps people align their actions with their principles by helping find, remind, and iterate on life principles. Currently, it uses AI (GPT3, BERT) to remind people of principles when they need them.
Our beta version software is running with a small but growing list of users

Why join us:

We are solving a significant problem: We believe that the lack of a systematic and principle-driven approach is one of the most significant factors that slow down the growth of EA-aligned people/organizations.
Work at your own hours for 5 ~ 25hrs / week depend on your role
Learn to build your own startup / product from 0 to 1 by working with an experienced team on an altruistic product with real users
Work with a group of extremely interesting and “principled” and EA-aligned people, with a lot of mentorships and internal paired life / career coaching buddies

Your job task as an AI Engineer could include and not be limited to the following:

Build an AI (Data pipeline) to extract principles from articles at scale
Fine-tuning NLP models with a large amount of user data
Host model and data pipeline on GCP / AWS
Experiment with different NLP models / architectures to solve engineering problems

Your job task as an Application Engineer could include and not be limited to the following:

Iterate app prototypes closely with our designers, business side members, and users using the most popular no-code app dev platform Bubble.io
Develop python backend server closely with our AI Engineers

Who are we: We are an EA-aligned Team of 4 members plus multiple volunteers, from all over the world. We are also serial entrepreneurs with experience starting NGOs, and venture-backed, YC-interviewed, and 20-under-20 Thiel Fellow startups.

If you are interested in joining us, please shoot me a message at guoty3310@gmail.com or https://www.linkedin.com/in/ti-guo/. Please send your resume / LinkedIn profile / personal website / fe...