AI and Tacit Knowledge

Useful, mostly non-technical paper on the topic.  We learned much about this when we trained with explicit rules.  How do we effectively leverage the statement:  'we know more than we can tell"?

Polanyi's Revenge and AI's New Romance with Tacit Knowledge  By Subbarao Kambhampati    Communications of the ACM, February 2021, Vol. 64 No. 2, Pages 31-32  10.1145/3446369

In his 2019 Turing Award Lecture, Geoff Hinton talks about two approaches to make computers intelligent. One he dubs—tongue firmly in cheek—"Intelligent Design" (or giving task-specific knowledge to the computers) and the other, his favored one, "Learning" where we only provide examples to the computers and let them learn. Hinton's not-so-subtle message is that the "deep learning revolution" shows the only true way is the second.

Hinton is of course reinforcing the AI Zeitgeist, if only in a doctrinal form. Artificial intelligence technology has captured popular imagination of late, thanks in large part to the impressive feats in perceptual intelligence—including learning to recognize images, voice, and rudimentary language—and bringing fruits of those advances to everyone via their smartphones and personal digital accessories. Most of these advances did indeed come from "learning" approaches, but it is important to understand the advances have come in spheres of knowledge that are "tacit"—although we can recognize faces and objects, we have no way of articulating this knowledge explicitly. The "intelligent design" approach fails for these tasks because we really do not have conscious theories for such tacit knowledge tasks. But, what of tasks and domains—especially those we designed—for which we do have explicit knowledge? Is it forbidden to give that knowledge to AI systems?

(Robot Image)  "Human, grant me the serenity to accept the things I cannot learn, data to learn the things I can, and wisdom to know the difference."

The polymath Polanyi bemoaned the paradoxical fact that human civilization focuses on acquiring and codifying "explicit" knowledge, even though a significant part of human knowledge is "tacit" and cannot be exchanged through explicit verbal instructions. His "we can know more than we can tell" dictum has often been seen as a pithy summary of the main stumbling block for early AI efforts especially in perception.

Polanyi's paradox explains to a certain extent why AI systems wound up developing in a direction that is almost the reverse of the way human babies do. Babies demonstrate aspects of perceptual intelligence (recognizing faces, voices and words), physical manipulation (of putting everything into their mouths), emotional intelligence, and social intelligence, long before they show signs of expertise in cognitive tasks requiring reasoning skills. In contrast, AI systems have demonstrated reasoning abilities—be they expert systems or chess—long before they were able to show any competence in the other tacit facets of intelligence including perception.

In a sense, AI went from getting computers to do tasks for which we (humans) have explicit knowledge, to getting computers to learn to do tasks for which we only have tacit knowledge. The recent revolution in perceptual intelligence happened only after labeled data (such as cats, faces, voices, text corpora, and so forth) became plentiful, thanks to the Internet and the World Wide Web, allowing machines to look for patterns when humans are not quite able to give them explicit know-how. .... '