The global healthcare Natural Language Processing (NLP) market is expected to grow from 1.10 billion in 2015 to 2.67 billion by 2020, according to a new report. "Natural Language Processing Market for Health Care and Life Sciences Industry by Type (Rule-Based, Statistical, and Hybrid NLP Solutions) – Worldwide Forecast and Analysis to 2015 – 2020" is published by MarketsandMarkets, The explosive growth in healthcare and life sciences industries, with their vast troves of unstructured clinical data in EHRs, are the main market drivers. As the report describes it, NLP technologies assist machines in understanding the language used by humans to communicate both reading and writing. This form of communication assists the computer in performing various other additional tasks. NLP techniques extract important information from the vast amount of clinical data and analyze it for enhanced processing and analytics.

This article focuses not what the athletes are putting into their bodies, but what they are putting on their bodies and shows how technology affects gears used by them. An hour long lecture by Demis Hassabis, the CEO of DeepMind, where he discusses what is happening at the cutting edge of AI research, including the recent historic AlphaGo match, and its future potential impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind. Here, Margaret Boden, a Professor of cognitive science at the University of Sussex, examines what it means to be "creative" and whether we can ever translate this into our computers. Steven Pinker believes there's some interesting gender psychology at play when it comes to the robopocalypse. Could artificial intelligence become evil or are alpha male scientists just projecting?

Five years ago, Tim Cook officially took over as CEO of Apple amid questions about his ability to live up to the legacy of Steve Jobs. This week, as Cook celebrates his fifth anniversary as head of the company (the exact date is Wednesday), questions continue to swirl. When Jobs officially stepped down from the top spot, he said he believed "Apple's brightest and most innovative days are ahead of it." Cook's track record leaves you scratching your head. Yes, Cook has had some big wins.

Attempting to locate and assist people living in impoverished parts of the world could be made easier by using satellite imagery and machine learning algorithms, according to a new study led by researchers at Stanford University and published in the journal Science. Traditionally, international aid group perform door-to-door surveys to record data on local incomes in developing nations, but as study author Marshall Burke of the Stanford Institute for Economic Policy Research explained, these methods can be expensive and time consuming. They believe they've found a more efficient alternative. "If you give a computer enough data it can figure out what to look for. We trained a computer model to find things in imagery that are predictive of poverty," Burke told BBC News.

An anonymous reader quotes a report from Quanta Magazine: An ambitious new program, funded by the federal government's intelligence arm, aims to bring artificial intelligence more in line with our own mental powers. Three teams composed of neuroscientists and computer scientists will attempt to figure out how the brain performs these feats of visual identification, then make machines that do the same. "Today's machine learning fails where humans excel," said Jacob Vogelstein, who heads the program at the Intelligence Advanced Research Projects Activity (IARPA). "We want to revolutionize machine learning by reverse engineering the algorithms and computations of the brain." By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex.

While walking around the larger industry shows, those hosting say more than 140 vendors, it doesn't take long to realise that artificial intelligence and machine-learning are the current'it' girls of the cyber-security industry. In an effort to define what'artificial intelligence' actually is, Luger & Stubblefield described in their 2004 book on artificial intelligence, that an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximise its chance of success at some goal based on a complex set of calculations. As notifications from UBA, SIEM and threat intelligence systems continue to grow, artificially intelligent systems are being touted as the solution to the fatigue experienced by SOC teams who have to try and figure out what to do with each threat, and whether or not they should investigate it further. Research from security company Hexadite, a security automation company, claimed that 37 percent of cyber-security professionals face 10,000 alerts per month" with 52 percent of alerts turning out to be false positive. He responded: "Highly repetitive and intricate tasks may be well suited for a machine rather than a human.

For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). At A360 this year, my expert on AI was Stephen Gold, the CMO and VP of Business Development and Partner Programs at IBM Watson. "We wanted to prove a point that you could bring together some very unique technologies: natural language technologies, artificial intelligence, the context, the machine learning and deep learning, analytics and data and do something purposeful that ideally could be commercialized." In this model, AIs will move from speech recognition to natural language interaction, to natural language generation, and eventually to an ability to write as well as receive information.

Pop culture's many takes on artificial intelligence New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

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