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.