Open thread, September 16-22, 2013

What's a fun job for someone with strong technical skills?

I just graduated with a PhD in pure math (algebraic topology), I've done 50 Project Euler problems, I know Java and Python although I've never coded anything that anyone else uses. I'm looking for work and making a list of nonacademic job titles that involve solving interesting problems, and would appreciate suggestions. So far I'm looking at:

• Data scientist / analytics
• Software engineer

http://sub.garrytan.com/its-not-the-morphine-its-the-size-of-the-cage-rat-park-experiment-upturns-conventional-wisdom-about-addiction is an article about a change in perspective about how rats act when given access to a morphine drip.

Basic concept: When given a larger cage with more space and potential things and other rats to interact with, rats are much less likely to only use a morphine drip, as compared to when they are given a small standard lab cage.

Edit per NancyLebovitz: This is evidence that offers a different perspective on the experiments that I had heard about and it seemed worth sharing. It is not novel though, since apparently it was done in the late 70's and published in 1980. See wikipedia link at: http://en.wikipedia.org/wiki/Rat_Park

Following up on a post I made last month, I've put up A Non-Technical Introduction to AI Risk, collecting the most engaging and accessible very short introductions to the dangers of intelligence explosion I've seen. I've written up a few new paragraphs to better situate the links, and removed meta information that might make it unsuitable for distribution outside LW. Suggestions for further improvements are welcome!

Does the average LW user actually maintain a list of probabilities for their beliefs? Or is Bayesian probabilistic reasoning just some gold standard that no-one here actually does? If the former, what kinds of stuff do you have on your list?

So I found this research a while ago saying, essentially, that willpower is only limited if you believe it is - subjects who believed their willpower was abundant were able to power through tasks without an extra glucose boost.

I was excited because this seemed different from the views I saw on LessWrong, and I thought based on what I'd seen people posting and commenting that this might warrant a big update for some people here. Without searching the site, I posted about it, and then was embarrassed to find out that it had been posted here before a couple of years before...

What puzzles me, though, is that people here still seem to talk about ego depletion as if it's the only model of "willpower" there is. Is it that not everyone has seen that study, or is it that people don't take it seriously compared to the other research? I'm curious.

I recently made a big update in my model of how much influence one can have on one's longevity. I had thought that genetics accounted for the vast majority of variance, but it turns out the real number is something like 20-30%. This necessitates more effort thinking about optimizing lifestyle factors. Does anyone know of a good attempt at a quantified analysis of how lifestyle factors affect lifespan? Most of the resources I find make vague qualitative claims, as such, it's hard to compare between different classes of risks.

Is there much value in doing psychological tests in any particular interval to catch any mental problem in its early stages even if one is not acutely aware of any problem?

Intellectual hygiene.

I am slowly coming to terms with the limits of my knowledge. Tertrium non datur is something that I should not apply outside of formal systems but always think or I could be wrong in a way I do not realize yet. In all my beliefs I should explicitly plant the seed of its destruction: If this event occurs I should stop believing in this or at least seriously doubt this.

A few years ago, in my introductory psych class in college, the instructor was running through possible explanations for consciousness. He got to Roger Penrose's theory of quantum computations in the microtubules being where consciousness came from (replacing another black box with another black box, oh joy). I burst out laughing, loudly, because it was just so absurd that someone would seriously propose that, and that other scientists would even give such an explanation the time of day.

The instructor stopped midsentence, and looked at me. So did 200-odd other students.

I kept laughing.

In hindsight, I think the instructor expected more solemnity.

"Hey Scott," I said. The technician was a familiar face, since I used the booths twice each day.

"Hey David," he replied. "Chicago Six?"

"Yup."

I walked into the booth, a room of sorts resembling an extremely small elevator, and the doors shut behind me. There was a flash of light, and I stepped out of the booth again--only to find that I was still at Scott's station in San Francisco.

"Shucks," said Scott. "The link went down, so the system sent you back here. So just wait a moment... oh shit. Chicago got their copy of you right before the link went down, so now there's one of you in Chicago, too."

"Well, uh... two heads are better than one, I guess?" I said.

"Yeah, here's what we do in this situation," said Scott, ignoring me. "We don't want two copies of you running around, so generally we just destroy the unwanted copy."

"Yeah... I guess that sounds like the way to go," I said.

"So yeah, just get back in the booth and we'll destroy this copy of you."

I stepped back into the booth again, and the doors closed. There was a fla--

Meanwhile, I was still walking to my office in Chicago, unaware that anything unusual had happened.

So... it turns out some people actually do believe that there are fundamentally mental quantities not reducible to physics, and that these quantities explain the behaviour of living things. I confess I'm a bit surprised. I had the impression that everyone these days agreed that physics actually does describe the motion of all the atoms, including those in living brains. But no, believers in the ghost in the machine walk among us, and claim that the motions of living things cannot be predicted even in principle using physics. Something to bear in mind when discussing simulations; obviously such a man will never be convinced that the upload is the person no matter how close the simulation, even unto individual atoms.

I thought this was interesting: perhaps the first use I've read of odds in a psychology paper. From Sprenger et al 2013:

8.1. A Bayesian analysis of WM training effectiveness

To our knowledge, our study is the first to include a Bayesian analysis of working memory training, which we view as particularly well suited for evaluating its effectiveness. For example, we suspect that at least some of the existing studies reporting positive transfer of WM training will fail the Bayesian “sniff test.” Indeed, even for studies that have faithfully observed statistically significant effects of training it is instructive to evaluate these findings in light of one's subjective prior probabilities. For illustrative purposes, suppose a pessimist adopts prior odds of 10:1 against the effectiveness of WM training, citing the plethora of historical evidence that cognitive abilities are stable. In contrast, suppose an optimist adopts a prior odds of 1:10 in favor of the effectiveness of WM training. How might these two individuals change their beliefs in light of the available evidence?

Chein and Morrison (2010, Table 2) report significant one-tailed t-tests on the gain scores for both Stroop (t(40) = 1.80) and reading comprehension (t(38) = 1.80). The corresponding BFs = 1.06 and BF = 1.067, respectively, using the JZS prior. These BFs are interpreted as providing equivalent support for the null and the alternative—that is, the BF indicates that the data are equally supportive of both the alternative and null hypotheses. The t-tests for fluid IQ (t(40) = 0.24) and reasoning (t(40) = 1.39) were both non-significant, and have corresponding BFs of 4.37 and 1.92 in favor of the null hypothesis. The average BF across all four tasks is 2.10 in favor of the null. Turning to the experiments reported above, across all measures of fluid abilities in Experiment 1, the average BF at post-test is 2.59 in favor of the null, and this includes operation span and symmetry span which arguably reflects stimulus specific training effects. Similarly, the average BF of the untrained assessment tasks in Experiment 2 across all three training groups is 4.18, again in favor of the null. Multiplying these BFs with the priors gives us the posterior odds ratios. For the pessimist, the posterior odds against the effectiveness of WM is over 227:1 (10 ∗ 2.10 ∗ 2.59 ∗ 4.18). This corresponds to a posterior probability p(null is true|data) = 227 / 228 = 0.996. But, even for the optimist, the posterior odds favors the null at a ratio of 2.27:1 (0.1 ∗ 2.10 ∗ 2.59 ∗ 4.18 = 2.27), with a posterior probability p(null is true|data) = 2.27 / 3.27 = 0.694. In other words, based on the result of Chein and Morrison (2010) and the experiments reported herein, even the optimist should express some skepticism in the hypothesis that WM-training is effective.3

Can blackmail kinds of information be compared to things like NashX or Mutually Assured Destruction usefully?

Most of my friends have information on me which I wouldn't want to get out, and vice versa. This means we can do favours for each other that pay off asynchronously, or trust each other with other things that seem less valuable than that information . Building a friendship seems to be based on gradually getting this information on each other, without either of us having significantly more on one than the other.

I don't think this is particularly original, but it seems a pretty elegant idea and might have some clues for blackmail resolution.

If you want to do something, at least one of the following must be true:

1. The task is simple.
2. Someone else has taught you how to do it.
3. You have a lot of experience performing similar tasks.
4. As you're trying to perform the task, you receive lots of feedback about how you're doing.
5. You've performed an extremely thorough analysis of the task which accounts for all possibilities.

If a task is complicated (1 is false), then it consists of many sub-tasks, all of which are possible points of failure. In order to succeed at every sub-task, either you must be able to correct failures after they show up (4 is true), or you must be able to avoid all failures before encountering any of them. In order to avoid all failures before encountering any of them, you must already know how to perform the task, and the only ways to obtain this knowledge are through experience (3), through being taught (2), and through analysis (5).

Except I'm not sure there aren't other ways to obtain the relevant knowledge. If you want to build a house, one option is to try building lots of houses until finally you're experienced enough that you can build good houses. Another option is to have someone else who already knows how to build a house teach you. Another is to think carefully about how to build a house, coming up with an exhaustive list of every way you could possibly fail to build a house, and invent a technique that you're sure will avoid all of those failure modes. Are there any other ways to learn to build a house, besides experience, being taught, and analysis? Pretty sure there isn't.

Is there much known about how to recall information you've memorised at the right time / in the right context? I can memorise pieces of knowledge just fine with Anki, and if someone asks me a question about that piece of information I can tell them the answer no problem. However, recalling in the right situation that a piece of information exists and using it -- that I'm finding much more of a challenge. I've been trying to find information on instilling information in such a way as to recall it in the right context for the last few days, but none of the avenues of inquiry I've searched have yielded anything on the level I'm wanting. Most articles I've found are talking about specific good habits, or memory, rather than their mechanisms and how to engage them.

I have sorted 50 US states on such a way, that their Levenshtein string difference is minimal:

Massachusetts, Mississippi, Missouri, Wisconsin, Washington, Michigan, Maryland, Pennsylvania, Rhode Island, Louisiana, Indiana, Montana, Kentucky, Connecticut, Minnesota, Tennessee, New Jersey, New Mexico, New Hampshire, New York, Delaware, Hawaii, Iowa, Utah, Idaho, Ohio, Maine, Wyoming, Vermont, Oregon, Arizona, Arkansas, Kansas, Texas, Nevada, Nebraska, Alaska, Alabama, Oklahoma, Illinois, California, Colorado, Florida, Georgia, Virginia, West Virginia, South Carolina, North Carolina, North Dakota, South Dakota

http://protokol2020.wordpress.com/2013/09/13/order-by-string-proximity/

I don't know, was it done before?

I hope people do not mind creating me these. I live in a timezone earlier than American ones and I do periodical thread on another forum anyway so I am in the zone.

Are there resources for someone who is considering running a free local rationality workshop? If not does anyone have any good ideas for things that could be done in a weekly hour-long workshop? I was surprised that there weren't any free resources from CFAR for exactly this.

Are people more productive using laptops or desktops?

There's a Culture fanfic thread on this month's Media Thread. I compiled a list of what little there is.

I am interested in how, in the early stages of developing an AI, we might map our perception of the human world (language) to the AI’s view of the world (likely pure maths). There have been previous discussions such as AI ontology crises: an informal typology, but it has been said to be dangerous to attempt to map the entire world down to values.

If we use an Upper Ontology and expand it slightly (as not to get too restrictive or potentially conflicting) for Friendly AI’s concepts, this would assist in giving a human view of the current state of the AI’s perception of the world.

Are there any existing ontologies on machine intelligence, and is this something worth exploring now to test on paper?

If anyone got that microeconomics vs macroeconomics comic strip, feel free to explain... Possible related: inefficient hot dogs.

I am still confused about aspects of the torture vs specks problem. I'll grant for this comment that I would be willing to choose torture for 1 person for 50 years to avoid a dust speck in the eye of 3^^^3 people. Numerically I'll just assign -3^^^3 utilons to specks and -10^12 utilons to torture. Where confusion sets in is if I consider the possibility of a third form of disutility between the two extremes, for example paper cuts.

Suppose that 1 paper cut is -100 utilons and 50 years of torture is -10^12 utilons so the expected utility in either case is the same*. However, my personal preference would be to choose 10^10 papercuts over 50 years of torture. Similarly, if a broken bone is worth -10^4 utilons I would rather that the same 10^10 people got a papercut instead of only 10^8 people having a broken bone. The best case would be if I could avoid 3^^^3 specks in exchange for somewhat fewer than 3^^^3 just-barely-more-irritating specks, instead of torturing, breaking, or cutting anyone.

Therefore, maximizing average or total expected utility doesn't seem to capture all my preferences. I think I can best describe it as choosing the maximum of the minimum individual utilities while still maximizing total or average utility. As such I am inclined to choose specks over torture, probably as a result of trying to find a more palatable tradeoff with broken bones or papercuts or slight-more-annoying specks. In real life there are usually compromises, unlike in hypotheticals. Still, I wonder if it might be acceptable (or even moral) to accept only 99% of the maximum possible utility if it allows significant maximin-ing of some otherwise very negative individual utilities.

*assume a universal population of 4^^^4 individuals and the roles are randomly selected so that utility isn't affected by changing the total number of individuals.