[randcraw]

Deep nets are deservedly big because they've managed to improve upon all of the decades-old state-of-the-art methods in the world of signal processing (DSP): voice, image, video, game play, and a much of natural language. No other single computational/algorithmic method has achieved so much, in so many domains, ever. That's revolutionary.

The advances made by deep nets in signal processing will likely slow down now, but they aren't going away, not in the foreseeable future.

The hype around DNNs arose when we took our unbridled enthusiasm for what's they've achieved in DSP and extend it to other domains with data that's less 'dense' and thus aren't as amenable to de/convolution in N-D space or time.

Will DNNs revolutionize or introduce all the techniques needed to achieve AGI/Strong AI? I very much doubt it. As yet, there's little sign that DNNs can perform relational operations on interdependent symbols, like the transforms available via type theory, bayesian nets, or predicate logic.

The multitude of disparate facts and semantics in a rich knowledgebase can't be organized into dense matrices the way that continuous signals can, so the SIMD operations that are so effective in DSP won't implement the rich transformations needed in a relational fact-based knowledge space equally as well, if at all. Thus DNNs almost surely aren't going to take us to the heights of logical or compositional thinking that human level intelligence requires.

But how far up relational mountain will DNNs take us? I suspect that won't be known for a decade or longer. But even if we don't reach the summit, it'll be higher than we were before.