Communications of the ACM
Deep Learning: Is It Approaching a Wall?
Deep learning is essentially a method for machines to learn from data thats loosely modeled on the way a biological brain learns to solve problems.
Credit: Saul Loeb/Agence France-Presse/Getty Images
Deep learning and related machine learning advances have played a central role in AI's recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed. In just the past two years, Google's deep-learning-based AlphaGo defeated the world's top Go players, surprising most AI experts who thought that it would take another five to 10 years to achieve such a milestone.
As is typically the case with major technology achievements, deep learning has quickly climbed to the top of Gartner's hype cycle, where all the excitement and publicity accompanying new, promising technologies often leads to inflated expectations, followed by disillusionment if the technology fails to deliver. Over the past several decades, AI has gone through a few such hype cycles, including the so-called AI winter in the 1980s that nearly killed the field.
In a recent article, Deep Learning: A Critical Appraisal, author and NYU professor Gary Marcus offers a serious assessment of deep learning. He argues that, despite its considerable achievements over the past five years, deep learning may well be approaching a wall, an opinion apparently shared by University of Toronto professor Geoffrey Hinton, the so-called Godfather of Deep Learning.
Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks. It's essentially a method for machines to learn from data that's loosely modeled on the way a biological brain learns to solve problems. Each artificial neural unit is connected to many other such units, and the links can be statistically strengthened or decreased based on the data used to train the system. Each successive layer in a multi-layer network uses the output from the previous layer as input.
"The technique excels at solving closed-end classification problems, in which a wide range of potential signals must be mapped onto a limited number of categories, given that there is enough data available and the test set closely resembles the training set," writes Mr. Marcus. "But deviations from these assumptions can cause problems; deep learning is just a statistical technique, and all statistical techniques suffer from deviation from their assumptions."
From The Wall Street Journal
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