Lately the term "Machine Learning" gets kicked around like a hackysack in a stoners basement. It's becoming a ubiquitous buzzword because it really is exciting technology that's finally making its way into our lives, but a large majority of the buzz is nothing more than lazy marketing. Apple is one of the tech giants that's bringing machine learning into the mainstream in effective and even mindblowing ways.
We’ve all been told that “you can’t teach an old dog new tricks,” but any competent dog trainer will tell you that most old dogs can indeed be taught new tricks, when properly motivated and rewarded. For a real teaching challenge, try teaching a computer to reliably identify a Chesterfield couch (or any couch, for that matter) in a photograph.
With all the buzz about machine learning, artificial neural networks, and other forms of artificial intelligence (AI), it’s fair to ask, “When will I have it on my smartphone?”
“Artificial intelligence.” “Artificial neural networks.” “Machine learning.” “Deep learning.” “Computer vision.” “Statistical pattern recognition.” There has been a whole lot of artificial intelligence terminology thrown about recently because of the accelerating pace of advancement in the field. These aren't just silly buzzwords we use to sound cool (well, not always). To the uninitiated, all the terms tend to sound alike, and many of them have been used more or less interchangeably in the popular press. However, there are subtle differences, and in this blog I'll sort a few of them out for you.
By this time, you’ve probably heard a whole lot of thought, talk, and speculation about the coming revolution in intelligent machines, from self-driving cars and other robots to facial recognition systems andmore. It’s not just science fiction anymore. We really are gettingcloser to a new reality where it will be harder to tell the difference between “natural” and “artificial” intelligence.
The debate rages on, in the technical press, mass media, social media, and philosophical circles, as to whether computers will ever be considered “intelligent,” and whether it would be a good thing if they were. Much of the debate hinges on definitions of what it means to be intelligent and how human-like a machine would have to be before it was considered intelligent. Arguments aside, much progress has been made in recent years on two technologies that together bring us closer to the goal of “artificial intelligence” in general, and intelligent robotics in particular: machine learning and computer vision.
