At this point, it's a bit of an open secret that Facebook will announce the arrival of chatbots for Messenger at F8, its annual developer conference. Well, the latest Messenger update all but confirms it. If you were to do a search in the latest version of Facebook's chat app, you'll find a new category heading called "Bots and Businesses." Prior to the update, this would simply read "Businesses," which was a listing of companies that you could message for customer support and general inquiries. Messaging businesses typically involves speaking to a human, however, while talking to chatbots would likely be a more automated experience -- sort of like chatting to the equivalent of a phone tree.

A recent study by researchers at Oxford University and consultants Deloitte may have given finance professionals pause for thought. It found that, based on the nature of their work and the expected advance of machine intelligence, it is 95% likely that charted accountants will be replaced by some form of automation over the next 20 years. That is a startling figure, but it should come as no surprise that the work of the finance department is ripe for automation. Since 2004 the median number of full-time employees working in the function at big companies has declined by 40% to about 71 people for every US 1bn of revenue, down from 119 people, according to Hackett Group, a consulting firm. This is due, in part at least, to the increased use of automation within the finance department, the researchers say.

Growing up in a small village in Vietnam, Quoc Le had no electricity till he was nine. A little over 20 years later he has helped design artificial intelligence used by millions everyday. The 32-year-old helps lead the Google Brain team, a specialised unit that attempts to give computers the kind of profound neural networks that human beings possess, or at least helps them simulate it. It is Google's attempt to build an artificial brain. It may not be humanoid-like machine that can think for itself that many will have in mind, but "intelligence" has already been integrated into Google products, the kinds of technology that Mr Le could only imagine as a child.

The last place you want to learn you have heart failure is where it often winds up being diagnosed: in the emergency room. Researchers analyzing electronic health records are using artificial intelligence and GPUs to get ahead of this curve. They've shown they can predict heart failure as much as nine months before doctors can now deliver the diagnosis. The stakes could hardly be higher. Each year, about 23 million people worldwide, including nearly 6 million Americans, have heart failure, according to the American Heart Association.

"The robots are coming" is not something Paul Revere said during the American Revolution, but it is certainly something many people have uttered over the years. So have we finally reached the tipping point where artificial intelligence and robots will begin to take over human jobs en masse? Perhaps not, but we are closer to the time when they will be even more essential assets and presences in the workforce, explains Martin Ford, the author of the book "Rise of the Robots." I caught up with Ford at The Economist magazine's Innovation Forum event, which was held earlier this month. He pointed out that artificial intelligence is making its way into sectors that were once manned by only man, including the legal profession, where computer systems such as Watson could muscle in on human territory to provide legal counsel, and even journalism where stories are being written without direct human input about some articles.

Amazon has acquired PhotoTime creator Orbeus, an artificial intelligence startup that specializes in photo-recognition technology. The acquisition took place in the fall of 2015, according to sources familiar with the matter. This would be the latest in a string of acquisitions in the area of deep learning, with deals already completed for high-performance computing company Nice and video processing company Elemental Technologies. Orbeus developed photo-recognition technology based on a powerful type of AI called neural networks. It automatically identifies people and objects in photos and videos.

With technological innovations emerging daily, experts predict that more people will be thrown out of jobs. They envisage that advanced technology will improve work efficiency and will replace the manpower of most organisations. A latest research by Avanade's affirms the predictions of forecasters that newly introduced smart technologies, connected things and intelligent automation are dramatically changing the digital workplace and benefiting the business enterprises. However, rather than leading to job losses, business and IT experts, who participated in the research, believe that the increased use of smart technologies will trigger dramatic shifts in how we work, when we work and what type of work we do. The Avanade report titled, 'Making digital work,' asserts that this evolution isn't the doom and gloom that others paint it to be. Avanade's global survey of business and IT leaders defined smart technologies as computers or machines that do the work of or make decisions traditionally made by humans.

Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However structure of social networks and node attributes are often interpreted separately in the research of community detection. When these two sources are interpreted simultaneously, one common as- sumption shared by previous studies is that nodes attributes are correlated with communities. In this paper, we present a model that is capable of combining topology information and nodes attributes information with- out assuming correlation. This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated. We derive the detectability threshold for this model and use Belief Propagation (BP) to make inference. This algorithm is optimal in the sense that it can recover community all the way down to the threshold. This new model is also with the potential to handle edge content and dynamic settings.

Humans may one day create superintelligence, artificially intelligent machines that surpass mankind's intellect. Would these artificial intelligences choose to play games with us, and if so, which games? We believe this question is relevant for the ethics of general AI, the current widespread integration of AI systems into daily life, and for game AI research. We present a catalog of scenarios, some good for humanity and some bad, in which various kinds of play might take place between humans and intelligent machines. We assume a superintelligence, because of its greater cognitive ability, would stand in a similar relation to us as an adult does to a child, an expert to a novice, or a human to an animal. We define friendly games, learning games, observational games, and domination games, and proceed to consider games adults play with children, experts play with novices, and humans play with animals. Reasoning by analogy, we imagine corresponding games that superintelligences might choose to play with us, finding that domination games would pose a significant risk to humanity.

Automatic evaluation of text for its innovative quality has been necessitated by the growing trend to organize open innovation contests by different organizations. Such online/offline contests are known to fuel major business benefits to many industries. However, open contests result in a huge number of documents of which only a few may contain potentially interesting and relevant ideas. Usually these entries are manually reviewed and scored by multiple experts. But manual evaluation process not only require a lot of time and effort but are also prone to erroneous judgments due to inter-annotator disagreements. To counter this issue, in this paper, we have proposed a new approach towards detecting novelty or innovativeness of textual ideas from a given collection of ideas. The proposed approach uses information theoretic measures and term relevance to domain to compute document level innovativeness score. We have evaluated the performance of the proposed approach with a real world collection of innovative ideas which were manually scored by experts. We have compared the performance of our proposed model with some of the commonly used baseline approaches that rely on distributional semantics and geometric distances. The result shows that the proposed method outperform the existing baseline models.