trentmkelly/LessWrong-43k huggingface dataset:
Resource on alcohol problems
by juliawise — 2020-02-27T18:17:56.947Z

A few people in the EA community have asked about how to help friends who seem to have an alcohol problem. I wrote this resource and am cross-posting it here.

I'm not an expert on this. Please treat this as the writing of someone with mental health training and some internet research skills, but not experience in treating substance abuse specifically.

About research:

There is no single best research-based treatment for drinking problems or other substance problems.

Any kind of substance addiction is a hard subject to research because there is a large industry in detox centers, and most of what you can easily find on this topic is written by companies trying to sell a particular program.

About other problems: Many people have both a drinking problem and a problem with using other substances, or mental health problems. In the medical system, someone who has problems with multiple substance might be diagnosed with polysubstance use disorder. Someone who has both a substance use disorder and a mental health condition would be considered “dual diagnosis.” This is common - for example someone might have both problems with drinking and also depression or another mental health problem.

Combining substances (particularly alcohol with other “downers” like benzodiazepines or opiate pain medications) can be deadly. Alcohol is involved in about 22% of deaths from prescription opioids (

source

).
More on

dual diagnosis

. More on

treatment for multiple substances

.
It’s best to find a provider (like a doctor, therapist, or treatment program) that knows about any relevant problems you have, including medical or mental health conditions, and is willing to provide care for those conditions.

About safety: if you’re regularly drinking large amounts, quitting suddenly is medically dangerous.

Alcohol withdrawal

is not a problem for everyone who stops drinking suddenly, but it can cause seizures and can kill you. Your doctor can advise you about safely tapering down drinking and may prescribe medication to help with the taper. You can also see guides like “

How to taper off alcohol

.”

Approaches

Two main approaches are abstinence and harm reduction. Both approaches seem to work for some people and not for others.

Abstinence

The abstinence approach advocates completely stopping drinking alcohol.

Harm reduction

The harm reduction approach a...
Response to Oren Etzioni's "How to know if artificial intelligence is about to destroy civilization"
by Daniel Kokotajlo — 2020-02-27T18:10:11.129Z

Oren Etzioni recently wrote a popular article titled "How to know if artificial intelligence is about to destroy civilization." I think it's a good idea to write publicly available responses to articles like this, in case interested people are googling around looking for such. If I get lucky, perhaps Oren himself will come across this! Then we could have a proper discussion and we'd probably both learn things.

For most readers of LW, it's probably not worth reading further, since you probably already agree with what I'm saying.

Here is the full text of the article, interspersed with my comments:

Could we wake up one morning dumbstruck that a super-powerful AI has emerged, with disastrous consequences? Books like Superintelligence by Nick Bostrom and Life 3.0 by Max Tegmark, as well as more recent articles, argue that malevolent superintelligence is an existential risk for humanity.

But one can speculate endlessly. It’s better to ask a more concrete, empirical question: What would alert us that superintelligence is indeed around the corner?

It is unfair to claim that Bostrom is speculating whereas your question is concrete and empirical. For one thing, Bostrom and others you denigrate have already asked the same question you now raise; there is not a consensus as to the answer yet, but plenty of arguments have been made. See e.g. Yudkowsky "There's No Fire Alarm for Artificial General Intelligence" More importantly though, how is your question any less speculative than the others? You too are making guesses about what future technologies will come when and in what order, and you too have zero historical data to draw from. Perhaps you could make arguments based on analogy to other technologies -- e.g. as Grace and Christiano have argued about "slow takeoff" and "likelihood of discontinuous progress" but funnily enough (looking ahead at the rest of the article) you don't even do that; you just rely on your intuition!

We might call such harbingers canaries in the coal mines of AI. If an artificial-intelligence program develops a fundamental new capability, that’s the equivalent of a canary collapsing: an early warning of AI breakthroughs on the horizon.

Could the famous Turing test serve as a canary? The test, invented by Alan Turing in 1950, posits that human-level AI will be achieved

...
Training Regime Day 13: Resolve Cycles
by Mark Xu — 2020-02-27T17:45:07.845Z

Introduction

Resolve cycles are a technique that channels and sharpens your innate ability to actually do things.

We're just going to jump right into doing it, then I'll explain why I think it works afterwards. This technique doesn't take very long and is potentially absurdly powerful, so I really encourage you to follow along.

Resolve Cycles

The zeroth step is to pick a bug from your bugs list. This should be your most medium sized bug.

The first step is to set a five minute timer. You should do this with a device that will make a noise at the end of five minutes.

The second step is to start the timer and fix your bug before the timer goes off. Are you ready?

GO!

Did you solve your bug? If yes, congratulations! If no, fear not, for there is more.

The third step is to set another five minute timer and use those five minutes to create a plan that will solve your bug in five minutes. For instance, if my bug was "I don't have a job", I could think of applications to fill out, people to email, etc.

Are you ready?

GO!

The obvious fourth step is obvious. Set another 5 minute timer and execute your plan.

Are you ready?

GO!

Tips

C. Terry Warner writes:

Except in a very few [tennis] matches, usually with world-class performers, there is a point in every match (and in some cases it's right at the beginning) when the loser decides he's going to lose. And after that, everything he does will be aimed at providing an explanation of why he will have lost. He may throw himself at the ball (so he will be able to say he's done his best against a superior opponent). He may dispute calls (so he will be able to say he's been robbed). He may swear at himself and throw his racket (so he can say it was apparent all along he wasn't in top form). His energies go not into winning but into producing an explanation, an excuse, a justification for losing.

The reason that resolves cycles work is that there's a difference between the appearance of the thing and the thing itself. When you're trying to solve a bug, there's a difference between looking like you're trying to solve the bug and solving the bug. Most of the time, when you "trying to solve your bug", you're probably optimizing for some combination of appearance and actuality; you're probably spending some energy trying to explain why you failed to solve the bug, before you've even failed.

The point of the resolve cycle is to strip away

...
If I were a well-intentioned AI...
II: Acting in a world
by Stuart_Armstrong — 2020-02-27T11:58:32.279Z

Classifying images is one thing. But what if I'm an agent that is actually active in some setting?

The previous approach still applies: detecting when I'm out of distribution, and trying to keep my behaviour compatible with the various reward function that could be compatible with the data I've seen.

The main difference is that, if I'm acting, it's much easier to push the setting into an out of distribution state, seeking out an extremal Goodhart solution to maximise reward. But that issue is for a next post.

Mazes and doors example

We'll use the maze and door example from this post. I've has been trained to go through a maze and reach a red door (which is the only red object in the environment); the episode then ends.

I'm now in an environment where the only door is blue, and the only red thing is a window. What should I do now?

My reward function is underspecified by its training environment - this is the old problem of unidentifiability of reward functions.

There are three potential reward functions I could extrapolate from the training examples:

Rrd1: reward for reaching a red door.

Rd1: reward for reaching a door.

Rro1: reward for reaching a red object.

The episode ended, in training, every time I reached the red door. So I can't distinguish "reaching" a point from "staying" at that point. So the following three reward functions are also possible, though less likely:

Rrd2: reward for each turn spent next to a red door.

Rd2: reward for each turn spent next to a door.

Rro2: reward for each turn spent next to a red object.

There are other possible reward functions, but these are the most obvious. I might have different levels of credence for these rewards; as stated before, the R∗2 seems less likely than the R∗1.

So, what is the optimal policy here? Note that Rrd1 and Rrd2 are irrelevant here, because the current environment doesn't contain any red doors.
So, initially, to go to the blue door and the red window - which one first depends on the layout of the maze and the relative probabilities of the reward functions Rd1 and Rro1.

After that, if the episode hasn't ended, the R1 rewards are irrelevant - either they are incorrect, or they have already been accomplished. So now only the rewards Rd2 and Rro2 are relevant. If the first one is the most likely, I maximise expected reward by standing by the door forever; if the second is more likely, then standing by

...
[AN #88]: How the principal-agent literature relates to AI risk
by Rohin Shah — 2020-02-27T09:10:02.018Z

Find all Alignment Newsletter resources

here

. In particular, you can

sign up

, or look through this

spreadsheet

of all summaries that have ever been in the newsletter. I'm always happy to hear feedback; you can send it to me by replying to this email.

Audio version

here

(may not be up yet).

Highlights

What can the principal-agent literature tell us about AI risk?

(Alexis Carlier and Tom Davidson) (summarized by Rohin): It has been

argued

(

AN #56

) that at least some AI risk scenarios rely on principal-agent problems becoming extremely large, and that this is incompatible with the existing academic literature on the principal-agent problem. This post examines this critique in detail.

Generally, the post finds that the principal-agent literature doesn't have much bearing on AI risk, because it usually doesn't consider weak principals with more capable agents, the models that do exist will probably not generalize to the cases we care about, and it doesn't consider the case where contracts can no longer be enforced.

We can consider the application to specific arguments, such as the

"going out with a bang" scenario

(

AN #50

) in which we accidentally train influence-maximizers that gradually gain power and then fail catastrophically (e.g. by executing a treacherous turn). In this situation, the principal-agent problem is relevant only in the first stage, where AI agents gradually gain power: this is the case where AI agents are executing some task, and are extracting agency rents to gain power. The second stage, in which the agent fails catastrophically, happens "outside" the principal-agent problem: this failure doesn't happen while performing some assigned task, but instead involves the agent exercising its accumulated power outside of any specific task.

What about the original scenario, in which an AI agent becomes very intelligent, and finds some solution to its task that the designers (principals) didn't think about and are surprised by? In the principal-agent setting, we might model this as the agent having an expanded action set that the principal doesn't know about. The principal-agent literature has not really studied such models, probably because it is immediately obvious that in such a situation the principal could give incentives that lead the agent to kill everyone.

Rohin's opinion: I've been conf...
Attainable Utility Preservation: Scaling to Superhuman
by TurnTrout — 2020-02-27T00:52:49.970Z

I think we're plausibly quite close to the impact measurement endgame. What do we have now, and what remains to be had?

AUP for advanced agents will basically involve restraining their power gain, per the catastrophic convergence conjecture (CCC). For simplicity, I'm going to keep writing as if the environment is fully observable, even though we're thinking about an agent interacting with the real world.

Consider the AUP equation from last time.

RAUP(s,a):=primary goalR(s,a)−scaling termλQ∗Raux(s,∅)change in auxiliary AU∣∣Q∗Raux(s,a)−Q∗Raux(s,∅)∣∣(1)

Suppose the agent is so smart that it can instantly compute optimal policies and the optimal AU after an action (Q∗R(s,a)). What happens if Raux is the survival reward function: 1 reward if the agent is activated, and 0 otherwise? This seems like a pretty good proxy for power.

It is a pretty good proxy. It correctly penalizes accumulating resources, avoiding immediate deactivation, taking over the world, etc.

In fact, if you extend the inaction comparison to e.g. "AU after waiting a week vs AU after doing the thing and waiting a week", this seems to correctly penalize all classic AGI catastrophe scenarios for power gain. This is cool, especially since we didn't have to put in any information about human values. This is a big part of why I've been so excited about AUP ever since its introduction. There's a good deal of relevant discussion in that post, but it's predicated on a much more complicated formalism which has consistently obscured AUP's conceptual core.

However, I think this equation can be gamed if the environment is sufficiently rich and the AUP agent is sufficiently smart. We're going to slowly work some of these problems out of the equation, explaining each improvement in detail.

Problems

Auxiliary loopholes

The real reason that agents often gain power is so that they can better achieve their own goals. Therefore, if we're selecting hard for good plans which don't gain power in general, we shouldn't be surprised if there are ways to better achieve one's goals without general power gain (according to our formal measurement thereof). If this kind of plan is optimal, then the agent still ends up overfitting the AU landscape, and we're still screwed.

Again supposing that Raux is the survival reward function, a superintelligent agent might find edge cases in which it becomes massively more able to achieve its own

...
KC Dojo 2-18-20
by Bae's Theorem — 2020-02-26T22:49:06.221Z

Attendees:

Me, A, W, S, C, J

Me:

As a follow up to last week, where I brought up the fact I am planning to change careers, I showed an example of the kind of job I would like to have: a position as a decision analyst at the Pentagon. (I am following the Career Path of ‘Improve Institutional Decision-Making’ as per 80,000hrs). I have sent emails to college advisors and am waiting on responses.

‘A’ recommended very highly that I emphasize networking. I am in the process of using Linkdin for this. As a spitball, he also suggested ‘doing research on people’ and ‘start doing stuff’. He doesn’t have any specific ideas, but it might be worth thinking about

‘W’ recommended I apply to work in the Census. Since they perform a background check, this will let me know if there are any flags on my record (I have troubled family members).

A:

A had a bad experience with an agency in his career, and has resolved to not interact with them again. He will have to adjust his long-term career goals, but has a solid plan of action and no new difficulties. He is trying to write a nonfiction book, and is currently resolved to ‘just write’. We’ll touch base with him again in a few weeks to see if he might need a more specific strategy.

W:

W is struggling with an ex of his who is posting on twitter in such a way that W knows she is having relationship problems. He is tempted to reach out and show concern (they are on friendly terms), but is convinced that it is a bad idea, and wants our feedback.

I asked for clarification on why he thinks it is bad idea, and he cited that his map/model of her is somewhat outdated, and might be mis-reading. Also, their relationship is mostly a passive one, which doesn’t provide precedent for this.

A noticed that when we affirm W’s intuitions, he doesn’t seem to fully like that answer. A asked why. W was silent for a while, and said “I want to help”. He doesn’t like that reaching out to help would likely result in drama/a worse situation. After talking a bit more about it, he verbalized that he also gets a certain level of satisfaction from understanding her, and having deduced that she is probably having relationship problems. He considers this to be an “impure” motivation, that may end up poisoning the well i...
Training Regime Day 12: Focusing
by Mark Xu — 2020-02-26T19:07:15.407Z

Note: Focusing for skeptics is a far better description than I could give. I will attempt my own description anyway, for triangulation reasons.

2nd note: this technique was invented by Eugene Gendlin. The book Focusing goes into more detail, although I'm told the audiobook is better.

Introduction

We can (fake) categorize all knowledge you have into two types: tacit knowledge and explicit knowledge. Roughly speaking, explicit knowledge is knowledge where you know why you know it: math, typing, writing, reading, skateboarding, etc. Tacit knowledge is knowledge where you are less sure of why you know it: intuition, vague emotional stuff, inner sim predictions, etc.

One extremely rough way to put it is that explicit knowledge is in the mind and tacit knowledge is in the body. This is false, but the useful sort of false. It's not like tacit knowledge isn't in your brain, but how tacit knowledge usually manifests itself is through sensations instead of thinking (think confusion, surprise, fear, excitement, etc.) To me, this makes it seem more like tacit knowledge is my body telling me things and explicit knowledge is my brain telling me things.

Focusing is a technique designed to allow tacit knowledge and explicit knowledge to interface. The goal of focusing is to turn vague bodily sensations into pieces of information that you can actually think about. I find this technique extremely hard to do, but extremely valuable if done correctly.

Focusing

Serious thinking usually begins by clearing the mind. Focusing begins by clearing the body. When focusing, there should be no physical sensations to distract/intrude upon the feelings generated by tacit knowledge. In practice, this probably involves finding a comfortable seat or lying down.

The first step in focusing is to locate the tacit-knowledge-feeling, also called a felt sense. This kind of feels like knowing you forgot something and trying to remember what it was. It also feels similar to looking for something without knowing what you're looking for, only that you'll recognize it when you find it. The felt sense usually begins extremely vague and blob-like. Almost always, felt sense are negative in some sense, i.e. your body is telling you that something is wrong.

The second step is to figure out what exactly the felt sense feels like. The process that this is most similar to is wanting to describe something and trying

...
Value of the Long Tail
by johnswentworth — 2020-02-26T17:24:28.707Z

Suppose we have a self-driving car which works 99% of the time. A human driver only needs to intervene to prevent an accident on one trip out of 100. How much economic value does this generate, relative to a full human-level-or-better self-driving car?

I would guess less than 10%, maybe even less than 1% of the value. Why? Because with a 1% error rate, we still need a human driver, and the vast majority of the value of self-driving cars comes from removing the human. Things like automated emergency braking, parallel parking, warning systems, or cruise control definitely have some value - but they’re nowhere near the value of giving every worker in the United States one extra hour every weekday (roughly the average round-trip commute time).

I think this sort of problem is far more common than most engineers intuitively expect. There’s a lot of areas where it seems like it should be easy to automate, if not all of the work, at least 90% of it. It looks like there’s metaphorical hundred-dollar bills lying on the ground. But I think, in most of these cases, automating 90% just doesn’t generate all that much value - because you still need a human watching everything, waiting to jump in as soon as the other 10% comes up. The vast majority of the value comes from taking the human out of the loop.

Personally, I ran into this at a mortgage startup. We wanted to automate as much of the approval process as possible; we figured at least 90% of approval conditions (weighted by how often they’re needed) should be tractable. In retrospect, that was true - 90% of it was pretty tractable. But we realized that, even with the easy 90% automated, we would still need humans most of the time. The large majority of our loans had at least some “hair” on them - something which was weird and needed special handling. Sometimes it was FHA/VA subsidies (each requiring a bunch of extra legwork). Sometimes it was income from a side-gig or alimony. Sometimes it was a condition associated with the appraisal - e.g. a roof repair in-progress. Sometimes it was an ex-spouse on the title. No single issue was very common, but most loans had something weird on them. And as soon as a human needed to be in the loop, at all, most of the automation value was gone - we couldn’t offer substantive instant results.

In general, when would we expect this p...