In the same year, a deep neural network achieved 16% error rate, a significant improvement over previous results, at the annual Imagenet Large Scale Visual Recognition Challenge (ILSVCR), a competition where research teams submit programs that classify and detect objects and scenes. They have also been around for a long time, but advances made by The Canadian Mafia (and others) over the last decade in training computers with big data using specialized processors have generated “the latest craze.” The tipping point(s) came in 2012 when two much-publicized breakthroughs occurred: The Google “Brain Team” has trained a cluster of 16,000 computers to train itself to recognize an image of a cat after processing 10 million digital images taken from YouTube videos.
“Artificial Neural Networks” and “Deep Learning” (and variations thereof) are the most hyped buzzwords today, more than any other tech buzzword, I would argue.
In 1959, per Wikipedia, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed." Yes, 1959-not exactly what one would call “an emerging technology.” Indeed, machine learning has been around for quite a while. In addition to spam filtering, machine learning has been applied successfully to problems such as hand-writing recognition, machine translation, fraud detection, and product recommendations. For example, after a period of training in which the computer is presented with spam and non-spam email messages, a good machine learning program will successfully identify, (i.e., predict,) which email message is spam and which is not without human intervention.
#Gartner hype cycle emerging technologies 2016 how to#
Machine learning is best defined as the transition from feeding the computer with programs containing specific instructions in the forms of step-by-step rules or algorithms to feeding the computer with algorithms that can "learn" from data and can make inferences “on their own.” The computer is “trained” by data which is labeled or classified based on previous outcomes, and its software algorithms “learn” how to predict the classification of new data that is not labeled or classified. Is machine learning an "emerging technology" and is there a better term to describe what most of the hype is about nowadays in tech circles? This year, Gartner has moved machine learning back a few notches, putting it at the peak of inflated expectations, still estimating 2 to 5 years until mainstream adoption.
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