trentmkelly/LessWrong-43k huggingface dataset:
Does anti-malaria charity destroy the local anti-malaria industry?
by Viliam — 2019-01-05T19:04:57.601Z

The usual argument against foreign aid to Africa is that randomly giving tons of free goods (such as food) ruins local producers; and when at some later moment the charity goes out of fashion (or decides to target a different part of Africa), the local situation becomes even worse than before, because the local producers have gone out of business. In addition, it hurts the local people psychologically to know that any local business, no matter how successful it could otherwise have been, can at any moment be destroyed by a well-meaning foreign charity.

Recently I heard the same argument made about anti-malaria nets recommended by GiveWell. If I understand it correctly, the donated nets put local net producers out of business (increasing local poverty and dependence on foreign aid), and the estimated number of lives saved is misleading (because in the alternative scenario, the same people could have been saved by locally produced nets).

I have one specific question, and one more general concern.

The specific question... well, I know nothing about the anti-malaria industry in Africa. It exists, I assume. But quantitatively -- how many nets it produces, how many nets it stops producing because it is pushed out of the market by GiveWell, whether the nets are of comparable quality, what is the best estimate of the scenario with no foreign aid compared to the scenario with foreign aid -- I have no idea. I supposed some of this was already discussed by some effective altruists, so I would love to hear the summary.

The meta concern is the following: I find the argument of foreign goods disrupting local market plausible. But seems to me that the problem is with high variance (one year a ton of goods, the very next year nothing), not with foreign goods per se. Because, anytime a country participates in foreign trade, the local producers of the stuff that is being imported, are pushed out of business. But we have the law of comparative advantages saying that in global, this is a good thing, for both countries. (Or to put it differently, trade sanctions are typically used as a punishment, not as a reward.) I worry that at some moment, the "stop destroying African economy by your disruptive aid" argument becomes effectively "stop trading with Africa", and I am not sure where exactly to draw that line.
Will humans build goal-directed agents?
by Rohin Shah — 2019-01-05T01:33:36.548Z

In the previous post, I argued that simply knowing that an AI system is superintelligent does not imply that it must be goal-directed. However, there are many other arguments that suggest that AI systems will or should be goal-directed, which I will discuss in this post.

Note that I don’t think of this as the Tool AI vs. Agent AI argument: it seems possible to build agent AI systems that are not goal-directed. For example, imitation learning allows you to create an agent that behaves similarly to another agent -- I would classify this as “Agent AI that is not goal-directed”. (But see this comment thread for discussion.)

Note that these arguments have different implications than the argument that superintelligent AI must be goal-directed due to coherence arguments. Suppose you believe all of the following:

Any of the arguments in this post.
Superintelligent AI is not required to be goal-directed, as I argued in the last post.
Goal-directed agents cause catastrophe by default.

Then you could try to create alternative designs for AI systems such that they can do the things that goal-directed agents can do without themselves being goal-directed. You could also try to persuade AI researchers of these facts, so that they don’t build goal-directed systems.

Economic efficiency: goal-directed humans

Humans want to build powerful AI systems in order to help them achieve their goals -- it seems quite clear that humans are at least partially goal-directed. As a result, it seems natural that they would build AI systems that are also goal-directed.

This is really an argument that the system comprising the human and AI agent should be directed towards some goal. The AI agent by itself need not be goal-directed as long as we get goal-directed behavior when combined with a human operator. However, in the situation where the AI agent is much more intelligent than the human, it is probably best to delegate most or all decisions to the agent, and so the agent could still look mostly goal-directed.

Even so, you could imagine that even the small part of the work that the human continues to do allows the agent to not be goal-directed, especially over long horizons. For example, perhaps the human decides what the agent should do each day, and the agent executes the instruction, which involves planning over the course of a day, but no longer. (I am not arguing that this is safe; on the contra...
I want it my way!
by nickhayes — 2019-01-04T18:08:43.955Z

First posted to notthinkable.com

I've been meaning to write a blog post for awhile (years), because more and more I'm finding that contributing to others, in whatever form that takes, is a really rewarding thing to do, and makes me feel connected.
But I've been putting it off, because I can't write Meditations on Moloch on my first try.

I have this really sticky idea that if I hold off writing a post, somehow, in the future, I'll be able to write something really good: that moves a conversation, or that is an authentic expression of who I am, or something. Even though I know this is silly. People tend to have to have practice writing blog posts for awhile before they produce something that touches people.
However, the feeling is still there. Because I don't want to give up on the idea that this won't be as good as I want it to be when I publish it. That would mean a kick to my ego, and that would be bad. Why can't I just write something and revise it endlessly, so that it can be good enough first time? I don't want blogging to be how it is. I want it my way!
-
It seems that I'm a slow learner when it comes to this.
Last year I got into online dating (briefly). I'd been putting it off for awhile (years). Eventually what it took was a close friend sitting down next to me, watching me, whilst I messaged people, because I just wouldn't do it alone.
It reminds me of a Jordan Peterson video where he talks about treating people who are afraid of needles. They'll start with the smallest possible step: let's just have a needle in the room, and reveal this fact. Not even a visible needle. Just create awareness that there's a needle in the room. And go from there: first having the needle visible. Then having it brought closer. Then having the patient touch it, etc. Each session taking a small step, making progress little by little.
I was originally going to write an essay called 'holding hands' awhile back, about this dynamic. My friend said that sounded like a great idea. But I didn't ever write it, because I didn't follow my own advice.
There's a lot of humility required in something like this. A previous Nick would have judged. Aren't you ashamed of yourself? You can't even do that? That's pretty pathetic. You should just get over yourself, and start dating. Man ...
Towards no-math, graphical instructions for prediction markets
by ryan_b — 2019-01-04T16:39:58.479Z

You are probably familiar with prediction markets. As of the last general update, Robin Hanson notes that while there is increasing research interest and a few general platforms are coming soon, the real goal of implementing inside organizations remains elusive.

I strongly suspect that it will remain so until companies are founded which use them from the beginning. This is because introducing people to new tools is hard. Introducing them to new tools that don’t even have a direct bearing on doing their jobs is even harder. We know that trivial inconveniences are important; it was the dominant theme of the crypto autopsy. If it happens in an environment like Lesswrong with crypto, I expect it will be worse in virtually all other contexts.

If prediction markets are going to be widely adopted, they need to be widely applicable, which basically means that any given schlub needs to be able to make a bet in a market without any math/economics/computing background. Happily, we have extremely robust evidence that any given schlub is perfectly capable of making a bet, courtesy of casinos.

One way to help with this problem is to make it as easy and intuitive as possible to learn. To wit: interactive visualizations. The biggest inspirations are Up and Down the Ladder of Abstraction by Brett Victor, and Metaculus. The first is about exploring system behavior without math, and the training for the second does a pretty good job of letting you make predictions based on the shape of the probability curve (though this doesn’t work as well for understanding the payoffs). Another relevant tool is the Sankey Diagram, which makes the relative sizes of flows apparent at a glance. I don't know of anything which makes such a thing interactive though - which is a shame, because I bet it would make controls comprehensible to people who don't know any differential equations or linear algebra. I am currently impressed by Seeing Theory. I ran across a few other websites which are designed to communicate math, and even one about risk, but for some reason all these savages are using Flash.

However, all of these is mostly for teaching math, and that is not actually the goal. Fundamentally the idea is information flows into the market, and only payments need to flow back out. So what we need is a way for people to specify what they think the outcome will be, with what confidence, with...
Two More Decision Theory Problems for Humans
by Wei Dai — 2019-01-04T09:00:33.436Z

(This post has been sitting in my drafts folder for 6 years. Not sure why I didn't make it public, but here it is now after some editing.)

There are two problems closely related to the Ontological Crisis in Humans. I'll call them the "Partial Utility Function Problem" and the "Decision Theory Upgrade Problem".

Partial Utility Function Problem

As I mentioned in a previous post, the only apparent utility function we have seems to be defined over an ontology very different from the fundamental ontology of the universe. But even on it's native domain, the utility function seems only partially defined. In other words, it will throw an error (i.e., say "I don't know") on some possible states of the heuristical model. For example, this happens for me when the number of people gets sufficiently large, like 3^^^3 in Eliezer's Torture vs Dust Specks scenario. When we try to compute the expected utility of some action, how should we deal with these "I don't know" values that come up?

(Note that I'm presenting a simplified version of the real problem we face, where in addition to "I don't know", our utility function could also return essentially random extrapolated values outside of the region where it gives sensible outputs.)

Decision Theory Upgrade Problem

In the Decision Theory Upgrade Problem, an agent decides that their current decision theory is inadequate in some way, and needs to be upgraded. (Note that the Ontological Crisis could be considered an instance of this more general problem.) The question is whether and how to transfer their values over to the new decision theory.

For example a human might be be running a mix of several decision theories: reinforcement learning, heuristical model-based consequentialism, identity-based decision making (where you adopt one or more social roles, like "environmentalist" or "academic" as part of your identity and then make decisions based on pattern matching what that role would do in any given situation), as well as virtual ethics and deontology. If you are tempted to drop one or more of these in favor of a more "advanced" or "rational" decision theory, such as UDT, you have to figure out how to transfer the values embodied in the old decision theory, which may not even be represented as any kind of utility function, over to the new.

Another instance of this problem can be seen in someone just wanting to be a bit more consequent

...
What are good ML/AI related prediction / calibration questions for 2019?
by james_t — 2019-01-04T02:40:28.921Z

I'm trying to come up with a set of questions for self-calibration, related to AI and ML.

I've written down what I've come up with so far below. But I am principally interested in what other people come up with -- thus the question metatype -- both for questions, and for predictions for the questions.

So far I have an insufficient number of questions to produce anything like a nice calibration curve. I've also struggled with coming up with meaningful questions.

I've rot13'ed my predictions to avoid anchoring anyone. I'm pretty uncertain about most of these as point estimates however.

On Explicitly Stated ML / Systems Goals

It is (relatively) easy to determine if these are fulfilled or not. The trade-off is that they likely have little relation to AGI.

1. OpenAI succeeds in defeating top pro teams on unrestricted Dota2

OpenAI has explicitly said that they wish to beat top human teams in the MOBA Dota2. Their latest attempt to do so used self-play and familiar policy-gradient strategies on an incredibly massive scale to train, but still lost to top teams who won (relatively?) easily.

I'm also interested in people's probabilities on whether OpenAI succeeds, conditional on OpenAI not including genuine algorithmic novelty in their learning methods, although that's a harder question to define because of cloudiness around "algorithmic novelty."

My prediction: Friragl-svir creprag.

2. Tesla succeeds in a self-driving car driving coast-to-coast without intervention.

Tesla sells cars with (ostensibly) all the hardware necessary for full self-driving, and an in-house self-driving research program that uses a mix of ML and hard-coded rules. They have a goal of giving a demonstration autonomous coast-to-coast drive, although this goal has been repeatedly delayed. There is widespread skepticism both of the sensor suite in Tesla cars and of the maturity of their software.

My prediction: Gra creprag.

3. DeepMind reveals a skilled RL-trained agent for StarCraft II.

After AlphaGo, DeepMind announced that they would try to create an expert-level agent for SCII. They've released preliminary research related to this topic, although what they've revealed is far from such an agent.

My prediction: Nobhg svir creprag.

On Goals Not So Clearly Marked As Targets

1. Someone gets a score on the Winograd Schema tests above 80%....
What is a reasonable outside view for the fate of social movements?
by Bird Concept — 2019-01-04T00:21:20.603Z

Epistemic status: very hand-wavy and vague, but confident there is a substantial and well-understood core. Hoping for an answer that elucidates that core more clearly.

It is something of a rationalist folk theorem that social movements face the risk of an "Eternal September", or of scaling into oblivion. (See e.g. this blog post by Leverage research, this paper by Owen Cotton-Barratt, David Chapman or Benjamin Hoffman on "Geeks, MOPs and sociopaths", and Scott Alexander on "the toxoplasma of rage").

I've had the sense that some cocktail of Hansonian/Dunbarian evolutionary psychology, basic game theory/microeconomics, memetic theory and Sturgeon's law, should predict this. That is, that some reasonably operationalised version of the claim "most social movements fail" is true.

Yet I am not able to point to >=5 historical examples of social movements that suffered this fate, along with some gears for what went wrong.

Hence I'm looking for links, historical examples, more fleshed-out gears, ... anything that might form a more rigorous reference point for an outside view on the fate of social movements.
Logical inductors in multistable situations.
by Donald Hobson — 2019-01-03T23:56:54.671Z

I was reading about logical induction at

https://intelligence.org/files/LogicalInduction.pdf

and understand how it resolves paradoxical self reference, but I'm not sure what the inductor will do in situations where multiple stable solutions exist.

Let f:[0,1]→[0,1]

If f is continuous then it must have a fixed point. Even if it has finitely many discontinuities, it must have an "almost fixed" point. An x such that ∀ϵ>0:infy∈(x−ϵ,x)f(y)≤x≤supy∈(x,x+ϵ)f(y)

However some f have multiple such points.

f(x)={0x<121x≥12

Has "almost fixed" points at 0, 12 and 1.

A similar continuous f is

f(x)=⎧⎪
⎪
⎪⎨⎪
⎪
⎪⎩0x≤133x−113≤x≤231x≥23

With

f(x)=x

Having every point fixed.

Consider ϕn="f(En(ϕn))"

These functions make ϕn the logical inductor version of "this statement is true". Multiple values can be consistently applied to this logically uncertain variable. None of the possible values allow a money pump, so the technique of showing that some behaviour would make the market exploitable that is used repeatedly in the paper don't work here.

Is the value of En(ϕn) uniquely defined or does it depend on the implementation details of the logical inductor? Does it tend to a limit as n→∞ ? Is there a sense in which

f(x)={0.830.82≤x≤0.840.1else

causes En(ϕn) has a stronger attractor to 0.1 than it does to 0.83?

Can En(ϕn) be 0.6 where

f(x)={0.6x=0.60.1else

because the smallest variation would force it to be 0.1?
Applied Rationality Workshop Cologne, Germany
by mschons — 2019-01-03T09:16:35.147Z

Together with Anne Wissemann we are going to do a 2-day weekend workshop on applied rationality in Cologne, Germany. If you cannot apply since you have already scheduled something important, feel free to leave your email to be informed about future workshops in the Cologne area.

Summary

Date: 19. & 20.01.2019, 9am - 6pm
Application-form: https://goo.gl/forms/DLfsznUTbWzoIJV12 (Deadline: Friday, 11.01.2018, confirmations will be send on Saturday)
Location: somewhere in Cologne... to be announced :)
Costs: 40-60€
Topic: CFAR-techniques and other useful rationality-concepts
Limited to 20 attendees
Target Group: Aspiring EAs & Rationalists
Housing available on request

Aims

We - the EA Köln/Cologne group - want to contribute to the positive development of the EA & Rationality community.

Over the past year we organized weekly Meetups including Socials, Book-Clubs as well as talks with discussion rounds. Recently we decided to make weekend seminars and workshops a priority since they seem to have multiple benefits:

real deep-diving into topics compared to evening events
'Unite' the EA & LessWrong communities addressing mutual interests
socialize with great people
filter for (really interested) newcomers
Due to weekend time frame: invite great speakers and people from other cities (who would not take the effort traveling for an evening only)

Overview

Anne works as a coach for members of the rationality and effective altruism communities. In addition to her own CFAR workshop, she has volunteered as a mentor at 5 other mainline workshops since 2014, has undergone and taught at CFAR's mentorship training, as well as helped develop her local weekly Rationality Dojo in Berlin. She plans to teach the participants:

- Double Crux
- Focusing
- Internal Double Crux
- Trigger-Action Plans (TAP)
- Flash classes/concepts: Mindfulness, Negative Visualisation, Decreasing Marginal Returns, helpful Debugging prompts, Frogs, How to notice things, experimental mindset, and more!
- Hamming Circles = Debugging on what's most important
- Goal Factoring

For a more detailed description see this document.

Lunch, snacks and drinks are included. If you need housing, please take the steps indicated in the application form.

Past events

In mid November we had our first weekend workshop with Stijn Bruers about 'Rational Ethics'. We had participants from a...
What's the best way for me to improve my English pronounciation?
by ChristianKl — 2019-01-02T23:49:19.428Z

I'm a native German speaker and learned a good portion of my English not in school but by reading English content.

As part of a New Years event we did a Doom Circle and one essential feedback I got was that I have to improve my English accent.

When it comes to my budget I'm willing to invest a few hundred Euro but not thousands.

What would you recommend to me to improve my English pronounciation?
Generalising CNNs
by James Crook — 2019-01-02T20:57:24.646Z

TL/DR; The invention of Convolution Neural Networks for image and audio processing was a key landmark in machine learning.

This topic is for people who already know what CNNs are, and are interested in how to innovate to riff on and extend the core reason (perhaps?) that CNNs learn faster. Probing the technology topic is one 'sub goal' in questioning where our AI knowledge is heading, and how fast. In turn, that's because we want it to progress in a good direction.

Sub Goal

Q: Can the reduction in number of parameters that a CNN introduces be achieved in a more general way?

A: Yes. Here are sketches of two ways:

1) Saccades. Train one network (layer) on attention. Train it to learn which local blocks of the image to give attention to. Train the second part of the network using those chosen 'local blocks' in conjunction with coordinates of their locations.

The number of blocks that have large CNN kernels applied to them is much reduced. Those blocks are the blocks that matter.

2) Parameter Compression. Give each layer of a neural network more (potential) connections than you think will actually end up being used. After training for a few cycles, compress the parameter values using a lossy algorithm, always choosing the compression which scores best on some weighting of size and quality. Uncompress and repeat this process till you have completed the training set.

The number of bits used to represent parameters is being kept low, helping to guard against over fitting.

Dialog

[Doubter] This all sounds very hand wavy. How exactly would you train a saccadic network on the right movements?

[Optimist] One stepping stone, before you get to a true saccadic network with the locus of attention following a temporal trajectory, is to train a shallow network to classify where to give attention. So this stepping stone outputs a weighting for how much attention to give to each location. For sake of being more concrete, it works on a down sampled image and gives 0 for no attention, 1 for convolution with a 3x3 kernel, 2 for convolution with a 5x5 kernel.

[Doubter] You still haven't said how you would do that attention training.

[Optimist] You could reward a network for robustness to corruption of the image. Reward it for zeroes in the attention layers.

[Doubter] That's not clear, and I think there is a Catch 22. You need to have...