Opinion | How Long Will A.I.'s 'Slop' Era Last? - The New York Times - 0 views
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Sequoia Capital, calculated that investments in A.I. were running short of projected profits by a margin of at least several hundred billion dollars annually. (He called this “A.I.’s $600 billion question” and warned of “investment incineration.”)
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In a similarly bearish Goldman Sachs report, the firm’s head of global equity research estimated that the cost of A.I. infrastructure build-out over the next several years would reach $1 trillion. “Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed,” he noted. “The crucial question is: What $1 trillion problem will A.I. solve?”
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that trillion-dollar A.I. expenditure, more than the United States spends annually on its military, and think: What exactly is that money going toward?
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What is A.I. even for?
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“A.I. slop”: often uncanny, frequently misleading material, now flooding web browsers and social-media platforms like spam in old inboxes. Years deep into national hysteria over the threat of internet misinformation pushed on us by bad actors, we’ve sleepwalked into a new internet in which meaningless, nonfactual slop is casually mass-produced and force-fed to us by A.I.
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It has already helped drive down the cost and drive up the performance of next-gen batteries and solar photovoltaic cells, whose performance can also be improved, even after the panels have been manufactured and installed on your roof, by as much as 25 percent
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while the internet was never perfectly trustworthy, one epoch-defining breakthrough of Google was that it got us pretty close. Now the company’s chief executive acknowledges that hallucinations are “inherent” to the technology it has celebrated as a kind of successor for ranked-order search results, which are now often found far below not just the A.I. summary but a whole stack of “sponsored” results as well.
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Where not long ago we used to find the very best results for Google searches, we can now find instead potentially plagiarized and often inaccurate paragraph summaries of answers to our queries
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Machine learning may help make our electricity grid as much as 40 percent more efficient at delivering power as it is today, when many of its routing decisions are made by individual humans on the basis of experience and intuition
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This month, KoBold Metals announced the largest discovery of new copper deposits in a decade — a green-energy gold mine, so to speak, delivered with the help of its own proprietary A.I., which integrated information about subatomic particles detected underground with century-old mining reports and radar imagery to make predictions about where minerals critical for the green transition might be found.
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.I. is designing new proteins, rapidly accelerating drug discovery and speeding up clinical trials testing new medicines and therapies.
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perhaps that a more optimistic perspective can be drawn by analogy to what economists call the “environmental Kuznets curve,” which suggests that, as nations develop, they tend to first pollute a lot more and then, over time, as they grow richer, they ultimately pollute less.
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Even in describing regular old pollution, this framework has its shortcomings, especially because it treats as automatic eventual progress that has always required tooth-and-nail fights against some very stubborn bad actors
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A.I. is generating an awful lot of genuine pollution, too — both Google and Microsoft, which each pledged in 2019 to reach zero emissions by 2030, have instead expanded their carbon footprints by nearly 50 percent in the interim.