A Brief History

Introduction

This chapter provides a brief history of artificial intelligence (AI) and cognitive computing (CC). These terms are often used interchangeably, but there the approaches and objectives of each differ. As the popularity of AI grows, there remains a misunderstanding of the technical jargon that comes with it. Examples include terms such as ‘deep learning’, ‘machine learning’ (ML), ‘speech recognition’, ‘text mining’, ‘neural networks’ and many more. In layman’s terms, AI is an understanding that is achieved by machines that interpret, mine and learn from external data in ways that the machine functionally imitates the cognitive processes of a human. These processes include learning from constantly changing data, reasoning to make sense of the data and related self-correction mechanisms. Human intelligence is rooted in sensing the environment, learning from it and processing its information. Thus, AI includes

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  A simulation of human senses: sight, hearing, smell, taste and touch

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  A simulation of learning and processing: deep learning, ML, etc.

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  Simulations of human responses: robotics

AI applications includes problem-solving, game playing, natural language processing (NLP), speech recognition, image processing, automatic programming and robotics. CC refers to the development of computer systems based on mimicking human brains. It is a science that was developed to train computers to think by analysing, interpreting, reasoning and learning without constant human involvement. CC represents the third era of computing. In the first era (19^th century) Charles Babbage, the ‘father of the computer’, introduced the concept of a programmable machine. Used for navigational calculation, his computer tabulated polynomial functions. The second era (1950s) resulted in digital programming computers like ENIAC¹ and ushered an era of modern computing and programmable systems.

CC utilises deep-learning algorithms and big-data analytics to provide insights. Thus, the brain of a cognitive system is a neural network: fundamental concept behind deep learning. A neural network is a system of hardware and software that mimics the central nervous system of humans to estimate functions that depend on a huge amount of unknown or learned inputs. By the 1980s, two trends affected the way experts and researchers began to unpack ‘the black box’ of the neural approaches to studying, thinking and learning. This was the advent of computing and cognitive sciences, and it gave rise to computer AI using a model of the mind (Block, 1990). Thus, CC refers to

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  Understanding and simulating reasoning

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  Understanding and simulating human behaviour

Using CC systems, we can make better human decisions at work. Applications include speech recognition, sentiment analysis, face detection, risk assessment and fraud detection. AI empowers computer systems to be smart. This, of course, has raised fears of human displacement in jobs and elsewhere. Conversely, CC includes individual technologies that perform specific tasks that facilitate and augment human intelligence, which perhaps counters some of the fears. The sub-sections below highlight several differences and similarities between CC and AI.