What Michel Jordan calls "an engineering discipline with principles of analysis and design" is, I think, more usually called "social science":

Michael Jordan: Artificial Intelligence—The Revolution Hasn’t Happened Yet: "Just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans, and the environment. Just as early buildings and bridges sometimes fell to the ground—in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws. Unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. What we’re missing is an engineering discipline with principles of analysis and design...

Realizing in software and hardware an entity possessing human-level intelligence. I will use the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificially-intelligent entity should seem to be one of us, if not physically then at least mentally (whatever that might mean).... The famous backpropagation algorithm.... One could simply refer to all of this as AI, and indeed that is what appears to have happened. Such labeling may come as a surprise to optimization or statistics researchers, who find themselves suddenly called AI researchers, but labels aside, the bigger problem is that the use of this single, ill-defined acronym prevents a clear understanding of the range of intellectual and commercial issues at play.

The past two decades have seen major progress—in industry and academia—in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). Here computation and data are used to create services that augment human intelligence and creativity. A search engine can be viewed as an example of IA, as it augments human memory and factual knowledge, as can natural language translation....

Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data, and physical entities exists that makes human environments more supportive, interesting, and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce, and finance, with implications for individual humans and societies....

We now come to a critical issue: is working on classical human-imitative AI the best or only way to focus on these larger challenges?... Although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited.... Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems.... Some say that the human-imitative AI aspiration subsumes IA and II aspirations, because a human-imitative AI system would not only be able to solve the classical problems of AI (e.g., as embodied in the Turing test), but it would also be our best bet for solving IA and II problems. Such an argument has little historical precedent. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer?...

We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. Such systems must cope with cloud-edge interactions in making timely, distributed decisions, and they must deal with long-tail phenomena where there is lots of data on some individuals and little data on most individuals. They must address the difficulties of sharing data across administrative and competitive boundaries. Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods.....

IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean)....

We need to realize that the current public dialog on AI—which focuses on narrow subsets of both industry and of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA, and II. This scope is less about the realization of science-fiction dreams or superhuman nightmares, and more about the need for humans to understand and shape technology as it becomes ever more present and influential...


#noted

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