"Smivs writes with word on a spider..."
My first thought when reading this was "What kind of computer is a spider?" followed by "Why isn't he using Open Office?"
The Structure of Scientific Revolutions by Thomas Kuhn is a must read for anyone trying to understand how a theory (like evolution) is formed and evolves. It talks in depth about how new ideas challenge and eventually overthrow the established science, the difficulties involved, and how a paradigm eventually solidifies. His examples are mostly in physics and chemistry, but evolutionary biology had a very similar path to those described: a new theory is posited with powerful explainatory powers, although it certainly can't explain everything. Eventually, it is generally recognized as the most powerful and parsimonious explaination, although significant changes are made to its initial hypotheses. Something very similar happened with Einstein and physics, and Copernicus and astronomy.
Of course, the problem is not that people believe stupid things about how science works, but that people in power believe stupid things about how science works. http://xkcd.com/154/
Several people have already mentioned this, but I have to add my vote for Goedel, Escher, Bach. I was 16 when I read it for the first time, and it completely blew my mind and made me want to understand mathematics as a way of understanding life. I think I'm a computer scientist today largely because of that book. After I read it, I tried to get everyone I knew to read it, but I don't think I got any takers. The large text deals with a very complex set of ideas, including Goedel's incompleteness theorem. But with a teacher's help, I think most highschoolers could get through it, and possibly have their minds expanded.
I also like Chaos: Making a New Science by James Gleick, for the accessible and personal introduction to chaos and fractals.
If you learn a programming language, at least you can write some useful programs. Also it's worth noting that learning a new programming language gives you insight and new perspective. Does someone who knows one religion have incentive to learn another?
People have already mentioned project euler, which is good, but most of those problems will be beyond the capacity of 12-year-olds, I think. Start really simple: write the absolute value function, the min and max functions, factorial, simple I/O interactions like "how old are you?". Build confidence in their ability to solve simple problems before you give them something more complex. A good one might be the "higher/lower" game. Write a program that picks a random number and then asks for a guess. It tells whether the guess is higher or lower than the true value. If you make them program an AI that can play the game, they may discover binary search all by themselves. I think problem solving is more important than being able to see pretty results.
For what it's worth, I've had good experiences with coq:
http://coq.inria.fr/
Although I've never used it for anything large, it has the nice ability to make proofs about code, and export the code to haskell, scheme, or ML. I had a fun time proving that the min function always returns the lesser of the two values.
I couldn't disagree more. All the questions you ask DO have a right answer, in the sense that they represent concrete choices whose outcomes change the quality of life of us all, and one choice will improve our quality of life more than another (or worsen it). We don't know which is the right answer, but that's a function of our ignorance, not the question.
For anyone interested, MoGo was the Phd thesis of Sylvain Gelly, and that thesis, which describes mogo, is available here: http://www.lri.fr/~gelly/
As was said before, alpha-beta search is the most common strategy in chess, but my understanding is that it doesn't parallelize very well. Monte Carlo has been around for a long time, but it hasn't ever really succeeded at Go. Gelly's main contribution was to borrow a successful solution to the multi-armed bandit problem, an algorithm called UCB, and apply to the search tree. Initial values are determined through Monte Carlo style search, and then for each branch the algorithm estimates the upper confidence bound on the reward of each move, and preferentially searches the parts of the tree that seem to have good potential.
This made it good, but still not quite as good as gnugo, so he used reinforcement learning offline to make a quick pattern-matching style evaluation to aid UCT. There are some other tweaks to improve play style mentioned in the thesis.
Anyway, it's worth a look if your interested, and especially if you feel like MoGo has an obvious or boring algorithm.
Slashdot Mirror
"Smivs writes with word on a spider..." My first thought when reading this was "What kind of computer is a spider?" followed by "Why isn't he using Open Office?"
The Structure of Scientific Revolutions by Thomas Kuhn is a must read for anyone trying to understand how a theory (like evolution) is formed and evolves. It talks in depth about how new ideas challenge and eventually overthrow the established science, the difficulties involved, and how a paradigm eventually solidifies. His examples are mostly in physics and chemistry, but evolutionary biology had a very similar path to those described: a new theory is posited with powerful explainatory powers, although it certainly can't explain everything. Eventually, it is generally recognized as the most powerful and parsimonious explaination, although significant changes are made to its initial hypotheses. Something very similar happened with Einstein and physics, and Copernicus and astronomy. Of course, the problem is not that people believe stupid things about how science works, but that people in power believe stupid things about how science works. http://xkcd.com/154/
Several people have already mentioned this, but I have to add my vote for Goedel, Escher, Bach. I was 16 when I read it for the first time, and it completely blew my mind and made me want to understand mathematics as a way of understanding life. I think I'm a computer scientist today largely because of that book. After I read it, I tried to get everyone I knew to read it, but I don't think I got any takers. The large text deals with a very complex set of ideas, including Goedel's incompleteness theorem. But with a teacher's help, I think most highschoolers could get through it, and possibly have their minds expanded. I also like Chaos: Making a New Science by James Gleick, for the accessible and personal introduction to chaos and fractals.
Why Amazon? Is it just name recognition? They're probably the largest online retailer. Are they "the online walmart" like the article says?
If you learn a programming language, at least you can write some useful programs. Also it's worth noting that learning a new programming language gives you insight and new perspective. Does someone who knows one religion have incentive to learn another?
People have already mentioned project euler, which is good, but most of those problems will be beyond the capacity of 12-year-olds, I think. Start really simple: write the absolute value function, the min and max functions, factorial, simple I/O interactions like "how old are you?". Build confidence in their ability to solve simple problems before you give them something more complex. A good one might be the "higher/lower" game. Write a program that picks a random number and then asks for a guess. It tells whether the guess is higher or lower than the true value. If you make them program an AI that can play the game, they may discover binary search all by themselves. I think problem solving is more important than being able to see pretty results.
I was going to post an exerpt, but /. destroys my formatting.
http://poetry.poetryx.com/poems/784/
It seems like way too often I go camping and it rains, and even the "waterproof" tent lets water in, especially seeping underneath where you sleep...
For what it's worth, I've had good experiences with coq: http://coq.inria.fr/ Although I've never used it for anything large, it has the nice ability to make proofs about code, and export the code to haskell, scheme, or ML. I had a fun time proving that the min function always returns the lesser of the two values.
None of these have a right and a wrong answer.
I couldn't disagree more. All the questions you ask DO have a right answer, in the sense that they represent concrete choices whose outcomes change the quality of life of us all, and one choice will improve our quality of life more than another (or worsen it). We don't know which is the right answer, but that's a function of our ignorance, not the question.
For anyone interested, MoGo was the Phd thesis of Sylvain Gelly, and that thesis, which describes mogo, is available here: http://www.lri.fr/~gelly/ As was said before, alpha-beta search is the most common strategy in chess, but my understanding is that it doesn't parallelize very well. Monte Carlo has been around for a long time, but it hasn't ever really succeeded at Go. Gelly's main contribution was to borrow a successful solution to the multi-armed bandit problem, an algorithm called UCB, and apply to the search tree. Initial values are determined through Monte Carlo style search, and then for each branch the algorithm estimates the upper confidence bound on the reward of each move, and preferentially searches the parts of the tree that seem to have good potential. This made it good, but still not quite as good as gnugo, so he used reinforcement learning offline to make a quick pattern-matching style evaluation to aid UCT. There are some other tweaks to improve play style mentioned in the thesis. Anyway, it's worth a look if your interested, and especially if you feel like MoGo has an obvious or boring algorithm.