This position seems to be popular among people that don't know the first thing about AI. So let me explain the situation from a point of view familiar to AI practitioners: A rational agent is one that acts as if it were maximizing the expected value of some utility function. That, in a nutshell, is the core of making decisions under uncertainty, and the basic recipe for a large part of AI. As part of the design of a utility-centered AI system, you define the utility function, which is precisely how you would tell the machine what to want. None of this "boggles the mind". It is almost trivial, actually. The difficult parts are perception, how to model future events to evaluate the expected value of the utility function in different scenarios, etc.

Don't worry. It is using a non-standard prototype for `main', and it forgot a few semicolons. It will stop working the next time the compiler is updated. Who knew that some obscure idiosyncrasies of the C programming language would save humanity? :)

I was programming at her age (BASIC on some 8-bit computer), and I turned out OK. My parents weren't very happy that I spent many hours a day in front of the computer, but that's what allowed me to have a great job as an adult.

I would just let the girl do whatever she is interested in.

That's why France doesn't allow children under the age of six to open both eyes at the same time.

If you don't agree with the parent, you need to watch this documentary: https://www.youtube.com/watch?...

If the only two choices are positive/negative (or thumbs up/thumbs down or some other equivalent 0/1 scheme), here's a formula that should work fairly well:

(n_positive + 1) / (n_positive + n_negative + 2)

So a single positive review gives you a score of .6667, and a single negative review gives you .3333. For large numbers of reviews, the score quickly converges to the actual fraction. If you don't have any reviews, you are at .5000.

The mathematical justification for this formula is that if you try to use a Bayesian approach to estimating the true probability of getting a positive review, and you start with a flat prior, this formula gives you the average of the posterior probability after observing the given number of positive and negative reviews. The full posterior distribution is a beta distribution with parameters alpha=n_positive+1 and beta=n_negative+1.

This formula is often used when applying Monte Carlo techniques to the game of go. I believe a lot of programmers simply start the counters of wins and losses at 1 to avoid corner cases (like division by 0), and they accidentally use the correct formula.

You forgot to divide by sqrt(2) in your erfc expression. The actual probability of IQ of a random human being over 197 is about 5e-11, which means about 0.35 humans should have it.

http://www.wolframalpha.com/in...

While that is indeed the solution, it is also true that it is too easy to forget. Perhaps one could modify all commands to require the use of the "--" separator, or to warn if it's not present, at least if some environment variable is set. That could be very helpful for people trying to write more secure code.