But what is new in recent months is the growing speed and accuracy of deep-learning programs, often called artificial neural networks or just “neural nets” for their resemblance to the neural connections in the brain.
John MarkoffThere has been a number of stunning new results with deep-learning methods, said Yann LeCun, a computer scientist at New York University who did pioneering research in handwriting recognition at Bell Laboratories.The kind of jump we are seeing in the accuracy of these systems is very rare indeed.
Skynet? Not so fast! says another article:
Deep learning excels at this sort of problem, known as unsupervised learning. In some cases it performs far better than its predecessors. It can, for example, learn to identify syllables in a new language better than earlier systems. But it’s still not good enough to reliably recognize or sort objects when the set of possibilities is large. The much-publicized Google system that learned to recognize cats for example, works about seventy per cent better than its predecessors. But it still recognizes less than a sixth of the objects on which it was trained, and it did worse when the objects were rotated or moved to the left or right of an image.Gary Marcus
Obviously I’m no expert, but the search to create artificial intelligence seems to be lacking a key ingredient: a good understanding of how the only intelligence we know – the human brain – works and how it evolved from our less intelligent ancestors. That is not to mean that intelligence cannot emerge in other ways, especially on a entirely different substrate; I would simply say that, when we’ll create or encounter an AI, it will likely be under surprising circumstances that very few among us can imagine.