Quote of the Week: What (& Who) Makes a Chair a “Chair”?

Tom Edsall writes data- and research-driven columns in the New York Times that frequently make my head spin and my heart sink. Today he did both by talking about a chair . . . .

Edsall: David Autor is an economist at M.I.T. who has written on the role of the trade shocks that have driven white working class voters into the arms of the Republican Party . . . .

In a July 2022 paper “The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty,” Autor describes how artificial intelligence radically enlarges the potential of robotics and automation to replace workers not only performing routine tasks but more complex procedures:

“What makes a task routine is that it follows an explicit, fully specified set of rules and procedures. Tasks fitting this description can in many cases be codified in computer software and executed by machines.”

Conversely, Autor goes on to say, tasks that rely on “tacit knowledge (e.g., riding a bicycle, telling a clever joke) have historically been challenging to program because the explicit steps for accomplishing these tasks are often not formally known.”

“Artificial intelligence,” Autor writes, “overturns the second piece of the task framework — specifically, the stipulation that computers can accomplish only explicitly understood (i.e., ‘routine’) tasks. A.I. tools surmount this longstanding constraint because they can be used to infer tacit relationships that are not fully specified by underlying software.”

Autor uses the manufacture of a chair to explain the power of A.I.:

It is extraordinarily challenging to explicitly define what makes a chair a chair: must it have legs, and if so, how many; must it have a back; what range of heights is acceptable; must it be comfortable; and what makes a chair comfortable, anyway? Writing the rules for this problem is maddening. If written too narrowly, they will exclude stools and rocking chairs. If written too broadly, they will include tables and countertops.

A chair. A beanbag?

A.I. cuts through the problem of computerizing the manufacture of a chair, according to Autor, by learning

the solution inductively by training on examples. Given a suitable database of tagged images and sufficient processing power, A.I. can infer what image attributes are statistically associated with the label ‘chair’ and can then use that information to classify untagged images of chairs with a high degree of accuracy What rules does A.I. use for this classification? In general, we do not know because the rules remain tacit. Nowhere in the learning process does A.I. formally codify or reveal the underlying features (i.e., rules) that constitute “chair-ness.” Rather, the classification decision emerges from layers of learned statistical associations with no human interpretable window into that decision-making process.

COMMENT: Which is to say, you still need some human workers to make many kinds of “chairs”. But with AI’s coming mastery of “chairness,” many or most of those remaining jobs will vanish — not sent to China, but just gone.

The chairmaking companies will thereby increase profits; the unemployed chairmakers will increasingly look for someone to blame and despise for their plight; and the red hats are ready and waiting to supply them, hammers included.

I’m not sure if I was ever frightened by a chair before. But this is a time of continued surprises.

And now, thanks to Tom Edsall, I can be reminded every time I sit down.

One thought on “Quote of the Week: What (& Who) Makes a Chair a “Chair”?”

Eames? Wright ? Robie ? Bauhaus?

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