When will we have artificial general intelligence, the kind of AI that can mimic the human mind in all aspect? Experts are divided on the topic, and answers range anywhere between a few decades and never. But what everyone agrees on is that current AI systems are a far shot from human intelligence. Humans can explore the world, discover unsolved problems, and think about their solutions. Meanwhile, the AI toolbox continues to grow with algorithms that can perform specific tasks but can't generalize their capabilities beyond their narrow domains.
Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. When will we have artificial general intelligence, the kind of AI that can mimic the human mind in all aspect? Experts are divided on the topic, and answers range anywhere between a few decades and never. But what everyone agrees on is that current AI systems are a far shot from human intelligence. Humans can explore the world, discover unsolved problems, and think about their solutions.
In 1958, the New York Times reported on a demonstration by the US Navy of Frank Rosenblatt's "perceptron" (a rudimentary precursor to today's deep neural networks): "The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence". This optimistic take was quickly followed by similar proclamations from AI pioneers, this time about the promise of logic-based "symbolic" AI. In 1960 Herbert Simon declared that, "Machines will be capable, within twenty years, of doing any work that a man can do". The following year, Claude Shannon echoed this prediction: "I confidently expect that within a matter of 10 or 15 years, something will emerge from the laboratory which is not too far from the robot of science fiction fame". And a few years later Marvin Minsky predicted that, "Within a generation...the problems of creating'artificial intelligence' will be substantially solved". John McCarthy promoted the term Artificial Intelligence with a wishful thinking that, 'Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve the kinds of problems now reserved for humans, and improve themselves.' AI was assumed to simulate human reasoning, giving the ability of a computer program to learn and think.
The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.