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The Impact of Artificial Intelligence on Innovation (nber.org)
Abstract of a paper [PDF] which was originally published last month: Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose "method of invention" that can reshape the nature of the innovation process and the organization of R&D. We distinguish between automation-oriented applications such as robotics and the potential for recent developments in "deep learning" to serve as a general-purpose method of invention, finding strong evidence of a "shift" in the importance of application-oriented learning research since 2009.
We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms. At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms. We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.
We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms. At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms. We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.
No matter what you think, we won't end up with people working less and living better. We will end up with 1% exclusively benefiting from all the increases in productivity and 99% getting hit by even higher unemployment because even conspicuous consumption by 1% has a limit.
use what ever definition you want, "Imitation" is not intelligence. Intelligence is adaptability, intelligence is dynamic and self evolving, intelligence is the ability to apply one data to a new and unrelated problem (aka, the EUREKA moment). What we have today is "task programming"- nothing more. The "task" of converting spoken words to text with zero comprehension or experience of the information that is attempting to be conveyed. Siri for example is saturated with errors, auto corrects are hilarious, suggestions are nonsensical, and you you want to call that AL? bah. Even the best AI "autonomous" vehicles drive into fixed objects. Stop being amused by parlor tricks and see them for what they are - task programming.
We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms.
It is cruel to have this (AI generated?) stuff just after another story telling "Papers today are longer than ever and full of jargon and symbols"