Before the company grew relatively flush from investors’ cash and hundreds of thousands of customers on more than 5,000 campuses, it had to resort to creative bootstrapping.

Chegg began renting books before it owned any, so when an order came in, its employees would surf the Web to find a cheap copy. They would buy the book using Mr. Rashid’s American Express card and have it shipped to the student. Eventually, Chegg automated the system.

http://www.nytimes.com/2009/07/05/business/05ping.html?_r=1&ref=business

In general, any organized team of people is going to form some sort of super-”application” (like an assembly line). When I’ve had to do my own mini-publishing for newsletters, conference packets, and even individualized paperwork I’ve had a group of people organized to perform page collation, binding, etc. It was very machine like, with procedures for exceptions and all. For example, if a page was defective or misprinted, the person who spotted it would call out “defect” and everyone would adjust their station accordingly. This made a lot of sense, especially when packet pages had to be exactly matched with another individual packet page (for individualized paperwork).

It was a fun experiment in Artificial Artificial Intelligence. I’ve also conducted similar projects on a LAN, which involved a kind of peer to peer dynamic updating system. We had a variety of topics to research and produce brief dossiers on, so we had a beefed up IRC chat and an updating procedure so that everyone could individually research the subject with minimal redundancy and stylistic clash.

I think it is described in a book with the name: “Barbarians Led by Bill Gates”

“Helle, I’m Ranhjid and your private automated kernel test suite – in my spare time I sell flowers … BTW wanna buy a rose? … kernel tests has been started!”

“Google TechTalks July 26, 2006 Luis von Ahn is an assistant professor in the Computer Science Department at Carnegie Mellon University, where he also received his Ph.D. in 2005. Previously, Luis obtained a B.S. in mathematics from Duke University in 2000. He is the recipient of a Microsoft Research Fellowship. ABSTRACT Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This talk introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve.”

IMHO, “Flintstoning” should only be used to refer to setups in which the backend is implemented by wisecracking prehistoric animals.

https://www.mturk.com/
http://en.wikipedia.org/wiki/Amazon_Mechanical_Turk

Similarly, auditory significance scanning. When you are in a crowded room full of chatter, if someone says your name, you’re immediately able to break out of the “box” you were in and detect it, even though you were effectively ignoring the noise of the room before. The psychology behind how that works is, again, extremely complex, but humans are very good at it. Imagine being in a noisy crowded room and having a computer listen to all the conversations it can hear at once, showing you what you missed, and indicating what was interesting.

- Determine the context of a conversation. This has huge implications not only for voice recognition in general (huge), but device interaction. If a device in my home/work can figure out I’m talking about an task it may be a part of, it can adjust itself accordingly so as to speed up the ramp up time of interaction should I decide to use it, or just isolate orders to one device at a time, such as searching my cable box for a show, and not my DVR.

- Seeing eye dog. I foresee a _very_ good system coming for people who need to navigate city streets without sight.

- Intelligent news alerts. I foresee the intelligence for news alerts ramping up greatly. A child could currently do a better job of forwarding me stories from feeds better than the algorithms I have used. If my mobile phone position is near a weather front that’s about to hit me in 20 minutes, guess what, I want to know about it.

That’s all for now. If you want more, ask.

Taylor