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NEW THOUGHTS ON A.I. PART 1
MY MOST RECENT thought on artificial intelligence is to add punctuation, A.I. Hitherto, depending upon type font, discussions of this subject here at SimanaitisSays resembled a bio of a guy named Al. My other thoughts about A.I. are more profound, arising from The New York Times Book Review and prompting me to reexamine an earlier London Review of Books.
In “Can Humans Be Replaced by Machines?,” The New York Times Book Review, March 19, 2021, James Fallows writes about two A.I. books: Genius Makers, by Cade Metz; and Futureproof: 9 Rules for Humans in the Age of Automation, by Kevin Roose.
In the London Review of Books, January 21, 2021, “Insanely Complicated, Hopelessly Inadequate,” Paul Taylor reviews three A.I. books: The Promise of Artificial Intelligence: Reckoning and Judgment, by Brian Cantwell Smith; Rebooting A.I.: Building Artificial Intelligence We Can Trust; by Gary Marcus and Ernest Davis; and The Book of Why: The New Science of Cause and Effect, by Judea Pearl and Dana MacKinzie.
I doubt I’d have the gumption to read all five, but here in Parts 1 and 2 today and tomorrow are tidbits from the two reviews (which I read word for word).
A Touching Error. Fallows writes, “The most persistent and touching error has been the ever-dashed hope that, as machines are able to do more work, human beings will be freed to do less, and will have more time for culture and contemplation.”
One example of this is “paperless” anything. Do we actually accumulate less paper?
The Reality. Fallows cites the way A.I. has already changed our lives: “Its implications range from utilities already routinized into daily life (like real-time updates on traffic flow), to ominous steps toward ‘1984’-style perpetual-surveillance states (like China’s facial recognition system, which within one second can match a name to a photo of any person within the country).”
Person Thinking? Machine Thinking? In its 60 years of development, A.I. has had two different approaches. Fallows says, “The longest running, most consequential debate is between proponents of two different approaches to increasing computerized ‘intelligence,’ which can be oversimplified as ‘thinking like a person’ versus ‘thinking like a machine.’ The first boils down to using ‘neural networks’—the neurons in this case being computer circuits…. The second boils down to equipping a computer with detailed sets of rules—rules of syntax and semantics for language translation, rules of syndrome-pattern for medical diagnosis.”
Computers Lagged, then Caught Up. Fallows describes the excitement of neural networks in the early 1960s leading to the A.I. winter of the 1970s “when the era’s computers proved too limited to do the job, to the recent revival of a neural-network approach toward ‘deep-learning,’ which is the result of the faster and more complex self-correction of today’s enormously capable machines.”
Tomorrow in Part 2, we’ll see what the other reviewer, Paul Taylor, has to say about A.I. and its future. ds
© Dennis Simanaitis, SimanaitisSays.com, 2021
Simanaitis Says 