The MIT researchers said their generative model requires less memory to store than datasets, which can cost millions of dollars to create. MIT researchers have found a way to classify images using synthetic data, which they claim can rival models trained from real data. In the study, the team created a special type of machine learning model to generate extremely realistic synthetic data, which can then train another model for vision-related tasks. The researchers said that currently, massive amounts of data is required to train a machine to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, the datasets required to train the model can cost millions of dollars to generate.

Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. CEO Sundar Pichai and other top executives brought Google's audacious ambition into sharper focus Wednesday at an annual conference attended by more than 7,000 developers who design apps to work with its wide array of digital services. Among other things, Google unveiled new ways for its massive network of computers to identify images, as well as recommend, share, and organize photos. It also is launching an attempt to make its voice-controlled digital assistant more proactive and visual while expanding its audience to Apple's iPhone, where it will try to outwit an older peer, Siri. The push marks another step toward infusing nearly all of Google's products with some semblance of artificial intelligence – the concept of writing software that enables computers to gradually learn to think more like humans.

Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. CEO Sundar Pichai and other top executives brought Google's audacious ambition into sharper focus Wednesday at an annual conference attended by more than 7,000 developers who design apps to work with its wide array of digital services. Among other things, Google unveiled new ways for its massive network of computers to identify images, as well as recommend, share, and organize photos. It also is launching an attempt to make its voice-controlled digital assistant more proactive and visual while expanding its audience to Apple's iPhone, where it will try to outwit an older peer, Siri. The push marks another step toward infusing nearly all of Google's products with some semblance of artificial intelligence -- the concept of writing software that enables computers to gradually learn to think more like humans.

A team of Stanford researchers trained a computer to identify images of skin cancer moles and lesions as accurately as a dermatologist, according to a new paper published in the journal Nature. In the future, this new research suggests, a simple cell phone app may help patients diagnose a skin cancer -- the most common of all cancers in the United States -- for themselves. "Our objective is to bring the expertise of top-level dermatologists to places where the dermatologist is not available," said Sebastian Thrun, senior author of the new study, founder of research and development lab Google X and an adjunct professor at Stanford University. He added that those who live in developing countries do not have the same level of care as can be found in the US and other industrialized nations. Melanomas represent fewer than 5% of all skin malignancies diagnosed in the US, yet they account for nearly three-quarters of all deaths related to this form of cancer.

TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service directly to businesses. The New York-based company is pitching studios and entertainment sites on a machine learning system that can identify products and locations as a way to generate revenue from product placements and experiential travel based on set locations. The new product is based on a year's worth of work that TheTake's development did to train a proprietary machine learning algorithm to identify images using a different technique than the industry standard, according to TheTake's chief executive Ty Cooper. Initially, the team behind TheTake would manually enter all the datasets and use an off-the-shelf computer visualization tool to identify images that fit the pre-defined parameters set by the company's staff. Companies like Universal Pictures, Comcast, Bravo, E!, Fandango, Sony Pictures and the Hallmark Channel, are testing out the AI-based service now, according to an email from Cooper.

Imagine seeing a dozen pictures flash by in a fraction of a second. You might think it would be impossible to identify any images you see for such a short time. However, a team of neuroscientists from MIT has found that the human brain can process entire images that the eye sees for as little as 13 milliseconds -- the first evidence of such rapid processing speed. That speed is far faster than the 100 milliseconds suggested by previous studies. In the new study, which appears in the journal Attention, Perception, and Psychophysics, researchers asked subjects to look for a particular type of image, such as "picnic" or "smiling couple," as they viewed a series of six or 12 images, each presented for between 13 and 80 milliseconds.