From Michael Wilson’s comments on the last Accelerating Future post:

‘The more stuff you’ve read about previous attempts the more contaminated your thinking may become with old failed paradigms.’ – In my experience this viewpoint is held mostly by cranks. In fact the spectrum of viewpoints goes something like this, ranked roughly from least competent to most competent;

* Ignore all that past work, it’s worse than useless and will only contaminate your thinking, we must brainstorm radical new solutions (cranks, mostly).

* It’s obvious that only predicate logic/neural networks/Bayes/whatever will produce AGI. Papers on those other approaches may be interesting, and there may even be salvagable techniques, but they’re essentially a dead end (most AI academics).

* Many past AI approaches have had local successes, but failed to generalise. They all have strengths and weaknesses. We must learn about as many approaches as possible, promote collaboration and attempt to combine them all into one diverse mega-architecture that will have all of their strengths and none of their weaknesses (some of the more daring AI academics, collaboration junkies, some of the more clueful beginners).

* Past approaches have had only limited success, despite seeming reasonable to their designers (who were often at least as bright as me). We need to understand why they (locally) succeeded, why they (ultimately) failed and why they looked like a good idea at the time despite being broken. You should read as much past work as possible until you can reliably answer these questions, as this will greatly improve your chances of designing an AGI system that can incorporate the core discoveries of past work while hopefully avoiding past misconceptions and misfeatures (the researchers I most admire either hold this view or are getting close to it).

From my limited knowledge, I feel that this is sound advice for approaching the field of Artificial General Intelligence – that is, software capable of reasoning generally. As Ben Goertzel has said, implementing AGI on a computationally limited substrate demands a heterogeneous, messy approach, a “heuristic soup”, as I’d call it, and ignoring the past successes of AI is not likely to help those who pursue it in the present day.