trentmkelly/LessWrong-43k huggingface dataset:
Instrumental Rationality 4.2: Creating Habits
by  — 2017-10-12T02:25:06.007Z

[Instrumental Rationality Sequence 4.2/7]

[Part two of a three-part series of habits.]

[We go over three techniques for creating habits: TAPs, Systematic Planning, and Scaling Up.]

Techniques: Creating Habits

[The Techniques section has been broken up into sections: one on creating new habits and one on breaking existing habits. They are split up based on what they do rather than what they are because I think it makes more sense.

Splitting things up by function allows the given examples for the techniques to be more focused on one of the two uses at a time, which I think makes the explanations easier to understand.]

As we touched upon in an earlier section, the process by which habits form, when simplified, looks roughly something like this:

Figure out what you want to do.

Identify the situation where you want the action to occur.

Actually perform said action in said situation.

Repeat the [Context cue → Response] loop until habituation occurs.

Therefore, from a theoretical standpoint, a useful intervention would try to affect at least one of the above steps.

The three evidence-backed techniques we’ll go over are Trigger Action Plans (TAPs), Systematic Planning, and Scaling Up.

Often, the best results occur when they’re all used in tandem, as they each affect different steps of habit formation.

++++

Trigger Action Plans (TAPs):

[TAPs are a way of framing intentional habit creation by focusing on both a salient trigger (i.e. context cue) and an action (i.e. response). They are well-backed by over two decades of research and build off the standard habit model.]

Research:

Trigger Action Planning is a technique by CFAR that is an adaptation of the implementation intention technique by psychologist Peter Gollwitzer.

Implementation intentions are also known as “if-then plans” because they take the form of a conditional, not unlike those used in programming, where one thing follows another. Basically it’s a way of chaining together actions and situations.

In writing, it roughly looks like:

“I intend to do action X when I encounter situation Y!”

As an example, a typical implementation intention for eating healthier might look like: “I intend to fill up half my plate with veggies whenever I am at a self-serve restaurant.”

Compared to many other behavioral change interventions, implementation have a stronger evidence base, and they’re simple to put into practice.

In a met

...
Gnostic Rationality
by Gordon Seidoh Worley — 2017-10-11T21:44:22.144Z

Ancient Greek famously made a distinction between 3 kinds of knowledge: doxa, episteme, and gnosis.

Doxa is basically what in English we might call hearsay. It's the stuff you know because someone told you about it. If you know the Earth is round because you read it in a book, that's doxa.

Episteme is what we most often mean by "knowledge" in English. It's the stuff you know because you thought about it and reasoned it out. If you know the Earth is round because you measured shadows at different locations and did the math that proves the only logical conclusion of the results is that the Earth is round, that's episteme.

Gnosis has no good equivalent in English, but the closest we come is when people talk about personal experience because gnosis is the stuff you know because you experienced it. If you know the Earth is round because you traveled all the way around it or observed it from space, that's gnosis.

Often we elide these distinctions. Doxa of episteme is often thought of as episteme because if you read enough about how others gained episteme you may feel as though you have episteme yourself. We discover this is not true, though, when we actually develop episteme of something we previously only had doxa of episteme of, like when we try to teach another person something and discover we didn't understand it as well as we thought we did. Similarly, episteme is sometimes mistaken for gnosis because episteme may allow you to always get the expected answer the way gnosis usually lets you, but only so long as you put in the effort to reckon epistemologically.

Many rationalist thinkers focus heavily on episteme and the doxastic logic used to combine facts. This is fine as far as it goes: you need to be able to develop well-formed episteme from facts if you want to have much chance of winning. Humans are notoriously bad at episteme and it takes considerable training to become good at it, and episteme requires constant maintenance to remain accurate. We cannot hope to be rationalists if we cannot master episteme.

But there is something more if you want to walk the Way. It's not enough to know about the Way and how to walk it; you need gnosis of walking. And I know this (doxastically, epistemically, and gnostically) from listening to others describe their experiences, reasoning about epistemology, and remembering my own expe

...
Mini-conference "Near-term AI safety"
by turchin — 2017-10-11T15:19:50.889Z

TL;DR: The event will be in Moscow, Russia, and near-term risks of AI will be discussed. The main language will be Russian, but Jonatan Yan will speak in English from HK. English presentations will be uploaded later on the FB page of the group "Near-term AI safety." Speakers: S. Shegurin, A. Turchin, Jonathan Yan. The event's FB page is here.

In the last five years, artificial intelligence has developed at a much faster pace in connection with the success of neural network technologies. If we extrapolate these trends, AI near-human level may appear in the next five to ten years, and there is a significant probability that this will lead to a global catastrophe. At a one-day conference at the Kocherga rationalist club, we'll look at how recent advances in the field of neural networks are changing our estimates of the timing of the creation of AGI, and what global catastrophes are possible in connection with the emergence of an increasingly strong AI. A special guest of the program Jonathan Yan is Hong Kong will tell (in English, via Skype) the latest research data on this topic.

The language of the conference: the first two reports in Russian, and the report Yan in English without translation, the discussion after it in English.

Registration: on the event page on Facebook.

Place: rationalist club "Kocherga", mail hall, Bolshaya Dorogomilovskaya ul., 5 корпус 2.

Participation in tariffs anticafe, 2.5 rubles a minute, coffee for free.

Videobroadcast will be on the Facebook.

Program:

October 14, Saturday 15.00 - the beginning.

15.00 - Shegurin Sergey. "Is it possible to create a human level AI in the next 10 years?"

16.00 - Turchin Alexey. "The next 10 years: the global risks of AI before the creation of the superintelligence"

17.00 - Jonathan Yan. "Recent Developments Towards AGI & Why It's Nearer Than You Think (in English)"

17.40 - Discussion
Mini-conference 
"Near-term AI safety"
by avturchin — 2017-10-11T14:54:10.147Z

TL;DR: The event will be in Moscow, Russia, and near-term risks of AI will be discussed. The main language will be Russian, but Jonatan Yan will speak in English from HK. English presentations will be uploaded later on the FB page of the group "Near-term AI safety." Speakers: S. Shegurin, A. Turchin, Jonathan Yan. The event's FB page is here.

In the last five years, artificial intelligence has developed at a much faster pace in connection with the success of neural network technologies. If we extrapolate these trends, AI near-human level may appear in the next five to ten years, and there is a significant probability that this will lead to a global catastrophe. At a one-day conference at the Kocherga rationalist club, we'll look at how recent advances in the field of neural networks are changing our estimates of the timing of the creation of AGI, and what global catastrophes are possible in connection with the emergence of an increasingly strong AI. A special guest of the program Jonathan Yan is Hong Kong will tell (in English, via Skype) the latest research data on this topic.

The language of the conference: the first two reports in Russian, and the report Yan in English without translation, the discussion after it in English.

Place: rationalist club "Kocherga", mail hall, Bolshaya Dorogomilovskaya ul., 5k 2.

Registration: on the event page on Facebook.

Participation in tariffs anticafe, 2.5 rubles a minute, coffee for free.

Video broadcast will be on the Facebook.

Program:

October 14, Saturday 15.00 - the beginning.

15.00 - Shegurin Sergey. "Is it possible to create an human level AI in the next 10 years?"

16.00 - Turchin Alexey. "The next 10 years: the global risks of AI before the creation of the superintelligence"

17.00. - Jonathan Yan "Recent Developments Towards AGI & Why It's Nearer Than You Think (in English)"

17.40 - Discussion
Winning is for Losers
by Jacob Falkovich — 2017-10-11T04:01:43.000Z

This post originally appeared on Ribbonfarm. It was written as part of the Ribbonfarm long-form writing course and edited by Joseph Kelly. I owe Joseph and the Ribbonfarm editors (Venkatesh Rao and Sarah Perry) huge thanks for spending the time to make me a better writer.

Our world is filled with competition, frenzied ambition in every domain. In Western nations, and above all in the United States, it animates not only economic and financial life, but scientific research and intellectual life as well. Despite the tension and the unrest it brings, these nations are inclined on the whole to congratulate themselves for having embraced the spirit of competition, for its positive effects are considerable.

— Rene Girard, The One by Whom Scandal Comes

I. Eating Dogs

Human life is all about competition, from the micro level to the macro.

We are built by genes that outcompeted their rivals over aeons of natural selection.

Children cooperate less and compete more as they grow older, even when competition is irrational. By the time boys and girls hit puberty they start mercilessly fighting for status, in addition to competing for resources and attention. As people enter the world of dating and finding mates, the competition for status only intensifies. With dating having moved online, everyone competes for the attention of their beloved against thousands of other Tinder matches. And sometimes also with the 5 other people they set up a date with in the same bar. The winner takes it all, and nice guys finish last.

We like exercise, music, and cooking. We like professional sports, American Idol, and cooking competitions even more.

Politics is war. The political right sees a war between barbarous foreigners and a civilized America. The left sees a war between economic classes, or among a multitude of identity groups fighting to oppress each other. The Libertarian Party is the only one that doesn’t look at politics as being primarily about fighting someone and they consistently gain less than 1% of the vote, the losers.

Our education system emphasizes competitive admissions, exams, and grading on a curve. This is done to prepare students to compete in the job market and the economy.

Our economy is based on companies competing with each other in the marketplace. But if you think that employees in the same company will cooperate for the good of the organization then you haven’t bee...
Centralisation shapes Idealists into Cynics
by  — 2017-10-10T20:01:27.691Z

So this is very tentative, this is mainly generated by thinking about the ways I would need to change to influence the centre.

By Cynics I mean what is being pointed at in the Gervais Principle, the Thick of It or Yes, Minister! They are not everywhere in the Centre.

It is not just cynicism, but a need for power.

There is something I'm going to call the Idealists journey, that I think turns some Idealists into Cynics.

An idealist wants to change the world

You an idealist notices something wrong with the world. It needs fixing and you are the one to fix it.

There are two paths you can go down. You can try and influence the centre and the stronger the centre the more tempting this is. Or you can try and influence the people. To influence the people you give them something, a new technology (like linux) or a new idea. You don't know exactly what people will do with the new thing, so this seems like a risky idea. So you try and influence the centre.

Power is in the centre

In our capitalist, statist system power is in the centre. If you can grab some of it you can redirect some of it to the fix the problem you've noticed. Power being in the centre is not a fact of the world. For the majority of life's history power has been decentralised, a tree mainly grew by itself, helped by the trees around it providing it shelter but also competing for resources. It didn't need planning permission to grow or rely on a central bank to maintain liquidity in a currency so that it could take out a loan to pay for its resources. Centralization is an artifact of our current technology level, with more technology we may be able to be more decentralized.

To get power you have to have influence

So once you have decided down the central path you need to place yourself in the centre. People in the centre just don't let other people come waltzing into the centre, psycopaths do not want to lose control and other Idealists need to be convinced that your idea is good. You need to convince them you should be in the centre in some way or for some reason. So you need influence.

To get influence you have to be ruthless

The more power you need the closer into the centre you want to go. So the more influence you need. Influence is zero sum, so you always have to be on the look out for people back stabbing you and you have to take those opportunities for influence that you get,

...
10/10/2017: Development update ("autosaving" & Intercom options)
by habryka — 2017-10-10T19:15:12.804Z

Hey Everyone,

Here is a quick summary of changes I just pushed:

Editor fields should now be saved locally on your computer, which means that if you reload the page while writing a comment or editing a post, your content will no longer be reset (however, over the course of a few hours your computer might flush that storage, so don't rely on it as a long-term solution for storing drafts)

Your edit-profile page now has a cleaner UI and should be slightly less confusing (though a more thorough rework is also in the works)

You can now hide the Intercom chat in your profile settings

We made some changes to how bullet lists render in comments and posts, they should look slightly less janky now

We are still working on performance improvements, are still improving autosave functionality and also have a larger font and style rework in the works that should fix rendering issues on windows and linux machines.
What would convince you you'd won the lottery?
by Stuart_Armstrong — 2017-10-10T13:45:44.996Z

The latest (06 Oct 2017) Euromillion lottery numbers were 01 - 09 - 15 - 19 - 25 , with the "Lucky Stars " being 01 - 07.

Ha! Bet I convinced no-one about those numbers. The odds against 01 - 09 - 15 - 19 - 25 / 01 - 07 being the true lottery numbers are about 140 million to one, so I must have been a fool to think you'd believe me. The odds that I decided to lie is far more that one in a 140 million, so it's very unlikely those are the true numbers.

Wait a moment. Something is wrong here. That argument could be used against any set of numbers; and yet one of them was the real winning set. The issue is that probabilities are not being compared properly.

Let S=(01,09,15,19,25,01,07), let W(S) be the fact these numbers won the lottery, let L be the fact that I lied about the winning number, and L(S) be the fact that I lied and claimed S as the winning numbers.

So though P(L) is indeed much higher than P(W(S)), generally P(W(S)) will be higher than P(L(S)). That's because while W(S) gets penalised by all the other values S could have been, so does L(S).

Note the key word "generally". The sum of P(L(S')), across all S', is P(L); this means that for most S', P(L(S')) must be low, less than 140 million times smaller than P(L). But it's possible that for some values of S', it might be much higher. If I'd claimed that the winning numbers were 01 - 02 -03 - 04 - 05 / 06 - 07, then you might have cause to doubt me (after taking into account selection bias in me reporting it, the number of lotteries going on around the world, and so on). The connections with Komogorov complexity should be obvious at this point: the more complex the sequence, the less likely it is that a human being will select it disproportionately often (and you can't select all sequences disproportionately often).

What if I'd claimed that I'd won that lottery? That seems like a 01 - 02 -03 - 04 - 05 / 06 - 07 - style claim. I'm saying that the 01 - 09 - 15 - 19 - 25 / 01 - 07 did win (fair enough) but that these numbers are special to me because I selected them. Here you should be more sceptical. But what if I linked to a few articles that showed me with a gigantic novelty check? It would still seem more likely that I'd hacked and faked those than that I'd actually won... or would it?

Let's make it more

...
Toy model of the AI control problem: animated version
by Stuart_Armstrong — 2017-10-10T11:12:07.207Z

Crossposted at LessWrong 2.0.

A few years back, I came up with a toy model of the AI control problem. It has a robot moving boxes into a hole, with a slightly different goal than it's human designers, and a security camera to check that it's behaving as it should. The robot learns to block the camera to get its highest reward.

I've been told that the model is useful for explaining the control problem quite a few people, and I've always wanted to program the "robot" and get an animated version of it. Gwern had a live demo, but it didn't illustrate all the things I wanted to.

So I programmed the toy problem in python, and generated a video with commentary.

In this simplified version, the state space is sufficiently small that you can explicitly generate the whole table of Q-values (expected reward for taking an action in a certain state, assuming otherwise optimal policy). Since behaviour is deterministic, this can be updated in dynamical programming, using a full width backup. The number of such backups essentially measures the depth of the robot's predictive ability.

The most interesting depths of planning are:

At depth 0, the robot behaves randomly.

At depth 3, the robot crashes itself efficiently, to avoid loosing too much reward.

At depth 6, the robot figures out how to get one box into the hole. The automated camera turns it off.

At depth 17, the robot finally starts to deceive/conceal/manipulate, by blocking the camera and pushing two boxes into the hole. Note that the robot's deception comes from the fact that its incentives are misaligned, and that humans tried to control it.

At depth 18, the robot efficiently does the plan from depth 17.

At depth 20, the robot does the maximally efficient plan: blocking the camera, and pushing all boxes into the hole.

At depth 32, the robot has the correct Q-values for the maximally efficient plan.

At depth 45, finally, the Q-value table is fully updated, and the robot will take maximally efficient, and, if need be, deceptive plans from any robot/box starting positions.

The code and images can be found here.
Toy model of the AI control problem: animated version
by Stuart_Armstrong — 2017-10-10T11:06:41.518Z

A few years back, I came up with a toy model of the AI control problem. It has a robot moving boxes into a hole, with a slightly different goal than it's human designers, and a security camera to check that it's behaving as it should. The robot learns to block the camera to get its highest reward.

I've been told that the model is useful for explaining the control problem quite a few people, and I've always wanted to program the "robot" and get an animated version of it. Gwern had a live demo, but it didn't illustrate all the things I wanted to.

So I programmed the toy problem in python, and generated a video with commentary.

In this simplified version, the state space is sufficiently small that you can explicitly generate the whole table of Q-values (expected reward for taking an action in a certain state, assuming otherwise optimal policy). Since behaviour is deterministic, this can be updated in dynamical programming, using a full width backup. The number of such backups essentially measures the depth of the robot's predictive ability.

The most interesting depths of planning are:

At depth 0, the robot behaves randomly.

At depth 3, the robot crashes itself efficiently, to avoid loosing too much reward.

At depth 6, the robot figures out how to get one box into the hole. The automated camera turns it off.

At depth 17, the robot finally starts to deceive/conceal/manipulate, by blocking the camera and pushing two boxes into the hole. Note that the robot's deception comes from the fact that its incentives are misaligned, and that humans tried to control it.

At depth 18, the robot efficiently does the plan from depth 17.

At depth 20, the robot does the maximally efficient plan: blocking the camera, and pushing all boxes into the hole.

At depth 32, the robot has the correct Q-values for the maximally efficient plan.

At depth 45, finally, the Q-value table is fully updated, and the robot will take maximally efficient, and, if need be, deceptive plans from any robot/box starting positions.

The code and images can be found here.
Robustness as a Path to AI Alignment
by abramdemski — 2017-10-10T08:14:31.054Z

[Epistemic Status: Some of what I'm going to say here is true technical results. I'll use them to gesture in a research direction which I think may be useful; but, I could easily be wrong. This does not represent the current agenda of MIRI overall, or even my whole research agenda.]

Converting Philosophy to Machine Learning

A large part of the work at MIRI is to turn fuzzy philosophy problems into hard math. This sometimes makes it difficult to communicate what work needs done, for example to math-savvy people who want to help. When most of the difficulty is in finding a problem statement, it's not easy to outsource the intellectual labor.

Philosophy is also hard to get traction on. Arguably, something really good happened to the epistemic norms of AI research when things switched over from GOFAI to primarily being about machine learning. Before, what constituted progress in AI was largely up to personal taste. After, progress could be verified by achieving high performance on benchmarks. There are problems with the second mode as well -- you get a kind of bake-off mentality which focuses on tricks to get higher performance, not always yielding insight. (For example, top-performing techniques in machine learning competitions often combine many methods, taking advantage of the complementary strengths and weaknesses of each. However, this approach leans on the power of the other methods.) Nonetheless, this is better for AI progress than armchair philosophy and toy problems.

It would be nice if AI alignment could be more empirically grounded. There are serious obstacles to this. Many alignment concerns, such as self-modification, barely show up or seem quite easy to solve when you aren't dealing with a superintelligent system. However, I'll argue that there is sometimes a way to turn difficult alignment problems into machine learning problems.

A second reason to look in this direction is that in order to do any good, alignment research has to be used by the people who end up making AGI. The way things look right now, that means they have to be used by machine learning researchers. To that end, anything which puts things closer to a shape which ML researchers are familiar with seems good.

To put it a different way: in the end, we need a successful pipeline from philosophy, to math, to implementation. So far, MIRI has focused on optimizing the

...