Me personally I'm not 100% convinced that the current ML/AI approaches (that I'm aware of) will yield new big steps forward.
Neuromorphic computing could have the potential I think, if we can build hardware that's good enough.
Neuromorphic computing could have the potential I think, if we can build hardware that's good enough.
I think the key fields in AI will become simulator based learning (RL) and graph processing neural nets, because graphs can express any kind of highly dimensional data and are useful for reasoning tasks. They marry the symbolic and connectionist approaches. These two subdomains have had rapid evolution over the last couple of years. They also solve the data problem - in simulation you can produce as much data as you want, and graphs have combinatorial generalisation, thus they work on new configurations without retraining.
I could show someone a single photograph of an animal they have never seen before and they will recognize it forever, from different angles, in different lighting, in black and white, probably even from a silhouette.
The more I dig in to ML the more it seems like it's cheating, like a mathematical trick. At least the way we are using it.
I can't help but feel like it's a game of Pachinko with pixels instead of balls, and neural weights instead of pins and holes.
I'm no expert though so take my opinion with a grain of salt.