Here, from PBS, is an interesting interview with Marvin Minsky, one of the key pioneers of Artificial Intelligence research. Although Minsky remains somewhat optimistic about developing a general artificial intelligence, he believes that the current approaches are misguided and too narrow – that researchers are now looking for “a magic bullet”, and that it’s going to take a lot longer to create generalized AI than if we applied a more general approach:
How hard is it to build an intelligent machine? I don’t think it’s so hard …. The basic idea I promote is that you mustn’t look for a magic bullet. You mustn’t look for one wonderful way to solve all problems. Instead you want to look for 20 or 30 ways to solve different kinds of problems. And to build some kind of higher administrative device that figures out what kind of problem you have and what method to use.
Now, if you take any particular researcher today, it’s very unlikely that that researcher is going to work on this architectural level of what the thinking machine should be like. Instead a typical researcher says, “I have a new way to use statistics to solve all problems.” Or: “I have a new way to make a system that imitates evolution. It does trials and finds the things that work and remembers the things that don’t and gets better that way.” And another one says, “It’s going to use formal logic and reasoning of a certain kind, and it will figure out everything.” So each researcher today is likely to have one particular idea, and that researcher is trying to show that he or she can make a machine that will solve all problems in that way.
I think this is a disease that has spread through my profession. Each practitioner thinks there’s one magic way to get a machine to be smart, and so they’re all wasting their time in a sense.
I was surprised to see his lack of optimism in the face of so much progress in areas I’d argue are very generalized indeed. The DARPA SyNAPSE project we’ve discussed several times here at Technology Report remains the best funded AI research to date, and lead researchers seem to feel optimistic that progress there could lead to a human scale general intelligence within several years rather than several decades that Minsky implies may be required given the new approaches.
Simply put, DARPA SyNAPSE is creating a computing infrastructure to rival the human brain in terms of connectivity, and counting on the possibility that we are dealing mostly with *quantity of connections* rather than *quality of connections* when we talk about human level intelligence.
The other very promising project for generalized AI is somewhat at odds with the DARPA SyNAPSE view. The Blue Brain project is also a promising development ground for general artificial intelligence, but the approach is very different as described by Dr. Henry Markram, the project manager at Blue Brain. The Blue Brain team is focusing more on “reverse engineering” animal brains and eventually a human brain.
Given the new level of enthusiasm and funding from DARPA, it seems likely that progress will continue at a faster pace that at anytime in the past.
Ironically I think Minsky’s early optimism in the 1950s was more justified than his current pessimism, though his observation that academics are working in too much isolation is certainly true. I’m often surprised how many technologists don’t seem to understand many simple aspects of human biology and evolution and vica versa. Human intelligence, though intriguing, continues to be overrated as an phenomenon of exceptional quality. We’re a somewhat arrogant creature by evolutionary design, but that does not justify our self importance. Machines surpass most of us in most compartmentalized aspects of intelligence and many aspects of creativity (mathematics / translation and language / game playing / music / information retrival, etc, etc). It seems reasonable that what we call “consciousness” may only require massive connectivity – perhaps something as simple as creating a fast, multitasked conversation between different parts of an artificial brain.
