By Michael I. Jordan | Full Article | 4500 words | Apr 18, 2018
1 minute read
Don’t evaluate AI as if it’s completely greenfield. Compare to the
I don’t agree with this personally but I see where the author is
coming from. I think this is hindsight bias
Whereas civil engineering and chemical engineering were built on
physics and chemistry, this new engineering discipline will be built
on ideas that the preceding century gave substance to — ideas such
as “information,” “algorithm,” “data,” “uncertainty,” “computing,”
“inference,” and “optimization.” Moreover, since much of the focus
of the new discipline will be on data from and about humans, its
development will require perspectives from the social sciences and
This confluence of ideas and technology trends has been rebranded as
“AI” over the past few years. This rebranding is worthy of some
I do agree with this. I think there isn’t so much of a “revolution”
as just good progress in a good field
Intelligence Augmentation: computation and data used to create
services that augment human intelligence and creativity, i.e. a really
good search engine or a sound generator
Intelligent Infrastructure: a web of computation, data, and
physical entites exists that makes human environments more supportive,
interesting, and safe. Medicine, transportation, etc.
Is working on classical human-imitative AI the best or only way to
focus on these larger challenges?
Has not been very successful
Success in human-imitative AI may not be enough for IA and II
problems. Lots of additional challenges, namely engineering
and scaling problems.
We need to realize that the current public dialog on AI — which
focuses on a narrow subset of industry and a narrow subset of
academia — risks blinding us to the challenges and opportunities that
are presented by the full scope of AI, IA and II.