trentmkelly/LessWrong-43k huggingface dataset:
A Day Without Defaults
by katydee — 2014-10-20T08:07:10.790Z

Author's note: this post was written on Sunday, Oct. 19th. Its sequel will be written on Sunday, Oct. 27th.

Last night, I went to bed content with a fun and eventful weekend gone by. This morning, I woke up, took a shower, did my morning exercises, and began eat breakfast before making the commute up to work.

At the breakfast table, though, I was surprised to learn that it was Sunday, not Monday. I had misremembered what day it was and in fact had an entire day ahead of me with nothing on the agenda. At first, this wasn't very interesting, but then I started thinking. What to do with an entirely free day, without any real routine?

I realized that I didn't particularly know what to do, so I decided that I would simply live a day without defaults. At each moment of the day, I would act only in accordance with my curiosity and genuine interest. If I noticed myself becoming bored, disinterested, or otherwise less than enthused about what was going on, I would stop doing it.

What I found was quite surprising. I spent much less time doing routine activities like reading the news and browsing discussion boards, and much more time doing things that I've "always wanted to get around to"-- meditation, trying out a new exercise routine, even just spending some time walking around outside and relaxing in the sun.

Further, this seemed to actually make me more productive. When I sat down to get some work done, it was because I was legitimately interested in finishing my work and curious as to whether I could use a new method I had thought up in order to solve it. I was able to resolve something that's been annoying me for a while in much less time than I thought it would take.

By the end of the day, I started thinking "is there any reason that I don't spend every day like this?" As far as I can tell, there isn't really. I do have a few work tasks that I consider relatively uninteresting, but there are multiple solutions to that problem that I suspect I can implement relatively easily.

My plan is to spend the next week doing the same thing that I did today and then report back. I'm excited to let you all know what I find!
Noticing
by casebash — 2014-10-20T07:47:54.388Z

The Selective Attention Test is a famous experiment in perceptual psychology that demonstrates how strongly attention shapes our perceptions. In light of this experiment, I think it is interesting to consider the question of how much valuable information we have thrown away because we simply didn't notice it or weren't in a position where we could appreciate the importance. My intuition is that we have missed more information than we've actually absorbed.

I would like to consider the question of what is there to notice, in particular what things will provide us with value once we gain the prerequisite knowledge and habits to allow us to effectively perceive it. One example is that after I started a course on Art History, I gained the ability to notice more about possible meanings and interesting aspects of art. This is fantastic, because art is everywhere. Now that I have a basic ability to appreciate art I gain some level of growth almost for free, just from seeing art in places where I'd have gone anyway. I'm hoping to do a film studies course next year, since, like almost everyone, I watch movies anyway and want to get as much out of them as I can.

Marketing is likely another example. Someone who has studied marketing may unconsciously evaluate every ad that they see, and after seeing enough examples, gain a strong understanding of what counts as a good ad and what counts as a bad ad. Perhaps this won't be totally free, perhaps they will sometimes see something and not know why it is good until they think about it for a bit. However, this knowledge is mostly free, in that after you understand the basic principles, you gain some level of growth for a minimal investment.

I think that another more general situation like this is those activities that are a form of creation. If you try writing a few stories, then when you read a story you'll have a greater appreciation of what the author is trying to do. If you've played guitar, then when you listen to music you'll learn about the different techniques that guitarists use. If you've played sport, then you'll probably have a greater appreciation for strategy when watching a game.

Professional comedy writers are always looking for jokes. Whenever unusual or bad or unexpected happens, they note it so that they can try to find a manner of forming it into a joke later. I've heard that actors become attuned to different ...
Meetup : Urbana-Champaign: Meta-systems and getting things done
by Manfred — 2014-10-20T04:16:05.928Z

Discussion article for the meetup : Urbana-Champaign: Meta-systems and getting things done

WHEN:
26 October 2014 02:00:00PM (-0500)

WHERE:
206 S. Cedar St., Urbana IL

Seed topic:

When I was a little kid doing chores, what worked was for my mom to tell me to do one thing. Then I'd do the thing, and come back and ask her "what next?" And then she'd tell me another thing, and I'd do that, and so on.

A few weeks ago Brienne posted a technique that can be used to do this with a mental model of someone smart. You build a model of what an effective person would do, and you ask your model what thing to do next, and the model tells you, and then you do that. This one of many ways of reminding yourself to follow the pattern "find the best strategy, and then do the next step in that strategy."

If you desire homework: try doing this for a few hours at some point during the week.

By contrast, consider the Getting Things Done family of productivity techniques, where you write down what you want to do in advance and then follow your written plan. How well has this worked for you in the past? (As one might predict, results vary).

Discussion article for the meetup : Urbana-Champaign: Meta-systems and getting things done
Alexander Berger on GiveWell Labs
by KatjaGrace — 2014-10-20T01:33:00.000Z

This post summarizes a conversation which was part of the Cause Prioritization Shallow, all parts of which are available here. Previously in this series, conversations with Owen Cotton-Barratt, Paul Christiano, Paul Penley, and Gordon Irlam.

Participants

Alexander Berger: Senior Research Analyst, GiveWell

Katja Grace: Research Assistant, Machine Intelligence Research Institute

Notes

Since this conversation, GiveWell Labs has become the Open Philanthropy Project.

This is a summary made by Katja of points made by Alexander Berger during a conversation about GiveWell Labs and cause prioritization research on March 5th 2014.

GiveWell Labs

Focus

GiveWell Labs is trying to answer the same basic question as GiveWell: “what’s the best way to spend money?” However GiveWell Labs is answering this question for larger amounts of money, which is less straightforward. Causes are a more useful unit than charities for very large donors. So instead of trying to figure out which charity one should give to this year, they are asking which program areas a foundation should work on.

Givewell’s relationship with Good Ventures is a substantial reason for focussing on the needs of big donors, and GiveWell Labs research has been done in partnership with Good Ventures. The long term vision is to have ongoing research into which areas that should be covered are not, while providing support for a wide range of foundations working on problems they have previously identified as important.

Approach to research

GiveWell Labs primarily aggregates information, rather than producing primary research. It also puts a small amount of effort into publicizing its research.

Their research process focuses on answering these questions:

how important is the issue?

how tractable is it?

how crowded is it?

They attempt to answer the questions at increasing levels of depth for a variety of areas. It is not certain that these are key criteria for determining returns through a program, but they seems correct intuitively.

Most research is done through speaking to experts (rather than e.g. reading research papers). The ‘importance’ question is the only one likely to have academic research on it.

The learning process

GiveWell Labs is prioritizing learning value and diversification at the moment, and not aiming to make decisions about cause priorities once and for all. Alexande...
European Community Weekend 2015
by Lachouette — 2014-10-19T15:02:46.069Z

The Berlin meetup group is organizing the LessWrong Community Weekend 2015. From June 12th to 14th awesome people from all across Europe are coming to Berlin to meet, exchange ideas and start projects. The focus is on forming new and strengthening existing ties between our local communities. In addition to being a vibrant social event, it’s also about sharing your world-improvement projects as well as about teaching and learning valuable skills.
If you are already attending a local meetup, you might find this to be similar in the way it mixes a social event with workshops and talks. And if you don’t have a meetup nearby, this is a great opportunity to get in touch with the community.
Our 2014 event had an unexpectedly huge turnout. This time we have planned for a larger number of participants yet might still be underestimating the size of the community and its growth since last year. So sign up quickly if you want to be sure to get in.
Building on our experience and the feedback from the last event we are making this event even more awesome: The new location offers several seminar rooms for parallel workshops, activities and discussions in smaller groups. Combined with shorter and more efficient talks this leaves more space for structured social time and activities.
Participants are encouraged to share their knowledge in workshops, tutorials and talks as well as exchange experiences in informal settings. Featured topics include practical rationality, self improvement, world improvement and other rationality related areas.
Giving a workshop or talk is a great way to introduce yourself to other attendants and start a discussion about a topic you care about. If you're unsure if the topic is valuable or a good fit, please err on the side of including it! The more content offers we get, the easier it will be to create a balanced, yet diverse program.
Next to the talks, there is plenty of opportunity to get to know your fellow participants better in structured and less structured social settings. The chosen location provides many opportunities to spend off-time outside, whether you'd rather take a few companions on a morning hike in the nearby forest while discussing AI, receive a tutorial in proper stone-skipping technique at lake Wannsee or if you'd prefer a game of ultimate frisbee on the premises. Which specific activities will be offered during the wee...
A few thoughts on a Friendly AGI (safe vs friendly, other minds problem, ETs and more)
by the-citizen — 2014-10-19T07:59:42.288Z

Friendly AI is an idea that I find to be an admirable goal. While I'm not yet sure an intelligence explosion is likely, or whether FAI is possible, I've found myself often thinking about it, and I'd like for my first post to share a few those thoughts on FAI with you.

Safe AGI vs Friendly AGI
-Let's assume an Intelligence Explosion is possible for now, and that an AGI with the ability to improve itself somehow is enough to achieve it.
-Let's define a safe AGI as an above-human general AI that does not threaten humanity or terran life (eg. FAI, Tool AGI, possibly Oracle AGI)
-Let's define a Friendly AGI as one that *ensures* the continuation of humanity and terran life.
-Let's say an unsafe AGI is all other AGIs.
-Safe AGIs must supress unsafe AGIs in order to be considered Friendly. Here's why:

-If we can build a safe AGI, we probably have the technology to build an unsafe AGI too.
-An unsafe AGI is likely to be built at that point because:

-It's very difficult to conceive of a way that humans alone will be able to permanently stop all humans from developing an unsafe AGI once the steps are known**
-Some people will find the safe AGI's goals unnacceptable
-Some people will rationalise or simply mistake that their AGI design is safe when it is not
-Some people will not care if their AGI design is safe, because they do not care about other people, or because they hold some extreme beliefs

-Most imaginable unsafe AGIs would outcompete safe AGIs, because they would not neccessarily be "hamstrung" by complex goals such as protecting us meatbags from destruction. Tool or Oracle AGIs would obviously not stand a chance due to their restrictions.
-Therefore, If a safe AGI does not prevent unsafe AGIs from coming into existence, humanity will very likely be destroyed.

-The AGI most likely to prevent unsafe AGIs from being created is one that actively predicted their development and terminates that development before or on completion.
-So to summarise

-An AGI is very likely only a Friendly AI if it actively supresses unsafe AGI.
-Oracle and Tool AGIs are not Friendly AIs, they are just safe AIs, because they don't suppress anything.
-Oracle and Tool AGIs are a bad plan for AI if we want to prevent the destruction of humanity, because hostile AGIs will surely follow.

(**On reflection I cannot be certain of this specific point, but I assume it would take a fairly restrict...
Fixing Moral Hazards In Business Science
by DavidLS — 2014-10-18T21:10:50.724Z

I'm a LW reader, two time CFAR alumnus, and rationalist entrepreneur.

Today I want to talk about something insidious: marketing studies.

Until recently I considered studies of this nature merely unfortunate, funny even. However, my recent experiences have caused me to realize the situation is much more serious than this. Product studies are the public's most frequent interaction with science. By tolerating (or worse, expecting) shitty science in commerce, we are undermining the public's perception of science as a whole.

The good news is this appears fixable. I think we can change how startups perform their studies immediately, and use that success to progressively expand.

Product studies have three features that break the assumptions of traditional science: (1) few if any follow up studies will be performed, (2) the scientists are in a position of moral hazard, and (3) the corporation seeking the study is in a position of moral hazard (for example, the filing cabinet bias becomes more of a "filing cabinet exploit" if you have low morals and the budget to perform 20 studies).

I believe we can address points 1 and 2 directly, and overcome point 3 by appealing to greed.

Here's what I'm proposing: we create a webapp that acts as a high quality (though less flexible) alternative to a Contract Research Organization. Since it's a webapp, the cost of doing these less flexible studies will approach the cost of the raw product to be tested. For most web companies, that's $0.

If we spend the time to design the standard protocols well, it's quite plausible any studies done using this webapp will be in the top 1% in terms of scientific rigor.

With the cost low, and the quality high, such a system might become the startup equivalent of citation needed. Once we have a significant number of startups using the system, and as we add support for more experiment types, we will hopefully attract progressively larger corporations.

Is anyone interested in helping? I will personally write the webapp and pay for the security audit if we can reach quorum on the initial protocols.

Companies who have expressed interested in using such a system if we build it:

Beeminder

HabitRPG

MealSquares

Complice (disclosure: the CEO, Malcolm, is a friend of mine)

General Biotics (disclosure: the CEO, David, is me)

(I sent out my inquiries at 10pm yesterday, and every one of these companies got back to me by...
Applications of logical uncertainty
by alex_zag_al — 2014-10-18T19:26:30.516Z

Lately I've been reading a lot about the problem of logical uncertainty. The problem is that there are logical consequences of your beliefs that you're uncertain about.

So, for statements like "the billionth digit of pi is even", it's provable from your beliefs. But you're still uncertain of it. So, the problem is, what's its probability? Well, 1/2, probably, but from what principled theory can you derive that?

That's the question. What probabilities we should assign to logical statements, and what laws of probability apply to them.

With all the time I've been spending on it, I've been wondering, will I ever have the opportunity to use it? My conclusion is, probably not me, personally. But I have come up with a decent list of ways other people might use it.

Combining information from simulation and experiment

This is why I first became interested in the problem--I was working in protein structure prediction.

The problem in protein structure prediction is figuring out how the protein folds. A protein is a big molecule. They're always these long, linear chains of atoms with relatively short side-branches. Interestingly, there are easy experiments to learn what they'd look like all stretched out in a line like this. But in reality, this long chain is folded up in a complex way. So, the problem is: given the linear, stretched out structure, what's the folded structure?

We have two general approaches to solve this problem.

One is a physics calculation. A protein folds because of electromagnetic forces, and we know how those work. So, you can set up a quantum physics simulation and end up with the correct fold. But you won't really end up with anything because your simulation will never finish if you do it precisely. So a lot of work goes into coming up with realistic approximate calculations, and using bigger computers. (Or distributed computing - Rosetta@Home distributes the calculations over the idle time of people's home computers, and you can download it now if you want your computer to help solve protein structures.)

Another is by experiment. We get some kind of reading from the protein structure--X-rays or radio waves, usually--and use that to solve the structure. This can be thought of as Bayesian inference, and, in fact, that's how some people do it--see a book dear to my heart, Bayesian Methods in Structural Bioinformatics. Some of the chapters are about P(obse...
Logical uncertainty reading list
by alex_zag_al — 2014-10-18T19:16:16.146Z

This was originally part of a post I wrote on logical uncertainty, but it turned out to be post-sized itself, so I'm splitting it off.

Daniel Garber's article Old Evidence and Logical Omniscience in Bayesian Confirmation Theory. Wonderful framing of the problem--explains the relevance of logical uncertainty to the Bayesian theory of confirmation of hypotheses by evidence.

Articles on using logical uncertainty for Friendly AI theory: qmaurmann's Meditations on Löb’s theorem and probabilistic logic. Squark's Overcoming the Loebian obstacle using evidence logic. And Paul Christiano, Eliezer Yudkowsky, Paul Herreshoff, and Mihaly Barasz's Definibility of Truth in Probabilistic Logic. So8res's walkthrough of that paper, and qmaurmann's notes. eli_sennesh like just made a post on this: Logics for Mind-Building Should Have Computational Meaning.

Benja's post on using logical uncertainty for updateless decision theory.

cousin_it's Notes on logical priors from the MIRI workshop. Addresses a logical-uncertainty version of Counterfactual Mugging, but in the course of that has, well, notes on logical priors that are more general.

Reasoning with Limited Resources and Assigning Probabilities to Arithmetical Statements, by Haim Gaifman. Shows that you can give up on giving logically equivalent statements equal probabilities without much sacrifice of the elegance of your theory. Also, gives a beautifully written framing of the problem.

manfred's early post, and later sequence. Amazingly readable. The proposal gives up Gaifman's elegance, but actually goes as far as assigning probabilities to mathematical statements and using them, whereas Gaifman never follows through to solve an example afaik. The post or the sequence may be the quickest path to getting your hands dirty and trying this stuff out, though I don't think the proposal will end up being the right answer.

There's some literature on modeling a function as a stochastic process, which gives you probability distributions over its values. The information in these distributions comes from calculations of a few values of the function. One application is in optimizing a difficult-to-evaluate objective function: see Efficient Global Optimization of Expensive Black-Box Functions, by Donald R. Jones, Matthias Schonlau, and William J. Welch. Another is when you're doing simulations that have free parameters, and you want...
Jean Tirole on Adaptive Biases
by feanor1600 — 2014-10-18T19:11:51.093Z

Jean Tirole, who just won the Nobel Prize in Economics, is mostly known for his work applying game theory to Industrial Organization (the subfield of economics that studies how firms compete and set prices). But he wrote very broadly, including on some subjects likely of interest to people here. Several of his "fun" papers linked here provide explanations for how biased beliefs could be beneficial to those who hold them- for instance, that overconfidence in your own abilities could reduce akrasia.
What is optimization power, formally?
by sbenthall — 2014-10-18T18:37:10.161Z

I'm interested in thinking formally about AI risk. I believe that a proper mathematization of the problem is important to making intellectual progress in that area.

I have been trying to understand the rather critical notion of optimization power. I was hoping that I could find a clear definition in Bostrom's Superintelligence. But having looked in the index at all the references to optimization power that it mentions, as far as I can tell he defines it nowhere. The closest he gets is defining it in terms of rate of change and recalcitrance (pp.62-77). This is an empty definition--just tautologically defining it in terms of other equally vague terms.

Looking around, this post by Yudkowksy, "Measuring Optimization Power" doesn't directly formalize optimization power. He does discuss how one would predict or identify if a system were the result of an optimization process in a Bayesian way:

The quantity we're measuring tells us how improbable this event is, in the absence of optimization, relative to some prior measure that describes the unoptimized probabilities. To look at it another way, the quantity is how surprised you would be by the event, conditional on the hypothesis that there were no optimization processes around. This plugs directly into Bayesian updating: it says that highly optimized events are strong evidence for optimization processes that produce them.

This is not, however, a definition that can be used to help identify the pace of AI development, for example. Rather, it is just an expression of how one would infer anything in a Bayesian way, applied to the vague 'optimization process' phenomenon.

Alex Altair has a promising attempt at formalization here but it looks inconclusive. He points out the difficulty of identifying optimization power with just the shift in the probability mass of utility according to some utility function. I may be misunderstanding, but my gloss on this is that defining optimization power purely in terms of differences in probability of utility doesn't say anything substantive about how a process has power. Which is important it is going to be related to some other concept like recalcitrance in a useful way.

Has there been any further progress in this area?

It's notable that this discussion makes zero references to computational complexity, formally or otherwise. That's notable because the informa...