This brings to mind the infamous case of Google censoring search results in China according to the government's will. That's an example of a deliberate human action, but examples will increasingly be "algorithmic byproduct" with zero human intervention. Unlike humans the algorithm can't questioned or intimidated by the media or taken to a court of law.

Legally and professionally, I suppose the product team could be taken responsible, but I definitely think there needs to be a push for more scrutinizable computation. (There have been discussion along these lines in terms of computer security. Sometimes open source is cited as a solution, but it hasn't necessary helped--e.g. Heartbleed.)

In fact, I'm just going to edit out that bit to de-emphasize the term itself.

As I replied to solipsist, I'm now wishing I had asked what experiences people here have at the intersection of interface design and machine intelligence and gone from there. I find UX design and other fields I mentioned as huge and nebulous--it could be equally about hex codes for button shadows as "humane representations of thought"--but my post is not necessarily reigning that in coherently.

Perhaps I overemphasized the "term introduction". Since the first two comments seem to be questioning whether this term and grouping of ideas should exist at all, now I'm wishing I could go back and frame the post as, "Is anyone here thinking about these kind of things?" Once the activity and attention of the community is better resolved, I could re-examine whether any part of it is worth promoting or rebranding.

Any suggestions of where to get programming gigs?

(Similar to Fluttershy) Culturally, there's a belief that college years are our formative years, and we should be learning to be good, well-rounded (in the liberal arts sense) people. But college is a huge time and money commitment, and the job market is competitive, so I think college ought to be used strategically for advancement in academic or well-paved professional tracks (doctor, lawyer). My college, Harvey Mudd, had a noticeable emphasis on ethics in science and technology and humanities as a hearty side heaping to technical topics. Ideally, ethics would be strategic for career advancement, but in the real world (software engineering), it's never seem to come up in my job placement. Harvey Mudd should be a pretty good model though since they manage to make it work anyway. Alan Kay also suggested a technical and humanities double major (somewhere in that interview...).

The politics of college aside, here's my list of things to learn as soon as and by any means possible:

• Rationality. Goes without saying here. In particular: using reasoning and empirical data for important questions. I just came across this today that decries the complete lack of empirical basis for programming language design (a topic that's collectively consumed hundreds of thousands of hours of debate, not to mention time developing mediocre solutions). You'll see the same thing in any field (at least fields that are mature enough to even ask the question).
• Career & finance. Understanding that there's a game to both and having knowledge about those games can get you opportunities and money that you wouldn't otherwise. I recommend Ramit Sethi's material and Tony Robbin's new book.
• Body & brain. You can often get away with research + rationality for a particular question, but it's good to have prior exposure to solutions to common problems: nutrition/fitness, body language, learning, mental health. For example: thinking "I'm depressed" leads you to: it could be due to a nutritional or neurochemical imbalance, or fixed with changing some thinking habits; instead of "I'm depressed because I'm a failure at life."
• Technical topics. If you want to make a contribution you really need to focus. Math is generally useful, but that's mostly as a symbolic and visual language rather than any particular deep math topic until you need it. Programming is often useful for automating technical tasks. I've observed people who study physics excelling in different topics. (Perhaps exposure to model building and data-driving theory testing. Perhaps selection bias.)
• Philosophical and spiritual things. I've only started to respect this recently, but I've found value in Taoist, Buddhist, Catholic, and Stoic teachings. Here's someone else exploring a variety of areas.
• Microeconomics and game theory come up a lot in the world and knowledge thereof may prevent you from making dumb "If I were in charge..." statements.

Lots of things I wish I knew more about still, like sociology/anthropology, politics, and history, where there's a lot of "why should I learn about this particular thing or another?" that are hard to answer on my own.

There is an academic field around this called intelligent tutoring systems (http://en.wikipedia.org/wiki/Intelligent_tutoring_system). The biggest company with an ITS, as far as I know, is Carnegie Learning, which provides entire K-12 curricula for it: books, teacher training, software. CL has had mixed evaluations in the past, but I think a fair conclusion at this point is that ITS significantly improves learning outcomes when implemented in an environment where they are able to use software as it's intended to be used (follow the training, spend enough time, etc).

As far as I know there isn't anything quite like this in a widely deployed online system with community discussion as you suggest. Grockit (http://grockit.com) is a social test prep site that is familiar with the ITS community and uses some principles. Khan Academy is continuing to improve, but I can't say whether they will reach the state of the art as far as intelligent tutors go. I'd say there's definitely an opportunity for more ITS in online learning now, but it isn't easy to build.

The Wikipedia article is OK. One example of a recent paper is http://users.wpi.edu/~zpardos/papers/zpardos-its-final22.pdf which also shows some of the human work that goes into modeling the knowledge domain for an ITS.

Your approach -- targeting home-schoolers who are "nonconsumers" of public K-12 education -- is exactly the approach advocated by disruption theory and specifically the book Disrupting Class. Using public education as analogous to established leaders in other industries, disruption always comes from the outside because the leaders aren't structurally able to do anything other than serve their consumers with marginal improvements.

ArtofProblemSolving.com is one successful example that's targeted gifted home-schoolers (and others looking for extracurricular learning) in math. I'm sure there are others. EdSurge.com is a good place to look for existing services, which you can sort by criteria including common core/state-standards aligned (you do have to register for free to get the list of resources). I also have thought about services that build on top of Khan Academy, but I wouldn't underestimate their ability to improve in that area. They just released a fantastic computer science platform. But they are a non-profit, so their growth depends, I suppose, on Bill Gates' mood and other philanthropy. To get to full disruption, it might take a for-profit with, as you suggest, monetization through tutoring and other valuable services.

I've explored using spaced repetition in various web-based learning interfaces, which are described at http://cicatriz.github.com I'd love to talk more with anyone who's interested. Based on my experiences, I have reservations about when and how exactly spaced repetition should be used and don't believe there's a general solution using current techniques to quickly go from content to SRS cards. But with a number of dedicated individuals working on different domains, there's certainly potential for better learning. I've been working on writing up a series of articles about this. Again, contact me if you want to be notified when that is released.

This seems to contradict the very powerful effect of learning from failure and corrective feedback. See http://www.wired.com/wiredscience/2011/10/why-do-some-people-learn-faster-2/ for an accessible overview.

I'd conjecture this works better when someone can already perform the desired behavior and wants to form a habit, whereas learning from failure comes in when new information needs to be stored and reorganized.