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Knowledge and Hardware

Common sense is not a simple thing. Instead, it is an immense society of hard earned practical ideas — of multitudes of life-learned rules and exceptions, dispositions and tendencies, balances and checks. If common sense is so diverse and intricate, what makes it seem so obvious and natural? This illusion of simplicity comes from losing touch with what happened during infancy, when we formed our first abilities. As each new group of skills matures, we build more layers on top of them. As time goes on the layers below become increasingly remote until, when we try to speak of them in later life, we find ourselves with little more to say than "I don’t know."

Marvin Minsky, The Society of Mind

Introduction

Throughout the 1980s, AI research started to make its way towards mainstream adoption in the form of expert systems, where computers would be expected to solve complex, domain-specific problems on behalf of human experts based on a large body of knowledge. While acquiring such data would prove to be difficult and expensive, expert systems were some of the first successful cases where end users would directly interact with fruits of AI research.

Emboldened by the success of expert systems, AI researchers were convinced that intelligence is defined by one's ability to deal with a specific set of tasks using an existing body of knowledge. Much of the era was dedicated to researching rigorous ways to acquire, store, and retrieve human knowledge of different contexts, and researchers began creating more projects that appeal to a wider audience, such as chess-playing computer programs.

As computing speed and capacity slowly paved way to much more powerful supercomputers, a group of scientists sought to return to the field of connectionism, where computing systems are designed to operate like a human brain with artificial neurons are bound together with artificial synapses. This line of research would lead to the rise of neural networks, deep learning, and other powerful AI techniques. Below are some of the reading materials that illustrate this era of revival.

Expert systems

MYCIN: A Quick Case Study
https://cinuresearch.tripod.com/ai/www-cee-hw-ac-uk/_alison/ai3notes/section2_5_5.html
"Mycin was one of the earliest expert systems, and its design has strongly influenced the design of commercial expert systems and expert system shells. Mycin was an expert system developed at Stanford in the 1970s. Its job was to diagnose and recommend treatment for certain blood infections.

Knowledge engineering

The Terminator and the Washing Machine
https://www.youtube.com/watch?v=wOjfXEL8BAA
"What the legendary matches between supercomputer Deep Blue and chess grandmaster Garry Kasparov reveal about today’s artificial intelligence and machine learning fears."

Rise of neural network

Geoffrey Hinton has a hunch about what’s next for AI
https://archive.is/O2LXB
"Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century’s worth of attempts—some wildly successful—he’d arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer."

Fin