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In short term we'll see a trend towards AI systems that blend handcrafted symbolic models of the world together with deep learning modules for perception/intuition (self-driving cars are a good example). But these are engineering-intensive.
In the longer term these hybrid systems will be automatically generated from data/experiments. The overall generation process will look a lot like existing software engineering workflows, but without the human component.
Could you elaborate?
We feel that one of the main impediments to this is architectural. End to end like many DL systems has infinite training required and suboptimizes each stage. Rather, better arch and better algorithms pace the way. See (1/2)
Our architecture conjecture here http://www.textician.com/texticians-four-rs-of-ai/ … and, on the latter, @filippie509 's great new post here: https://blog.piekniewski.info/2018/06/20/rebooting-ai-postulates/ … (2/2)
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