In our last post, I stated, “While AI as a field of research experienced funding surges and recessions, the infrastructure that ultimately fuels AI, integrated circuits, and computer software continued to follow Moore’s law. In the next post, we’ll discuss Moore’s law and its role in ending the second AI Winter.” This post will describe how Moore’s law ended the second AI Winter.
Intel co-founder Gordon E. Moore was the first to note a peculiar trend: the number of components in integrated circuits had doubled every year from the 1958 invention of the integrated circuit until 1965. In 1970 Caltech professor, VLSI (i.e., Very-Large-Scale Integration) pioneer, and entrepreneur Carver Mead coined the term “Moore’s law,” referring to Gordon E. Moore’s observation, and the phrase caught on within the scientific community. In 1975, Moore revised his prediction regarding the number of components in integrated circuits doubling every year to doubling every two years. Intel executive David House noted that Moore’s latest prediction would cause computer performance to double every eighteen months due to the combination of more transistors and the transistors themselves becoming faster.
This means that while the research field of AI experienced surges and recessions, the fundamental building blocks of AI, namely integrated circuit computer components, continued their exponential growth. Even today, Moore’s law is still applicable. In fact, many semiconductor companies use Moore’s law to plan their long-term product offerings. There is a deeply held belief in the semiconductor industry that they must adhere to Moore’s law must remain competitive. In effect, it has become a self-fulfilling prophecy.
In the strictest sense, Moore’s law is not a physical law of science. Rather, it delineates a trend or a general rule. This begs a question, “How long will Moore’s law continue to apply?” For approximately the last half-century, each estimate has predicted that Moore’s law would hold for another decade at various points in time. This has been occurring for almost five decades. I worked in the semiconductor industry for more than thirty years and over 20 years as a director of engineering for Honeywell’s Solid State Electronics Center, which developed and manufactured state-of-the-art integrated circuits for computers, missiles, and satellites. As a director of engineering, I was responsible for developing some of the world’s most sophisticated integrated circuits and sensors. During my over thirty years in the semiconductor industry, Moore’s law always appeared as if it would reach an impenetrable barrier. This, however, did not happen. New technologies constantly seemed to provide a stay of execution. We know that the trend may change at some point, but no one really has made a definitive case as to when this trend will end. The difficulty in predicting the end has to do with how one interprets Moore’s law. In my judgment, Moore’s law is not about integrated circuits, but rather it is an observation about human creativity as it relates to technology development. In fact, American author and Google’s director of engineering, Ray Kurzweil, showed via historical analysis that technological change is exponential. He termed this “The Law of Accelerating Returns” (Reference: The Age of Spiritual Machines, 1999, Ray Kurzweil).
As computer hardware and software continued its relentless exponential improvement, the AI field focused its development on “intelligent agents” or, as it often referred to, “smart agents.” The smart agent is a system that interacts with its environment and takes calculated actions to achieve its goal. Smart agents also can be combined to form multi-agent systems, with a hierarchical control system to bridge lower-level AI systems to higher-level AI systems. This became the game-changer. Using smart agents, AI technology has equal and exceed human intelligence in specific areas, such as playing chess. However, the current state of AI technology still falls short of general human intelligence, but this will change in the coming decades. We’ll discuss this further in the next post.