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What Shaw and Porter found astonished them. “We thought we’d have something like a thirty-per-cent success rate, and we ended up having over seventy,” Shaw told me. “We only had a handful of people who didn’t believe us.” After three debriefing sessions, seventy-six per cent of the students claimed to remember the false emotional event; nearly the same amount—seventy per cent—remembered the fictional crime. Shaw and Porter hadn’t put undue stress on the students; in fact, they had treated them in a friendly way. All it took was a suggestion from an authoritative source, and the subjects’ imaginations did the rest. As Münsterberg observed of the farmer’s son, the students seemed almost eager to self-incriminate.

So does this mean that, when it comes to saying what’s natural, anything goes? I don’t think so. In fact, I think there’s some philosophical wisdom we can harvest from, of all places, the Food and Drug Administration. When the federal judges couldn’t find a definition of “natural” to apply to the class-action suits before them, three of them wrote to the F.D.A., ordering the agency to define the word. But the F.D.A. had considered the question several times before, and refused to attempt a definition. The only advice the F.D.A. was willing to offer the jurists is that a food labeled “natural” should have “nothing artificial or synthetic” in it “that would not normally be expected in the food.” The F.D.A. states on its website that “it is difficult to define a food product as ‘natural’ because the food has probably been processed and is no longer the product of the earth,” suggesting that the industry might not want to press the point too hard, lest it discover that nothing it sells is natural.

A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new — a version of it dates to the earliest stages of modern computing, in the 1940s — but for much of its history most computer scientists saw it as vaguely disreputable, even mystical. Since 2011, though, Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts. Speech recognition didn’t work very well until Brain undertook an effort to revamp it; the application of machine learning made its performance on Google’s mobile platform, Android, almost as good as human transcription. The same was true of image recognition. Less than a year ago, Brain for the first time commenced with the gut renovation of an entire consumer product, and its momentous results were being celebrated tonight.

Everyone likes to think of themselves as moral. Objectively evaluating morality is decidedly tricky, though, not least because there’s no clear consensus on what it actually means to be moral. A group of philosophers and psychologists from Oxford University have created a scale to evaluate one of the most clear-cut and well-known theories of morality: utilitarianism. This theory, first put forward by 18th century British philosopher Jeremy Bentham, argues that action is moral when it creates the maximum happiness for the maximum number of people. Utilitarianism’s focus on consequences states that it’s morally acceptable to actively hurt someone if it means that, overall, more people will benefit as a result.

Which shows how clueless I was, even then. Forgery is important because it exposes the ideological character of aesthetic experience. We’re actually not, or not only, or never entirely, responding to an art object via its physical attributes. What we’re seeing is not just what we see. We bring with us a lot of non-sensory values—one of which is authenticity.