AI Pragmatism vs Philosophy of Learning

All approaches to learning have upper and lower bounds.

Deep learning has verily thawed any ice left from previous AI Winters. This approach, having been given new life through a combination of new insight and new hardware, is formidable against many of the tasks that occupy human staff today. AI is disrupting nearly every industry simultaneously.

Economically, this is a great and/or terrible thing. Philosophically, it may be somewhat more limited. For deep learning, which has seemingly infinite learning capacity, to approach human intelligence in the most broad applications, we will need further breakthroughs of equal if not greater ingenuity.

The biggest (economic) current problem being addressed by AI, is the problem of locomotion and mobility. Agile robots are not too far from becoming a reality. This is where deep-learning can shine. Deep learning is a great way to provide muscle memory for machines. It just works.

The other big problem that is not in the hype-cycle is contemplation. That is to say: computers never ask why. Contemplation is the gap in our chatbots and voice-assistants. As we start to address this problem, we will surely encounter the shortcomings of deep learning and realize it for the crutch that it is. Deep thinking is more complex than moving around physically.

We can see this in the animal kingdom. Only animals with large brains and social structures are able to identify themselves in mirrors. How far from that state must we be as humans to communicate through symbols. Day by day, we learn and read and transfer emotions through this medium. What structure and form must we be using to accomplish this!

So this is where I am watching. My voice assistants currently are adorably simple. At least we have the technology to use human sounding voices now. Other than a few more lines of programmed questions, I am not hoping for much.

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