is a great book, and a nice complement to Norvig and Russell, which is somewhat more theory-oriented.
It is ironic that the man who wrote the book uses different methods (maths) to extract meaning from data at google ~ http://news.ycombinator.com/item?id=90298
Something smells about AI.
It has been well funded, attracted the best minds (Minsky, McCarthy, Newell) at places like MIT, Stanford and resulted in lots of useful languages & tools. [0] From Cyc to Powerset companies have tried to use these techniques trying to assign human like intelligence to machines yet have failed.
Why?
Bruce Stirling reckons it has to do a lot with the language. [1] Assigning intelligence to what is essentially a 21'st century equivalent of knitting mills or steam engines of yesteryear. Maybe thats why Google has taken the approach it has. Instead of creating intelligent software to find the meaning, take an insight into users who have pre-assigned intelligence to documents. And use well known sorting, linking techniques and software engineering along with not-so-well applied math techniques. Thus make google appear to make sense of what we search for.
[0] http://en.wikipedia.org/wiki/Artificial_intelligence
[1] ITConversations, 'Bruce Stirling, The Internet of Things, 58m, 26.4 mb, 2006MAR06' ~ http://www.itconversations.com/shows/detail717.html
Our genetic code isn't that huge, but the amount of information contained in our brains is. All that information must be coming for somewhere. I refuse to believe that evolution has managed to create _that_ awesome compression algorithms.
Maybe this is what Google does, in a way. They happen to have a huge corpus of machine-readable data, which is in a sense equivalent to what a sophisticated sensory apparatus would give you. I intended to disagree with you when I started writing this comment, but now I'm not so sure. Human intelligence obviously doesn't spend its time reading petabytes of information on other people's search habits, but maybe Google is actually onto something.
The classical AI stuff is still interesting, of course, and still has practical applications (ie A* search for games). But I tend to count "the other stuff" (ie Data Mining) as AI, too.
Perhaps similar, and available as a free ebook: McKay, Information Theory, Inference Theory and Learning Algorithms, http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html
Keep in mind that AI is a broad field. As such you might find it more helpful to identify which computer intelligence problem(s) you want to solve, then start accessing the AI writings that discuss how to solve your problem. This won't give you a general overview, but it at least would reduce what you'd need to learn about to produce something useful.