This week’s Sunday Reading is a technology-themed two-parter. 

The first piece, out last week on The Atlantic from social psychologist Jonathan Haidt, offers a bleak chronicle of the social media age and its role in dividing our country; the second, a recent New York Times Magazine article from contributor Steven Johnson, lays out an even bleaker preview of where we’re headed.

Haidt compares the rise of social media to the fall of Babel. 

“Babel is not a story about tribalism; it’s a story about the fragmentation of everything,” he writes. “It’s about the shattering of all that had seemed solid, the scattering of people who had been a community.”

He’s equally critical of liberals and conservatives, dating the “fall of the tower” to 2015—“a year marked by the ‘great awokening’ on the left and the ascendancy of Donald Trump on the right.”

Each faction has become dominated by activist wings, Haidt writes, whose rigid values and propensity toward public shaming have functioned to not only deprive themselves of the ability to understand the opposing party, but also silence vast swathes of their own communities.

Within the right, a wave of threats toward those who renounce popular right-wing conspiracy theories—namely that the 2020 election was “stolen” from Donald Trump—has caused “remaining moderates to quit or go silent, giving us a party ever more divorced from the conservative tradition, constitutional responsibility, and reality.”

And on the left, callout culture has created a narrative that focuses solely on “equality of outcomes” and turns a blind eye to nuance and individual rights.

“When our public square is governed by mob dynamics unrestrained by due process, we don’t get justice and inclusion,” Haidt writes. “We get a society that ignores context, proportionality, mercy, and truth.”

Though you’re likely already aware of the ideas presented by Haidt, his story is a sobering refresher on the state of things, and it’s a perfect set-up to Johnson’s piece, which is a more eye-opening look at how the Internet’s current climate will shape the future of artificial intelligence.

Johnson takes a deep dive into an organization called OpenAI, founded in 2015 with a mission to “advance digital intelligence in the way that is most likely to benefit humanity as a whole.” 

OpenAI’s crown jewel is a “large language model” called GPT-3 that can generate complex, human-like prose. GPT-3 was trained using a surprisingly simple method: pulling from a massive set of web data, the model was prompted with trillions of unfinished sentences and tasked with guessing the missing word.

“The software then strengthens whatever random neural connections generated that particular suggestion and weakens all the connections that generated incorrect guesses,” Johnson writes. “And then it moves on to the next prompt. Over time, with enough iterations, the software learns.”

If you ask GPT-3 to write an essay discussing stream of consciousness as a narrative technique in James Joyce’s Ulysses, it will generate a concise, thoughtful, and articulate composition in half a second. It will do this as many times as you like. And each essay will be unique.

But the model isn’t perfect; since it was trained on our own digital content, it carries biases and falls victim to misinformation in the same way we do. It’s been known to spew racial slurs and create persuasive arguments founded entirely on conspiracy theories. In light of Haidt’s story, GPT-3 could be an incredibly dangerous tool—our digital selves are the most outraged, the least factual, and the most divided. Is unfiltered Internet data really the best way to teach an A.I. about humanity?

Or—and this is the nagging, unsettling question the article leaves you with—could we, humans, be the result of a similar model?

Critics say the model isn’t actually intelligent, arguing that it relies on blind mimicry and is bereft of the everyday knowledge integral to human intelligence. But, as Johnson counters, “maybe predicting the next word is just part of what thinking is.”

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