Abstract
Artificial Intelligence (AI) was the inspiration that shaped computing as we know it today. In this article, I explore why and how AI would continue to inspire computing and reinvent it when Moore's Law is running out of steam. At the dawn of computing, Alan Turing proposed that instead of comprising many different specific machines, the computing machinery for AI should be a Universal Digital Computer, modeled after human computers, which carry out calculations with pencil on paper. Based on the belief that a digital computer would be significantly faster, more diligent and patient than a human, he anticipated that AI would be advanced as software. In modern terminology, a universal computer would be designed to understand a language known as an Instruction Set Architecture (ISA), and software would be translated into the ISA. Since then, universal computers have become exponentially faster and more energy efficient through Moore's Law, while software has grown more sophisticated. Even though software has not yet made a machine think, it has been changing how we live fundamentally. The computing revolution started when the software was decoupled from the computing machinery. Since the slowdown of Moore's Law in 2005, the universal computer is no longer improving exponentially in terms of speed and energy efficiency. It has to carry ISA legacy, and cannot be aggressively modified to save energy. Turing's proposition of AI as software is challenged, and the temptation of making many domain-specific AI machines emerges. Thanks to Deep Learning, software can stay decoupled from the computing machinery in the language of linear algebra, which it has in common with supercomputing. A new universal computer for AI understands such language natively to then become a Native Supercomputer. AI has been and will still be the inspiration for computing. The quest to make machines think continues amid the slowdown of Moore's Law. AI might not only maximize the remaining benefits of Moore's Law, but also revive Moore's Law beyond current technology.
Highlights
What exactly are the discrete state machines to win the imitation game? Apparently, he did not know during his time; but witnessing the extreme difficulty of building a non-human, electronic computer himself [3], he envisioned only one machine, the Universal Digital Computer that could mimic any discrete state machine
Unless we reduce the core aggressively to compensate for the lagging improvement in energy efficiency, there might be no incentive to go with the fourth generation of transistor shrinking as there will be negligible performance improvement
Turing was not specific about the performance and energy efficiency of a universal computer. He assumed that computers would always be sufficiently fast, and would not be a gating factor for the quest for human-level Artificial Intelligence (AI); but if passing the Turing Test is the ultimate criteria for machine intelligence, he would have suggested that the computers must achieve a certain level of performance and efficiency to exhibit intelligence; otherwise, the interrogator would be suspicious if it takes too long for a computer to respond to questions or consumes too many resources in the effort
Summary
At the dawn of computing, one of the founding fathers, Alan Turing, believed that AI could be approached as software running on a universal computer This was a revolutionary idea given that during his time, the term “computer” was generally referred to as a human hired to do calculations with pencil on paper. Turing’s Universal Computer inspired von Neumann to come up with a powerful computing paradigm, in which complex functions were expressed in a simple yet complete language, the Instruction Set Architecture (ISA), that computing machinery could understand and execute. It brought us computers, as well as the software industry. There is abundant parallelism in AI with Deep Learning as we will see later
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