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the cost function. If this slope is negative, then you know you are on the left hill of the valley and need to step to the right to get closer to the bottom. Conversely, if the slope is positive, you know that you are on the right hill of the valley and need to step to the left to reach the local minimum. Depending on the steepness of the slope, you know how close you are to reaching the bottom, as the slope flattens out near the bottom.
It is this autonomous iterative process that makes modern day neural networks so powerful. It is worth noting, however, that these mathematical techniques, whilst not in and of themselves particularly complex, could not until recently be performed effectively to the extent that real progress was made in simulating intelligence. This is mainly owing to two important factors. Firstly, the enormous data sets required to effectively train these systems did not exist before the explosion of the internet and social media, and secondly, the processing capability required to perform the many, many rounds of backpropagation required across these enormous data sets was not yet available to AI researchers.
Thus, it was only when these two requirements were met that artificial intelligence was kick-started to the point where it could have a significant impact on society. This is especially true of more complex, unsupervised neural networks that do not make use of user-defined training sets as a guide, but rather rely on large volumes of data to refine their own training sets and outcomes through continuous refinement of predictions without the guidance of an external source. The benefits of these more data-intensive unsupervised models is that the neural net can identify previously unrecognised paths or tactics to a desired outcome far better than a human, and can be used for more than one strictly-designed task because of their open and more generally applicable methodologies.
The AI Revolution
Even though the earliest roots of artificial intelligence can be found as far back as in the 1930s with Alan Turing’s various research papers concerning the ideas and proofs for an intelligent machine, it would not be until the late 1990s and early 2000s that the mainstream media and big industry players would take AI seriously. Whilst there existed some useful applications in the technology
sector before this time, especially in image and voice recognition, it was very much behind the scenes and out of the eye of the public. But despite the less-than- enthusiastic attitude of big corporations and government towards funding artificial intelligence projects, especially given the underwhelming results it had provided in the twentieth century, there were always a few isolated believers in AI who truly understood the potential of deep learning.
One such important group of researchers, who would struggle through the fallow times in artificial intelligence research and whose steady belief in these methodologies would ultimately be justified, is known to the AI community as the Canadian Mafia. This tightly-knit group of artificial intelligence evangelists –
THE HISTORY AND SCIENCE OF ARTIFICIAL INTELLIGENCE
... it would not be until the
late 1990s and early 2000s that
the mainstream media and big industry players would take
AI seriously.
including such luminaries as Geoffrey Hinton, Yoshua Bengio and Yann LeCun – are today considered to be the rockstars of the AI field. They, for example, were the researchers that would make great strides in developing the critically important backpropagation method that would significantly advance the learning capabilities of neural networks.
Geoffrey Hinton, as an example – an English- Canadian cognitive psychologist and computer scientist – is regarded by many as the godfather of neural networks. And whilst his research has today been recognised as fundamental to the success of AI in recent times, this was not always the case. For many years, Hinton and those who studied under him, including LeCun and Bengio, were considered academics in a dying field of study. As the funding freeze of the AI Winter set in and all other researchers set their sights on what were considered more promising areas of speciality, Hinton’s group pressed on regardless with their research into mathematical methodologies to improve neural networks.
Their continued research, however, was ultimately to pay off when, in 1997, a massive turning point came for AI, especially in the mind of the public. This was the year
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