In analog computing, complexity resides in network topology, not in code

In analog computing, complexity resides in network topology, not in code. Information is processed as continuous functions of values such as voltage and relative pulse frequency rather than by logical operations on discrete strings of bits. Digital computing, intolerant of error or ambiguity, depends upon error correction at every step along the way. Analog computing tolerates errors, allowing you to live with them. Nature uses digital coding for the storage, replication, and recombination of sequences of nucleotides, but relies on analog computing, running on nervous systems, for intelligence and control. The genetic system in every living cell is a stored-program computer. Brains aren’t. Digital computers execute transformations between two species of bits: bits representing differences in space and bits representing differences in time. The transformations between these two forms of information, sequence and structure, are governed by the computer’s programming, and as long as computers require human programmers, we retain control. Analog computers also mediate transformations between two forms of information: structure in space and behavior in time. There is no code and no programming. Somehow—and we don’t fully understand how—Nature evolved analog computers known as nervous systems, which embody information absorbed from the world. They learn. One of the things they learn is control. They learn to control their own behavior, and they learn to control their environment to the extent that they can. Computer science has a long history—going back to before there even was computer science—of implementing neural networks, but for the most part these have been simulations of neural networks by digital computers, not neural networks as evolved in the wild by Nature herself. This is starting to change: from the bottom up, as the threefold drivers of drone warfare, autonomous vehicles, and cell phones push the development of neuromorphic microprocessors that implement actual neural networks, rather than simulations of neural networks, directly in silicon (and other potential substrates); and from the top down, as our largest and most successful enterprises increasingly turn to analog computation in their infiltration and control of the world. While we argue about the intelligence of digital computers, analog computing 1s quietly supervening upon the digital, in the same way that analog components like vacuum tubes were repurposed to build digital computers in the aftermath of World War II. Individually deterministic finite-state processors, running finite codes, are forming large-scale, nondeterministic, non-finite-state metazoan organisms running wild in the real world. The resulting hybrid analog/digital systems 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. Analog is back, and its nature is to assume control. Governing everything from the flow of goods to the flow of traffic to the flow of ideas, these systems operate statistically, as pulse-frequency coded information is processed in a neuron or a brain. The emergence of intelligence gets the attention of Homo sapiens, but what we should be worried about is the emergence of control. 38 HOUSE_OVERSIGHT_016841