trentmkelly/LessWrong-43k huggingface dataset:
Drugs are Sometimes Good, Actually
by Gordon Seidoh Worley — 2023-02-08T02:24:24.152Z

A casual post in a series about what I wish someone had told me when I was younger.

(Legal disclaimer: I'm not advocating you do illegal things. If you do illegal things you're on your own.)

Like a lot of people who were kids in 90s America, I first learned about drugs from DARE. And here's what I learned:

If you do drugs one time you will be addicted forever and probably die.
If you do drugs you'll instantly become a loser.
The only good drug is the drug you never do.

Now, to be clear, they're only talking about recreational drug use, not medical use of drugs. But kids aren't usually prescribed anything fun like Percocet, just boring medicine you can't have fun with, so the distinction was not super important to make to a bunch of 10 year olds.

I was pretty well bought into this. I didn't know anyone who did drugs recreationally. My parents didn't drink or smoke. I had no first hand experience to know that the link between underage drinking and heroin addiction might be non-zero but also was very weak.

Additionally, my only experience with "recreational" drug use was robotripping, and that was terrible and entirely by accident. In case you don't know, the cough suppressant dextromethorphan (DXM for short), commonly found in cold medicine like Robitussin, is also a hallucinogen. It's not a very pleasant hallucinogen, but sometimes people do it because they don't have access to anything else. Some things it does to you:

causes vertigo
causes scary hallucinations
at high doses puts you into a "coma" and you feel like you're dead while remaining kind of conscious

As a kid, I didn't know this. What I did know is that when I got sick sometimes I saw things and heard voices. It wasn't until I was 15 that I accidentally took two doses of cough medicine about an hour apart and proceeded to be chased around the house by giant pink capybaras that I wondered if something was up. I found an FAQ about DXM and it told me two important facts: it's a hallucinogen and some people have very low tolerance to it due to luck of the genetic draw.

But that's not how most drugs are. Most recreational drugs create positive experiences on net in the short term. There's a selection effect here: the drugs that become popular to use recreationally are those that create positive short term experiences most of the time for most people. That's why people like to take opioids and smoke weed for fun rath...
House Covid Infection Retrospective
by jefftk — 2023-02-08T02:20:02.150Z

On Saturday 2023-01-14 our 6yo tested positive for covid. Three weeks
later we're all fine, but five of us ended up getting it despite
relatively strong efforts to isolate. In retrospect I think this
wasn't worth it, and even though I didn't get sick I would rather have
gotten covid than isolate from the rest of the family for two weeks.

Here's the timeline:

Saturday 2023-01-14: The 6yo is unusually sleepy in the
morning, cranky in the afternoon, and tests positive in the evening.
I'm away with her and the 8yo. We start masking around each other,
though we're in a hotel room. I open the windows overnight, put the
6yo on the far side of the room from me and the 8yo, and we sleep
unmasked.

Sunday 2023-01-15: We travel
home together, masking consistently. When we arrive home the 6yo
isolates in her room with an air purifier, and everyone wears masks
around each other and in common areas, except for the 1.5yo. One of
our housemates is away, and I start sleeping in their room downstairs.
We stop doing house dinners and
plan to test everyone each morning. I plan to work from home until
this is over: while my work doesn't require that it seemed
considerate. I'm mostly the one bringing things, since I was already
exposed.

Tuesday 2023-01-17: The 8yo tests positive. She isolates in
her room. We do let her out masked for about 3min to visit the 1.5yo,
where she stood masked in front of a window with a fan blowing out and
the 1.5yo was on the other side of the room:

Our nanny decides to keep coming in, watching only the 1.5yo and
keeping on a different floor from the 6yo and 8yo.

Friday 2023-01-20: Julia and the 1.5yo test positive in the
morning. They start isolating upstairs and we tell our nanny not to
come in. At this point all four of them are testing positive and I
stay off that floor except to drop things outside people's doors. The
older two are excited that they can now play with their baby sister
again.

Saturday 2023-01-21: The 6yo tests negative in the morning and is
released from isolation.

Sunday 2023-01-22: Our nanny texts us that she tested positive.

Monday 2023-01-23: The 6yo is back in school, masked until day
ten. Lily tests negative in the evening.

Tuesday 2023-01-24: The 8yo is back in school, masked until day
ten.

Saturday 2023-01-28: Our nanny tests negative.

Tuesday 2023-01-31: Julia and the 1

...
Noting an error in Inadequate Equilibria
by Matthew Barnett — 2023-02-08T01:33:33.715Z

I think I've uncovered an error in Eliezer Yudkowsky's book Inadequate Equilibria that undermines a key point in the book. Here are some of my observations.

First, let me provide some context. In the first chapter, Yudkowsky states that prior to Shinzo Abe's tenure as Prime Minister of Japan, the Bank of Japan had implemented a bad monetary policy that cost Japan trillions of dollars in real economic growth.

His point was that he was able to spot this mistake, and confidently know better than the experts employed at the Bank of Japan, despite not being an expert in economic policy himself. In a dialogue, he wrote,

CONVENTIONAL CYNICAL ECONOMIST: So, Eliezer, you think you know better than the Bank of Japan and many other central banks around the world, do you?

ELIEZER: Yep. Or rather, by reading econblogs, I believe myself to have identified which econbloggers know better, like Scott Sumner.

C.C.E.: Even though literally trillions of dollars of real value are at stake?

ELIEZER: Yep.

To demonstrate that he was correct on this issue, Yudkowsky said the following,

When we critique a government, we don’t usually get to see what would actually happen if the government took our advice. But in this one case, less than a month after my exchange with John, the Bank of Japan—under the new leadership of Haruhiko Kuroda, and under unprecedented pressure from recently elected Prime Minister Shinzo Abe, who included monetary policy in his campaign platform—embarked on an attempt to print huge amounts of money, with a stated goal of doubling the Japanese money supply.5

Immediately after, Japan experienced real GDP growth of 2.3%, where the previous trend was for falling RGDP. Their economy was operating that far under capacity due to lack of money.6

However, that last part is not correct, as far as I can tell.

According to official government data, Japan's RGDP had not been falling prior to 2013, other than the fall caused by the Great Recession. RGDP did grow by ~2.0% in 2013, but I cannot discern any significant change in the trend after Haruhiko Kuroda began serving as governor at the Bank of Japan.

In his footnote, Yudkowsky cites this article from 2017 to provide a "more recent update" about Japan's successful monetary policy. However, I don't think the article demonstrates that Yudkowsky was correct in any major way about the point he made.

The article never pre...
Living Nomadically: My 80/20 Guide
by KatWoods — 2023-02-08T01:31:49.193Z

I’ve been living nomadically for three years, and I’m often asked what my advice is for people trying it. Here’s the 80/20 of all my advice:

Work out of restaurants and 5-star resorts. They’re gorgeous and if you pay for a meal, ~99.9% of restaurants and resorts are fine with you working there.
Ask ~20 different AirBnBs for a 40% discount if you stay for a month. ~5-10% will say yes, then you’ll get to stay in way nicer places for cheaper.
Travel with a folding bike. It will help you get around and really see the cities.
Use

NomadList.com

to find the best places to stay. It allows you to search cities by cheapness, safety for women, weather, number of nomads, etc. Nomad List is the best and I wish everything were Nomad List.
Social contact is the main reason people stop nomading. To fix that a) travel with at least one friend/partner and b) become excellent at location-independent friendships. The main way to do this is to set up recurring calls. Make it the default to talk to people. If you have to choose to hangout with people each time, you will just forget to. You should probably already be doing this.

Full short article on how to do this well

.
Some of the best places to nomad, according to Nomad List / me

Bali (particularly Canggu and Ubud). Tropical paradise, tons of nomads, insanely cheap and beautiful living arrangements.

Thailand (particularly Chiang Mai). Same as Bali.
Istanbul. The Mediterranean with mosques and cats. (So many cats!) Very safe for women, filled with history. First world infrastructure and third world prices. Better time zone for working remotely in Europe.

Buenos Aires. European style city, very cheap and safe. Better time zone for working remotely from North America
Medellín. Jungle in a city. Cheap and gorgeous. Better time zone for working remotely from North America

Other articles you might like:

How to maintain long-distance friendships without losing touch

App and book recommendations for people who want to be happier and more productive

EA Houses: the AirBnB of EA

Cross-posted from my personal blog

Reminder that you can listen to this post on your podcast player using the Nonlinear Library if it reaches above 35 upvotes
OpenAI/Microsoft announce "next generation language model" integrated into Bing/Edge
by LawrenceC — 2023-02-07T20:38:08.726Z
Link: https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/

TL;DR: Microsoft and OpenAI announced a new version of Bing featuring "a new, next-generation OpenAI large language model [..] more powerful than ChatGPT", and that Microsoft Edge will feature a Copilot-like assistant that helps with composing and summarizing content.

Brief thoughts/comments/notes:

Microsoft's attitude during their press meeting seemed pretty aggressive and targeted directly at racing with Google. This seems kind of bad. For example, a quote from Nadella:

"The race starts today, and we’re going to move and move fast. Most importantly, we want to have a lot of fun innovating again in search, because it’s high time."

Microsoft built a scaffold for the new LM called "Prometheus", that lets them "best leverage its power".
Microsoft has also used the new LM in their Bing search engine, though it's not clear exactly how.
This seems way more hype than Google's Bard announcement.
You can register for the new Bing beta on the Bing.com site.
How evals might (or might not) prevent  catastrophic risks from AI
by Orpheus16 — 2023-02-07T20:16:08.253Z

Epistemic status: Trying to synthesize my thoughts as someone without any private information about evals. I expect this to be wrong in several ways, lacking detail, and missing a lot of context on specific things that AGI labs and independent teams are thinking about. I post it in the hopes that it will be useful for sparking discussion on high-level points & acquiring clarity.

Inspired by the style of

what 2026 looks like

, my goal was to write out a detailed future history (“trajectory”) that is as realistic (to me) as I can currently manage. The methodology was roughly:

Condition on a future in which evals counterfactually avoid an existential catastrophe.
Imagine that this existential catastrophe is avoided in 2027 (why 2027? Partially for simplicity, partially because I find short timelines plausible, and partially because I think it makes sense to focus our actions on timelines that are shorter than our actual timeline estimates, and partially because it gets increasingly hard to tell realistic stories as you get further and further away from the present).
Write a somewhat-realistic story in which evals help us avoid the existential catastrophe.
Look through the story to find parts that seem the most overly-optimistic, and brainstorm potential failure modes. Briefly describe these potential failure modes and put (extremely rough) credences on each of them.

I’m grateful to Daniel Kokotajlo, Zach Stein-Perlman, Olivia Jimenez, Siméon Campos, and Thomas Larsen for feedback.

Scenario: Victory

In 2023, researchers at leading AGI labs and independent groups work on developing a set of evaluations. There are many different types of evaluations proposed, though many of them can be summarized in the following format:

A model is subject to a test of some kind.
If we observe X evidence (perhaps scary capabilities, misalignment, high potential for misuse), then the team developing the model is not allowed to [continue training, scale, deploy] the model until they can show that their model is safe.
To show that a model is (likely to be) safe, the team must show Y counterevidence.

There is initially a great deal of debate and confusion in the space. What metrics should we be tracking with evals? What counterevidence could be produced to permit labs to scale or deploy the model?

The field is initially pre-paradigmat...
Conditioning Predictive Models: Making inner alignment as easy as possible
by evhub — 2023-02-07T20:04:20.272Z

This is the fourth of seven posts in the Conditioning Predictive Models Sequence based on the paper “Conditioning Predictive Models: Risks and Strategies” by Evan Hubinger, Adam Jermyn, Johannes Treutlein, Rubi Hudson, and Kate Woolverton. Each post in the sequence corresponds to a different section of the paper.

4. Making inner alignment as easy as possible

At the beginning, we posited the assumption that large language models could be well-understood as predictive models of the world. At the time, however, that was just an assumption—now, we want to return to that assumption and try to understand how likely it is to actually be true.

Furthermore, in addition to needing a predictive model (as opposed to e.g. a deceptive agent), we also want our predictor to have a fixed, physical understanding of its cameras rather than operate as a general inductor to avoid the problem of anthropic capture. Additionally, as we’ll discuss in more depth in this section, we’ll also need a prediction model that is managing its own internal cognitive resources in the right way.

Though we think that ensuring these desiderata could be quite difficult, we nevertheless think that this presents the easiest inner alignment problem that we are aware of among any potentially safe and competitive approaches. Furthermore, since we believe that inner alignment—and deceptive alignment in particular—pose some of the most dangerous and hardest to address of all known AI safety problems, we think that any improvement in the overall difficulty of that problem should be taken quite seriously as a reason to favor predictive model approaches.

Plausible internal structures

There are many possible ways large language models could work internally. Previously, we suggested some examples—specifically:

an agent minimizing its cross-entropy loss;

an agent maximizing long-run predictive accuracy;

a deceptive agent trying to gain power in the world;

a general inductor;

a predictive model of the world (with fixed, physical “cameras” translating world states into observed tokens);

a loose collection of heuristics;

a generative model of token transitions;

a simulator that picks from a repertoire of humans to simulate;

a proxy-aligned agent optimizing proxies like grammatical correctness.

To start with, for our purposes here, we’ll eliminate those internal structures that don’t scale with capabilities—that is...
On The Current Status Of AI Dating
by Nikita Brancatisano — 2023-02-07T20:00:47.243Z

In the past months there has been a number of posts and stories regarding AI dating. People tried (with various degrees of success) to simulate a romantic partner using LLMs[1] and the topic has been explored with various degrees of controversy in many discussion boards.

Even here on LW(https://www.lesswrong.com/posts/9kQFure4hdDmRBNdH/how-it-feels-to-have-your-mind-hacked-by-an-ai) there has been some discussion on the topic, and in my opinion we can classify the phenomenon in many different ways, but it's undeniable that many people started feeling (or believing to feel) emotional attachment of sorts to AIs.

And I, by reading all of this, slowly started becoming interested in the topic. I was curious about the details of the interaction, about whether I also could "fall for it", and about what it means exactly to be "dating" an AI.

Just for context: I'm currently living abroad alone. This means that my social interactions are happening mostly through online means; and I also have been single for the past three years.

So I decided to perform some tests. The main contenders here are Replika, Character.ai and ChatGPT. There are a couple of other ones, but they all seemed way less developed, so I decided to focus my efforts on those three.

Replika

Replika was one of the first chatbots introduced on the market. It was marketed as a "virtual friend for lonely people" and used its own proprietary LLM. I remember seeing it a couple of years ago and trying it, but I uninstalled it after a couple of days because it felt dumb at the time.

Now, while still being advertised as a virtual friend, the app clearly caters for a different audience. Their promotional materials are almost all oriented at a male audience, and they offer a girlfriend experience (with sexting) as a paid plan.

The main feature that separates Replika from other bots is that it learns from the conversations you have with it. It learns to mimic your speech pattern, remembers some key moments from the conversations and tries to bring up topics you like talking about.

With a paid subscription[2] you can also get access to video calls (more on this later), coaching scenarios (self-help conversations) and a couple of other stuff. On top of the paid subscription there is also a "currency" system that allows you to buy clothes and items for real life money.

I subscribed for a month ...
Framing AI strategy
by Zach Stein-Perlman — 2023-02-07T19:20:04.535Z
Link: https://aiimpacts.org/framing-ai-strategy/

Strategy is the activity or project of doing research to inform interventions to achieve a particular goal.1 AI strategy is strategy from the perspective that AI is important, focused on interventions to make AI go better. An analytic frame is a conceptual orientation that makes salient some aspects of an issue, including cues for what needs to be understood, how to approach the issue, what your goals and responsibilities are, what roles to see yourself as having, what to pay attention to, and what to ignore.

This post discusses ten strategy frames, focusing on AI strategy. Some frames are comprehensive approaches to strategy; some are components of strategy or prompts for thinking about an aspect of strategy. This post focuses on meta-level exploration of frames, but the second and last sections have some object-level thoughts within a frame.

Sections are overlapping but independent; focus on sections that aren’t already in your toolbox of approaches to strategy.

Epistemic status: exploratory, brainstormy.

Make a plan

See Jade Leung’s Priorities in AGI governance research (2022) and How can we see the impact of AI strategy research? (2019).

One output of strategy is a plan describing relevant (kinds of) actors’ behavior. More generally, we can aim for a playbook– something like a function from (sets of observations about) world-states to plans. A plan is good insofar as it improves important decisions in the counterfactual where you try to implement it, in expectation.

To make a plan or playbook, identify (kinds of) actors that might be affectable, then figure out

what they could do,

what it would be good for them to do,

what their incentives are (if relevant), and then

how to cause them to act better.

It is also possible to focus on decisions rather than actors: determine what decisions you want to affect (presumably because they’re important and affecting them seems tractable) and how you can affect them.

For AI, relevant actors include AI labs, states (particularly America), non-researching non-governmental organizations (particularly standard-setters), compute providers, and the AI risk and EA communities.2

Insofar as an agent (not necessarily an actor that can take directly important actions) has distinctive abilities and is likely to try to execute good ideas you have, it can be helpful to focus on what the agent can do or how to l...
Review of AI Alignment Progress
by PeterMcCluskey — 2023-02-07T18:57:41.329Z
Link: https://bayesianinvestor.com/blog/index.php/2023/02/06/review-of-ai-alignment-progress/

I'm having trouble keeping track of everything I've learned about AI
and AI alignment in the past year or so. I'm writing this post in part
to organize my thoughts, and to a lesser extent I'm hoping for feedback
about what important new developments I've been neglecting. I'm sure
that I haven't noticed every development that I would consider
important.

I've become a bit more optimistic about AI alignment in the past year
or so.

I currently estimate a 7% chance AI will kill us all this century.
That's down from estimates that fluctuated from something like 10% to
40% over the past decade. (The extent to which those numbers fluctuate
implies enough confusion that it only takes a little bit of evidence to
move my estimate a lot.)

I'm also becoming more nervous about how close we are to human level
and transformative AGI. Not to mention feeling uncomfortable that I
still don't have a clear understanding of what I mean when I say human
level or transformative AGI.

Shard Theory

Shard theory is a paradigm that seems destined to replace the focus (at
least on LessWrong) on utility functions as a way of describing what
intelligent entities want.

I kept having trouble with the plan to get AIs to have utility functions
that promote human values.

Human values mostly vary in response to changes in the environment. I
can make a theoretical distinction between contingent human values and
the kind of fixed terminal values that seem to belong in a utility
function. But I kept getting confused when I tried to fit my values, or
typical human values, into that framework. Some values seem clearly
instrumental and contingent. Some values seem fixed enough to sort of
resemble terminal values. But whenever I try to convince myself that
I've found a terminal value that I want to be immutable, I end up
feeling confused.

Shard theory tells me that humans don't have values that are well
described by the concept of a utility function. Probably nothing will go
wrong if I stop hoping to find those terminal values.

We can describe human values as context-sensitive heuristics. That will
likely also be true of AIs that we want to create.

I feel deconfused when I reject utility functions, in favor of values
being embedded in heuristics and/or subagents.

Some of the posts that better explain these ideas:

Shard Theory in Nine Theses: a Distillation and Critical
Appraisal

The shard theory of human
value

...