trentmkelly/LessWrong-43k huggingface dataset:
The Codex Skeptic FAQ
by Michaël Trazzi — 2021-08-24T16:01:18.844Z

Most of my programmer friends believe that Language Models trained on code will not affect their day job anytime soon. In this post, I make the case that 1) code generation is already useful (assuming minimal prompt engineering skills) 2) even if you do not believe in 1), code generation will increase programmers' throughput way sooner than it will fully automate them.

Language Models trained on Code do not bring us closer to Full Code Automation

This misconception comes from thinking linearly instead of exponentially. Language models are good enough at generating code to make the very engineers building such models slightly more productive, for instance when dealing with a new API. In other words, the returns (aka the improvements in the algorithm) from investing more resources in code generation directly helps (with better developer tools) create a better code-generating algorithm.

Code generation does not automate the part of my workday where I think hard

It still accelerates “glue code” or “API work”—a substantial fraction of large codebases.
Besides, only a set of privileged engineers get to think about the broad picture every day.
Plus, hard thinking is mostly required at the start, when designing the architecture.
And thinking seldom happens in a silo. It instead requires many iterations, through coding.

I asked a model to generate code but it doesn't seem to be able to solve it

More often than not, the issue is not about the model. Try another prompt. (Example)

The output is outdated code from average programmers

Code quality (length, variable naming, taste) is prompt and hyperparameter dependent. Generally, language models use variables from the prompt and you can rename those yourself.

Only developers who repeat the same tasks will be automated so it will not affect me

You might still see gains in productivity in learning how to use a more advanced version.

My job does not involve solving simple coding tests from docstrings

You should be capable of separating your code in smaller functions and write docstrings.

Codex cannot solve my problem since it has only access to a limited training set

Github Copilot

stores

your data. Supposedly, the same applies to the Codex beta.

Current Language Models still make silly mistakes

If the mistake is silly, then fixing it is trivial.

Anyway, it is error prone so it cannot be used for critical software

It generates less error than ...
Philosophy Web - Project Proposal
by SocratesDissatisfied — 2021-08-24T15:36:28.160Z

TLDR: I’m interested in creating an online map of philosophical concepts and their interrelations; which could be used to automatically identify contradictions within, and implications of, given belief systems. I am looking for interested collaborators - especially those with coding capacities – and development advice. I believe there are compelling reasons for rationalists to be interested in this proposal.

[If you’re interested in reading the full Philosophy Web proposal, please see the following link: https://drive.google.com/file/d/1X9fdGUMFase_GGPlXqJH6CcydREaSaDb/view?usp=sharing]

What is Philosophy Web?

Philosophy Web is a proposal to create an interactive online map of philosophical concepts, and the relationships of support and opposition between them. This map would take the form of a node and spoke diagram, with nodes representing concepts, and spokes representing relations of support or opposition.

Users would be able to add these concepts to their own personalised webs of belief. Philosophy Web would then automatically highlight potential contradictions and implications of users' personalised conceptual maps; helping users expand their intellectual horizons, discover errors in their thinking, and incorporate a broader evidential base in formulating their theories (or do the same for other belief systems they were interested in investigating).

Why Philosophy Web?

Philosophy Web has the potential to assist philosophers in several ways; each of which are expanded upon in the above linked proposal document:

Philosophy Web would facilitate research into the underexplored conceptual space between philosophical specialisms, to pluck the low hanging intellectual fruit which grows there.
Philosophy Web would reveal “long range”, implications of, and contradictions within, philosophical theories; which might otherwise be difficult for supporters (or critics) to discern.
Philosophy Web would support comprehensive philosophical theory building. Comprehensive philosophical theories draw upon - and test themselves against - broader evidential foundations, and are thus likely to constitute epistemic improvements on extant (predominantly very narrow) theories.
Philosophy Web would provide a valuable educative resource for Philosophers.

As a rationalist, you might find this compelling because:

If you in

...
Yet More Modal Combat
by Donald Hobson — 2021-08-24T10:32:49.078Z

Strongly recommended prior reading.

Robust Cooperation in the Prisoner’s Dilemma:
Program Equilibrium via Provability Logic

I think I have come up with a modal agent that has these nice properties.

Cooperates against itself, and similar designs.
Cooperates against some fairbots (see notes later)
Defects against cooperate bot
Never gets the suckers payoff (C,D)
Only references the situation its actually in.

The design makes use of 2 different proof systems W and S . These are short for weak and strong. For example, you could consider W=PA and S=PA+1 .

Although W= intuitionistic Robinson arithmetic, and S=ZFC+GCH+IO would work too.

The algorithm

If W[C] then DefectElse If S[C] then CooperateElse Defect

Where C= My opponent cooperates against me.

The last point is key here. Let's suppose this game is being played by actual computers made of physical atoms. The Justbot and Prudentbot in the paper linked above consider what the opponent would do against a player that isn't yourself. For example, Prudentbot imagines a hypothetical world where its own code was deleted and replaced with the code for a defect bot. The hypothetical opponent may notice this apparent anomaly and decide investigating it is far more important than the prisoners dilemma.

My bot (any good names in the comments?) solves this by only considering its opponent against itself.

Consider two versions of this bot playing against each other. Not necessarily using the same W and S as each other.

Lets assume all 4 proof systems in use are consistent. We also want the criteria that none of the proof systems can ever prove a false statement. (Although I suspect this can be weakened somewhat.)

Neither bot ever gets the suckers payoff (C,D), as each bot will only cooperate if its S can prove that the other bot does too. If W[C] on either bot, then this would mean the other bot got the suckers payoff. This can't happen. As neither W actually succeeds, if both of the S are sufficiently strong enough to prove this ( S1[¬W2[C1]] and S2[¬W1[C2]] ) (Note: some technical detail work on when exactly this is true, still I expect it to be true for most sensible choices of proof system) then the situation reduces to fairbot v...
Better Password Peppering
by Yoav Ravid — 2021-08-24T10:19:19.000Z

Epistemic status: confidant. I'm not a cryptographer, but no one raised strong objections and I thought about it a bunch since first thinking about it.

Everyone talking about password peppering is missing a better method to implement it.

whether you think peppers are good or bad, knowing this is valuable. If you think they're good, this is a better method to use them. If you think they're bad, this is a stronger version to argue against. The main point of this post isn't to endorse peppers (though I do think they're good), but to compare the common methods to an alternative one.

Terms and background, in short

Skip if you're familiar

Hash: Secure password storage doesn't store passwords, so if the database leaks, the passwords aren't revealed. Instead you use a hashing function, which takes any password, and turns it into a seemingly random (very large) number, this is your hash, and this is what you store instead of the password. Hashes are hard to reverse, and unlike encryption, hashes don't have keys to decrypt them. When the user logs in, the password is hashed again and compared to the stored hash.

Salt: A random string that is added to to the password before it's hashed, and is usually stored in plain text near the now salted hash (so each salt is connected to a user). When the user logs in the salt is again hashed with their password and compared to the stored hash. Salts prevent rainbow table attacks, and make the hashes of passwords shared by multiple users unique so each password has to be cracked individually. However, if an attacker targets a specific user and is willing to bruteforce their password, a salt will do nothing to stop them, and the user will have to rely on the strength of their own password.

Pepper: A pepper is a secret salt that is stored without connection to a user (for example at the top of the database[1]), or not stored at all. Peppers make it harder to crack individual passwords even in the case of a leak. Peppers should always be used together with salts.

Common Approaches

There's two main approaches to peppering, both should be in addition to salts.

You generate a really long number (128+ bits) and store it outside the database, then whenever you need to hash a user's password you add it to the password before hashing.

Pros: if attackers don't know the pepper it's impossible to crack any of the passwords.
Cons: If the a

...
Information Assets
by ozziegooen — 2021-08-24T04:32:40.087Z

Epistemic Status: This represents fairly early work. The terminology isn't at all set in stone.
Scholarship Status: I've spent several hours attempting to investigate this topic, and in the past have spent quite a while researching Information Theory and Applied Information Economics. I'm sure I'm missing very valuable literature, but I can't find it. Comments well appreciated.

Thanks to David Manheim and Nuño Sempere for comments on this

Introduction

As anyone who’s read an arduous textbook knows, learning comes with costs as well as benefits. As anyone who’s decided against reading at least one textbook knows, often the expected costs outweigh the expected benefits.

The interplay of the costs and benefits of information leads to an environment of efficient trade-offs. Tweets and infographics often do most of the work of popular nonfiction books. Lossy computational compression schemes offer good-enough quality for greatly reduced storage costs. Common verbal communication is typically far less precise and rigorous than formal philosophical proofs, but is far more practical.

As the old saying goes “All models are false, but some are useful.” A cost-benefit approach would say, “Models represent trade-offs in situations where absolute accuracy doesn’t justify its cost.” I think this basic fact; that educational materials, models, theories, and language all represent trade-offs between accuracy and costs, should really be fairly obvious and commonly acknowledged by now.

Interestingly enough, fairly little work around Information Theory seems to have gone deep into modeling these trade-offs. It’s often assumed that information is either freely absorbed, or in some situations limited to a particular fixed communication channel. Some work around Value of Information analyses estimate the cost one might be interested in paying for specific information, but often makes some very large assumptions for some particular settings.

I’m interested in developing better intuitions and vocabulary around these tradeoffs. Here are some initial attempts. I’m sure I’m missing a lot of great work, but I’ve had problems finding it. If you have thoughts or recommended references, I’d very much appreciate you sharing them.

A Very Simple Model

We’re going to begin with models of textbooks; not because these are particularly important, but because I think these are particularly easy to in...
COVID/Delta advice I'm currently giving to friends
by Rob Bensinger — 2021-08-24T03:46:09.053Z

[Epistemic status: US-centric. gathering various impressions even though I haven't deeply investigated this topic and am not an expert, so it's all in one place and others can better use or critique the claims.]

1. Figure things out for yourself

Figure out what risk level you're comfortable with, and use microCOVID.org to get a sense of what policy makes sense for you personally.

Consider sharing your Fermi estimates with others (e.g., make a small Facebook group or chat for the purpose, or a LW thread if you're happy to do it publicly). This is a way to compare notes, get feedback, and share info without there needing to be a single big Policy Proposal For Everyone blog post people are passing around.

2. Be much more wary of COVID when hospitals are full

Keep an eye on confirmed COVID-19 cases, hospitalizations, and deaths in your area, and put effort into avoiding catching or transmitting COVID-19 if it looks like hospitals in your area will be overloaded 2-4 weeks from now.

3. Consider mostly not worrying about it

For young healthy vaccinated people in places with relatively good health care and relatively high vaccination rates (e.g., the US), if it doesn't look like your local hospitals will be overloaded in the next few weeks, then I think COVID-19 is mostly not worth worrying about right now.

(Likely exceptions: you're spending a lot of face-to-face time with an immunocompromised friend; you're visiting your seventy-five-year-old parents for the holidays in two weeks; etc.)

I emphasize this point mostly because my friends are in left/liberal spaces, where I think social forces encourage people to voice their "worry more" thoughts and keep quiet about their "worry less" thoughts.

This makes it extra valuable to speak up about "worry less" when you think it's true. (Though, again, all of this is too individual-specific for a public blog post to be able to say much about the exactly correct level of "worry".)

People in my social circle have mostly been taking large precautions throughout the pandemic. I see rationalist friends worrying a lot about whether it's OK to host get-togethers without rapid-testing all attendees (which I'd tentatively guess is not worth the trouble for the vast majority of gatherings). I see non-rationalist friends posting about how sad they are to not get to see friends again until things 'go back to normal'.

What I ...
What are some good rationalist ice breaker questions?
by konstell — 2021-08-24T02:47:55.940Z

This question has been asked, but I will ask again. In light of the upcoming meetups everywhere, I thought it would be a good idea to compile a list of ice breaker questions, so we can make it easier for people to start conversations.

A few questions that have worked well for me:

What do you think about when you're not thinking about work?

How did you get into ACX/SSC/LW/EA?

What's something you've been putting lots of effort in recently?

What have you been thinking about lately?

Any other prompts?
18 possible meanings of "I Like Red"
by ozziegooen — 2021-08-23T23:25:24.718Z

A speaker says, "I like red". Here are a few of the many things they could mean[1] by that.

I like red.
I want you to believe that I like red.
I want myself to believe that I like red, and this can be assisted by a public statement.
I think that the community valence of red should be higher. I’m ready to defend this position if sufficiently provoked, but this will be a battle for both parties, so I don’t recommend attacking it unless you are very confident in your position.
I want to hint that I like Communism, but for various reasons I feel uncomfortable saying that more plainly.
You should associate me with the cool people (who like red).
I’ve recognized that you identify with liking red. As such, I want to express my commitment to support you.
Due to incentives related to (7), our culture has internalized norms around people professing likes for things unique to specific tribes. When I say "I like red", I'm showing that I respect these norms, even though none of us might understand the origins or purposes of these norms.
I’m sure you recognize that by all accounts I should probably hate red. But I know that liking it would be more convenient for your situation. This discrepancy makes my saying a more clear signal of loyalty.
I’ve recognized that group X is associated with the liking or disliking of redness, and would like to express my loyalties accordingly. This will help craft very specific alliances, though at the possible expense of making some enemies.
I recognized that you've developed a positive connotation towards people who do (10). I'd like to position myself with these people, even though I don't understand the background context.
(Related to 11): Fred from accounting says "I like red" a lot, and he's successful. I might as well say it and hope for the best.
I just want you to know that I’m talking to you, the particular information doesn’t matter much. This could be useful to signal that I'm willing to be social, and/or that I see you as a friend/colleague/potential partner.

A listener might pick up that intended meaning, or they might learn different things than the speaker intended. These could include:

B1. I have background knowledge that people who like red are overwhelming represented by tribe Y, which have characteristics M and K, so I can infer that the speaker has characteristics M and K.

B2. The speaker thinks they like red. However, I ca...
D&D.Sci August 2021 Evaluation and Ruleset
by abstractapplic — 2021-08-23T22:49:20.528Z

This is a followup to the D&D.Sci post I made ten days ago; if you haven’t already read it, you should do so now before spoiling yourself.

Here is the web interactive I built to let you evaluate your solution; below is an explanation of the rules used to generate the dataset. You’ll probably want to test your answer before reading any further.

Ruleset

Below is a summary of how the dataset works; full generation code is available here.

(Note: to make writing this easier, I’m using standard D&D dice notation, in which “4d8+3” means “roll four eight-sided dice, sum the results, then add three”.)

Mana types

Solar

Solar is the strongest and most complicated mana type. It can be decomposed into an average (40), a small amount of random noise (+ 1d2 - 1d2), two seasonality components, and the effects of distant supernovae.

The power of Solar on Day 384 is 45 + 1d2 - 1d2

Lunar

The moon reflects and inverts the effects of the sun, with a delay. It provides a power of 75 minus the amount of Solar power 14 days ago.

The power of Lunar on Day 384 is 16, because the power of Solar on Day 370 was 59.

Earth and Ocean

Earth and Ocean combine to create the World. World Mana can be decomposed into an average (68), some random noise (1d4 - 1d4), and a seasonality effect.

On a given day, Earth takes (15+1d80)% of World Mana, rounded down; Ocean gets the rest.

The combined power of Earth and Ocean on Day 384 is 77 + 1d4 - 1d4.

Breeze

The breeze varies, as does its variance. It is composed of an average (13), and some random noise which varies seasonally (1dN - 1dN, where N is given in the graph below).

The power of Breeze on Day 384 is 13 + 1d4 - 1d4

Flame

Flame catches, sputters and starts, building and waning randomly. Today’s Flame mana is yesterday’s, minus 25% (rounded down), plus the lowest of two d20 rolls.

The spread of possibilities at Day 384 looks like this:

Ash

Ash is what remains when Flame is extinguished. Today’s Ash is the 25% removed from yesterday’s Flame.

The spread of possibilities at Day 384 looks like this:

Void

Void is timeless; all motion within it is random, and all apparent trends or correspondences are merely noise. The power of Void on Day 384 – and all other days – is 24+1d8-1d8.

Doom

Doom builds gradually, then diminishes sharply as it is fulfilled. On a typical day, it can be decomposed into a starting point (24), some random noise (1d6), and a jagged seasonality effe...
The Roots of Progress is now a nonprofit organization
by jasoncrawford — 2021-08-23T21:33:32.535Z
Link: https://rootsofprogress.org/nonprofit-announcement

I’m excited to announce that The Roots of Progress is now a nonprofit organization.

The Roots of Progress started in 2017 as a side project, not much more than a blog. After Tyler Cowen and Patrick Collison published their call for “progress studies”, and the progress community started to form, it became my full-time job. For the first year or so I was an independent writer, supported by grants. Now I have enough support not only to fund myself, but also to hire help and sponsor programs. The new nonprofit organization is the vehicle for this.

The mission of The Roots of Progress is to establish a new philosophy of progress for the twenty-first century. The world needs a clearer understanding of the nature of progress, its causes, its value and importance, how we can manage its costs and risks, and ultimately how we can accelerate progress while ensuring that it is beneficial to humanity.

My focus now is on two priorities. First is the intellectual content, the history and philosophy of progress itself. I’m writing a book on this topic, The Story of Industrial Civilization, and the new organization is sponsoring this work. But much more is needed: more books, articles, talks, journals, documentaries. We need more histories of different aspects of progress, to make the story accessible to a broader audience. We need progress-oriented solutions to the problems facing the world, such as poverty, climate, pollution, job loss, and pandemics. And we need an ambitious, inspiring vision of the future, of where progress can take us. If you’d like to write on any of these topics, get in touch.

My second priority is building out and strengthening the progress network and community. Stay tuned for announcements here.

For advice and governance, I’ve formed a board of directors. The people I invited, while not in the limelight of the progress community, have been some of my strongest supporters: Ray Girn, CEO of Higher Ground Education (which commissioned my high school progress course), and Anil Varanasi, CEO of Meter. Patrick Collison and Tyler Cowen are serving as advisers (as they have informally, ever since I was considering going full-time).

Our first-year fundraising goal is $500,000, and thanks to generous donations from Patrick and John Collison among others, we’re already more than halfway to that goal. You can support us through Patreon, or get in touch to talk about...