Artificial Intelligence: The Revolution Hasn't Happened Yet


:star: :star: :star: :star:

Notes

  • Don’t evaluate AI as if it’s completely greenfield. Compare to the past.
    • 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 humanities.
  • This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. This rebranding is worthy of some scrutiny.
    • 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.