trentmkelly/LessWrong-43k huggingface dataset:
A brief tutorial on preferences in AI
by lukeprog — 2012-02-21T05:29:03.556Z

Preferences are important both for rationality and for Friendly AI, so preferences are a major topic of discussion on Less Wrong. We've discussed preferences in the context of economics and decision theory, but I think AI has a more robust set of tools for working with preferences than either economics or decision theory has, so I'd like to introduce Less Wrong to some of these tools. In particular, I think AI's toolset for working with preferences may help us think more clearly about CEV.

In AI, we can think of working with preferences in four steps:

Preference acquisition: In this step, we aim to extract preferences from a user. This can occur either by preference learning or by preference elicitation. Preference learning occurs when preferences are acquired from data about the user's past behavior or past preferences. Preference elicitation occurs as a result of an interactive process with the user, e.g. a question-answer process.

Preferences modeling: Our next step is to mathematically express these acquired preferences as preferences between pairwise choices. The properties of a preferences model are important. For example, is the relation transitive? (If the model tells us that choice c1 is preferred to c2, and c2 is preferred to c3, can we conclude that c1 is preferred to c3?) And is the relation complete? (Is any choice comparable to any other choice, or are there some incomparabilities?)

Preference representation: Assuming we want to capture and manipulate the user's preferences robustly, we'll next want to represent the preferences model in a preference representation language.

Preferences reasoning: Once a user's preferences are represented in a preference representation language, we can do cool things like preferences aggregation (involving the preferences of multiple agents) and preference revision (a user's new preferences being added to her old preferences). We can also perform the usual computations of decision theory, game theory, and more.

Preference acquisition

Preference learning is typically an application of supervised machine learning (classification). Throw the algorithm at a database containing a user's preferences, and it will learn that user's preferences and make predictions about the preferences not listed in the database, including preferences about pairwise choices the user may never have faced befor...
[SEQ RERUN] Wrong Questions
by MinibearRex — 2012-02-21T05:02:20.747Z

Today's post, Wrong Questions was originally published on 08 March 2008. A summary (taken from the LW wiki):

Where the mind cuts against reality's grain, it generates wrong questions - questions that cannot possibly be answered on their own terms, but only dissolved by understanding the cognitive algorithm that generates the perception of a question.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Dissolving the Question, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Ambiguity in cognitive bias names; a refresher
by nerfhammer — 2012-02-21T04:37:24.759Z

This came on the nyc list, I thought I would adapt it here.

Cognitive biases have names. That's what makes them memetic. It's easier to think about something that has a name. Though I think the benefits outweigh the costs, there is also the risk of a little Albert: a concept living on after the original research has been found to be much more ambiguous than first realized.

There are many errors that are possible with respect to named ideas, and despite being studied generally scientifically, cognitive biases are no exception. There is no equivalent to cognitive biases as the Académie Française is to French.

Let's describe some. Here they are:

different people in different fields will "discover" virtually the same bias but not be aware of each other and assign it different names. For example, see the Curse of Knowledge which I think George Loewenstein came up with vs. the Historian's Fallacy by David Hackett Fischer, presentist bias, creeping determinism, and probably many others, not all of them scientific. Sometimes researchers in seemingly closely related subfields are remarkably insular to each other.

researchers will use one term predominantly while an offshoot will decide they don't like the name and use a different one. For example the Fundamental Attribution Error has also been called the overattribution effect, the correspondence bias, the attribution bias, and the actor-observer effect. In this case the older term still predominates, and is used in intro textbooks without asterisks. Of the naming errors this is one of the least harmful, since everyone agrees what the FAE is, some just prefer a different name for it.

an author will decide he doesn't like the names of some biases will invent idiosyncratic names of his own. Jonathan Baron has a good textbook on cognitive bias but he uses names of his own invention half the time.

the same term will sometimes have different polysemous meanings. For example the "Zeigarnik Effect" has been used to refer to a memory bias in having a superior recall for unfinished tasks, and the term has also been used to refer to an attentional bias in which unfinished tasks tend intrude on consciousness; almost, but not quite exactly, the same thing. The term "confirmation bias" has several different but related meanings, for example, to seek out confirming information, to notice confirmin

...
An interesting, novel approach to designing computer processors
by D_Alex — 2012-02-21T02:37:12.193Z

Here is presentation from a researcher at MIT on a novel way of designing computer processors. It relies on performing approximate, rather than exact, mathematical operations (like the meat-based processor in our heads!). Claimed benefits are a 10,000-fold improvement in speed, while the errors introduced by the approximations are postulated to be insignificant in many applications.

http://web.media.mit.edu/~bates/Summary_files/BatesTalk.pdf

Slide #2 of the presentation offers a fascinating insight: We currently work around the limitations of the processing substrate to implement a precise computation, and it is becoming increasingly difficult:

------------------

THE MOTIVATING PROBLEM:

Computations speciﬁed by programmers are implemented as behavior in physical material

• Hardware designer’s job:

efﬁciently implement Math (what sw wants) using Physics (what silicon offers)

(near) perfect arith noisy, approximate

uniform mem delay delay ~ distance

• Increasingly difﬁcult as decades passed and transistor counts exploded

• Now each instruction (increment, load register, occasionally multiply) invokes >10M transistor operations, even though a single transistor can perform, for instance, an approximate exponentiate or logarithm

--------------------

The parallels and contrasts with our own brain are what interested me the most. Perhaps one day the most powerful computers will be running on "corrupted hardware" of sorts.
Evaluating Multiple Metrics (where not all are required)
by imbatman — 2012-02-20T19:38:05.684Z

This is my first article, and I'm submitting it in the discussion forum, so hopefully I've done this correctly and we can discuss!

Anyway, I have a group of friends who are really interested in movies, and they feel very strongly about them. I find their convictions interesting. Specifically the way they adamantly argue that, for instance, Midnight in Paris is a "better" movie than Bridesmaids or whatever. I got to thinking about how one would create metrics by which you could evaluate any movie.

First attempt: A simple scale by which you give rankings (1-10) to a list of movie attributes (the metrics), sum up the total, and highest number is the best movie.

Some metrics might be:

Plot/Story

Acting

Effects, Costumes, Editing, etc.

Script/Dialogue

Humor

Drama/Passion

Suspense

So we can argue about what the metrics should be and how many we need, but since we're not worried about justifying our system objectively we can include whatever criteria we want. We could even add a weighting component so some metrics are worth more than others. My system can even be different than yours. The problem, though, is that in reality movies don't need to excel at all metrics to be perfect for what they are. Would Schindler's List be a better movie if they were cracking jokes the whole time? Would 12 Angry Men be better if it had more special effects? And it's a little weird to evaluate the acting in Up or Toy Story 3. (No offense to voice actors.)

The idea of ranking movies is really about the challenge of comparing things that are the same class (movies) but very different types (comedy, horror, drama, etc) -- in content, goal, method, etc. Is it possible to come up with metrics by which to compare anything in the class regardless of type? Assuming you can come up with which metrics you find valuable/relevant, some of them will apply to one type but not another. But you also can't completely disregard metrics that are not common between all types, because you've just said you find them valuable/relevant (in this case, to your enjoyment of a movie).

These thoughts led me to the question which I will pose here: How do you evaluate items in a class based on multiple metrics when not all metrics are ALWAYS relevant?

Some brainstorming to try to answer that question (modifying the system proposed above):

Al...
Model Thinking class [link]
by Dr_Manhattan — 2012-02-20T18:27:51.909Z

As part of the expansion of Stanford's effort to open up some of their courseware Scott Page is giving Model Thinking course, which I suspect a lot of people here might like. There is quite a bit of Schelling-type modeling applied to a wide range of problems. The course is live, but it's not too late to sign up - http://www.modelthinker-class.org.
[SEQ RERUN] Dissolving the Question
by MinibearRex — 2012-02-20T04:58:42.584Z

Today's post, Dissolving the Question was originally published on 08 March 2008. A summary (taken from the LW wiki):

Proving that you are confused may not make you feel any less confused. Proving that a question is meaningless may not help you any more than answering it. Philosophy may lead you to reject the concept, but rejecting a concept is not the same as understanding the cognitive algorithms behind it. Ask yourself, as a question of cognitive science, why do humans make that mistake?

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Variable Question Fallacies, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
TEDxLive Opportunities
by daenerys — 2012-02-20T04:31:10.652Z

TED is live broadcasting one day of the TED conference to sites around the world. Entrance to the viewings should be free. I thought many LWers would be interested in this opportunity. It would also make a great meetup activity.

Here is the website with the info: http://www.ted.com/pages/tedxlive

>The idea for TEDxLive grew out of a question we at TED were asking ourselves: What would happen if we made the TED Conference more open, its impact more immediate and tangibly global? What would happen if communities around the world gathered to engage with -- and build on -- the TED Conference experience while it happened live?

You can find a local viewing by clicking on the "Find an Event" tab at the top.

If you think your city is too small to be hosting this, check anyway! Here in Columbus we have two viewings, one hosted by TEDxColumbus, and another hosted by TEDxOhioStateUniversity. They are at slightly different times-- Due to the time difference, they both decided to show the broadcast the next day, rather than exactly live.

The Downside- The broadcasts are all either on Wednesday or Thursday, and in about a week.

Awesome Idea- A chat room or Skype conversation of LWers from around the world watching the broadcast simultaneously (depending on whether other places are going with the "not quite live" idea or not).

I won't be able to go to most of this, due to work, but am interested in knowing if other people are planning to participate. (And if so, how was the experience?)
Longevity Insurance
by canadaduane — 2012-02-20T00:30:18.441Z

Let's say we (as a country) ban life insurance and health insurance as separate packages [1] and require them to be combined in something I'll call "Longevity Insurance". The idea is that as a person/consumer, you can buy a "life expectancy" of 75 years, or 90 years, or whatever. In addition, you specify a maximum dollar amount that the longevity insurance will ever pay out--say, $2 million. If you have any medical issues throughout your life, up to the life expectancy threshold, the insurance plan will pay for your expenses. If it fails to keep you consciously alive for the duration of your "life expectancy", then upon your death, the policy guarantees that the company will pay the full remaining amount to your next of kin.

As an example, suppose you (let's say you're a woman) had purchased a 75-year policy, but you had a car accident. The paramedics tried to save you, and the hospital bill came to $100k, but even after that noble effort, you still died. As a result, your husband and children get $1.9M. Alternatively, if in our hypothetical situation they succeed in resuscitating you, the company would keep the $1.9M for future medical bills, and, if they fulfill their promise of life expectancy, they pocket the remainder as profit on your 75th birthday.

It seems like this arrangement would put all of the right incentives [2] in place for both companies and individuals. Most individuals would want to avoid trivial medical expenses in order to maximize payout to family in case of accidental death. Companies would want to maximize health and longevity in order to profit from the end-of-life payout. And our society would have a way to rationally consider the value of life without resorting to arguments that essentially conclude "life is of infinite value," and in doing so, prevent sensible gerontological triage. To put it into perspective, it makes little sense that we spend $1M (as a society) trying to save a 92-year-old when that same amount could have saved 10 teenagers.

Longevity Insurance companies would be incentivized to become heavily involved in medical research that prevents disease, prolongs life, and keeps people healthy. I can imagine a whole array of things that make sense in this context. For example, it would be the right place to fund studies on genetics, it could be the right vehicle for getting 'free' immunizations, and it could ...
MITx class(es): [LINK] and discussion
by  — 2012-02-19T11:29:49.783Z

MIT is offering online classes under the name MITx, the first of which is MITx 6.002: Circuits and Electronics. I think the idea is similar in philosophy to that of the Stanford AI class, which I saw interested LessWrong-ers, though the subject matter (intro EE class) may appeal less to the LessWrong programming/AI/logic demographic. The class itself runs from March 5 - June 8 of this year.

Link: MITx 6.002

I've registered for the class already; I've always felt as though circuit theory was a weak point for me compared to EE friends, as physics classes gave me the bare bones by comparison. Feel free to bug me with questions like, "So how much progress have you made recently with the MITx class?" (I'll take any free sources of motivation that I can get.) Also, if any LessWrong-ers are interested, we could start a study group when the class actually begins to keep ourselves motivated and on top of the material.
Quantified Health Prize results announced
by Zvi — 2012-02-19T08:10:08.454Z

Follow-up to: Announcing the Quantified Health Prize

I am happy to announce that Scott Alexander, better known on Less Wrong as Yvain, has won the first Quantified Health Prize, and Kevin Fischer has been awarded second place. There were exactly five entries, so the remaining prizes will go to Steven Kaas, Kevin Keith and Michael Buck Shlegeris.

The full announcement can be found here until the second contest is announced, and is reproduced below the fold.

While we had hoped to receive more than five entries, I feel strongly that we still got our money’s worth and more. Scott Alexander and Kevin Fischer in particular put in a lot of work, and provided largely distinct sets of insight into the question. In general, it is clear that much time was usefully spent, and all five entries had something unique to contribute to the problem.

We consider the first contest a success, and intend to announce a second contest in the next few weeks that will feature multiple questions and a much larger prize pool.

Discussion of all five entries follows:Place ($500):

5th Place ($500): Full report

Steven Kaas makes a well-reasoned argument for selenium supplementation. That obviously wasn't a complete entry. It's very possible this was a strategic decision in the hopes there would be less than five other entries, and if so it was a smart gamble that paid off. I sympathize with his statements on the difficulty of making good decisions in this space.

4th Place ($500):

4th Place ($500): Full report

Kevin Keeth’s Recommendation List is as follows: “No quantified recommendations were developed. See ‘Report Body’ for excruciating confession of abject failure.” A failure that is admitted to and analyzed with such honesty is valuable, and I’m glad that Kevin submitted an entry rather than giving up, even though he considered his entry invalid and failure is still failure. Many of the concerns raised in his explanation are no doubt valid concerns. I do think it is worth noting that a Bayesian approach is not at a loss when the data is threadbare, and the probabilistic consequences of actions are highly uncertain. Indeed, this is where a Bayesian approach is most vital, as other methods are forced to throw up their hands. Despite the protests, Kevin does provide strong cases against supplementation of a number of...
[SEQ RERUN] Variable Question Fallacies
by MinibearRex — 2012-02-19T05:57:14.776Z

Today's post, Variable Question Fallacies was originally published on 05 March 2008. A summary (taken from the LW wiki):

You use a word that has different meanings in different places as though it meant the same thing on each occasion, possibly creating the illusion of something protean and shifting. "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable grabbed from the surrounding context. Whose "left" is meant, Bob's or Martin's?

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Words as Mental Paintbrush Handles, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Brain shrinkage in humans over past ~20 000 years - what did we lose?
by Dmytry — 2012-02-18T22:17:41.406Z

The human brain volume has been shrinking over the past 20 000 years or so, after millions years of increase in volume. Not just the brain size, but the brain size relatively to body size as well. We are lacking a tennis-ball sized piece of our earlier brain (and it might even be God-shaped).

Brain is expensive in many ways: energy consumption, birth complications and locomotion impairment for females, lower survival of head impacts i'd guess. The damn thing along with supporting structures is heavy and awkwardly located, etc.

And the big brain can only be advantageous if it improves reproduction substantially, with larger brained individuals being sufficiently more successful at surviving and reproducing than smaller brained individuals, as to negate the above-mentioned cost.

That must have been the case through the evolution up to a couple tens thousands years ago, to produce the big brains that we have. It is clear to see that in past 20 000 years, the environment in which humans live has undergone very significant change due to emergence of societies; the new environment may not be pushing us as hard [in the direction of intelligence], at least on the individual level. [and may have been pushing us too hard for smaller brains, thanks Nornagest for making that point]

We were evolving ability to think, until it got just about to the point of being barely able - with great difficulty and many falls - to think useful thoughts. If we were species that were evolving flight, we'd be the species that could just barely fly, and recently flew over a river, entering new land. In the new land, everything is different. And our wings were shrinking at very rapid rate.

The important question is - Did we lose any functionality since then? Are we dumber? Are we less sane in some way? (The palaeolithic humans did not seem to do any really insane religious stuff)

The notion that our brains just got more efficient and 'therefore' could shrink in size appears very shaky to me. This 'therefore' comes from fallacy of anthropomorphizing the evolution. Evolution doesn't work to a goal of optimizing some sub-unit in the organism while preserving specifications, in the way that a team on an engineering project would.

The optimization could as well instead make brain even larger, if said improvement made larger brain pay off more. One would have to show that some improvement in brain effi...
RAND Health Insurance Experiment critiques
by Dustin — 2012-02-18T17:52:59.330Z

I have neither the qualifications nor the access to properly understand these two paywalled critiques of the RAND Health Insurance Experiment.

Health Plan Switching and Attrition Bias in the RAND Health Insurance Experiment

The Rand Health Insurance Study: A Summary Critique

Has there been any talk about either of these on OB/LW? If not, why not and could anyone with access to the papers make any comments about how much weight they carry?

I post this here because the RAND results are brought up so often in discussions here, I hope others find it to be an appropriate venue.