For much of the last decade, the hot space in A.I. was machine vision. The release of ImageNet gave researchers a common target to work on, and a very large data set, and spurred dramatic advancements in machine vision accuracy as a result.
As machine vision for common problems started to reach human levels of performance, many of the best and brightest in the field realized that the marginal contributions to machine vision were low, and turned to other areas. Natural language understanding started receiving a lot of attention. A.I. related work on natural language has been an explosive space and has moved forward rapidly over the past few years. I suspect that in another 3 years we will have made remarkable progress as an industry.
In parallel, some fields that are lumped together with A.I., like predictive analytics, have moved forward pretty rapidly as well. With all the investment in these spaces, they are moving forward at a nice pace, which raises the question... what is next? A.I. technologies rise and fall in popularity. Neural approaches are peaking and will probably start becoming less popular by late 2017. Bayesian approaches are getting a fresh look and starting to ramp up again. Genetic algorithms are stale, and I think will remain so for a while. So what technology will be the next to start rising in popularity again?
My guess is A.I. analogy makers will start to rise again in 2017. Many of you probably read Doug Hofstadter's "Godel, Escher, Bach: An Eternal Golden Braid." If you haven't, it's an amazing book so check it out. Hofstadter was an early champion of analogy making as intelligence. The reason analogy making is primed to rise is that, neural networks have primarily improved our accuracy on classification. Classification is at the bottom of the intelligence hierarchy and so, it makes sense to start climbing that hierarchy again and perpetuating our advancements upward. We can build on the better classifiers we have to do better analysis, reasoning, and analogy making.
This time around, analogy making will start by focusing on microdomains. Just as neural networks made dramatic progress by focusing on particular data sets that were conceptually narrow, analogy making will make progress by focusing on microdomain analogy making, where success will be easier to classify and results will be more useful.
The best thing I ever read about analogy making in microdomains is Robert French's "Subtlety of Sameness", which is French's PhD dissertation turned into a book. French created a program called Tabletop. The way it worked is, a table is set a certain way on each end. French then points to an object on his end of the table, and the program has to point to "the most similar object." When the table ends are set identically, say, both with a plate, a fork on the left and a spoon on the right, this is easy. Touch the spoon and the computer will touch the spoon as well. But what happens when the table ends are setup differently? What if there is no spoon? In that case, the program may touch the item in a similar location on the table. But what if that item is of a different class? Maybe it's a glass rather than a utensil. In that case, the computer may touch the closest utensil.
Tabletop explores analogy making in interesting ways. Something could be similar in substance, or in a similar class, or in a similar location, or have a similar function. The algorithm works both tops down and bottoms up, which is an approach I think will become more common as A.I. moves away from neural network approaches into other areas. (Or maybe applies bottoms up and tops down NNs to analogy making somehow?)
Analogy making is the next step up from classification and prediction, and will enable us to solve a new level of problem in A.I. On top of that, advances in lower levels of A.I. technology haven't yet pushed analogy making forward, but I think they will. That's why I expect it to be fertile ground for innovation starting soon.
I think 2017 will see the early seeds of analogy making rising again on the A.I. front. If you want to be part of that rise, or bet on it as an investor, it makes sense to start refreshing yourself now. Reading about Tabletop is a great place to start.