This powerful technology needs to be treated with care.
Deep learning, which is a subset of AI (Artificial Intelligence), has been around since the 1950s. It’s focused on developing systems that mimic the brain’s neural network structure.
Yet it was not until the 1980s that deep learning started to show promise, spurred by the pioneering theories of researchers like Geoffrey Hinton, Yoshua Bengio and Yann Lecun. There was also the benefit of accelerating improvements in computer power.
Despite all this, there remained lots of skepticism. Deep learning approaches still looked more like interesting academic exercises that were not ready for prime time.
But this all changed in a big way in 2012, when Hinton, Ilya Sutskever, and Alex Krizhevsky used sophisticated deep learning to recognize images in an enormous dataset. The results were stunning, as they blew away previous records. So began the deep learning revolution.
Nowadays if you do a cursory search of the news for the phrase “deep learning” you’ll see hundreds of mentions. Many of them will be from mainstream publications.
Yes, it’s a case of a 60-plus-year-old overnight success story. And it is certainly well deserved.
But of course, the enthusiasm can still stretch beyond reality. Keep in mind that deep learning is far from a miracle technology and does not represent the final stages of true AI nirvana. If anything, the use cases are still fairly narrow and there are considerable challenges.
“Deep learning is most effective when there isn’t an obvious structure to the data that you can exploit and build features around,” said Dr. Scott Clark, who is the co-founder and CEO of SigOpt. “Common examples of this are text, video, image, or time series datasets. The great thing about deep learning is that it will automatically build and exploit patterns in the data in order to make better decisions. The downside is that this can sometimes take a lot of data and a lot of compute resources to converge to a good solution. It tends to be the most effective in places where there is a lot of data, a lot of compute power, and there is a need for the best possible solution.”
