trentmkelly/LessWrong-43k huggingface dataset:
How to use ChatGPT to get better book & movie recommendations
by KatWoods — 2023-07-15T08:55:08.826Z

I find ChatGPT gives me better movie recommendations than Netflix when I do the following:

1) Keep a list of books/movies that you like and why you like them

2) Use the prompt "Recommend X movies/books that [CRITERIA]. Here's a list of things I like and dislike and why: [LIST] "

The reason it works better is that you can tell ChatGPT why you like them. This is better than Netflix, which only has access to whether you liked the show and how long you watched it.

Like, I might say I really liked Catch Me If You Can, and Netflix will often infer that I want to watch DiCaprio, when really I want to watch biographical dramas.

I keep a list in a spreadsheet, then just copy paste it when needed. Here’s screenshot example of bit of the spreadsheet and the prompts and the results:

Works for books, TV shows, movies, video games, and all sorts of entertainment.

Other examples to give you a flavor of how specific you can be:

Tell me one of the most famous novels or books to come from every Asian country, listed in order of population of country, starting with China. Ideally classics, but also memoirs, biographies, or essays that are really well-known in that country.

Memoirs of scientists who probably would have been rationalists had they been born in the modern era.

The only problem with this is about 1 in 10 books or so will sound amazing and then turn out to totally not exist. This will be devastating, but hopefully my warning has prepared you for the inevitable heartbreak.

You can also add to your list things you don't like and why. For example, I have a second part of my spreadsheet which describes the things I don't like, so that it doesn't recommend things in that category.

If anybody wants to build a recommendation system that makes this easy, I bet it could really take off. Not much of a moat around it probably, but the general idea could make finding perfect content for you amazing.

Like, imagine feeding in all of your reviews of books or movies on IMDB/Amazon/etc into it. Or instead of giving thumbs up or down for things, giving quick written feedback, that is continuously added to a database which feeds into your reccs. (Code Interpreter or Claude 2 seem likely to be able to do this)

Maybe this is already being done. If so, links in the comments would be appreciated!

In the meantime, this...
Would you take a job making humanoid robots for an AGI?
by Super AGI — 2023-07-15T05:26:27.678Z

I just finished reading the transcript from this interview with Carl Shulman, and found these parts particularly interesting.

“Converting the car industry to making Humanoid Robots.”

https://youtu.be/_kRg-ZP1vQc?t=6363

“Where the Human workers are more valuable for their hands than their heads...”

https://youtu.be/_kRg-ZP1vQc?t=6469

What percentage of people do you think would be interested in taking a job to convert a factory from making cars to manufacturing humanoid robots? And/or making humanoid robots in those factories?

Would you take a job making humanoid robots for an AGI? Or Elon Musk, or another large Corporation? How much would you need to be paid per hour? Are there any other concessions or terms you would require, beyond the pay?
Rationality, Pedagogy, and "Vibes": Quick Thoughts
by Nicholas / Heather Kross — 2023-07-15T02:09:46.677Z

I just read this book review of Egan's The Educated Mind. Here are some thoughts I had, written for all but grammatically directed at the review's anonymous author (like a typical comment!).

I'd love to go to that middle school, and that high school. It would set a lower-bound on how many different areas someone has passing knowledge of. (This sounds like faint praise, but Gell-Mann Amnesia partly depends on people not having this.)

I don't know if this would really help all the students, even if I mostly-agree with the Bruner(/HPMOR) statement that basically anyone can learn anything, after the age where pouring-water-into-a-taller-container-looks-like-adding-water no longer applies. But aside from the credential/signaling model of education (which, itself, could explicitly select against anything remotely pleasant or fun or effective), I think a lot of school's function is just daycare. The school does its "job" of keeping a kid off the streets and out of their parents' hair for 8 hours, but adding this curriculum to the existing system doesn't e.g. solve a kid's learning problems from being in an abusive home. (This probably isn't for the curriculum itself to have to solve, but I will say that The Bottleneck could just be on mental/physical health. E.g. schools fuck up teens' sleep.)

This sorta flatters my bias in favor of my own idea for how to teach math, so that's something. Ditto for my ADHD, as you discussed; That "curiosity about everything" is indeed quite fun. (No wonder TVTropes and social media feeds are both addictive!)

I got most of the Really Cool Stuff (taught in these Cool Intuitive Ways!) from a combination of pop-science books, YouTube videos, and blog posts on LessWrong and elsewhere. I was lucky that some of the core parts were already thrown into my middle/high school (e.g. how Bayes' formula can be derived from looking at a Venn Diagram). Now that I'm trying to specialize and amp-up my skills (for AI alignment), I can go into more depth using textbooks etc.

The overall idea of "learn through diverse examples" definitely fits well with other things in my memeplex belief system, like "the scaling hypothesis is basically true" and "most of our knowledge has to come from our prior knowledge (and, implicitly, we should learn about lots of things)" and "multiple different kinds of explanations should be used when teaching/learning". I still thin...
(redacted) Anomalous tokens might disproportionately affect complex language tasks
by Nikola Jurkovic — 2023-07-15T00:48:10.002Z

Disclaimer: This is a final writeup I did for a college class. Everything past the summary is written in studentspeak and thus might not be worth reading.

Correction (read this!)

When I recently revisited my code, I noticed there was a mistake where the rote repetition task was testing non-glitch tokens when it was supposed to test glitch tokens, making the performance basically the same (91% vs 90%). Rerunning the experiments reveals that the relative performance decrease is similar across anomalous and non-anomalous tokens. I now have no strong reasons to think that the main claim of the post is true.

Summary

I started with a hypothesis: "Anomalous/glitch tokens (such as SolidGoldMagikarp) affect performance on complex language tasks more than they do on simple language tasks."
Potential implications: If my hypothesis is actually true, and keeps being true for more complex models, then keeping anomalous tokens around on purpose might provide a consistent and easy way to make LLMs perform badly.

This might be a really janky way to add a partial off-switch to a model, especially if we make it so that the model never actually "finds out" which tokens are anomalous.
Additionally, if anomalous tokens are simply a result of specific tokens from the tokenizer dictionary not being in the training set (or being followed by random sets of tokens in the training set), then it would be easy to add anomalous tokens to a model during training.

I tested GPT-2 small on two tasks:

a simple rote repetition task where random sequences of tokens are repeated multiple times, and
an instructed repetition task where the model is asked (through a prompt) to repeat a string.

Intuitively, I judged instructed repetition to be a more complex task than rote repetition, as rote repetition relies mostly on induction heads, but instructed repetition relies on stuff like "knowing what an instruction is" and "knowing which part of the text to repeat". For some reason, I thought that "more moving parts" would mean that anomalous tokens degrade performance more.
For rote repetition (the simple task), there wasn't a noticable performance difference for anomalous tokens (91% accuracy for normal tokens, 90% accuracy for anomalous tokens).
For instructed repetition (the complex task), there was a noticable performance difference for anomalous tokens (34% accuracy for normal tokens, 16% accuracy for ano

...
Why was the AI Alignment community so unprepared for this moment?
by Ras1513 — 2023-07-15T00:26:29.769Z

Our epistemic rationality has probably gotten way ahead of our instrumental rationality

-Scott Alexander

A Lesswrong Crypto autopsy

This is a question post:
Why was the AI Alignment community so unprepared for engaging with the wider world when the moment finally came?
EDIT Based on comment feedback: This is a genuine question about why something that is so obvious now with hindsight bias, was not clear back then and understand why not. Not an attempt to cast blame on any person or group.

I have been a LW reader for at least 10 years, but I confess that until the last ~1.5 years I mostly watched the AI alignment conversation float by. I knew of the work, but I did not engage with the work. Top people were on it, and I had nothing valuable to add.
All that to say: Maybe this has been covered before and I have missed it in the archives.
Lately (throughout this year), there have been a flurry of posts essentially asking: How do we get better at communicating to and convincing the rest of the world about the dangers of AI alignment?

Catching the Eye of Sauron
An AI Realist Manifesto
The Social Alignment Problem

All three of which were posted in April 2023.

The subtext being: If it is possible to not-kill-everyone this is how we are going to have to do it. Why are we failing so badly at doing this?
At this risk of looking dumb or ignorant, I feel compelled to ask: Why did this work not start 10 or 15 years ago?
To be clear: I do not mean true nuts and bolts ML researcher Alignment work, which this community and MIRI were clearly the beginning and end for nearly 2 decades.
I do not even mean outreach work to adjacent experts who might conceivably help the cause. Again, here I think great effort was clearly made.
I also do not mean that we should have been actively doing these things before it was culturally relevant.
I am asking: Why did the Alignment community not prepare tools and plans years in advance for convincing the wider infosphere about AI safety? Prior to the Spring 2023 inflection point.
Why were there no battle plans in the basement of the pentagon that were written for this exact moment?
It seems clear to me, based on the posts linked above and the resulting discussion generated, that this did not happen.
I can imagine an alternate timeline where there was a parallel track of development within the community circa 2010-2020(?) where much dis...
Physics is Ultimately Subjective
by Gordon Seidoh Worley — 2023-07-14T22:19:01.151Z

N.B. A basic and perhaps obvious point that nonetheless I think people get confused about. Despite the somewhat provocative title, my goal is not to say anything new to the average Less Wrong reader, only to emphasize a point and try to explain it so that it can be clearly seen.

Summary: Physics, by which I mean models of how reality works at the most fundamental level, is a subjective endeavor. Physics seems to be objective, but that's because there's high intersubjective consensus about which models best explain and predict reality. Rounding this off to objective causes confusion, and the point generalizes for all seemingly objective things.

Art, and in particular modern art, is highly subjective. Some people are impressed by the artistry of paint splatters, blank canvases, and signed toilets, while others roll their eyes. People like different things and only sometimes agree, so we generally accept that art is subjective.

Assessing a work of art looks sort of like this:

A person sees some art and they subjectively judge it good or bad.

Physics, by contrast, seems totally objective. The world is how it is, and our models of it are good or bad insofar as they accurately and precisely describe the world. Whether or not you like general relativity, for example, has no bearing on whether it's a good theory. All that matters is how well general relativity explains and predicts what we observe.

The picture for physics might look like this:

Physics theories describe reality and are objectively good or bad.

But both of these pictures are wrong! The first leaves out the detail that art has to exist in reality—it's not art first, but atoms first, and it only becomes art when it's observed by someone who thinks of it as art. The second leaves out that theories of physics don't exist on their own, they exist in our heads. To suppose otherwise is to suppose the existence of a paradoxical view from nowhere. So really both pictures are the same picture:

Judgments are mediated by agents.

What does this mean for art and physics? Art is just as it was. The picture only makes it clearer that art doesn't exist independently: it exists within someone's experience of the world. For physics, though, it's a bigger deal. Physics feels like it's objective, and physical theories seem like they should be good or bad regardless of who does the assessing. And our experience matches this: people ...
How should a rational agent construct their utility function when faced with existence?
by Aman Rusia — 2023-07-14T19:48:41.022Z

Intuitively there are some utility functions that should be discarded based on principle of indifference. For example, my utility function shouldn't change based on my angular position with respect to the sun (given everything else the same) because I don't have a reason to prefer one direction over the other.
It also feels like my utility function should not change with time either.

I'm wondering if there can be some symmetry and invariance based arguments one can use to restrict one's construction of their own utility function. Is there any literature or posts that I can read?
In other words, is there an objectively rationally better purpose one can give to their own life based on symmetries in the real world?
AI Risk and Survivorship Bias - How Andreessen and LeCun got it wrong 
by Štěpán Los — 2023-07-14T17:43:15.998Z

Purpose of this post/TL;DR: Many people who argue against the existence of large-scale risks associated with AI, including Marc Andreessen or Yann LeCun, use the following argument template (call this the Argument from Overestimation, AFO): "since people were wrong in estimating high risk for *insert safe technology that we use today*, AI is probably safe too". I was surprised that this argument is being used by some of the leading figures in the AI Safety debate because as I argue in this paper, its logic relies on the survivorship bias. This, in turn, means that the argument begs the question by grouping AI with a skewed sample of only safe technologies, making it a flawed argument. I conclude that for the sake of improving the current debate on AI Safety, this argument should be abandoned.

Epistemic status: While I tend to be more on the “x-risks side” in AI Safety debates (to make this clearer, I was more in agreement with Tegmark and Bengio in the debate I reference), I attempted to give the most charitable reconstructions and strongest responses for AFO and gave concrete examples of the argument in action to avoid the straw-man fallacy. Note also that just because a particular argument in favour of AI safety fails, we are not warranted to conclude that AI is unsafe. The main point of this post is simply that we should not assume safety/unsafety without providing further arguments. I invite feedback and potential improvements on my position in the comments!

Argument from Overestimation

Consider the following passage from

Marc Andreessen’s essay about concerns regarding AI risk

, titled ‘Why AI will Save the World’:

“The fear that technology of our own creation will rise up and destroy us is deeply coded into our culture. [Recounts the myth of Prometheus about the technology of fire as an example.] The presumed evolutionary purpose of this mythology is to motivate us to seriously consider potential risks of new technologies – fire, after all, can indeed be used to burn down entire cities. But just as fire was also the foundation of modern civilization as used to keep us warm and safe in a cold and hostile world, this mythology ignores the far greater upside of most – all? – new technologies, and in practice inflames destructive emotion rather than reasoned analysis. Just because premodern man freaked out like this doesn’t mean we have to; we ...
Unsafe AI as Dynamical Systems
by Robert_AIZI — 2023-07-14T15:31:48.482Z
Link: https://aizi.substack.com/p/unsafe-ai-as-dynamical-systems

[Thanks to Valerie Morris for help editing this post.]

Overview

Large Language Models (LLMs) and their safety properties are often studied from the perspective of

a single pass

: what is the single next token the LLM will produce? But this is almost never how they are deployed. In contrast, LLMs are almost always run autoregressively: they produce tokens sequentially, taking earlier outputs as part of their input, until a halting condition is met (such as a special token or a token limit). In this post I discuss how one might study LLMs as dynamical systems, which emphasizes how an LLM can become more or less safe as it is run autoregressively.

Why Dynamics are Critical: Multi-step plans

Agents that are able to create and execute multi-step plans are far more capable than those that cannot. Current AI risk evaluations

test for planning ability

, and many threat models depend on the assumption that the AI

can generate complicated plans

. At the same time, the

capabilities

and

behavior

of language models are strongly influenced by the text in their context window. By the time a language model attempts to carry out a later step, its context window may be different enough that it has different values, goals, or capabilities. A language model writing a plan has to trust its “future self” to carry it out, a future self that may be different in key ways, such as refusing to perform unsafe actions. Thus, for an AI to successfully execute a dangerous multi-step plan, it must be consistently unsafe, across the steps of the plan.

For a simplified example: suppose an AI formulates a 100-step plan for world domination, but it would normally refuse to carry out any individual step because of a preference for ethical behavior, only carrying out a given step 1% of the time. A horribly naive prediction would be that these safety checks are independent, so the AI only has a (0.01)^100 chance of carrying out its plan, and is therefore sufficiently safe. More plausibly, these odds would be correlated - perhaps something external sets the AI into an “unsafe mode” where it will perform each step 100% of the time, or (even worse,) perhaps the trigger for “unsafe mode” is taking the first unsafe step. Identifying how earlier unsafe actions impact the odds of later unsafe actions is critical to AI safety, and is the goal of the dynamical systems framework for AI safety.

The Dynamical Systems

...
A Short Summary of "Focus Your Uncertainty"
by Stephen James — 2023-07-14T11:18:50.384Z

The first piece in the Noticing Confusion Sequence frames the reader as a novice pundit that wants to do good punditry with minimal effort. The fictional reader-as-novice-pundit quickly surmises that there's no math to leverage to estimate which outcomes are likely to occur: that the price of an asset to will rise, stay the same, or fall before your next show. As such, you have a lot of discomfort in deciding how to dedicate preparation time to the upcoming segment of punditry. After all, you have finite time to prepare for the segment, finite time for the segment (and a perfect division by 3 would underserve the reality of the price move), and your success is dependent on your ability to tell a good story about what happened. All this despite your inability to know the future before it occurs.

The article proceeds to a pronounced pause in our fictional self's thinking that festers a mental discomfort about our inability to solve the aforementioned problem sans mathematics. We are encouraged to investigate the mental discomfort of this haplessness, and follow it along the probabilistic intuition that thinking people tend to have. From the article

And yet… even in your uncertain state of mind, it seems that you anticipate the three events differently; that you expect to need some excuses more than others. And—this is the fascinating part—when you think of something that makes it seem more likely that bond prices will go up, then you feel less likely to need an excuse for bond prices going down or remaining the same.
It even seems like there’s a relation between how much you anticipate each of the three outcomes, and how much time you want to spend preparing each excuse. Of course the relation can’t actually be quantified.

This agitation will point us towards a heuristic or methodology for updating our beliefs in correspondence to this intuition: this felt sense that you are more drawn to certain outcomes and your willingness to expect some outcomes more than others must be founded on something - even if you haven't exactly put words to what that something is.

This piece is laying the early bricks of the Bayesian thinking and the idea of a Bayesian update. Indeed, in a mathematics-light fashion, the next sequence makes both the barriers to changing belief and seeing with fresh eyes the main target for the bulk of 50 articles before taking a different tact in...
Do the change you want to see in the world
by TeaTieAndHat — 2023-07-14T10:19:07.029Z

[Epistemic status: old truth, still seemed to deserve to be repeated a zillionth time in different words]

Really, I’m just going to state the obvious here. Most people have know and applied this very well for decades. On the other hand, it’s an obvious thing I personally forgot for a very long time, so, maybe I’m not the only one in need of a reminder.

[drum roll]

You shouldn’t focus on who you want to be, but on what you want to do.

I did say it was obvious.

As you may have noticed, the general idea of ‘doing things’ is already very present on LW. Like, very, very, very present. Everyone knows that if you want to improve the world, your life, or the life of your pet hamster, you’ve got to get off your chair and do something at some point. It’s true. It’s obvious. And it’s very hard, and so there are lots of posts telling you how to do more of it and how to do it better.

And I read those, and I saw that they were good. Even tried to apply some of that advice, for a while. However, I had read them to solve a problem I had, and after reading them, I still had my problem. To be precise, I still had some anxiety, in the form of an ugly little minx sitting on my shoulder, whispering into my ear: [ominous voice] "What are you going to do with your life? Will you be a doctor, an archeologist, a cashier at Starbucks?" "Are those people friends or foes? Are you with them?" "Are you happy?" "No, seriously, just what are you going to do with your life?" "Do you want to be like them?"

(Ok, Anxiety, you can shut up now, I think they’ve got the idea)

Anyway. Do stuff, and then see what you become and how happy it makes you. Then do more stuff. That’s pretty much the end of this post already. The only thing I have to say in this post is that doing things you find interesting or useful is better than LARPing someone you think is cool. It’s not just that it seems obvious, it’s, like, a contender for the Most Obvious Statement Ever Awards. Yet, we keep forgetting it (At least I do?).

By the way: seems like the anxiety minx put a curse on this post. Anyone who reads it has 30 seconds to come up with an idea of something to do that they wouldn’t have done unprompted, and will then feel compelled to do it straight away.