While still a vast field, a huge part of machine learning exists for what may seem to be a relatively narrow subset of problems. These are problems involving visual processing: character recognition, facial recognition, the generation of trippy images dominated by populations of dogslugs, birdlegs, and spidereyes. Image data is unique in its suitability for machine learning tasks. It naturally occurs as multidimensional arrays--tensors, really--of pixel data. It's more at the fringes of machine learning that audio data gets a turn. Part of the problem is that, despite the vast amounts of digital audio data that exists in the world, there is a relative lack of openly accessible computational datasets.

Even if you're not a machinist, you've probably had a crisis at home or with your car where you only needed one weird, tiny screw to fix the problem. You bring the part to a store and stand in line only to discover the part isn't in stock. So they call it in, and the part that arrives maybe a week later is the wrong one. Then the whole process starts over again. It all sounds terribly inefficient!

Frances Haugen was part of the first wave of people to use Google back in 1996. Her mother, a faculty member at the University of Iowa1, showed her the search engine, which was still a research project at Stanford University. Haugen was blown away at what Larry Page and Sergey Brin had built. "The idea that you could actually peer into a giant mountain of data was amazing," she says. Haugen has been obsessed with search technology ever since.

Researchers dreaming up such high-tech innovations to make the lives of senior citizens easier are convening this week at an unusual technology exhibition at the Marriott Wardman Park Hotel in Woodley Park. The event, timed to coincide with a once-a-decade White House Conference on Aging, is open to the public today. While new tech products are usually focused on the young and hip, the technologists at the 30 or so companies making an appearance here are taking the same components used in, say, the latest flashy "smart phone" to help those in their golden years maintain control over their lives. A watch from Intel Corp. could beam medication reminders to a patient's television set -- or place a discreet reminder phone call, for those wanting more privacy. Chester the Talking Pill, designed at the University of Rochester, is a wall-mounted LCD screen with a built-in software avatar trained to tell patients anything about their prescription drugs.

Image recognition has become increasingly critical in applications ranging from smartphones to driverless cars, and on Wednesday UCLA opened up to the public a new algorithm that promises big gains. The Phase Stretch Transform algorithm is a physics-inspired computational approach to processing images and information that can help computers "see" features of objects that aren't visible using standard imaging techniques. It could be used to detect an LED lamp's internal structure, for example--something that would be obscured to conventional techniques by the brightness of its light. It can also distinguish distant stars that would normally be invisible in astronomical images, UCLA said. Essentially, the algorithm works by performing a mathematical operation that identifies objects' edges and then detects and extracts their features.

Now that machine-learning algorithms are moving into mainstream computing, the Massachusetts Institute of Technology is preparing a way to make it easier to use the technique in everyday programming. In June, MIT researchers will present a new programming language, called Picture, that could radically reduce the amount of coding needed to help computers recognize objects in images and video. It is a prototype of how a relatively novel form of programming, called probabilistic programming, could reduce the amount of code needed for such complex tasks. In one test of the new language, the researchers were able to cut thousands of lines of code in one image recognition program down to fewer than 50. With probabilistic programming, "we're building models of what faces look like in general, and use them to make pretty good guesses about what face we're seeing for the first time," said Josh Tenenbaum, an MIT professor of computational cognitive science who assisted in the work.

Did you ever think that the next hot technology field would be the ability for a machine to "see" a picture and describe it in words? Google may have kicked off the latest wave of interest in automated image recognition, but several teams of researchers, including Microsoft and Baidu, also plan to participate. Microsoft said late Tuesday that the company launched a research project over the summer, where the results were convincing enough to fool humans about 20 percent or so of the time. Microsoft will publish its results in a paper, which will be presented at the Computer Vision and Pattern Recognition conference in June 2015. However, John Platt, a deputy managing director at Microsoft Research, also wrote that he expects papers to be submitted by a team of Baidu and UCLA researchers, as well as teams from U.C. Berkeley; Google; and Stanford and the University of Toronto.

Ray Kurzweil is, by all accounts, a genius. He holds nineteen honorary doctorates, has founded a half-dozen successful companies, and was a major contributor to the field of artificial intelligence. He built some of the first practical systems for recognizing speech and scanning text. Time magazine recently featured Kurzweil on its cover, and Fortune described him as "a legendary inventor with a history of mind-blowing ideas." And now he has a new book, with a subtitle that suggests he has found another such idea: "How to Create a Mind: The Secret of Human Thought Revealed."

The International Association for Pattern Recognition (IAPR) is an international association of non-profit, scientific or professional organizations (being national, multi-national, or international in scope) concerned with pattern recognition, computer vision, and image processing in a broad sense. Normally, only one organization is admitted from any one country, and individuals interested in taking part in IAPR's activities may do so by joining their national organization.

While most would point to home security cameras as the primary application for imaging in the smart home - just this week, after all, smart home darling Nest launched their own home security cam - there appears to be a new focus in the connected home when it comes to imaging tech: our food. Just consider: Last month it was revealed by Science that had been doing research into machine learning around food identification, and had released a new app called Im2Calories, which examines an image and attempts to quantify the amount of calories on a plate. It uses "deep learning" technology - essentially a form of machine learning. Im2Calories can draw connections between what a given piece of food looks like, and vast amounts of available caloric data." And while we're used to Google doing crazy bleeding edge stuff, they're definitely not the only ones who see cameras as a natural fit in the kitchen. Last week we learned of a new product called the June Intelligent Oven, which uses images captured from an in-oven camera to identify food and then automatically program cooking time and temperature. And then there's the SmartPlate, a new product currently on Kickstarter from Fitly that includes three cameras in the plate itself. The cameras are used to detect food quantity and type the image across a database of food and associated caloric content. Wait, a plate with cameras? How exactly does that work? CEO Anthony Ortiz told me that the cameras will be recessed within the plate on the rim. "Think about the cameras having lenses .