The Occult Sciences and Mysteries of Greece by Phillip White [epub download]
. Philip White. S P. Shortly after they meet, he and Elaine marry, and leave their. though they are bound to one another by the divine.At its best, deep learning has enabled computers to recognize patterns in data the way that people do — by sifting through enormous troves of numerical, visual or spoken information. In other areas, however, it has struggled to generalize, meaning it doesn't automatically produce the correct answer the way a human or a child can.
A group of researchers from institutions including MIT and Stanford recently won't be deterred by those challenges. One of the group's newest advances — it was just published in the journal Nature — is a new deep-learning algorithm that can recognize images of human faces with an accuracy that's nearly as good as that of humans.
The researchers, led by University of Washington computer science professor Salvatore Puozzo, work with two main neural network models: one that learns and recognizes objects, and another that learns and recognizes images of animals. The researchers presented both systems with images of faces, and found that the animal-learning model produced a whopping 96 percent accuracy in identifying faces while the object-learning model had around 92 percent accuracy.
When the algorithms applied this learning to images of all human beings in a population, they were able to identify all of them above 90 percent accuracy.
The researchers argue that their algorithm has broad application in facial-recognition technology and image recognition generally.
"This is the most general and powerful algorithm in the field we know, except of course for humans," Puozzo says. "It can be used for anything that you want to classify as a real image of an object."
He explains that while many deep-learning algorithms learn a system to recognize a specific object, this algorithm can learn to recognize a wide variety of objects because it handles the images abstractly.
"They don't become abstractions by learning rules about what is in the image, but by learning where the image is in the space of all possible images," he says. "I would argue that many other types of algorithms are like this."
Puozzo and his colleagues have also developed a new algorithm for machine translation that they believe could have even broader implications for machine intelligence.
Their original algorithm, known as UniLM, represents a radical shift in the way that researchers have thought about machine translation 0b46394aab