(Roughly) Daily

AI isn’t human, but that doesn’t mean, Nathan Gardels argues (citing three recent essays in Noema, the magazine that he edits), that it cannot be intelligent…

As the authors point out, “the dominant technique in contemporary AI is deep learning (DL) neural networks, massive self-learning algorithms which excel at discerning and utilizing patterns in data.”

Critics of this approach argue that its “insurmountable wall” is “symbolic reasoning, the capacity to manipulate symbols in the ways familiar from algebra or logic. As we learned as children, solving math problems involves a step-by-step manipulation of symbols according to strict rules (e.g., multiply the furthest right column, carry the extra value to the column to the left, etc.).”

Such reasoning would enable logical inferences that can apply what has been learned to unprogrammed contingencies, thus “completing patterns” by connecting the dots. LeCun and Browning argue that, as with the evolution of the human mind itself, in time and with manifold experiences, this ability may emerge as well from the neural networks of intelligent machines.

“Contemporary large language models — such as GPT-3 and LaMDA — show the potential of this approach,” they contend. “They are capable of impressive abilities to manipulate symbols, displaying some level of common-sense reasoning, compositionality, multilingual competency, some logical and mathematical abilities, and even creepy capacities to mimic the dead. If you’re inclined to take symbolic reasoning as coming in degrees, this is incredibly exciting.”

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The philosopher Charles Taylor associates the breakthroughs of consciousness in that era with the arrival of written language. In his view, access to the stored memories of this first cloud technology enabled the interiority of sustained reflection from which symbolic competencies evolved.

This “transcendence” beyond oral narrative myth narrowly grounded in one’s own immediate circumstance and experience gave rise to what the sociologist Robert Bellah called “theoretic culture” — a mental organization of the world at large into the abstraction of symbols. The universalization of abstraction, in turn and over a long period of time, enabled the emergence of systems of thought ranging from monotheistic religions to the scientific reasoning of the Enlightenment.

Not unlike the transition from oral to written culture, might AI be the midwife to the next step of evolution? As has been written in this column before, we have only become aware of climate change through planetary computation that abstractly models the Earthly organism beyond what any of us could conceive out of our own un-encompassing knowledge or direct experience.

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For Bratton and Agüera y Arcas, it comes down in the end to language as the “cognitive infrastructure” that can comprehend patterns, referential context and the relationality among them when facing novel events.

“There are already many kinds of languages. There are internal languages that may be unrelated to external communication. There are bird songs, musical scores and mathematical notation, none of which have the same kinds of correspondences to real-world referents,” they observe.

As an “executable” translation of human language, code does not produce the same kind of intelligence that emerges from human consciousness, but is intelligence nonetheless. What is most likely to emerge in their view is not “artificial” intelligence when machines become more human, but “synthetic” intelligence, which fuses both.

As AI further develops through human prompt or a capacity to guide its own evolution by acquiring a sense of itself in the world, what is clear is that it is well on the way to taking its place alongside, perhaps conjoining and becoming synthesized with, other intelligences, from homo sapiens to insects to forests to the planetary organism itself…

AI takes its place among and may conjoin with other multiple intelligences: “Cognizant Machines: A What Is Not A Who.” Eminentl worth reading in full both the linked essay and the articles referenced in it.

* Dostoyevsky, Crime and Punishment

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As we make room for company, we might recall that it was on this date in 1911 that a telegraph operator in the 7th floor of The New York Times headquarters in Times Square sent a message– “This message sent around the world”– that left at 7:00p, traveled over 28,000 miles, and was relayed by 16 different operators. It arrived back at the Times only 16.5 minutes later.

The “around the world telegraphy” record had been set in 1903, when President Roosevelt celebrated the completion of the Commercial Pacific Cable by sending the first round-the-world message in just 9 minutes. But that message had been given priority status; the Times wanted to see how long a regular message would take — and what route it would follow.

The building from which the message originated is now called One Times Square and is best known as the site of the New Year’s Eve ball drop.

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