2010 – 2019: The rise of deep learning Posted on : Jan 03 - 2020

No other technology was more important over the past decade than artificial intelligence. Stanford’s Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become “AI-first” companies. In the next decade, all technology will be considered “AI technology.” And we can thank deep learning for that.

Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brain’s neural network. Rather than simply running algorithms to completion, deep learning lets us tweak the parameters of a learning system until it outputs the results we desire.

The 2019 Turing Award, given for excellence in artificial intelligence research, was awarded to three of deep learning‘s most influential architects, Facebook’s Yann LeCun, Google’s Geoffrey Hinton, and University of Montreal’s Yoshua Bengio. This trio, along with many others over the past decade, developed the algorithms, systems, and techniques responsible for the onslaught of AI-powered products and services that are probably dominating your holiday shopping lists.

Deep learning powers your phone’s face unlock feature and it’s the reason Alexa and Siri understand your voice. It’s what makes Microsoft Translator and Google Maps work. If it weren’t for deep learning, Spotify and Netflix would have no clue what you want to hear or watch next.

How does it work? It’s actually simpler than you might think. The machine uses algorithms to shake out answers like a series of sifters. You put a bunch of data in one side, it falls through sifters (abstraction layers) that pull specific information from it, and the machine outputs what’s basically a curated insight. A lot of this happens in what’s called the “black box,” a place where the algorithm crunches numbers in a way that we can’t explain with simple math. But since the results can be tuned to our liking, it usually doesn’t matter whether we can “show our work” or not when it comes to deep learning.

Deep learning, like all artificial intelligence technology, isn’t new. The term was brought to prominence in the 1980s by computer scientists. And by 1986 a team of researchers including Geoffrey Hinton managed to come up with a back propagation-based training method that tickled at the beginnings of an unsupervised artificial neural network. Scant a few years later a young Yann LeCun would train an AI to recognize handwritten letters using similar techniques. View More