Harley-ml/lesswrong huggingface dataset:
# [Link] Persistence of Long-Term Memory in Vitrified and Revived C. elegans worms

http://online.liebertpub.com/doi/pdf/10.1089/rej.2014.1636

This is a paper published in 2014 by Natasha Vita-More and Daniel Barranco, both associated with the Alcor Research Center (ARC).

The abstract:

> Can memory be retained after cryopreservation? Our research has attempted to answer this long-standing question by using the nematode worm _Caenorhabditis elegans_ (_C. elegans_), a well-known model organism for biological research that has generated revolutionary findings but has not been tested for memory retention after cryopreservation. Our study’s goal was to test _C. elegans_’ memory recall after vitrification and reviving. Using a method of sensory imprinting in the young _C. elegans_ we establish that learning acquired through olfactory cues shapes the animal’s behavior and the learning is retained at the adult stage after vitrification. Our research method included olfactory imprinting with the chemical benzaldehyde (C₆H₅CHO) for phase-sense olfactory imprinting at the L1 stage, the fast cooling SafeSpeed method for vitrification at the L2 stage, reviving, and a chemotaxis assay for testing memory retention of learning at the adult stage. Our results in testing memory retention after cryopreservation show that the mechanisms that regulate the odorant imprinting (a form of long-term memory) in _C. elegans_ have not been modified by the process of vitrification or by slow freezing.
# Update on the Brain Preservation Foundation Prize

[Brain Preservation Foundation](http://www.brainpreservation.org/) President Kenneth Hayworth just [wrote a synopsis](http://blog.brainpreservation.org/2015/05/26/may-2015-bpf-prize-update/) of the recent ongoings from the major two competitors for the BPF prizes. Here is the summary: 

Brain Preservation Prize competitor Shawn Mikula just published his whole mouse brain electron microscopy protocol in Nature Methods ([paper](http://www.nature.com/nmeth/journal/v9/n12/full/nmeth.2213.html), [BPF interview](http://blog.brainpreservation.org/2015/04/27/shawn-mikula-on-brain-preservation-protocols/)), putting him close to winning the mouse phase of our prize.

Brain Preservation Prize competitor 21st Century Medicine has developed a new “Aldehyde-Stabilized Cryopreservation” technique–preliminary results show good ultrastructure preservation even after storage of a whole rabbit brain at -135 degrees C.

This work was funded in part from donations from LW users. In particular, a grant to support the work of [LW user Robert McIntyre](/user/aurellem/overview/) at 21st Century Medicine that the BPF was able to provide has been instrumental. 

In order to continue this type of research and to bolster it, BPF welcomes your support in a variety of different ways, including awareness-raising, [donations](http://www.brainpreservation.org/content/donate), and volunteering. Please [reach out if you would like to volunteer](http://www.brainpreservation.org/content/volunteer), or you can PM me and I will help put you in touch. And if you have any suggestions for the BPF, please feel free to discuss them in the comments below.
# [Link] Small-game fallacies: a Problem for Prediction Markets

[Nick Szabo writes](http://unenumerated.blogspot.com/2015/05/small-game-fallacies.html) about the dangers of taking assumptions that are valid in small, self-contained games and applying them to larger, real-world "games," a practice he calls a _small-game fallacy_.

> Interactions between small games and large games infect most works of game theory, and much of microeconomics, often rendering such analyses useless or worse than useless as a guide for how the "players" will behave in real circumstances. These fallacies tend to be particularly egregious when "economic imperialists" try to apply the techniques of economics to domains beyond the traditional efficient-markets domain of economics, attempting to bring economic theory to bear to describe law, politics, security protocols, or a wide variety of other institutions that behave very differently from efficient markets. However as we shall see, small-game fallacies can sometimes arise even in the analysis of some very market-like institutions, such as "prediction markets."

This last point, which he expands on later in the post, will be of particular interest to some readers of LW. The idea is that while a prediction market does incentivize feeding accurate information into the system, the existence of the market also gives rise to parallel external incentives. As Szabo glibly puts it,

> A sufficiently large market predicting an individual's death is also, necessarily, an assassination market...

Futarchy, it seems, will have some kinks to work out.
# Request for Advice : A.I. - can I make myself useful?

I'm a PhD student wrapping up a doctorate in Genomics. I started in biology and switched to analysis because I have stupid hands. My opinion of my field is low. Working in it has, on on the bright side, taught me some statistics and programming.  I'm roughly upper 5% on math ability, relative to my college class. Once upon a time I could solve ODEs, now most of my math is gone. However, I'm good with R, and can talk intelligently about mixed linear model, bayesianism vs. frequentism and about genetics, biochemistry and developmental biology. It's also taught me that huge segments of the biology literature are a mixture of non reproducible crap, and uninteresting, street-light science, dressed up as progress with deceptive plots and statistics. I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence are going to make human-run biology irrelevant before long. I think the ultimate problems we're tackling (predicting genotype from phenotype, reliable manipulation of biology, curing cancer/aging/death) are insoluble with our current methods - we need effective robots to do the experiments, and A.I. to interpret the results.

  
Here's what I want to ask the lesswrong hivemind:

1)Do you agree? Do you think there are important problems being tackled now in biology that someone with my skillset could be useful in? E.g. analyzing the brain with genetics to try and get a handle on how it's algorithms work? (I'm skeptical of this bottom up approach to the brain myself)

2)Do you think there are areas closer to the AI problem (or say, cryonics...) I could be usefully working on?

Sorry for bothering you with my personal problems, but I recall a thread a while ago inviting this sort of thing, so I thought I'd give it a try. I'm leaning towards the default option right now, which is to do some more courses, so I can say, bluff my way through Hadoop and Java, and then see how much cash I can earn in a boring private sector job. However, I'd prefer to do something I find intrinsically interesting.

Edit: Thanks guys - this has already been helpful.
# Learning takes a long time

I recently realized that I had greatly underestimated the inferential distance between most of my readers and myself. Thinking it over, I realize that the bulk of the difference comes from a difference in perspectives on **how long it takes to learn substantive things**.

People often tell me that they're bad at math. I sometimes respond by saying that they didn't spend enough time on it to know one way or the other. I averaged ~25+ hours a week thinking about math when I was 16 and 17, for a total of ~2,500+ hours. I needed to immerse myself in the math to become very good at it, in the same way that I would need to live in French speaking country to get very good at French. If my mathematical activity had been restricted exclusively to coursework, I never would have become a good mathematician.

Math grad students who want to learn algebraic geometry often spend spend **two years** going through Hartshorne's dense and obscure textbook. it's not uncommon for students to learn interesting applications only **after** having gone through it. I find this practice grotesque, and I don't endorse it. I bring it up only to explain where I'm coming from. With the Hartshorne ritual as a standard practice, it's felt to me like a very solid achievement to present substantive material that readers can understand after only ~10 hours of reading and reflecting deeply.

It was so salient to me that _one can't hope to become intellectually sophisticated without engaging in such activity on a regular basis_ that it didn't occur to me that it might not be obvious everyone. I missed the fact that most of my readers aren't in the habit of spending ~10 hours carefully reading a dense article and grappling with the ideas therein_, _so that even though I **felt** like I was making things accessible, I was still in the wrong ballpark altogether.

Thinking it over, I'm bemused by the irony of the situation. Even as I was exasperated by some readers' apparent disinclination to read articles very carefully and think about them deeply, I was blind to the fact that **I was failing because I hadn't put thousands of hours into learning how to communicate to a general audience**. Seeing how large my blindspot was made me realize "Oh... just as I had no ideahow much time I need to put into developing my communication abilities to reach my readers_,_ some of my readers who appeared to me to be trolling probably just had no way of knowing of how much time it takes to learn really deep things."

The tens of thousands of hours that I put into developing intellectually didn't feel like a slog – it was fascinating. It was the same for all of the deepest thinkers who I know. If you haven't had this experience, and you're open to it, you're in for a wonderful treat.
# Realistic epistemic expectations

When I state a position and offer evidence for it, people sometimes complain that the evidence that I've given doesn't suffice to establish my position. The situation is usually that _I'm **not** **trying** to give a rigorous argument for my position, and I don't intend to claim that the evidence that I provide suffices to establish my position_.

My goal in these cases is to offer a high-level summary of my thinking, and to provide enough evidence so that readers have reason to [Bayesian update](/lw/2b0/bayes_theorem_illustrated_my_way/) and to find the view sufficiently intriguing to investigate further.

In general, when a position is non-obvious, a single conversation is [nowhere near enough time](/lw/m9z/learning_takes_a_long_time/) to convince a rational person that it's very likely to be true. As Burgundy recently [wrote](/lw/m93/rationality_is_about_pattern_recognition_not/cer7):

_When you ask Carl Shulman a question on AI, and he starts giving you facts instead of a straight answer, he is revealing part of his book. The thing you are hearing from Carl Shulman is really only the tip of the iceberg because he cannot talk fast enough. His real answer to your question involves the totality of his knowledge of AI, or perhaps the totality of the contents of his brain._

If I were to restrict myself to making claims that I could substantiate in a mere ~2 hours, that would preclude the possibility of me sharing the vast majority of what I know.

In math, one can give rigorous proofs starting from very simple axioms, as Gauss described:

_I mean the word proof not in the sense of lawyers, who set two half proofs equal to a whole one, but in the sense of mathematicians, where 1/2 proof = 0, and it is demanded for proof that every doubt becomes impossible'._

Even _within_ math, _as a practical matter_, proofs that appear to be right are sometimes undercut by subtle errors. But outside of math – the only reliable tool that one has at one's disposal is [Bayesian inference](http://en.wikipedia.org/wiki/Bayesian_inference).  In 2009, charity evaluator GiveWell made very strong efforts to apply careful reasoning to identify its top rated charity, and gave a "conservative" cost-effectiveness estimate of $545/life saved, which turned out to have been _[wildly optimistic](/lw/hif/robustness_of_costeffectiveness_estimates_and/). _Argumentation that looks solid on the surface often breaks down on close scrutiny. This is closely related to why GiveWell [emphasizes](http://blog.givewell.org/2011/11/10/maximizing-cost-effectiveness-via-critical-inquiry/) the need to look at giving opportunities from many angles, and gives more weight to robustness of evidence than to careful chains of argumentation.

Eliezer named this website **Less** Wrong for a reason – one can never be _certain_ of anything – all rational beliefs reflect degrees of confidence. I believe that discussion advances rationality the most when it involves sharing perspectives and evidence, rather than argumentation.
# Roadmap: Plan of Action to Prevent Human Extinction Risks

Let’s do an experiment in "reverse crowdfunding”. I will pay 50 USD to anyone who can suggest a new way of X-risk prevention that is not already mentioned in this roadmap. Post your ideas as a comment to this post.

Should more than one person have the same idea, the award will be made to the person who posted it first.

The idea must be endorsed by me and included in the roadmap in order to qualify, and it must be new, rational and consistent with modern scientific data.

I may include you as a co-author in the roadmap (if you agree).

The roadmap is distributed under an open license GNU.

Payment will be made by PayPal. The total amount of the prize fund is 500 USD (total 10 prizes).

The competition is open until the end of 2015.

The roadmap can be downloaded as a pdf from:

UPDATE: I uploaded new version of the map with changes marked in blue.

[http://immortality-roadmap.com/globriskeng.pdf](http://immortality-roadmap.com/globriskeng.pdf)

Email: alexei.turchin@gmail.com
# Measuring open-mindedness

Recently we have opened an [experimental website](http://www.omnilibrium.com/) for [Rational Discussion of Politics](/lw/m3s/rational_discussion_of_politics/ "Rational Discussion of Politics"). A special feature of the new website is an automated recommendation system which studies user preferences based on their voting records.  The purpose of this feature is to enhance the quality of discussion without using any form of censorship.

The recommendation system was previously tested with the help of 30 members of a political discussion forum. The tests have shown that most user preferences can be reasonably well described by just two parameters. The system chooses the parameters (principal vectors) independently based only on the numerical data (comment ratings), but it was easy to see that one vector corresponded to the “leftwing - rightwing” and another to the “well written – poorly written” axis.

About a month ago we started discussions on the new website. This time, all our participants were LW members and the results were very different. There was relatively little variation along “well written – poorly written” axis. There was significant variation along what seemed to be the political views axis, but it could no longer be perfectly described by the conventional “leftwing - rightwing” labels. For the moment, we adopted “populares” and “optimates” terms for the two camps (the former seems somewhat correlated with “left-wing/liberal” and the latter with “right-wing/libertarian”).

The results have shown an interesting asymmetry between the camps. In the previous tests, both left and right leaning users upvoted users from their own camp much more frequently. However, one group was several times more likely to upvote their opponents than the other.  Among “populares” and “optimates” the asymmetry was a lot weaker (currently 27%), but still noticeable.

In both cases our sample sizes were small and may not be representative of the LW community or the US population. Still, it would be interesting to find an explanation for this asymmetry. One possibility is that, on average, one side presents significantly better arguments. Another possibility is that the other group is more open-minded.  

Can anyone suggest a test that can objectively decide which (if any) hypothesis is correct?
# Confession Thread: Mistakes as an aspiring rationalist

We looked at the cloudy night sky and thought it would be interesting to share the ways in which, in the past, we made mistakes we would have been able to overcome, if only we had been stronger as rationalists. The experience felt valuable and humbling. So why not do some more of it on Lesswrong?  
  
An antithesis to the Bragging Thread, this is a thread to share where we made mistakes. Where we knew we could, but didn't. Where we felt we were wrong, but carried on anyway.

As with the recent group bragging thread, anything you've done wrong since the comet killed the dinosaurs is fair game, and if it happens to be a systematic mistake that over long periods of time systematically curtailed your potential, that others can try to learn avoiding, better. 

This thread is an attempt to see if there are exceptions to the cached thought that life experience cannot be learned but has to be lived. Let's test this belief together!
# Agency is bugs and uncertainty

(_Epistemic status: often discussed in bits in pieces, haven't seen it summarized in one place anywhere._)

Do you feel that your computer sometimes has a mind of its own? "I have no idea why it is doing that!" Do you feel that, the more you understand and predict someone's action, the less intelligent and more "mechanical" they appear? 

My guess is that, in many cases, agency (as in, the capacity to act and make choices) is a manifestation of the observer's inability to explain and predict the agent's actions. To Omega in the Newcomb's problem humans are just automatons without a hint of agency. To a game player some NPCs appear stupid and others smart, and the more you play and the more you can predict the NPCs, the less agenty they appear to you.

Note that randomness is not the same as uncertainty, since if you can predict that someone or something behaves randomly, it is still a prediction. What I mean is more of a Knightian uncertainty, where one fails to make a useful prediction at all. Something like a tornado may appear to intentionally go after you if you fail to predict where it will be going and you have trouble escaping.

If you are a user of a computer program, and it does not behave as you expect it to, you often get a feeling of there being a hostile intelligence opposing you, occasionally resulting in an aggressive behavior toward it, usually with verbal violence, though occasionally getting physical, the way we would confront an actual enemy. On the other hand, if you are the programmer who wrote the code in question, you think of the misbehavior as bugs, not intentional hostility, and treat the code by debugging or documenting. Mostly. Sometimes I personalize especially nasty bugs.

I was told by a nurse that this is also how they are taught to treat difficult patients: you don't get upset at someone's misbehavior and instead treat them not as an agent, but more like an algorithm in need of debugging. Parents of young children are also advised to take this approach.

This seems to also apply to self-analysis, though to a lesser degree. If you know yourself well, and can predict what you would do in a specific situation, you may feel that your response is mechanistic or automatic and not agenty or intelligent. Or maybe not. I am not sure. I think if I had the capacity for full introspection, not just the surface level understanding of my thoughts and actions, I would ascribe much less agency to myself. Probably because it would cease to be a useful concept. I wonder if this generalizes to a superintelligence capable of perfect or near perfect self-reflection.

This leads us to the issue of feelings, deliberate choices, free will and ability to consent and take responsibility. These seem to be useful, if illusory, concepts for when you live among your intellectual peers and want to be treated at least as having as much agency as you ascribe to them. But this is a topic for a different post.
# [link] Baidu cheats in an AI contest in order to gain a 0.24% advantage

Some of you may already have seen this story, since it's several days old, but MIT Technology Review seems to have the best explanation of what happened: [Why and How Baidu Cheated an Artificial Intelligence Test](http://www.technologyreview.com/view/538111/why-and-how-baidu-cheated-an-artificial-intelligence-test/)

> Such is the success of deep learning on this particular test that even a small advantage could make a difference. Baidu had reported it achieved an error rate of only 4.58 percent, beating the previous best of 4.82 percent, [reported by Google](http://arxiv.org/abs/1502.03167) in March. In fact, some experts have noted that the small margins of victory in the race to get better on this particular test make it increasingly meaningless. That Baidu and others continue to trumpet their results all the same - and may even be willing to break the rules - suggest that being the best at machine learning matters to them very much indeed.

(In case you didn't know, Baidu is the largest search engine in China, with a market cap of $72B, compared to Google's $370B.)

The problem I see here is that the mainstream AI / machine learning community measures progress mainly by this kind of contest. Researchers are incentivized to use whatever method they can find or invent to gain a few tenths of a percent in some contest, which allows them to claim progress at an AI task and publish a paper. Even as the AI safety / control / Friendliness field gets more attention and funding, it seems easy to foresee a future where mainstream AI researchers continue to ignore such work because it does not contribute to the tenths of a percent that they are seeking but instead can only hinder their efforts. What can be done to change this?
# How much do we know about creativity?

A lot of Less Wrong frames becoming more rational in terms of correcting biases. When [Scott Alexander](http://www.slatestarcodex.com) is asked how he does it, he doesn't seem to actually have an answer-- if I recall correctly, he's just said that all he's got in his life is his job, his girlfriend, and his blog, which doesn't begin to explain his remarkable flow of interesting posts.

It's a good thing to have fewer and weaker biases, but it's better if de-biasing can be applied to new ideas which have a good chance of paying off.  
  
Is there LW material about creativity that I'm not remembering? Any recommendations for information about creativity elsewhere? I'm especially interested in material which you've seen help you or other people become more creative, as distinct from material which has been plausible and/or fun to read.

Edited to add: While I think this is a generally applicable topic, I also have a local interest. I'm fond of LW, but it seems to be in a doldrums, and at least part of the cause is a lack of interesting new material.
# The Joy of Bias

What do you feel when you discover that your reasoning is flawed? when you find your recurring mistakes? when you find that you have been doing something wrong for quite a long time?

Many people feel bad. For example, here is a quote from a recent [article](/lw/mau/optimizing_the_twelve_virtues_of_rationality/) on LessWrong:

> By depicting the self as always flawed, and portraying the aspiring rationalist's job as seeking to find the flaws, the virtue of perfectionism is framed negatively, and is bound to result in negative reinforcement. Finding a flaw feels bad, and in many people that creates ugh fields around actually doing that search, as reported by participants at the Meetup.

But actually, when you find a serious flaw of yours, you should usually jump for joy. Here's why.

If you are an ambitious person, you have likely exhausted all the easy ways to improve your performance known to you; all the shortcuts, all the low-hanging fruits. If not, what are you waiting for? So, what's left for you is the hard ways, which require a lot of effort for relatively small improvements. Think harder, work harder, have more willpower, be more restless, et cetera. Such improvements are neither easy nor fast.

Now, suppose that you have found a serious flaw in your reasoning. 

What has happened, actually?

Firstly, you have lost nothing. All your achievements are still with you; as well as all your abilities; as well as anything else. However, one possibility is that your predictions about the future may become worse; maybe, you have just noticed that your road is leading to the abyss. But I'm talking here about noticing biases, errors and flaws that you have had for a long time, and such biases are unlikely to have irreversible forthcoming consequences. For example, if you have had a biased estimate of radioactivity effects on health and have gotten a dose -- that's an unpleasant bias to find, indeed. But most long-term biases aren't that malicious.

Secondly, now you have an opportunity to fix it. Whatever you achieved before, you achieved while being impaired by the flaw. And now, when the bias is eliminated, you can do _even better_. Fixing a bias might require some effort, but usually way less effort than your default hard ways of improvement. Or maybe you just got rid of some risk which could hit you in the future -- that's good too.

When I find a flaw or a recurring mistake of mine, I imagine how my life will be improved after I eliminate it. That feels good, certainly.
# [Link] Word-vector based DL system achieves human parity in verbal IQ tests

A research team in China has [created a system](http://arxiv.org/abs/1505.07909) for answering verbal analogy questions of the type found on the GRE and IQ tests that scores a little above the average human score, perhaps corresponding to an IQ of around 105 or so.  This improves substantially on the reported SOTA in AI for these types of problems.

This work builds on deep word-vector embeddings which have led to large gains in translation and many NLP tasks.  One of their key improvements involves learning multiple vectors per word, where the number of specific word meanings is simply grabbed from a dictionary.  This is important because verbal analogy questions often use more rare word meanings.  They also employ modules specialized for the different types of questions.

I vaguely remember reading that AI systems already are fairly strong at solving visual raven-matrix style IQ questions, although I haven't looked into that in detail.

The multi-vector technique is probably the most important take away for future work.  

Even if subsequent follow up work reaches superhuman verbal IQ in a few years, this of course doesn't immediately imply AGI.  These types of IQ tests measure specific abilities which are correlated with general intelligence in humans, but these specific abilities are only a small subset of the systems/abilities required for general intelligence, and probably rely on a smallish subset of the brain's circuitry.
# Surprising examples of non-human optimization

I am very much interested in examples of non-human optimization processes producing working, but surprising solutions. What is most fascinating is how they show human approach is often not the only one and much more alien solutions can be found, which humans are just not capable of conceiving. It is very probable, that more and more such solutions will arise and will slowly make big part of technology ununderstandable by humans.

I present following examples and ask for linking more in comments:

1\. Nick Bostrom describes efforts in evolving circuits that would produce oscilloscope and frequency discriminator, that yielded very unorthodox designs:  
http://www.damninteresting.com/on-the-origin-of-circuits/  
http://homepage.ntlworld.com/r.stow1/jb/publications/Bird_CEC2002.pdf (IV. B. Oscillator Experiments; also C. and D. in that section)

2\. Algorithms learns to play NES games with some eerie strategies:  
https://youtu.be/qXXZLoq2zFc?t=361 (description by Vsause)  
http://hackaday.com/2013/04/14/teaching-a-computer-to-play-mario-seemingly-through-voodoo/ (more info)

3\. Eurisko finding unexpected way of winning Traveller TCS stratedy game:  
http://aliciapatterson.org/stories/eurisko-computer-mind-its-own  
http://www.therpgsite.com/showthread.php?t=14095
# European Community Weekend 2015 Impressions Thread

The [European Community Weekend in Berlin](/lw/l4s/european_community_weekend_2015/) is over and was plain awesome.

This is no complete report of the event but a place where you can e.g. comment on the event, link to photos or what else you want to share.

I'm not the organizer of the Meetup but I have been there and for me it was the most grand experience since last years [European Community Weekend](/lw/k5d/european_community_weekend_2014_retrospective/). Meeting so many energetic, compassionate and in general awesome people - some from last year or many new. Great presentations and workshops. And such a positive and open athmosphere.

Cheers to all participants!

See also the [Facebook Group for the Community Event](https://www.facebook.com/groups/1613214895589346/).
# Seeking geeks interested in bioinformatics

I work at a small but feisty research team whose focus is biomedical informatics, i.e. mining biomedical data. Especially anonymized hospital records pooled over multiple healthcare networks. My personal interest is ultimately life-extension, and my colleagues are warming up to the idea as well. But the short-term goal that will be useful many different research areas is building infrastructure to massively accelerate hypothesis testing on and modelling of retrospective human data.

We have a job posting here (permanent, non-faculty, full-time, benefits):

https://www.uthscsajobs.com/postings/3113

If you can program, want to work in an academic research setting, and can relocate to San Antonio, TX, I invite you to apply. Thanks.

Note: The first step of the recruitment process will be a coding challenge, which will include an arithmetical or string-manipulation problem to solve in real-time using a language and developer tools of your choice.

edit: If you tried applying and were unable to access the posting, it's because the link has changed, our HR has an automated process that periodically expires the links for some reason. I have now updated the job post link.
# Two Zendo-inspired games

LW [has](/lw/3lg/zendolike_induction_game_for_playing_online/) [often](/lw/ilw/games_for_rationalists/9qnq) [discussed](/lw/q9/the_failures_of_eld_science/jyf) the inductive logic game [Zendo](https://en.wikipedia.org/wiki/Zendo_(game)), as a possible way of training rationality. But I couldn't find any computer implementations of Zendo online.

So I built two (fairly similar) games inspired by Zendo; they generate rules and play as sensei. [The code is on GitHub](https://github.com/StephenBarnes/Zendo-inspired), along with some more explanation. To run the games you'll need to install Python 3, and Scikit-Learn for the second game; see the readme.  
  
All bugfixes and improvements are welcome. For instance, more rule classes or features would improve the game and be pretty easy to code. Also, if anyone has a website and wants to host this playable online (with CGI, say), that would be awesome.
# Can You Give Support or Feedback for My Program to Alleviate Poverty?

Hi LessWrong,

Two years ago, when I travelled to Belize, I came up with an idea for a self-sufficient scalable program to address poverty. I saw how many people in Belize were unemployed or getting paid very low wages, but I also saw how skilled they were, a result of English being the national language and a mandatory education system. Many Belizeans have a secondary/high school education in Belize, and the [vast majority have at least a primary school education](https://en.wikipedia.org/wiki/Education_in_Belize#Access_to_education) and [can speak English](https://www.ethnologue.com/country/BZ). I thought to myself, "it's too bad I can't teleport Belizeans to the United States, because in the U.S., they would automatically be able to earn many times more the minimum wage in Belize with their existing skills."

But I knew there was a way to do it: "virtual teleportation." My solution involves using computer and internet access in conjunction with training and support to connect the poor with high paying international work opportunities. My tests of virtual employment using [Upwork](https://www.upwork.com/) and [Amazon Mechanical Turk](https://www.mturk.com) show that it is possible to earn at least twice the minimum wage in Belize, around $3 an hour, working with flexible hours. This solution is scalable because there is a consistent international demand for very low wage work (relatively speaking) from competent English speakers, and in other countries around the world like South Africa, many people matching that description can be found and lifted out of poverty. The solution could become self-sufficient because running a virtual employment enterprise or taking a cut of the earnings of members using virtual employment services (as bad as that sounds) can generate enough income to pay for the relatively low costs of monthly internet and the one-time costs of technology upgrades.

If you have any feedback, comments, suggestions, I would love to hear about it in the comments section. Feedback on my fundraising campaign at [igg.me/at/bvep](http://igg.me/at/bvep) is also greatly appreciated.

If you are thinking about supporting the idea, my team and I need your help to make this possible. It may be difficult for us to reach our goal, but every contribution greatly increases the chances our fundraiser and our program will be successful, especially in the early stages. All donations are tax-deductible, and if you’d like, you can also opt-in for perks like flash drives and t-shirts. It only takes a moment to make a great difference: [igg.me/at/bvep](http://igg.me/at/bvep).

Thank you for reading!
# [link] Essay on AI Safety

I recently wrote an essay about AI risk, targeted at other academics:

[Long-Term and Short-Term Challenges to Ensuring the Safety of AI Systems](https://jsteinhardt.wordpress.com/2015/06/24/long-term-and-short-term-challenges-to-ensuring-the-safety-of-ai-systems/)

I think it might be interesting to some of you, so I am sharing it here. I would appreciate any feedback any of you have, especially from others who do AI / machine learning research.
# Parenting Technique: Increase Your Child’s Working Memory

I continually train my [ten-year-old son’s](/user/Alex_Miller/overview/) working memory, and urge parents of other young children to do likewise.  While I have succeeded in at least temporarily improving his working memory, I accept that this change might not be permanent and could end a few months after he stops training.  But I also believe that while his working memory is boosted so too is his learning capacity.    

I have a horrible working memory that greatly hindered my academic achievement.  I was so bad at spelling that they stopped counting it against me in school.  In technical classes I had trouble remembering what variables stood for.  My son, in contrast, has a fantastic memory.  He twice [won his school’s spelling bee](https://www.facebook.com/james.d.miller.104/videos/10100148894161760/), and just recently I wrote twenty symbols (letters, numbers, and shapes) in rows of five.  After a few minutes he memorized the symbols and then (without looking) repeated them forward, backwards, forwards, and then by columns.    

My son and I have been learning different programming languages through Codecademy.  While I struggle to remember the required syntax of different languages, he quickly gets this and can focus on higher level understanding.  When we do math learning together his strong working memory also lets him concentrate on higher order issues then remembering the details of the problem and the relevant formulas.     

You can easily train a child’s working memory.  It requires just a few minutes of time a day, can be very low tech or done on a computer, can be optimized for your child to get him in flow, and easily lends itself to a reward system.  Here is some of the training we have done:     

*   I write down a sequence and have him repeat it.
*   I say a sequence and have him repeat it.
*   He repeats the sequence backwards.
*   He repeats the sequence with slight changes such as adding one to each number and “subtracting” one from each letter.
*   He repeats while doing some task like touching his head every time he says an even number and touching his knee every time he says an odd one.
*   Before repeating a memorized sequence he must play repeat after me where I say a random string.
*   I draw a picture and have him redraw it.
*   He plays N-back games.
*   He does mental math requiring keeping track of numbers (i.e. 42 times 37).
*   I assign numerical values to letters and ask him math operation questions (i.e. A*B+C).        

The key is to keep changing how you train your kid so you have more hope of improving general working memory rather than the very specific task you are doing.  So, for example, if you say a sequence and have your kid repeat it back to you, vary the speed at which you talk on different days and don’t just use one class of symbols in your exercises.
# [link] FLI's recommended project grants for AI safety research announced

http://futureoflife.org/misc/2015awardees

You may recognize several familiar names there, such as Paul Christiano, Benja Fallenstein, Katja Grace, Nick Bostrom, Anna Salamon, Jacob Steinhardt, Stuart Russell... and me. (the $20,000 for my project was the smallest grant that they gave out, but hey, I'm definitely not complaining. ^^)
# A Federal Judge on Biases in the Criminal Justice System.

A well-known American federal appellate judge, Alex Kozinski, has written a [commentary](http://georgetownlawjournal.org/files/2015/06/Kozinski_Preface.pdf) on systemic biases and institutional myths in the criminal justice system.

The basic thrust of his criticism will be familiar to readers of the sequences and rationalists generally. Lots about cognitive biases (but some specific criticisms of fingerprints and DNA evidence as well). Still, it's interesting that a prominent federal judge -- the youngest when appointed, and later chief of the Ninth Circuit -- would treat some sacred cows of the judiciary so ruthlessly. 

This is specifically a criticism of U.S. criminal justice, but, ceteris paribus, much of it applies not only to other areas of U.S. law, but to legal practices throughout the world as well.
# Crazy Ideas Thread

This thread is intended to provide a space for 'crazy' ideas. Ideas that spontaneously come to mind (and feel great), ideas you long wanted to tell but never found the place and time for and also for ideas you think should be obvious and simple - but nobody ever mentions them.

This thread itself is such an idea. Or rather the tangent of such an idea which I post below as a seed for this thread.

Rules for this thread:

1.  Each crazy idea goes into its own top level comment and may be commented there.
2.  Voting should be based primarily on how original the idea is.
3.  Meta discussion of the thread should go to the top level comment intended for that purpose. 

* * *

If this should become a regular thread I suggest the following :

*   Use "Crazy Ideas Thread" in the title.
*   Copy the rules.
*   Add the tag "crazy_idea".
*   Create a top-level comment saying 'Discussion of this thread goes here; all other top-level comments should be ideas or similar'
*   Add a second top-level comment with an initial crazy idea to start participation.
# The Other Path - a poem

Inspired by [the call to rationalist poetry fans](http://lesswrong.com/lw/3s/rationalist_poetry_fans_unite/) and informed by years of writing satire.

  

  

**The Other Path**

When you ask for truth and are offered illusion,

When senses deceive you and reasoning lies

I'll show you the path through the murky confusion,

Just follow and close your eyes.

On matters of fact there's no fact of the matter,

All moral and virtue are fashion and fad,

So dress in the creed that will fit you and flatter

No one can argue with that.

Some puzzles unyielding and mysteries ancient

No formula ever could hope to describe.

How proudly the scientist seeks explanations

How clearly in vain she strives.

Make cases like fortifications of metal,

No rival assertion shall ever go past.

Be carefree in choosing the side of the battle

But guard it until your last.

The sages declared that to know is to suffer,

Where wisdom is gained there is innocence lost

And learning is danger – best leave it to others,

Avoid it at any cost.

Some fools declare war on their very own nature

Their weapons are evidence, reason and math.

Don't offer compassion to those wretched creatures,

They've chosen the other path.
# Examples of AI's behaving badly

Some past examples to motivate thought on how AI's could misbehave:

An [algorithm pauses the game](http://techcrunch.com/2013/04/14/nes-robot/) to never lose at Tetris.

In "[Learning to Drive a Bicycle using Reinforcement Learning and Shaping](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.3038&rep=rep1&type=pdf)", Randlov and Alstrom, describes a system that learns to ride a simulated bicycle to a particular location. To speed up learning, they provided positive rewards whenever the agent made progress towards the goal. The agent learned to ride in tiny circles near the start state because no penalty was incurred from riding away from the goal.

A similar problem occurred with a soccer-playing robot being trained by David Andre and Astro Teller ([personal communication to Stuart Russell](http://luthuli.cs.uiuc.edu/~daf/courses/games/AIpapers/ng99policy.pdf)). Because possession in soccer is important, they provided a reward for touching the ball. The agent learned a policy whereby it remained next to the ball and “vibrated,” touching the ball as frequently as possible. 

Algorithms [claiming credit in Eurisko](https://aliciapatterson.org/george-johnson/eurisko-the-computer-with-a-mind-of-its-own/): Sometimes a "mutant" heuristic appears that does little more than continually cause itself to be triggered, creating within the program an infinite loop. During one run, Lenat noticed that the number in the Worth slot of one newly discovered heuristic kept rising, indicating that had made a particularly valuable find. As it turned out the heuristic performed no useful function. It simply examined the pool of new concepts, located those with the highest Worth values, and inserted its name in their My Creator slots.

[There was something else going on, though](https://www.digitalspy.com/videogames/a796635/elite-dangerous-ai-super-weapons-bug/). The AI was crafting super weapons that the designers had never intended. Players would be pulled into fights against ships armed with ridiculous weapons that would cut them to pieces. "It appears that the unusual weapons attacks were caused by some form of networking issue which allowed the NPC AI to merge weapon stats and abilities," according to a post written by Frontier community manager Zac Antonaci. "Meaning that all new and never before seen (sometimes devastating) weapons were created, such as a rail gun with the fire rate of a pulse laser. These appear to have been compounded by the additional stats and abilities of the engineers weaponry."

Programs classifying gender based on photos of irises may have been [artificially effective due to mascara in the photos](https://arxiv.org/pdf/1702.01304v1.pdf).
# Should you write longer comments? (Statistical analysis of the relationship between comment length and ratings)

A few months ago we have launched an experimental [website](http://www.omnilibrium.com/). In brief, our goal is to create a platform where unrestricted freedom of speech would be combined with high quality of discussion. The problem can be approached from two directions. One is to [help users navigate through content ](/lw/m3s/rational_discussion_of_politics/)and quickly locate the higher quality posts. Another, which is the topic of this article, is to help users improve the quality of their own posts by providing them with meaningful feedback.

One important consideration for those who want to write better comments is how much detail to leave out. Our statistical analysis shows that for many users there is a strong connection between the ratings and the size of their comments. For example, for [Yvain ](/user/Yvain/overview/)([Scott Alexander](http://slatestarcodex.com/)) and [Eliezer_Yudkowsky](/user/Eliezer_Yudkowsky/overview/), the average number of upvotes grows almost linearly with increasing comment length.

![](http://www.omnilibrium.com/LW1.jpg)

This trend, however, does not apply to all posters. For example, for the group of top ten contributors (in the last 30 days) to LessWrong, the average number of upvotes increases only slightly with the length of the comment (see the graph below).  For quite a few people the change even goes in the opposite direction – longer comments lead to lower ratings.

![](http://www.omnilibrium.com/LW2.jpg)

Naturally, even if your longer comments are rated higher than the short ones, this does not mean that inflating comments would always produce positive results. For most users (including popular writers, such as Yvain and Eliezer), the average number of downvotes increases with increasing comment length. The data also shows that long comments that get most upvotes are generally distinct from long comments that get most downvotes. In other words, long comments are fine as long as they are interesting, but they are penalized more when they are not.

![](http://www.omnilibrium.com/LW3.jpg)

The rating patterns vary significantly from person to person. For some posters, the average number of upvotes remains flat until the comment length reaches some threshold and then starts declining with increasing comment length. For others, the optimal comment length may be somewhere in the middle. (Users who have accounts on both Lesswrong and Omnilibrium can check the optimal length for their own comments on both websites by using [this link](http://www.omnilibrium.com/stats.php).)

Obviously length is just one among many factors that affect comment quality and for most users it does not explain more than 20% of variation in their ratings. We have a few other ideas on how to provide people with meaningful feedback on both the style and the content of their posts. But before implementing them, we would like to get your opinions first. Would such feedback be actually useful to you?