FUNDAMENTAL QUESTIONS IN COMPUTING

THE WORD “ADVERTORIAL” SETS an alarm in my mind: Is this thing trying to bamboozle me or what? On the other hand, AAAS Science magazine uses it in the pure sense: a purchased bit of editorial. As a most informative example, Zhejiang Lab sponsored a three-page advertorial in the October 21, 2022, issue of Science titled “10 Fundamental Scientific Questions on Intelligent Computing.” Here are tidbits gleaned from several of them.

Background. Zhejiang Lab is a research institute in China, jointly established by the Zhejiang Provincial Government, Zhejiang University, and Alibaba Group in September, 2017. Zhejiang University evolved from Qiushi Academy, one of the oldest universities in China. Alibaba Group is an international e-commerce and retail service platform founded by Jack Ma in 1999.

The advertorial notes, “The Zhejiang Lab and Science have jointly solicited fundamental scientific questions with great significance for the future of intelligent computing. The following 10 questions, found to be the most profound and challenging, were put forward by a panel of experts from around the world.”

The panel’s 28 contributors included those from Chinese institutions as well as Duke, University of Southern California, Syracuse, Notre Dame, and Carleton. Quoted passages that follow come from the advertorial produced by the Science/AAAS Custom Publishing Office.

Intelligence and Intelligent Computing. “Broadly speaking, intelligence is the ability to analyze and appropriately respond to input (data)…. The traditional evaluation of whether a system is intelligent is the Turing Test—can a human distinguish whether the system is a human or computer? Other weaker metrics exist such as asking whether the system performs its designated tasks accurately, or whether it can generalize beyond the data it has been trained on.”

“Whether a standard framework for intelligent computing can be established is still an open question, as there is no universally agreed-upon metric upon which to conduct the debate. The rules pertinent to one system may run afoul of rules established for another, and the sands upon which that system is built may shift.” 

Count or Measure? “Analog computing uses hardware to simulate algorithms, measuring continuous signals such as voltage or light intensity. It offers the advantages of low energy consumption and high computing efficiency in solving specific problems.”

“But it fell from favor many years ago with the advent of digital computing (which counts instead of measures), in part because at that time it was difficult to scale up and to verify analog systems.” 

“Yet because of its ability to mimic components of biological networks such as synapses and neurons, analog computing has seen a resurgence…. At present, though, it is an unrefined practice, using many kinds of physical carriers and calculation methods for simulation and calculation. It awaits a unified theoretical model to held promote its standardization and large-scale application.”

Will Quantum Computing Approach the Power of the Human Brain? “Some new devices may not be useful for conventional computing, but might make neural networks efficient…. For example, new architecture will be needed to emulate the behavior of astrocytes (star-shaped glial cells in the central nervous system), which have been found to play an important role in cognition and differ in significant ways from neurons.”

“Quantum computers are operated differently from general purpose computers. It is still early in their development—currently they are mostly used for massive number-crunching activities such as encryption. Whether they will someday be able to simulate the cognitive-computing and even emotive ability of the human brain is a matter of active research.” 

A Path to Converge Silicon-based and Carbon-based Learning? “Silicon-based computing is gradually reaching its physical limits. Meanwhile, the human brain—the highest known form of carbon-based computing—lacks the speed, accuracy, and reliability of silicon.”

“Carbon- and silicon-based computing platforms differ from each other in myriad ways. The former relies on a sparse but highly connected network of neurons, which is slow in terms of signal processing but very good at certain applications. Silicon platforms, on the other hand, rely on a highly integrated two-dimensional layout that boasts much faster transfer speeds.” 

“Researchers are investigating at least two pathways to converge these systems: One is to build a mathematical model of the neural network based on silicon-based architecture. Another is to build deep neural networks with layers upon layers of network connections…. Perhaps one path of convergence would include building components that act more like neuronal synapses, integrating information and participating in the computational processes, rather than just acting as a relay.” 

Heady stuff, this. But an advertorial that’s doing more than just pitchin’ the product. ds 

© Dennis Simanaitis, SimanaitisSays.com, 2022