The promise of artificial intelligence: reckoning and judgment

ZotWeb	book
Src Url	Smith (2019)

Abstract

An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent.

In this provocative book, Brian Cantwell Smith argues that artificial intelligence is nowhere near developing systems that are genuinely intelligent. Second wave AI, machine learning, even visions of third-wave AI: none will lead to human-level intelligence and judgment, which have been honed over millennia. Recent advances in AI may be of epochal significance, but human intelligence is of a different order than even the most powerful calculative ability enabled by new computational capacities. Smith calls this AI ability “reckoning,” and argues that it does not lead to full human judgment—dispassionate, deliberative thought grounded in ethical commitment and responsible action. Taking judgment as the ultimate goal of intelligence, Smith examines the history of AI from its first-wave origins (“good old-fashioned AI,” or GOFAI) to such celebrated second-wave approaches as machine learning, paying particular attention to recent advances that have led to excitement, anxiety, and debate. He considers each AI technology's underlying assumptions, the conceptions of intelligence targeted at each stage, and the successes achieved so far. Smith unpacks the notion of intelligence itself—what sort humans have, and what sort AI aims at.

Smith worries that, impressed by AI's reckoning prowess, we will shift our expectations of human intelligence. What we should do, he argues, is learn to use AI for the reckoning tasks at which it excels while we strengthen our commitment to judgment, ethics, and the world.

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Annotations

Brian Cantwell Smith,

Smith received his BS, MS and PhD degrees from the Massachusetts Institute of Technology. He was a founder of the Center for the Study of Language and Information at Stanford University, and a founder and first president of Computer Professionals for Social Responsibility. Smith served as principal scientist at the Xerox Palo Alto Research Center, in the 1980s.

(https://en.wikipedia.org/wiki/Brian\_Cantwell\_Smith)

Citer : (Smith, 2019)

FTag: Smith-2019

APA7: Smith, B. C. (2019). The promise of artificial intelligence: Reckoning and judgment. The MIT Press.

Machine Learning 47

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Epistemological Challenges (p. 71)

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ML is essentially a suite of statistical techniques

statistical classification and prediction of patterns

based on sample data

using an interconnected fabric of processors

arranged in multiple layers.

implemented in architectures often known as “neural networks,”

5 — Machine Learning

Figure 7 - représentation d'une couche de réseau de neurone avec ses entrées et sorties

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7 — Epistemological Challenges

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articulated reasoning

chains of structured propositions involving implications, negatives, quantification, hypotheticals,

Characteristics of Articulated Reasoning

Identity and nonidentity

Quantification

Variables

Logical operators

Sets

Opacity

Categories and subcategories

Possibility and necessity

Default reasoning

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