That is why we have not been able to directly program a computer to do many of the things that we humans do so easily-be it understanding speech, images or language or driving a car. Why did AI hit so many roadblocks in previous decades? The reason is that most of the knowledge we have of the world around us is not formalized in written language as a set of explicit tasks-a necessity for writing any computer program. A newly developed deep-learning algorithm is purported to diagnose heart failure from magnetic resonance imaging as well as a cardiologist. Applications are already expanding to encompass other fields of human expertise-and it is not all games. The triumph of a neural network over top-ranked player Lee Se-dol at the game of Go received prominent headlines. Working through the challenges of deep learning has led to stunning successes. Many university professors with expertise in this area-by some counts, the majority-have been pulled from academia to industry and furnished with well-appointed research facilities and ample compensation packages. Companies compete fiercely for talent, and Ph.D.s specializing in deep learning are a rare commodity that is in extremely high demand. ![]() These advances have suddenly opened the door to further commercialization of the technology, and the excitement only continues to grow. I often just speak to my phone, and sometimes it even answers back. Personally, I rarely type messages anymore. AI software, in fact, has now become a familiar fixture in the lives of millions of smartphone users. And for those who remember how poor object recognition was just a few years ago-software that might mistake an inanimate object for an animal-strides in computer vision have been incredible: we now have computers that, under certain conditions, can recognize a cat, a rock or faces in images almost as well as humans. And shortly afterward came applications for identifying the contents of an image, a feature now incorporated into the Google Photos search engine.Īnyone frustrated by clunky automated telephone menus can appreciate the dramatic advantages of using a better personal assistant on a smartphone. The first products rolled out in 2012 for understanding speech-you may be familiar with Google Now. The technology of deep learning has transformed AI research, reviving lost ambitions for computer vision, speech recognition, natural-language processing and robotics. Instead they are based on general mathematical principles that allow them to learn from examples to recognize people or objects in a photograph or to translate the world's major languages. Artificial neural networks do not mimic precisely how actual neurons work. The technique relies on so-called artificial neural networks-a core element of current AI research. Major information technology companies are now pouring billions of dollars into its development.ĭeep learning refers to the simulation of networks of neurons that gradually “learn” to recognize images, understand speech or even make decisions on their own. In recent years deep learning has become a singular force propelling AI research forward. That was when deep learning, an approach to building intelligent machines that drew inspiration from brain science, began to come into its own. Beginning in 2005, AI's outlook changed spectacularly. Scientists and writers describe the dashed hopes of the period from the 1970s until the mid-2000s as a series of “AI winters.” At the time, even the term “AI” seemed to leave the domain of serious science. ![]() Computer processing was also too tepid to power machines that could perform the massive calculations needed to approximate something approaching the intricacies of human thought.īy the mid-2000s the dream of building machines with human-level intelligence had almost disappeared in the scientific community. The algorithms of those early years lacked sophistication and needed more data than were available at the time. Software designed to help physicians make better diagnoses and networks modeled after the human brain for recognizing the contents of photographs failed to live up to their initial hype. That optimism, of course, turned out to be premature. In 1967 Marvin Minsky of the Massachusetts Institute of Technology, who died earlier this year, proclaimed that the challenge of AI would be solved within a generation. In the 1960s the hope grew that scientists might soon be able to replicate the human brain in hardware and software and that “artificial intelligence” would soon match human performance on any task. Computers generated a great deal of excitement in the 1950s when they began to beat humans at checkers and to prove math theorems.
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