trentmkelly/LessWrong-43k huggingface dataset:
Comparing AI Alignment Approaches to Minimize False Positive Risk
by Gordon Seidoh Worley — 2020-06-30T19:34:57.220Z

Introduction

Based on the method I used in "Robustness to Fundamental Uncertainty in AGI Alignment", we can analyze various proposals for building aligned AI and determine which appear to best trade off false positive risk for false negative risk and recommend those which are conservatively safest. We can do this at various levels of granularity and for various levels of specificity of proposed alignment methods. What I mean by that is we can consider AI alignment as a whole or various sub-problems within it, like value learning and inner alignment, and we can consider high-level approaches to alignment or more specific proposals with more of the details worked out. For this initial post in what may become a series, I'll compare high-level approaches on addressing alignment as a whole.

Some ground rules:

By "alignment" I mean "caring about the same things".

If you like I attempted to be more formal about this in "Formally Stating the AI Alignment Problem", but here I'm going to prefer the less formal statement to avoid getting tripped up by being overly specific in just one part of the analysis since we're not being very specific about the alignment methods.
I say "cares about the same things" rather than "aligned with human interests" to both taboo "align" in the definition of "alignment" and reflect my philosophical leaning that "caring" is the fundamental human activity we are interested in when we express a desire to build aligned AI.

I definitely have some unpublished idea about what "caring" means in terms of predictive coding in terms of valence. This is a good recent approximation of them written by someone more understandable than me, and accords with some things I've previously written. Hopefully not relevant beyond disclosing my background assumptions.
Care synonyms for the scrupulous: interest, purpose, telos, concern, value, and preference as long as we use it in the everyday sense of the word to point at a broad category of human activity and not as jargon about choice (even though this broad category does influence how we choose).

By "high-level approaches" I don't mean specific mechanisms or methods for building aligned AI, but general directions or ways in which specific methods are intended to work.

For example, I consider iterated distillation and amplification an approach, and various ways of using IDA to build aligned AI specific methods.
Abstract

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How ought I spend time? 
by Quinn — 2020-06-30T16:53:53.787Z

What factors go into your reasoning about assigning relative weights to

grinding through books

projects, taking a shot at the possibility of a contribution

reading current research

Personally, I never maintain 1 and 2 at the same time, and I tend to have >95% of my resources on either one or the other, leaving 5% or less for 3. The vast majority of the last 4 years I've been in 1-mode, my reasoning being that I can't make a contribution if I don't know anything, and I don't want to sink my time into bad projects.
Sick of struggling
by dqups1 — 2020-06-30T16:47:00.498Z

I’m just sick of struggling through life. The inefficiencies all around me are staggering and overwhelming. Why do we limit ourselves so much when our possibilities are endless? This forums seems like the closest place I’ve come to finding the best of humanity. That being said, curious how many have contemplated suicide at one point or another? I know I have which makes me think it to be a prevalent consideration among a group such as this.
Somerville Mask Usage
by jefftk — 2020-06-30T14:50:10.238Z

Two months ago, Somerville

made
facecoverings mandatory, both indoors and out. A few weeks later
the state of Massachusetts required them "

in public
places where social distancing is not possible". A week ago
Somerville

reduced
its requirements in light of the heat: "during the summer months,
when you are outside and able to social distance at least six feet
from others, you may temporarily remove your face covering but must
put it back on when others are nearby."

I was curious what people were actually doing, so while Anna was
playing in the "woods" along the edge of the bike path, I gathered
some statistics. As each person passed along the path, I tracked mode
of transportation (walk, run, bike, scooter/skateboard), apparent
gender (female, male, child, unclear), and face covering status
(covered, mouth only, removed, absent). Raw data is here.

In forty minutes on June 28th, from 9:41am to 10:21am I saw 179 people
pass. Of these, 73% (131) were masked, 5% (9) had their nose exposed,
15% (27) had masks on their chin or otherwise removed, and 7% (12) had
no mask at all:

With a person passing every 13 seconds, the path was a pretty crowded
place. I only very rarely saw people putting on their masks, however,
when coming close to others. For the analysis below I want to talk
about people as being masked or not, and people with masks removed
wouldn't qualify. Masks worn mouth-only are less clear, but since a
large fraction of transmission seems to be via talking and coughing, I
decided to count someone as masked if their mouth was covered, even if
their nose was sticking out.

When looking at people by transportation, walkers (74%, 62/84) were a bit less
likely to be masked than others (82%, 78/95), but not by much:

Looking by gender/age, women were less likely to be masked (72%,
56/78) than men (85%, 74/87). [1] Children were in between (77%,
10/13), though I didn't count children in bicycle trailers or
strollers:

I'm curious what numbers look like in other areas. Talking impressions
with friends, it sounds like this is a higher fraction of people
wearing masks than in most places?

[1] I categorized people by apparent gender, and one person didn't
read immediately as male or female.
Web AI discussion Groups
by Donald Hobson — 2020-06-30T11:22:45.611Z

After the success of Web Taisu, I have decided to organize a similar event.

Here are the rules, this document is an attempt to create common knowledge among the participants.

If you have an Idea for a topic to discuss

Post it to reddit on https://www.reddit.com/r/AI_Safety_Meetups/

Comment on these posts to add points that you think are interesting, vote up to indicate that you want to discuss a topic.

Scheduling

On Friday 3 July, about 12:00 am BST, I looked at this subreddit, and select all the topics. I will created a google doc where you can fill in what topics you are interested in, and when you are busy.

Timeslots will be between 2:00 to 9:30 pm BST (including breaks) (9:00 am to 4:30 pm EST) (6:00 am to 1:30pm PDT)

Timeslot 1:

2:00 pm to 3:30 pm BST, 9:00 am to 10:30 am EST, 6:00 am to 7:30 am PDT.

Timeslot 2:

4:00 pm to 5:30 pm BST, 11:00 am to 12:30 am EST, 8:00 am to 9:30 am PDT.

Timeslot 3:

6:00 pm to 7:30 pm BST, 1:00 pm to 2:30 pm EST, 10:00 am to 11:30 am PDT.

Timeslot 4:

8:00 pm to 9:30 pm BST, 3:00 pm to 4:30 pm EST, 12:00 am to 1:30 pm PDT.

Each timeslot will be available on Tuesday 7, Wednesday 8, and Friday 10 of July.

I placed the exact timeslots, and a link to the google doc here on Friday 3 July.

The doc will close, and a timetable will be placed here on Monday 6 of July, at around 12:00 am BST.

Scheduling Form

Please fill in your utilities for how much you want to attend each event, and for whatever else you would be doing at that time.

On Monday I will run the scheduling program found at https://github.com/DonaldHobson/Simple_scheduler . (You don't need to follow this link unless you want to double check my code, use the same code for your own event, or examine the details to respond strategically. )

The form is here: This form no longer does anything. Do not use.

https://forms.gle/ZNsZtDhqppL64Dpw8

So You need to

12:00 am BST Friday 3 July - 12:00 am BST Monday 6 July:

Visit this page again, find link to google doc, fill it in.

Carry on discussing the topics on reddit

12:00 am BST Monday 6 July - onwards

Look here for timetable and links to videocalls.

Timetable

Meeting password="guest"

Part 2 starts 50 minutes after part 1

Part 1 starts when the timeslot starts, look up

If you want to talk about some other aspect of AI, your still welcome.

Tuesday 7 July, Slot 1
Can we put any upper bounds on the capabilities of realworld AI systems.

https://...
How do takeoff speeds affect the probability of bad outcomes from AGI?
by KR — 2020-06-29T22:06:17.781Z

Introduction

In general, people seem to treat slow takeoff as the safer option as compared to classic FOOMish takeoff (see e.g. these interviews, this report, etc). Below, I outline some features of slow takeoff and what they might mean for future outcomes. They do not seem to point to an unambiguously safer scenario, though slow takeoff does seem on the whole likelier to lead to good outcomes.

Social and institutional effect of precursor AI

If there’s a slow takeoff, AI is a significant feature of the world far before we get to superhuman AI.[1] One way to frame this is that everything is already really weird before there’s any real danger of x-risks. Unless AI is somehow not used in any practical applications, the pre-superhuman but still very capable AI will lead to massive economical, technological, and probably social changes.

If we expect significant changes to the state of the world during takeoff, it makes it harder to predict what kinds of landscape the AI researchers of that time will be facing. If the world changes a lot between now and superhuman AI, any work on institutional change or public policy might be irrelevant by the time it matters. Also, the biggest effects may be in the AI community, which would be closest to the rapidly changing technological landscape.

The kinds of work needed if everything is changing rapidly also seem different. Specific organizations or direct changes might not survive in their original, useful form. The people who have thought about how to deal with the sort of problems we might be facing then might be well positioned to suggest solutions, though. This implies that more foundational work might be more valuable in this situation.

While I expect this to be very difficult to predict from our vantage point, one possible change is mass technological unemployment well before superhuman AI. Of course, historically people have predicted technological unemployment from many new inventions, but the ability to replace large fractions of intellectual work may be qualitatively different. If AI approaches human-level at most tasks and is price-competitive, the need for humans reduces down to areas where being biological is a bonus and the few tasks it hasn’t mastered.[2]

The effects of such unemployment could be very different depending on the country and political situation, but historically mass unemployment has often led to un

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AI Benefits Post 2: How AI Benefits Differs from AI Alignment & AI for Good
by Cullen — 2020-06-29T17:00:42.150Z

This is a post in a series on "AI Benefits." It is cross-posted from my personal blog. For other entries in this series, navigate to the AI Benefits Blog Series Index page.

This post is also discussed on the Effective Altruism Forum.

For comments on this series, I am thankful to Katya Klinova, Max Ghenis, Avital Balwit, Joel Becker, Anton Korinek, and others. Errors are my own.

If you are an expert in a relevant area and would like to help me further explore this topic, please contact me.

How AI Benefits Differs from AI Alignment & AI for Good

The Values Served by AI Benefits Work

Benefits plans need to optimize for a number of objectives.[1] The foremost is simply maximizing wellbeing. But AI Benefits work has some secondary goals, too. Some of these include:

Equality: Benefits are distributed fairly and broadly.[2]

Autonomy: AI Benefits respect and enhance end-beneficiaries’ autonomy.[3]

Democratization: Where possible, AI Benefits decisionmakers should create, consult with, or defer to democratic governance mechanisms.

Modesty: AI benefactors should be epistemically modest, meaning that they should be very careful when predicting how plans will change or interact with complex systems (e.g., the world economy).

These secondary goals are largely inherited from the stated goals of many individuals and organizations working to produce AI Benefits.

Additionally, since the rate of improvements to wellbeing probably decreases with income, the focus on maximizing wellbeing implies a focus on the distributional aspects of Benefits.

How AI Benefits differs from AI Alignment

Another important clarification is that AI Benefits differ from AI Alignment.

Both alignment and beneficiality are ethically relevant concepts. Alignment can refer to several different things. Iason Gabriel of DeepMind provides a useful taxonomy of existing conceptions of alignment. According to Gabriel, “AI alignment” can refer to alignment with:

“Instructions: the agent does what I instruct it to do.”

“Expressed intentions: the agent does what I intend it to do.”

“Revealed preferences: the agent does what my behaviour reveals I prefer.”

“Informed preferences or desires: the agent does what I would want it to do if I were rational and informed.”

“Interest or well-being: the agent does what is in my interest, or what is best for me, objectively speaking.”

“Values: the agent does what it moral

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Thoughts as open tabs
by ryan wong — 2020-06-29T12:13:54.132Z

I work better at night. At around midnight, I'm more prone to entering a state of relaxed focus, my mind making more lateral connections when reading or coming up with ideas that will likely have great positive impact on my personal life and work.

This happens after a hard workout too, or after having a stimulating discussion with a trusted, unbiased, and driven friend.

The conflict comes every day I wake up in the morning, and it's as if the open tabs of useful thoughts in my mind have closed, refreshed, but with no Command+Shift+Tab to reopen them exactly the way they were. I feel different in the morning; without the vision and subjective "charged" state I felt the night before, my mind doesn't give me a clear path to execute on the useful thoughts from yesterday. I don't have full access to that action-taking state.

It's logical that the subjective impact of thought and motivation changes from day-to-day. I wouldn't want to remember and feel the embarrassment from a social faux pas I made last week, every day. I just want to be able to capture and feel more of the good stuff from the day or week before. To feel the same emotional impact as when I first came across particular insights, so that I'll follow through on that with fervor and persistence.

Is there a way of hacking the brain to get the upside, without incurring the downside?

It's almost as if I existed in two states: one which thinks that it has done enough and deserves surface pleasures and rest - akrasia, and its opposite, non-akrasia - buzzing with quiet energy, working towards fulfillment, constantly alert to ideas that I can implement.

I know that being in a state of non-akrasia for sustained periods of time is possible. There are the Elon Musks of the world, those who work 80 hour work weeks towards their very specific cause of choice. I choose to believe that they were not so much genetically predisposed to do so, as they had something to protect as well as systems that ruthlessly reduced friction and distraction and ritualized the state of Flow.

The importance of colonizing mars and reversing climate change are likely kept as open tabs at the forefront of Musk's mind, thoughts with deep significance to him, that has guided every business or engineering decision he has made.

Given the limitations of working memory, how do I keep my usefu...
Optimized Propaganda with Bayesian Networks: Comment on "Articulating Lay Theories Through Graphical Models"
by Zack_M_Davis — 2020-06-29T02:45:08.145Z

Derek Powell, Kara Weisman, and Ellen M. Markman's "Articulating Lay Theories Through Graphical Models: A Study of Beliefs Surrounding Vaccination Decisions" (a conference paper from CogSci 2018) represents an exciting advance in marketing research, showing how to use causal graphical models to study why ordinary people have the beliefs they do, and how to intervene to make them be less wrong.

The specific case our authors examine is that of childhood vaccination decisions: some parents don't give their babies the recommended vaccines, because they're afraid that vaccines cause autism. (Not true.) This is pretty bad—not only are those unvaccinated kids more likely to get sick themselves, but declining vaccination rates undermine the population's herd immunity, leading to new outbreaks of highly-contagious diseases like the measles in regions where they were once eradicated.

What's wrong with these parents, huh?! But that doesn't have to just be a rhetorical question—Powell et al. show how we can use statistics to make the rhetorical hypophorical and model specifically what's wrong with these people! Realistically, people aren't going to just have a raw, "atomic" dislike of vaccination for no reason: parents who refuse to vaccinate their children do so because they're (irrationally) afraid of giving their kids autism, and not afraid enough of letting their kids get infectious diseases. Nor are beliefs about vaccine effectiveness or side-effects uncaused, but instead depend on other beliefs.

To unravel the structure of the web of beliefs, our authors got Amazon Mechanical Turk participants to take surveys about vaccination-related beliefs, rating statements like "Natural things are always better than synthetic alternatives" or "Parents should trust a doctor's advice even if it goes against their intuitions" on a 7-point Likert-like scale from "Strongly Agree" to "Strongly Disagree".

Throwing some off-the-shelf Bayes-net structure-learning software at a training set from the survey data, plus some ancillary assumptions (more-general "theory" beliefs like "skepticism of medical authorities" can cause more-specific "claim" beliefs like "vaccines have harmful additives", but not vice versa) produces a range of probabilistic models that can be depicted with graphs where nodes representing the different beliefs are connected by arrows that show which beliefs "cause" oth

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Abstractions on Inconsistent Data
by  — 2020-06-29T00:30:01.815Z

[I’m not sure this makes any sense – it is mostly babble, as an attempt to express something that doesn’t want to be expressed. The ideas here may themselves be an abstraction on inconsistent data. Posting anyway because maybe somebody else will prune it into something useful.]

i. Abterpretations

Abstractions are (or at least are very closely related to) patterns, compression, and Shannon entropy. We take something that isn’t entirely random, and we use that predictability (lack of randomness) to find a smaller representation which we can reason about, and predict. Abstractions frequently lose information – the map does not capture every detail of the territory – but are still generally useful. There is a sense in which some things cannot be abstracted without loss – purely random data cannot be compressed by definition. There is another sense in which everything can be abstracted without loss, since even purely random data can be represented as the bit-string of itself. Pure randomness is in this sense somehow analogous to primeness – there is only one satisfactory function, and it is the identity.

A separate idea, heading in the same direction: Data cannot, in itself, be inconsistent – it can only be inconsistent with (or within) a given interpretation. Data alone is a string of bits with no interpretation whatsoever. The bitstring 01000001
is commonly interpreted both as the number 65, and as the character ‘A’, but that interpretation is not inherent to the bits; I could just as easily interpret it as the number 190, or as anything else. Sense data that I interpret as “my total life so far, and then an apple falling upwards”, is inconsistent with the laws of gravity. But the apple falling up is not inconsistent with my total life so far – it’s only inconsistent with gravity, as my interpretation of that data.

There is a sense in which some data cannot be consistently interpreted – purely random data cannot be consistently mapped onto anything useful. There is another sense in which everything can be consistently interpreted, since even purely random data can be consistently mapped onto itself: the territory is the territory. Primeness as an analogue, again.

Abstraction and interpretation are both functions, mapping data onto other data. There is a se...
Gödel's Legacy: A game without end
by Hazard — 2020-06-28T18:50:43.536Z

Cross-posted at Brick. A fun exploration, hopefully requiring only a medium level of technical familiarity.

"Not with a bang but a whimper"

It's 1930 and Kurt Gödel has just delivered a talk at Königsberg that carpet bombed Hilbert's Program.

...and no one noticed.

Godel's announcement, delivered during the summarizing session on the third and last day of the conference, was so understated and casual—so thoroughly undramatic—that it hardly qualified as an announcement, and no one present, with one exception, paid it any mind at all.
(source)

Well, almost no one. John von Neumann came up to Gödel after the talk, presumably to double check if he was going crazy or if Gödel had in fact just proved the existence of unprovable mathematical truths.

To understand what Gödel's Incompleteness Theorem says, it helps to see the meta-mathematical opinions of some of his peers. Wittgenstein opens his big famous book (not that I've actually read it) with:

One could put the whole sense of the book perhaps in these words: What can be said at all, can be said clearly; and whereof one cannot speak, thereof one must be silent.

This was a bold move in its own way. Contrast it with the following take, "Everything can be spoken of, you just need to get smarter" which is a deeply held ideal of Strawman STEM-Lord. Interestingly, both Wittgenstein and STEM-Lord agree on something, the exact something that Gödel smashed up in Königsberg. It's a very technical claim, best communicated with the following GIF:

Witt and STEM-Lord disagree on what bleeds, but they are in fervent agreement that what bleeds can be killed.

Unraveling Self-reference

Take the classic liar's paradox:

Oh shit, it's true and false, or neither, paaaaaaaaaaaaaaaradox! The Wittgenstein-esque approach might be to claim the liar's sentence doesn't actually mean anything. There is no reality to it to speak of, so you can't speak of it. You are just getting yourself tied into a linguistic knot, tricking yourself into thinking that just because you can write this sentence it must mean something. Natural language is not a formal system and this sentence need not have a coherent "logical" "truth value". BOOM, checkmate hypothetical opponent!

Hidden in the explanation "Language isn't meant to avoid paradox and self-reference" is the claim "but formal math does avoid them!" The first part is true, the s

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