Some previous posts provide a quick postcard from the early days of AI and the rise of the first commercial AI applications: Expert Systems. However for all the initial hype around expert systems, their domain of expertise was (by definition) limited, they were expensive to build and maintain, and impossible to formally prove complete or correct. Moreover their most lacking feature, one that threw serious doubt as to whether expert systems could be classified as intelligent machines, was their inability to learn from the problem domain or from experience.
Feeling as if they had all rushed down a blind alley, researchers once again looked to the functioning of the human brain for inspirations and resumed work on replicating its neural structure inside machines. Even though the work of early pioneers had documented the essential concepts of neural networks, they had lacked the powerful computing infrastructure needed to implement the theory. Furthermore, mathematical analysis of neural network models appeared to demonstrate the computing limitation of such structures.
However, with more modern computing technology and the renewed enthusiasm directed at neural networks, fresh breakthroughs seems to happen simultaneously. Important theoretical advances were made in the 1980s such a Adaptive Reasoning Theory (Grossberg), Hopfield Networks (Hopfield), Self-Organising Maps (Kohonen), Reinforced Learning (Barto), and the high influential Back-Propagation Learning Algorithm (Bryson and Ho). All these resulted in a new breed of neural networks that could be trained and learn for themselves.
Other AI research postulated that since human intelligence emerged from evolutionary forces nascent in the natural world, i.e. Charles Darwin’s theory of evolution and natural selection, then intelligent machines would arise from an synthetic evolutionary environment. This approach to developing AI solution involves simulating a population of objects, allowing for evolutionary relationship to occur (selection, crossover and mutation), adding a healthy amount of entropy and letting generations evolve. The evolution based approach encapsulates three main techniques: genetic algorithms, evolutionary strategies, and genetic programming where the computer doesn’t produce answers but outputs programmes as the solution.
It would be unfair to say that neural networks, with their ability to learn from experience, discover patterns, and operate in the face of incomplete information, superseded expert systems. In fact the two technologies complement each other rather well. As was discussed in a previous post, knowledge elicitation from human expert is time consuming, can be expensive, and may lead to contradictions if multiple experts contribute to the knowledge base. Furthermore, experts may themselves make decision in the face of a great amount of uncertainty and are only able to explain their actions by vague explanations, lacking the preciseness that rules-based systems need. Neural networks can be used to discover hidden knowledge in the system, manage vagueness in the rule definition, and also correct rules where the entered expertise is contradictory.
It seems that human experts are able to reason and make decisions in the face of uncertainty because the natural language used in the reasoning process supports the expression of concepts with are vague and subjective. And so the theory of fuzzy logic became of primary interest for expert system developers. Fuzzy logic and fuzzy set theory is not a new discovery and had been established by Lotfi Zadeh in 1965. However the concept of fuzzy logic had not been well received by Zadeh’s contemporaries, possibly because the word “fuzzy” was offensive to scientists who wanted to be taken seriously. However by the 1980’s, the idea had travelled east to Japan where it had been successfully implemented in consumer goods (such as air conditioners and washing machines). Hence fuzzy logic had a proven commercial track record and significantly reduced the development effort and complexity of expert systems.
Nowadays expert systems use fuzzy rules and neural networks to create more powerful AI solutions. The field has matured and new expert systems development is based on existing theories rather than the expression of new ones. But processing potential had taken an exponential leap forwards with the advent of cloud computing, resulting in new powerful AI frameworks and solutions (e.g. deep learning). Though it may take an infinity of computers to replicate the power of the human mind, such a superlative seems to be within our grasp and AI is now more relevant in society than ever.
This post is based on the first chapter of “Artificial Intelligence -A Guide to Intelligent Systems” (2nd Edition) by Michael Negnevitsky.