It's that time of year again.

If you are reading this post and self-identify as a LWer, then you are the target population for the Less Wrong Census/Survey. Please take it. Doesn't matter if you don't post much. Doesn't matter if you're a lurker. Take the survey.

This year's census contains a "main survey" that should take about ten or fifteen minutes, as well as a bunch of "extra credit questions". You may do the extra credit questions if you want. You may skip all the extra credit questions if you want. They're pretty long and not all of them are very interesting. But it is very important that you not put off doing the survey or not do the survey at all because you're intimidated by the extra credit questions.

It also contains a chance at winning a MONETARY REWARD at the bottom. You do not need to fill in all the extra credit questions to get the MONETARY REWARD, just make an honest stab at as much of the survey as you can.

Please make things easier for my computer and by extension me by reading all the instructions and by answering any text questions in the simplest and most obvious possible way. For example, if it asks you "What language do you speak?" please answer "English" instead of "I speak English" or "It's English" or "English since I live in Canada" or "English (US)" or anything else. This will help me sort responses quickly and easily. Likewise, if a question asks for a number, please answer with a number such as "4", rather than "four".

The planned closing date for the survey is Friday, November 14. Instead of putting the survey off and then forgetting to do it, why not fill it out right now?

Okay! Enough preliminaries! Time to take the...

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[EDIT: SURVEY CLOSED, DO NOT TAKE!]

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Thanks to everyone who suggested questions and ideas for the 2014 Less Wrong Census/Survey. I regret I was unable to take all of your suggestions into account, because of some limitations in Google Docs, concern about survey length, and contradictions/duplications among suggestions. The current survey is a mess and requires serious shortening and possibly a hard and fast rule that it will never get longer than it is right now.

By ancient tradition, if you take the survey you may comment saying you have done so here, and people will upvote you and you will get karma.

Through a series of diagrams, this article will walk through key concepts in Nick Bostrom’s Superintelligence. The book is full of heavy content, and though well written, its scope and depth can make it difficult to grasp the concepts and mentally hold them together. The motivation behind making these diagrams is not to repeat an explanation of the content, but rather to present the content in such a way that the connections become clear. Thus, this article is best read and used as a supplement to Superintelligence.

Note: Superintelligence is now available in the UK. The hardcover is coming out in the US on September 3. The Kindle version is already available in the US as well as the UK.

Roadmap: there are two diagrams, both presented with an accompanying description. The two diagrams are combined into one mega-diagram at the end.

Figure 1: Pathways to Superintelligence

Figure 1 displays the five pathways toward superintelligence that Bostrom describes in chapter 2 and returns to in chapter 14 of the text. According to Bostrom, brain-computer interfaces are unlikely to yield superintelligence. Biological cognition, i.e., the enhancement of human intelligence, may yield a weak form of superintelligence on its own. Additionally, improvements to biological cognition could feed back into driving the progress of artificial intelligence or whole brain emulation. The arrows from networks and organizations likewise indicate technologies feeding back into AI and whole brain emulation development.

Artificial intelligence and whole brain emulation are two pathways that can lead to fully realized superintelligence. Note that neuromorphic is listed under artificial intelligence, but an arrow connects from whole brain emulation to neuromorphic. In chapter 14, Bostrom suggests that neuromorphic is a potential outcome of incomplete or improper whole brain emulation. Synthetic AI includes all the approaches to AI that are not neuromorphic; other terms that have been used are algorithmic or de novo AI.

It was a pleasure to see all major strands of the effective altruism movement gathered in one place at last week's Effective Altruism Summit.

Representatives from GiveWell, The Life You Can Save, 80,000 Hours, Giving What We Can, Effective Animal Altruism, Leverage Research, the Center for Applied Rationality, and the Machine Intelligence Research Institute either attended or gave presentations. My thanks to Leverage Research for organizing and hosting the event!

What do all these groups have in common? As Peter Singer said in his TED talk, effective altruism "combines both the heart and the head." The heart motivates us to be empathic and altruistic toward others, while the head can "make sure that what [we] do is effective and well-directed," so that altruists can do not just some good but as much good as possible.

Effective altruists (EAs) tend to:

1. Be globally altruistic: EAs care about people equally, regardless of location. Typically, the most cost-effective altruistic cause won't happen to be in one's home country.
2. Value consequences: EAs tend to value causes according to their consequences, whether those consequences are happiness, health, justice, fairness and/or other values.
3. Try to do as much good as possible: EAs don't just want to do some good; they want to do (roughly) as much good as possible. As such, they hope to devote their altruistic resources (time, money, energy, attention) to unusually cost-effective causes. (This doesn't necessarily mean that EAs think "explicit" cost effectiveness calculations are the best method for figuring out which causes are likely to do the most good.)
4. Think scientifically and quantitatively: EAs tend to be analytic, scientific, and quantitative when trying to figure out which causes actually do the most good.
5. Be willing to make significant life changes to be more effectively altruistic: As a result of their efforts to be more effective in their altruism, EAs often (1) change which charities they support financially, (2) change careers, (3) spend significant chunks of time investigating which causes are most cost-effective according to their values, or (4) make other significant life changes.

Despite these similarities, EAs are a diverse bunch, and they focus their efforts on a variety of causes.

Below are four popular focus areas of effective altruism, ordered roughly by how large and visible they appear to be at the moment. Many EAs work on several of these focus areas at once, due to uncertainty about both facts and values.

Though labels and categories have their dangers, they can also enable chunking, which has benefits for memory, learning, and communication. There are many other ways we might categorize the efforts of today's EAs; this is only one categorization.