We call them “AI”, but that confuses and distracts us: Michael Jordan: Artificial Intelligence—The revolution hasn’t happened yet

“Societal-scale, inference-and-decision-making systems that involve machines, humans and the environment”: We call them “AI”, but that confuses and distracts us: Michael Jordan: Artificial Intelligence—The Revolution Hasn’t Happened Yet: “Artificial Intelligence (AI) is the mantra… intoned by technologists, academicians, journalists and venture capitalists alike…

…The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us—enthralling us and frightening us in equal measure. And, unfortunately, it distracts us…. Whether or not we come to understand “intelligence” any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While this challenge is viewed by some as subservient to the creation of “artificial intelligence,” it can also be viewed more prosaically—but with no less reverence—as the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and doing so safely. 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.

While the building blocks have begun to emerge, the principles for putting these blocks together have not yet emerged, and so the blocks are currently being put together in ad-hoc ways. Thus, 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. And, 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 its principles of analysis and design.

The current public dialog… uses “AI” as an intellectual wildcard… that makes it difficult to reason…. Most of what is being called “AI”… is… “Machine Learning” (ML)…. That ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction, and building innovative consumer-facing services…. ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase “Data Science” began to be used…. This confluence of ideas and technology trends has been rebranded as “AI”…. This rebranding is worthy of some scrutiny….

Since the 1960s much progress has been made, but… not… from the pursuit of human-imitative AI…. Optimization… statistics researcher… find themselves suddenly referred to as “AI researchers.”… We now come to a critical issue: Is working on classical human-imitative AI the best or only way to focus?… [But] success in human-imitative AI has in fact been limited… and success in these domains is neither sufficient nor necessary…. On the sufficiency side… self-driving cars… engineering problems… may have little relationship to human competencies…. As for… necessity argument… did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer?… Humans are in fact not very good at some kinds of reasoning…. We did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts…. 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. This scope is less about the realization of science-fiction dreams or nightmares of super-human machines, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives…

April 21, 2018


Brad DeLong
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