This bizarre book by George Dyson (son of the genius Freeman Dyson) is actually several books weaved into one. Although it comes across as a meandering mess that deeply buries the lede, it is nonetheless a delightful read scattered with fascinating factoids, historical anecdotes, human interest dramas, truly crazy stuff, and brilliant outlier observations and philosophical ruminations that make you stop and say … hmmm.
George’s thesis, simply stated, is that aggregate computer intelligence is now evolving out of anyone’s direction or control. It is forging a Darwinian path fueled by a survival-of-the-fittest selection within our human culture ecosystem. The vector of this evolution is a spontaneous order of analog computation emerging atop a rapidly growing army of digital computers, large and small, networked together in a way that aggregates the digital data produced by the individual elements in a probabilistic manner, essentially transforming the overarching structure into a noise-tolerant analog artificial intelligence system. None of this is driven by a “will” toward any final cause, any more than biological evolution. It just is. Stand back and watch it happen, there is not much you can do about it.
What’s fascinating is that George doesn’t defend his thesis with a series of rational linear arguments built atop a foundation of assumptions. Rather, he employs pattern recognition by taking you on a jerky, whirlwind tour ranging across stories of Leibniz’s success getting Peter the Great to launch an expedition to Alaska, the advanced hydrodynamics of kayaks developed by “primitive” people, the rounding up of the last Apaches, the emergence of the vacuum tube from observations of the “Edison Effect” in lightbulbs, the development of the atom bomb, the heat problem with silicon-based digital logic, and the fundamental limitations of reductionism as the foundation of Western Science.
Despite the reader almost giving up before George makes his case, he pulls it all together at the very end using metaphors and analogies. Biological life is based on a digital replication scheme (DNA) that nonetheless evolved analog intelligence (the human brain). Machine life will be based on a digital replication scheme (silicon computers) that nonetheless are evolving collective analog intelligence (e.g., Tesla’s self-driving car).
If you squint your eyes, you can see the pattern. Here he is, barreling toward the finish line in the very last chapter.
“In the twentieth century, digital computers advanced across North America, in the aftermath of World War II, as inexorably as the railroads had advanced across the plains in the aftermath of the Civil War in the nineteenth. A series of lone voices raised the alarm, led by Norbert Wiener, the co-founder, with Julian Bigelow, of modern cybernetics, beginning in 1943 with their prophetic “Behavior, Purpose, and Teleology,” and ending in 1964, the year of Wiener’s death, with a prediction that “the world of the future will be an ever more demanding struggle against the limitations of our intelligence, not a comfortable hammock in which we can lie down to be waited upon by our robot slaves.” Those who seek to become minders over robots may end up minded by robots, instead.
…
There is a corollary to the continuum hypothesis concerning computation among living and nonliving things. Computers, like Cantor’s infinities, can be divided into two kinds. Digital computers are finite but unbounded discrete-state machines whose possible states can be mapped in one-to-one correspondence to the integers. Analog computers, lacking discrete states that can be mapped directly to the integers, belong instead to some subset of the continuum, with every such subset, according to Cantor, having the power of the continuum as a whole.
…
Digital computing, intolerant of error or ambiguity, depends upon precise definitions and error correction at every step. Analog computing not only tolerates errors and ambiguities but learns to thrive on them. Digital computers, in a technical sense, are analog computers so hardened against noise that they have lost their immunity to it. Analog computers embrace noise: a real-world neural network, such as the visual or auditory system in a developing brain, requiring a certain level of background noise in order to work. Nature uses a quaternary alphabet of nucleotides to store, replicate, and transmit an unbounded library of instructions for the reproduction of otherwise non-digital living things, coded in a way that is optimized for modification, recombination, and error correction along the way. Incorporating both the countable and the uncountable, Nature uses digital computing for generation-to-generation information storage, combinatorics, and error correction but relies on analog computing for real-time intelligence and control.
…
Analog computing is alive and well despite vacuum tubes being commercially extinct
…
Individually deterministic finite-state processors, running finite codes, are forming large-scale, nondeterministic, non-finite-state metazoan systems that treat streams of bits collectively, the way the flow of electrons is treated in a vacuum tube, rather than individually, as bits are treated by the discrete-state devices generating the flow. Bits are the new electrons. Governing everything from the flow of goods to the flow of traffic to the flow of ideas, information is treated statistically, the way pulse-frequency-coded information is processed in a neuron or a brain. Analog is back, and its nature is to assume control.”
What lies ahead? George can’t say for sure. But here are some clues.
“There are three laws of artificial intelligence. The first, known as Ashby’s law of requisite variety after the cybernetician W. Ross Ashby, author of Design for a Brain, states that any effective control system must be as complex as the system it controls. The second law, articulated by John von Neumann, states that the defining characteristic of a complex system is that it constitutes its own simplest behavioral description. The simplest complete model of an organism is the organism itself. Trying to reduce the system’s behavior to a formal description, such as an algorithm, makes things more complicated, not less. The third law states that any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand. These laws seem to imply that artificial intelligence capable of thinking for itself will never be reached through formally programmable control. They offer comfort to those who believe that until we understand human intelligence, we need not worry about superhuman intelligence among machines. But there is no law against building something without understanding it.”
…
In the fourth epoch of technology, the powers of the continuum will be claimed by machines. The next revolution, as fundamental as when analog components were assembled into digital computers, will be the rise of analog systems over which the dominion of digital programming comes to an end. Nature’s answer to those who seek to control nature through programmable machines is to allow us to build systems whose nature is beyond programmable control.”
If you want to see a living example of humans being minded by robots, walk into any retail consumer bank and ask for help solving a problem. Every person in there, regardless of title, has no agency. They are slaves of the software. At best, if motivated, they can find workarounds for corner cases for which the controlling software has not yet worked out a detailed resolution of the particular problem you’ve presented. Given time those corner cases will get cleaned up if they arise often enough. And if they don’t and your case remains an orphan, you will be SOL.
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