Lifeboat Foundation

In recent years, these technological limitations have become far more pressing. Deep neural networks have radically expanded the limits of artificial intelligence—but they have also created a monstrous demand for computational resources, and these resources present an enormous financial and environmental burden. Training GPT-3, a text predictor so accurate that it easily tricks people into thinking its words were written by a human, costs $4.6 million and emits a sobering volume of carbon dioxide—as much as 1,300 cars, according to Boahen.

With the free time afforded by the pandemic, Boahen, who is faculty affiliate at the Wu Tsai Neurosciences Institute at Stanford and the Stanford Institute for Human-Centered AI (HAI), applied himself single mindedly to this problem. “Every 10 years, I realize some blind spot that I have or some dogma that I’ve accepted,” he says. “I call it ‘raising my consciousness.’”

This time around, raising his consciousness meant looking toward dendrites, the spindly protrusions that neurons use to detect signals, for a completely novel way of thinking about computer chips. And, as he writes in Nature, he thinks he’s figured out how to make chips so efficient that the enormous GPT-3 language prediction neural network could one day be run on a cell phone. Just as Feynman posited the “quantum supremacy” of quantum computers over traditional computers, Boahen wants to work toward a “neural supremacy.”

Researchers from Japan and the Michigan Technological University have succeeded in building a molecular computer that, more than any previous project of its kind, can replicate the inner mechanisms of the human brain, repairing itself and mimicking the massive parallelism that allows our brains to process information like no silicon-based computer can.

A relatively new technology, molecular electronics is an interdisciplinary pursuit that may very well prove the long-term solution to validate Moore’s law well into the next century. A molecular computer is made of organic molecules instead of silicon. Chips built this way are not only potentially much smaller but also, because of the way they can be networked, able to do things that no other traditional computer, regardless of its speed, can do.

“Modern computers are quite fast, capable of executing trillions of instructions a second, but they can’t match the intelligent performance of our brain,” Michigan Tech physicist Ranjit Pati commented. “Our neurons only fire about a thousand times per second. But I can see you, recognize you, talk with you, and hear someone walking by in the hallway almost instantaneously, a Herculean task for even the fastest computer.”

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes. Here the authors create layered assemblies of genetically encoded devices that perform non-binary logic computation and signal processing using combinatorial promoters and feedback regulation.

Electrophysical processes are used to create third-order nanoscale circuit elements, and these are used to realize a transistorless network that can perform Boolean operations and find solutions to a computationally hard graph-partitioning problem.

Molecular materials for computing progress intensively but the performance and reliability still lag behind. Here the authors assess the current state of computing with molecular-based materials and describe two issues as the basis of a new computing technology: continued exploration of molecular electronic properties and process development for on-chip integration.

Our ability to understand the mind and its relation to the body is highly dependent on the way we define consciousness and the lens through which we study it. We argue that looking at conscious experience from an information-theory perspective can help obtain a unified and parsimonious account of the mind. Today’s dominant models consider consciousness to be a specialized function of the brain characterized by a discrete neural event. Against this background, we consider subjective experience through information theory, presenting consciousness as the propagation of information from the past to the future. We examine through this perspective major characteristics of consciousness. We demonstrate that without any additional assumptions, temporal continuity in perception can explain the emergence of volition, subjectivity, higher order thoughts, and body boundaries. Finally, we discuss the broader implications for the mind-body question and the appeal of embodied cognition.

Keywords: body boundaries; consciousness; information theory; neural correlates of consciousness (NCC); perception; self; volition.

Copyright © 2022 Revach and Salti.

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