Page 19 - Monocle Quarterly Journal Vol 3 Issue 2 Spring

In many ways, the history of AI begins with the very first manifestations of the digital electronic computer, dating back to Turing’s earliest research in the 1930s. But in the strictest sense, the highly effective and decorated Bombe machine built by Turing’s team at Bletchley Park could not truly be called a computer. For one thing, the Bombe could only solve one problem. And secondly, it could not store or retrieve data, these being the critical functions that allow modern computers to achieve the level of programmability that makes them so powerful today.
An AI Winter
Despite the Bombe not quite being classified as the first ever computer, Turing’s truly visionary work after the war demonstrated incredible foresight into the future of computing. In a paper called “On Computable Numbers, with an Application to the Entscheidungsproblem” (1936), Turing detailed mathematical proofs that there could exist a machine that could calculate any conceivable computation, given that it was representable in the form of an algorithm. These theoretical machines were to be called Universal Turing Machines (UTM), a seminal idea that would later be used by John Von Neumann to create the Electronic Discrete Variable Automatic Computer (EDVAC) in 1949. Built for the US Army’s Ballistics Research Laboratory in Pennsylvania, EDVAC was the first ever electronic stored-program computer, and unlike previous manifestations, used a binary numbering system as opposed to a decimal system – the format still used in modern computer programming today.
As was the case with the EDVAC, the first ever machine intended to “learn” was also funded by the US Military, this time through the Office of Naval Research and built by Frank Rosenblatt at the Cornell Aeronautical Laboratory in 1957. The Perceptron, as it was called, was an early prototype for machine learning, making use of a rudimentary neural network for image recognition. Unlike modern AI, the Perceptron was a machine, not a program. And although the “learning” aspect of the machine works similarly to neural networks of today, with neurons processing incoming data and altering the weights (or relative importance of inputs) attached to these neurons depending on the resultant output,
the weightings connected to neurons of the Perceptron were physically altered (as opposed to digitally) via small electrical motors. This early form of AI was called connectionism. But what seemed at first to be a signifi- cant breakthrough in machine learning and artificial intelligence, would ultimately, but unintentionally, be a massive burden to the entire field of study.
After a very promising and fruitful period for artificial intelligence research and development from the mid-1950s to late-1960s, what ensued was to be called the “AI Winter”, largely catalysed by the reception and review of the Perceptron machine by one single book in particular – Perceptrons: An Introduction to Computational Geometry (1969). The famous work – produced by American cognitive scientist Marvin Minsky and the South African-born American mathematician Seymour Papert – focused on the limitations of the Perceptron system, specifically providing mathematical proofs that such a neural network was not capable of learning an exclusive disjunction (XOR) function.
So influential was this book that it would change the course of AI research for decades to come. The result was a significant slowdown in sponsorships and a general feeling of pessimism around the discipline, with most experts on the matter espousing the limited capabilities
THE HISTORY AND SCIENCE OF ARTIFICIAL INTELLIGENCE
Despite the Bombe not quite being classified as the first ever
computer, Turing’s truly
visionary work after the war
demonstrated incredible foresight into the future of computing.
of the earliest forms of neural networks – in the form of connectionist systems such as the Perceptron – resulting in an industry-killing funding freeze.
Between the release of Perceptrons in 1969 and the eventual revival of AI research in the mid-1980s, funding for connectionism-type projects – as the earliest forms of neural networks – was near-impossible to attain. It would not be until the advent of multi-layered neural networks (capable of deep learning) that artificial intelligence research and optimism surrounding machine learning would make a revival, thanks in no small part to a few stubborn and dedicated researchers on the ground who
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