trentmkelly/LessWrong-43k huggingface dataset:
[LDSL#1] Performance optimization as a metaphor for life
by tailcalled — 2024-08-08T16:16:27.349Z

Followup to: Some epistemological conundrums. Response to: The omnigenic model as a metaphor for life. Part 1 in this post will introduce a concept from programming, part 2 will apply it to life in general. This post is also available on my Substack.

Part 1: Performance optimization

In computer programming, there is a tradeoff between software that is quick and easy for humans to write and read, versus software that is fast for the computer to execute. Moving from the former to the latter is called “performance optimization”.

Moving from the latter to the former is called “high-level rewriting”. This is rarely done intentionally, but a substantial part of the reason computers are sometimes still sluggish despite having gotten orders of magnitude faster is that high-level rewriting has happened implicitly as old software that focused more on performance has been replaced with new software that focuses more on developer speed.

Because programming is done at such a high level, there is almost always a way to do performance optimization in any piece of code. Yet, it’s considered a beginner mistake to just pick a random piece of code to go optimize, which is sometimes encapsulated in sayings like “premature optimization is the root of all evil” or “the epsilon fallacy”. Instead, a recommended approach is to run the code, measure what takes the most resources, and fix that. But why is that so important?

Computers are fast, or alternatively, software/data/people/society is very slow and long-term-oriented compared to individual instructions. Let’s say there’s something a computer can do in 1 microsecond, but you write the code inefficiently, so it takes 1000x too long. This means that it takes… 0.001 seconds, aka basically nothing in most circumstances.

But let’s say that it’s an operation that you need to apply to 10000 datapoints, so you need to repeat it 10000 times. Now suddenly it takes 10 seconds, which is most likely worth optimizing, as otherwise you wouldn’t e.g. want to wait 10 seconds for a page to load. If you need to apply it to each pair of the 10000 datapoints, then you’ve got a 10000 by 10000 grid, or 100000000 repetitions, which will take more than a day to execute, so this absolutely needs to be optimized.

Usually, an execution of some code consists of many pieces whose resource use adds up. Some of these pieces just run a quick operation once, whereas o...
Four Randomized Control Trials In Economics
by Maxwell Tabarrok — 2024-08-08T15:59:23.250Z
Link: https://www.maximum-progress.com/p/four-randomized-control-trials-in

Randomized Control Trials have some drawbacks. For many important questions, like

causes of the industrial revolution

, a randomized trial is impossible. For many others, RCTs are expensive and cumbersome, leading to low sample sizes or experimental designs that precisely answer irrelevant questions. Still, when RCTs with large sample size and generalizable designs are possible, their advantages justify deference to their results even when observational evidence disagrees. This is the case with the four trials in this post. They each have hundreds to tens of thousands of participants and budgets big enough to test treatments that are relevant to the real world.

The largest RCT

in this group, run by Harvard economists and the charity

RIP Medical Debt

, tests the effects of medical debt cancellation. They relieved $169 million dollars of debt for 83,401 people over two years 2018-2020. Medical debt has extremely low recovery rates, so the $169 million dollar face value only cost 2 or 3 million dollars to relieve, but this is still a large treatment size. The researchers followed up with the recipients of this debt relief with several surveys tracking their mental, physical, and financial health.

There are two other elements which make the evidence from this trial compelling. First, their analyses are pre-registered. This means they submitted the list of regressions they would run before they got the data back from their survey. This is important because it prevents them from putting inconvenient results in the

file drawer

and is a check against running

100 extra tests

where the null hypothesis is true and reporting the 5 that happen to have p < .05. They also ran an expert survey of economists and scientists who predicted the results so we can quantify exactly how much of a narrative violation these results are.

So what did this trial find?

First, we find no impact of debt relief on credit access, utilization, and financial distress on average. Second, we estimate that debt relief causes a moderate but statistically significant reduction in payment of existing medical bills. Third, we find no effect of medical debt relief on mental health on average, with detrimental effects for some groups in pre-registered heterogeneity analysis. [emphasis added]

So wiping out thousands of dollars of medical debt per person had precise null effects on just about anything positiv...
Cheap Whiteboards!
by Johannes C. Mayer — 2024-08-08T13:52:59.627Z

Take a large thick cardboard box. Cut it into planar pieces. Take transparent tape and cover the cardboard's surface with it. Tadaa! A whiteboard:

Because transparent tape is smooth the whiteboard pen is erasable.

Sticky notes stick a lot better on the transparent tape than on the cardboard, which is useful for Kanban Boards!

Ideally, put white paper between the cardboard box and the tape to get an actual white board.

This method can be applied to convert any object into a whiteboard, like a big piece of wood, the side of a shelf, your forehead, etc.

(Originally my intention was to make sticky notes stick better, and accidentally discovered that I made a whiteboard. That's why I did not use white paper.)

(I'm currently unsure if there is a problem with the pens becoming unerasable when leaving them on for a long period of time, or any other problems, as I just came up with this.)
AI #76: Six Shorts Stories About OpenAI
by Zvi — 2024-08-08T13:50:04.659Z

If you’re looking for audio of my posts, you’re in luck. Thanks to multiple volunteers you have two options.

Option one is Askwho, who uses a Substack. You can get an ElevenLabs-quality production, with a voice that makes me smile. For Apple Podcasts that means you can add them here, Spotify here, Pocket Casts here, RSS here.

Alternatively, for a more traditional AI treatment in podcast form, you can listen via Apple Podcasts, Spotify, Pocket Casts, and RSS.

These should be permanent links so you can incorporate those into ‘wherever you get your podcasts.’ I use Castbox myself, it works but it’s not special.

If you’re looking forward to next week’s AI #77, I am going on a two-part trip this week. First I’ll be going to Steamboat in Colorado to give a talk, then I’ll be swinging by Washington, DC on Wednesday, although outside of that morning my time there will be limited. My goal is still to get #77 released before Shabbat dinner, we’ll see if that works. Some topics may of course get pushed a bit.

It’s crazy how many of this week’s developments are from OpenAI. You’ve got their voice mode alpha, JSON formatting, answering the letter from several senators, sitting on watermarking for a year, endorsement of three bills before Congress and also them losing a cofounder to Anthropic and potentially another one via sabbatical.

Also Google found to be a monopolist, we have the prompts for Apple Intelligence and other neat stuff like that.

Table of Contents

Introduction.

Table of Contents.

Language Models Offer Mundane Utility. Surveys without the pesky humans.

Language Models Don’t Offer Mundane Utility. Ask a silly question.

Activate Voice Mode. When I know more, dear readers, so will you.

Apple Intelligence. We have its system prompts. They’re highly normal.

Antitrust Antitrust. Google found to be an illegal monopolist.

Copyright Confrontation. Nvidia takes notes on scraping YouTube videos.

Fun With Image Generation. The days of Verify Me seem numbered.

Deepfaketown and Botpocalypse Soon. OpenAI built a watermarking system.

They Took Our Jobs. We have met the enemy, and he is us. For now.

Chipping Up. If you want a chip expert ban, you have to enforce it.

Get Involved. Safeguard AI.

Introducing. JSONs, METR, Gemma, Rendernet, Thrive.

In Other AI News. Google more or less buys out Character.ai.

Q

...
What the cost difference in processing input vs. output tokens with LLMs?
by kotrfa — 2024-08-08T10:43:18.049Z

Hi,

I am trying to understand the difference in the cost of producing a single input token vs output token.

Based on some articles, I came to the following conclusion:

Input tokens processing scales quadratically, there’s no way around it, you have to compute attention (K and V by passing it through encoders) for each token with each other token.

Output tokens scale linearly thanks to using KV cache (otherwise quadratic without KV cache, as linear tokens do) which is what everyone seems to do when hosting these models. I.e. you trade compute for memory, and try to be clever about how you store and compute all these stuff (like sparse, cache eviction…). I believe this is simply an empirically practical way around the quadratic scaling.

Therefore, realistically, it’s really the memory capacity and bandwidth being the problem for output tokens rather than raw FLOPS. KV cache grows linearly with the sequence length regardless input/output tokens ratio.

I am confused here why input tokens are almost universally cheaper - 2 to 5 times - to output tokens. Also, is the evidence that providers are pricing things linearly for memory being the problem here (as they do now, you pay the same price for 11th token as for 10001)? If not and they were FLOPS bounded, I would expect the providers to price stuff quadratically, not linearly. Or is it just for client-pricing simplicity/marketing?

I want to be able to estimate the cost of processing a single token, and I cannot wrap my head around this. I theoretically estimated based on GPU rent price and separately based on power consumption (assuming some utilization such as 10%), and I believe I somehow need to differentiate between input/output tokens here.

In one tweet from LeptonAI who hosts these LLM, I also saw that there are usually 3-10 times more input tokens than output tokens. Again, if input tokens dominate the sequence and it was FLOPS the issue, I would expect to reflect that in the pricing. Not sure what role it plays in these calculations so far.

Any help is appreciated, thanks!
Meno's Paradox
by Hudjefa — 2024-08-08T05:59:24.373Z

https://en.wikipedia.org/wiki/Meno

A succinct argument form of Meno's paradox that I found online follows:

We wish to make an inquiry into/about x (x being an object of epistemology, covers stuff that can be known)

Either we know x or we don't know x
If we know x, inquiry is unnecessary
If we don't know x, inquiry is impossible
Inquiry is unnecessary or inquiry is impossible

As far as I can see, a nfr argument that thoroughly explicates Meno's paradox.

How do we solve this paradox?
Case Story: Lack of Consumer Protection Procedures

AI Manipulation and the Threat of Fund Concentration in Crypto

Seeking Assistance to Fund a Civil Case to Establish Facts and Protect Vulnerable Consumers from Damage Caused by Automated Systems

by Petr 'Margot' Andreev — 2024-08-08T05:55:09.775Z

Good time of the day. And thank you for the wonderful work you do!

I really like the concept of making fewer mistakes. I would appreciate it if you could point out any of my errors. I know I am definitely wrong, and my worldview is completely or largely incorrect. My cognitive abilities are sufficient only to read HPMOR 3 times and less wrong articles mostly in translation. Please point out where I am making mistakes. If any part is incorrectly formatted, please feel free to delete it, but please do not ignore it.
In some countries, there are actual consumer protection procedures at the local level.

This is due to the necessity of protecting their rights, especially from large companies, natural and common monopolists. There are rules that allow for the annulment of any actions of public contracts by monopolists to prevent them from endlessly abusing countless rules and running endlessly within innumerable corridors.

There are even European directives on consumer protection rights and English laws on unfair terms in consumer protection. They are created so that the consumer can always theoretically protect their rights, but in reality, this is very difficult.

However, this does not work at all at the international level of civil law. People have no rights or protection in other jurisdictions. You can google international consumer law and you will find out how to bypass it rather than how to comply with it, let alone find actual procedures.

And this is a big problem in the context of AI scalability.

In 2021, during what was called a bear market in the cryptocurrency market, companies were collapsing like a house of cards one after another. For many reasons, people were withdrawing liquidity as much as they could, even from their own users.
https://x.com/TomDwan/status/1592026755107520512

I see that the LessWrong community, 80,000 Hours, and Effective Altruism are involved in the regulation of artificial intelligence and automated procedural agents, but there are many wonderful laws in the world that do not work.

Typically, organizations such as states or corporations in everyday life often resemble automatically generative artificial intelligence models, similar to the Vogons from Douglas Adams' "The Hitchhiker's Guide to the Galaxy," who systematically destroy everything in their path.

https://upload.wikimedia.org/wikipedia/en/3/39/Vogon_poetry2.jpg

With one hand,...
Motivation Theory
by Zero Contradictions — 2024-08-08T05:05:04.741Z
Link: https://thewaywardaxolotl.blogspot.com/2022/02/motivation.html

This is a brief sketch of a theory of motivation.

Motivation drives action. It also causes the experiences of pleasure and pain. We experience pain when motivation increases, and pleasure when motivation decreases.

Motivation is generated by emotions, such as hunger, thirst and lust. Emotions generate motivation, and motivation generates action. Each emotion has a biological function. Hunger motivates eating. Thirst motivates drinking water. Lust motivates sex.

Some emotions react to stimuli. For example, if you are pricked with a pin, then you will experience pain. The pin activates sensory receptors in the skin (nociceptors), which send a signal to the brain, where it generates an emotional reaction. That reaction will motivate you to act in a way that avoids the noxious stimulus.

Behavior can be divided into two broad categories: avoidance and pursuit. Some emotions generate avoidance behaviors. Fear is a generic emotion that motivates avoidance. Other emotions, such as hunger, generate pursuit behaviors, such as seeking food. Emotions that motivate pursuit tend to build up over time, while emotions that motivate avoidance tend to be immediate reactions to stimuli. However, because we have complex, goal-directed behavior, we can act in advance to prevent future danger and harm, rather than just reacting to it.

Emotions are heuristic problem recognizers. They recognize biological problems, and generate the motivation to solve them.

Why do emotions generate motivation instead of action? Why is there an intermediate step? In some cases, a stimulus directly generates action, such as shivering when you are cold, or jerking your hand away from a hot stove. However, that stimulus-response mechanism can only generate simple behaviors.

(see the rest of the post in the link)

Although I'm not the author of this post (a friend of mine wrote it), I have created a PDF version of the essay that has a table of contents and headers to make it even easier to read.
It's time for a self-reproducing machine
by Carl Feynman — 2024-08-07T21:52:22.819Z

I've wanted to build a self-reproducing machine since I was 17. It's forty-five years later, and it has finally become feasible. (I've done a few other things along the way.) I'm going to describe one such device, and speculate as to its larger implications. It's a pretty detailed design, which I had to come up with to convince myself that it is feasible. No doubt there are better designs than this.

The Autofac

Here's a top-level description of the device I'm thinking of. It's called an Autofac, which is what they were called in the earliest story about them. It looks like a little metal shed, about a meter cubed. It weighs about 50 kilograms. There's a little gnome-sized door on each end. It's full of robot arms and automated machine tools. It's connected to electricity and by WiFi to a data center somewhere. It has a front door, where it accepts material, and a back door, where it outputs useful objects, and cans of neatly packaged waste. You can communicate with it, to tell it to make parts and assemble them into useful shapes. It can do all the metalworking operations available to a machinist with a good shop at their disposal. In return, it occasionally asks for help or clarification.

One particular thing it can be told to make is another one of itself. This is of course the case we're all interested in. Here's what that looks like. You feed a 60kg package of steel castings, electronics, and other parts, into the door at one end. It starts by building another shed, next to the other end. The two sheds are butted up next to each other, so the rain can't get in. Once it's enclosed, there is no visible progress for about a month, but it makes various metalworking noises. Then it announces that it's done. The second shed is now another Autofac, and can be carried away to start the process elsewhere. There's also a can full of metal scrap and used lubricant, which has to be disposed of responsibly. This process can be repeated a number of times, at least seven, to produce more offspring. Eventually the original Autofac wears out, but by then it has hundreds of descendants.

The software

The key part of the Autofac, the part that kept it from being built before, is the AI that runs it. Present-day VLMs (vision-language models) are capa...
[LDSL#0] Some epistemological conundrums
by tailcalled — 2024-08-07T19:52:55.688Z

This post is also available on my Substack.

When you deal with statistical science, causal inference, measurement, philosophy, rationalism, discourse, and similar, there’s some different questions that pop up, and I think I’ve discovered that there’s a shared answer behind a lot of the questions that I have been thinking about. In this post, I will briefly present the questions, and then in a followup post I will try to give my answer for them.

Why are people so insistent about outliers?

A common statistical method is to assume an outcome is due to a mixture of observed factors and unobserved factors, and then model how much of an effect the observed factors have, and attribute all remaining variation to unobserved factors. And then one makes claims about the effects of the observed factors.

But some people then pick an outlier and demand an explanation for that outlier, rather than just accepting the general statistical finding:

In fact, aren’t outliers almost by definition anti-informative? No model is perfect, so there’s always going to be cases we can’t model. By insisting on explaining all those rare cases, we’re basically throwing away the signal we can model.

A similar point applies to reading the news. Almost by definition, the news is about uncommon stuff like terrorist attacks, rather than common stuff like heart disease. Doesn’t reading such things invert your perception, such that you end up focusing on exactly the least relevant things?

Why isn’t factor analysis considered the main research tool?

Typically if you have a ton of variables, you can perform a factor analysis which identifies a set of variables which explain a huge chunk of variation across those variables. If you are used to performing factor analysis, this feels like a great way to get an overview over the subject matter. After all, what could be better than knowing the main dimensions of variation?

Yet a lot of people think of factor analysis as being superficial and uninformative. Often people insist that it only yields aggregates rather than causes, and while that might seem plausible at first, once you dig into it enough, you will see that usually the factors identified are actually causal, so that can’t be the real problem.

A related question is why people tend to talk in funky discrete ways when careful quantitative analysis generally finds everything to be continuous. Why do people wa...
Help us seed AI Safety Brussels
by gergogaspar — 2024-08-07T06:32:24.760Z

crossposted on the EA forum: https://forum.effectivealtruism.org/posts/EwdgPJfeieCxe4cxa/help-us-seed-ai-safety-brussels

TLDR:

Brussels is a hotspot for AI policy and hosts several think tanks doing good work on AI safety. However, there is no established AI Safety group with paid staff that brings the community together or works on reaching out to students and professionals. For this reason, the European Network for AI Safety (ENAIS) and members from EA Belgium are teaming up to seed AI Safety Brussels.

This is a call for expressions of interest for potential collaborators to make this happen, with a wide range of ways to contribute, such as being a potential founder, policy/technical lead, volunteer, advisor, funder, etc. If you are interested in helping out in some capacity, please fill out this 2-minute

form

.

Potential priorities

I (Gergő) will caveat this by saying that the exact strategy (and name) of the organisation will be determined by the founding team and advisors. However, we think there are several potential pathways in which someone doing community building full or part-time could add a lot of value.

Running courses for professionals

The AIS community, as well as the EU AI Office, are bottlenecked by senior talent. Currently, there are only a few groups and organizations working to do outreach towards professionals.[1]

Brussels has a lot of senior people working on AIS. Understandably, they don't run courses to onboard others to AI Safety, as they are busy with object-level work. Conditional on seniority, if someone gets funded to start AIS Brussels, they could leverage the existing network and create an environment that is quite attractive for (policy) professionals to join, who are new to the field of AI Safety. By running courses similar to AI Safety Fundamentals by Bluedot, such a person (or team) could introduce AIS to hundreds of professionals per year and support them in their journey of upskilling and help them get into high-impact roles.

For clarification, by professional outreach, we don't mean actively reaching out to policy professionals working at e.g., the European Parliament to request meetings etc. The existing think tanks are in a better position to do this kind of work.

Seeding university groups

To our knowledge, Brussels has no AI Safety university groups at the moment. The founders could help seed such groups, by doing

city-

...