
Andrew Ng: AI Winter Isn’t Coming - dirtyaura
https://www.technologyreview.com/s/603062/ai-winter-isnt-coming/
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daly
AI was huge in the 1980s. Expert systems (rule-based progamming), Logic
programming (Prolog), and Case-based reasoning were spawning startups at a
furious pace. There were great breakthru systems such as Emycin. Large
companies were developing advanced systems (IBM did a Financial and Marketing
Expert). AI was taking over the world. AI based on reasoning and understanding
was the true path. Eventually the limits became apparent and the hype died.
Money fled the market. Winter arrived.

Today we see great strides in perception and manipulation (P-and-M). We can
recognize faces. We can "compile action" into networks that can perform tasks.
There is a belief that this is "all". "Reasoning and understanding" is now
seen as "Good old-fashioned AI" (GOFAI). The limits of the P-and-M approach
are obvious if you look at them with a clear eye. Unsupervised learning is one
glaring example. Eventually the limits will become apparent and the hype will
die. Money will flee the market. Winter will arrive.

This will be dismissed as reasoning by false analogy. But consider trying to
use P-and-M in manufacturing. A robot would be excellent at putting something
together using deeply-learned perception of parts and deeply-learned actions
on parts. But the technology is useless for attacking related problems. For
example, given a CAD drawing of a new toaster, develop a plan to assemble the
toaster from parts and create the robot motions to complete the assembly. How
do you create P-and-M actions on unique parts? How do you reason about plans?
How do you handle one-of-a-kind problems that don't have big-data sources?

The limits are obvious. The primary market will saturate. Hard problems will
arrive that P-and-M can't address. Winning technology areas will become
mundane. Fast money will move elsewhere. Winter will arrive.

The "next generation" of AI will arrive when we start combining things like
knowledge representation (e.g. the concept of a wrench) with perception (is
this a wrench?) and manipulation (how to use it). The concept of a wrench will
be grounded by attaching deep-learning P-and-M routines. Knowing the concept
of a wrench will also mean knowing how to recognize one and knowing how to use
one. Self-modifying systems that learn by changing their structure will
arrive. New experience will add new knowledge and modify existing knowledge
structures. Planning based on these structures will lead to experience which
will lead to modified knowledge. The system will learn to ride a bicycle,
"compile the knowledge into manipulation", attach it to the "bicycle riding
concept" as grounding, and be permanently modified. Facts learned through
conversation will be used to modify the knowledge base, leading to new
behavior, just as we teach new students now. Self-modifying systems will
arrive. But first we have to let the deep-learning hype burn out. Sigh.

