Doing what has been done in the field would repeat the mistakes.
Doing something original might actually be, you know, original.
Doing something original might actually be, you know, original.
If you follow the classical path, you will become bogged down in the vast field of AI.
Ramanujan started from first principles and worked from there. My guess is that the person(s) who create "actual" AI will use that approach.
But there's value in studying things that didn't work. A huge number of obvious approaches to AI have been tried already, and the insights on why they don't work, or why they work on this problem but not this other one, are often the result of huge quantities of time spent on subtle traps and dead ends. To ignore them risks wasting your time all over again.
I think AI boils down to just the problem of managing complexity that all of cs is about. The eventual solution will be vast, and will require designing lots of separate subsystems that work in very different ways and yet need to communicate in subtle ways.
That emphasis on scale goes for research in general, I think. I recommend this video of Malcolm Gladwell talking about the nature of genius and how it's changed over the years. http://www.newyorker.com/online/video/conference/2007/gladwe... He compares the decoding of the rosetta stone and linear B 40 years ago with Andrew Wiles proving Fermat's last theorem in the past decade, and how fundamentally different their respective approaches are.
Both approaches may work, but you have to decide what attitude you want to take. You can either try to be a single monster-mind like Ramanujam sweeping through vast areas of research, or you can assume that won't happen and resign yourself to a lot of effort and learning before you're able to synthesize something useful.
To summarize: I agree that you want to avoid cluttering your mind with the ideology of past approaches. There's huge value though in simply studying the episodic history of a field, to be aware of what has been tried, what worked and what didn't.
I'm not saying that you have to learn everything before doing anything. That's a strawman. I'm saying you shouldn't ignore any source of knowledge, that you should look everywhere taking the good and leaving out the bad. And that's a Bruce Lee quote as well.
The concern about contagion is valid; my attitude towards it is that it happens not so much from what you read but who you talk to and the social circles you move in. Keep those diverse and you should be fine.
If you don't learn everything, then you must have some independent criterion with which to pick and choose; and having such a facility, one may as well simply start down whichever path one feels is most appropriate anyway.
In other words, if you can somehow rule out large swaths of possibilities without even trying them, then you must have some intuitive feel for the problem (or at least, have an intuitive feel for what you think the solution is likely to resemble, whether or not you turn out to be correct). In such a case, you are investigating based on your own intimations and your efforts will develop from there, rather than from from a top-down view -- which has so far been amply demonstrated not to work.
Edit: The recommendation to look "everywhere" is untenable, because at some point you have to stop casting about if you are going to get anything done yourself.
The thing that is independent/unique about somebody isn't a static criterion that remains unchanging over time, it's more the trajectory you end up taking that's constantly evolving over time. All I'm saying is: don't exclude reading about past failures when you make your choices on what to do next. It's fine to say ok, let's stop reading for a while and try building this one thing my mind has settled on. That's a great habit to acquire.
The unpronounceable author of Flow: http://www.amazon.com/Flow-Psychology-Experience-Mihaly-Csik... says the most successful people alternate periods in closed states when they focus on a specific concrete project with periods in open states where they demonstrate huge appetite for new ideas.
I'm starting to think we're saying the same thing in different words and with slightly different emphasis. It is thus with most interesting conversations.
I LIKE TURTLES
In fact, reading what somebody did will often help you avoid going down a path. I've read of many many more algorithms, successful and unsuccessful, than I've ever implemented.
Yup. We've already established that one has to prioritize what to implement. The priority clearly shouldn't always go towards doing something rather than reading what somebody else did. (Children would never learn to write that way.) Instead we prioritize differently at different times, and we try to have lots of feedback between reading and doing, by things like active reading.