David Weinberger

We are not today close to producing a computer that could convince us that it is intelligent. Some philosophers have argued that we are not even appreciably closer to this goal than we were ten years ago. But why should artificial intelligence even be considered possible? In this paper I shall argue that the temptation to believe in the possibility of AI stems from a misunderstanding about the nature of ideas; further, this misunderstanding can be traced back at least to Plato’s presentation of the metaphor of the cave. A phenomenological view of the nature of ideas removes the temptation to believe in the possibility of AI as presently considered.

According to John Howie, who compiled these first six annual Wayne Leys Memorial Lectures, “These essays invite the reader to discover the relevance of clearly stated, balanced, and reasonable ethical principles to controversial issues of our time.” Although the essays are indeed clearly stated, balanced, and reasonable, it is unlikely the book’s purchasers will be discovering for the first time that philosophy has something to say about social issues. But an anthology such as this will be bought and appreciated by scholars interested in reading some fairly recent work by six major contemporary American figures in the field of philosophical ethics.

Machine learning (ML) trains itself by discovering patterns of correlations that can be applied to new inputs. That is a very powerful form of generalization, but it is also very different from the sort of generalization that the west has valorized as the highest form of truth, such as universal laws in some of the sciences, or ethical principles and frameworks in moral reasoning. Machine learning’s generalizations synthesize the general and the particular in a new way, creating a multidimensional model that often retains more of the complex differentiating patterns it has uncovered in the training process than the human mind can grasp. Particulars speak louder in these models than they do in traditional generalizing frameworks. This creates an odd analogy with recent movements in moral philosophy, particularly the feminist ethics of care which rejects the application of general moral frameworks in favor of caring responses to the particular needs and interests of those affected by a moral decision. This paper suggests that our current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing our culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care.