Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8]

It is set to be a monumental battle for the next generation of smart assistants. Samsung has fired the latest salvo in its AI phone battle with Apple, revealing more details of Bixby, it's competitor for Siri. The AI is said to have visual search capabilities to analyze the images, identify objects and performing optical character recognition on visible text. Although the Samsung Galaxy S8 reveal is around the corner, many users are still feeling the burn from the Galaxy Note 7 fiasco. However, the firm may redeem itself, as the flagship smartphone is rumored to have an assistant more powerful than Apple's Siri Bixby could be used for a wide variety of functions in a similar way to Apple's Siri.

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."