After guessing your age, classifying dog breeds, and finding celebrity likenesses, Microsoft researchers have launched a new tool for identifying the contents of photos. With CaptionBot, users can upload any photo, and Microsoft will use various recognition services to describe what's happening. This includes identifying celebrities, recognizing emotions, and describing basic objects that appear in the scene. We've seen this type of party trick before. Last year, Wolfram Alpha released a similar tool, which remains available at ImageIdentify.com.

Google has employed machine learning on its Calendar application in an effort to help its users better keep track of, and complete, long-term goals. Users simply add a personal goal -- like hitting the gym three times a week, for example -- and Google Calendar will help them find the time and stick to it. Setting a goal requires the user to answer a few questions, specifying duration and times. From there Calendar will look at the user's schedule and find the best windows to schedule time to help complete the goal. It's another example of a major technology company using machine learning -- the concept of pattern recognition and computational learning theory -- to make its users' lives easier.

We have seen the birth to a generation of enterprises that are data-rich and analytically driven, eagerly following trends in big data and analytics. Let's take a closer look as I provide some use cases demonstrating how IBM is helping clients find innovative big data solutions. Enterprises that use data and sophisticated analytics turn insight into innovation, creating efficient new business processes, informing strategic decision making and outpacing their peers on a variety of fronts. According to the International Data Corporation (IDC), rich media (video, audio, images) analytics will at least triple in 2015 to emerge as a key driver for big data and analytics technology investment. And such data requires sophisticated analytics tools.

If robots are taking our jobs, they may also support our retirement. Bank of America Merrill Lynch estimates that 47 per cent of US jobs and 33 per cent of UK jobs have the potential to be automated. But the technology that could fill those jobs – robotics and artificial intelligence – is both a threat to some workers and a potential area of growth for investors, notably pension funds, seeking to capitalise on the new wave of industrialisation. "It's the next generation," says Philippe Cerf, Europe, Middle East and Africa co-head of the technology, media and telecommunications group at investment bank Credit Suisse. "Machine-learning is applied to some extent across all tech these days."

In intensive care units (ICU), electrocardiogram (ECG) waveforms show diverse variationsunder different patients' physical conditions.In general, physicians can diagnose patients efficientlyby detecting any disorder of heart rate or rhythm and any change in the morphological pattern of ECG data,which contain underlying semantics.To help physicians better analyze ECG data in a fairly short time,it is essential to develop a novel method for mining semantics from ECG patterns.This paper is the very first time to characterize ECG patterns by using Prefix Scalable Pattern Tree (PSP-Tree).Comparing with similar currently existing methods, PSP-Tree can mine significant semantics,such as scalability, temporality and hierarchy over ECG patterns.We conduct extensive experiments on real ECG data set which are obtained from PhysioBank Community and Beijing No.3 People Hospital.The experiment results show that our method performs more feasibly and effectively than other related work.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we propose the outlines of an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which both the example of action and its justification influence the moral intuitions of others. The signals an agent provides to others, and the signals received from others, help each agent determine which others are suitable cooperative partners, and which are likely to defect. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

Since the seminal work on frequent itemset mining, there has been considerable effort on mining more structured patterns such as sequences or graphs. Additionally, the field of constraint programming has been linked to the field of pattern mining resulting in a more general and declarative constraint-based itemset mining framework. As a result, a number of recent papers have proposed to extend the declarative approach to structured pattern mining problems. Because the formalism and the solving mechanisms can be vastly different in specialised algorithm and declarative approaches, assessing the benefits and the drawbacks of each approach can be difficult. In this paper, we introduce a framework that formally defines the core components of itemset, sequence and graph mining tasks, and we use it to compare existing specialised algorithms to their declarative counterpart. This analysis allows us to draw clear connections between the two approaches and provide insights on how to overcome current limitations in declarative structured mining.

John Markoff recently wrote in the New York Times, "Misconception: Computers will outstrip human capabilities within many of our lifetimes. Actually: You won't be obsolete for a long time, if ever, most researchers say. In March when Alphago, the Go-playing software program designed by Google's DeepMind subsidiary defeated Lee Se-dol, the human Go champion, some in Silicon Valley proclaimed the event as a precursor of the imminent arrival of genuine thinking machines. The achievement was rooted in recent advances in pattern recognition technologies that have also yielded impressive results in speech recognition, computer vision and machine learning. The progress in artificial intelligence has become a flash point for converging fears that we feel about the smart machines that are increasingly surrounding us.

Imagine that you are searching for a brown leather sandal online. You know what it should look like, but don't know how to describe it. You search "brown sandal" in Google, which serves up many results--but none of them are it. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India. If the company's ambitions for the space are realized, it could drive a massive change in global manufacturing.

This week Stephen Wolfram, founder and chief executive of Wolfram Research, announced a new component of the Wolfram Language for programming called ImageIdentify. Wolfram also introduced a new website, dubbed The Wolfram Language Image Identification Project, that demonstrates the language's new capabilities. The new site lets you upload images and get inferences and definitions in response. You can provide feedback, which should help it become more accurate. You can hit buttons like "Great!," "Could be better," "Missed the point," and "What the heck?!" After you choose one, the service offers a few more guesses, and a text box where you can type in a tag.