Neural networks expand far beyond feline photos
By Rick Merritt – “We need to get to real AI because most of today’s systems don’t have the common sense of a house cat!” The keynoter’s words drew chuckles from an audience of 3,000 engineers who have seen the demos of systems recognizing photos of felines.
There’s plenty of room for skepticism about AI. Ironically, the speaker in this case was Yann LeCun, the father of convolutional neural networks, the model that famously identified cat pictures better than a human.
It’s true, deep neural networks (DNNs) are a statistical method — by their very nature inexact. They require large, labeled data sets, something many users lack.
It’s also true that DNNs can be fragile. The pattern-matching technique can return dumb results when the data sets are incomplete and misleading results when they have been corrupted. Even when results are impressive, they are typically inexplicable.
The emerging technique has had its share of publicity, sometimes bordering on hype. The fact remains that DNNs work. Though only a few years old, they already are being applied widely. Facebook alone uses sometimes simple neural nets to perform 3×1014 predictions per day, some of which are run on mobile devices, according to LeCun.
Deep learning is with us to stay as a new form of computing. Its applications space is still being explored. Its underlying models and algorithms are still evolving, and hardware is trying to catch up with it all. more>