Studies suggest that relying on your smartphone heart monitor may not be the wisest move. The widespread use of smartphones to collect healthcare data has been thrown into doubt by a couple of recent studies. In some fields -- such as the management of chronic diseases and mental health monitoring smartphones have enabled a greater level of patient self-control, and personalised clinical intervention. Success in one area, however, does not imply success in all – especially as smartphone health apps are often rolled out before evidence of their effectiveness has been rigorously analysed. A major study into the use of mobile phone data as a tool for predicting clinical decisions, released in June, came to a scathing conclusion, characterising the practice as "voodoo machine learning".

A recent Evans Data report shows that 36 percent of developers working with big data and analytics are also using machine learning.

The United States Air Force wants robots and service members to be best buds on the battlefield. Last Friday, the Air Force announced a grant of 7.5 million for research on ways to make humans trust artificial intelligence (AI) so that people and machines can collaborate on missions. Soon service members in every branch of the Armed Forces will be working with AI on a daily basis--be it unmanned ariel vehicles, underwater drones, or robot soldiers (the U.S. Military had to shelve the Boston Dynamic L3 "robotic mule" because it was too loud, but last week the company revealed the much stealthier SpotMini). On November 1, 2014, (one week after Elon Musk compared developing AI to "summoning the demon") Undersecretary of Defense Frank Kendall issued a memo asking the Defense Science Board to study what issues must be solved in order to expand the use of AI "across all war-fighting domains." But robotic weapons and soldiers wont be as effective if their human counterparts don't trust them.

In this paper, we consider a variant of belief propagation algorithm in a tree graphical model where computations are carried out in the negative log-likelihood domain. Unlike the min-product algorithm, our goal is not limited to estimating the mode of the marginal distribution. We would like to obtain the entire marginal distribution as the sum-product algorithm does. We applied the algorithm to learn effective users features for A/B testing. We discussed scalable extension to the proposed algorithm for processing large amount of data.The primary goal of a parallel program is to reduce running time comparing to the sequential program by taking full advantage of computing power of multiprocessors.

On 7 May, a Tesla Model S was involved in a fatal accident in Florida. At the time of the accident, the vehicle was driving itself, using its Autopilot system. The system didn't stop for a tractor-trailer attempting to turn across a divided highway, and the Tesla collided with the trailer. In a statement, Tesla Motors said this is the "first known fatality in just over 130 million miles [210 million km] where Autopilot was activated" and suggested that this ratio makes the Autopilot safer than an average vehicle. Early this year, Tesla CEO Elon Musk told reporters that the Autopilot system in the Model S was "probably better than a person right now."

I have been researching the notion of Intelligent Automation and, in particular, the rapid uptake of Robotic Process Automation (RPA) for more than 4 years. It was this work that eventually brought me to HfS. Over the years, I have made many good friends and acquantances in the automation community – and many of those good folks have graciously suggested I have become a spokesperson for this community. Yet, it is time to take a stance and declare: RPA is dead! This is not meant to try to grandstand my esteemed colleague Phil, who's eloquently stated that RPA 1.0 is a done discussion.

When you hear the term "artificial intelligence", you probably think of the 2001 Spielberg film, the agents in The Matrix, or even time-traveling robots with Austrian accents and shotguns. The concept can be divided into two very broad meanings. The first is the blanket definition applied by those outside the fields of computer engineering and robotics, which includes a machine or program's ability to absorb data, take its meaning, and adjust its behavior according to ideal outcomes. The second definition, the one that really matters, is more scientific. "Intelligence" in the term has much deeper implications than absorbing information and changing behaviors.

Now on Tap was one of the marquee features of Android Marshmallow when Google first announced this update to its mobile operating system last year (which is now already being eclipsed by the next version: Android Nougat). It allows you to quickly find more relevant information and context about what's currently on your phone's or tablet's screen when you press and hold the home button. Today, Google is adding three new features to Now on Tap that should make the service even more useful and smarter. The first of these new features allows you to easily translate text from any application. Chrome has long featured a built-in translation feature, but that's obviously not available in other native apps.

On June 27, the US Office of Science and Technology Policy (OSTP) issued a Request For Information on Artificial Intelligence (RFI). The RFI seeks public input on the tools, technologies, and training needed to further research and implementation of artificial intelligence (AI). Public feedback on these important questions will enable the OSTP to develop guidance on how the law should balance AI's potential benefits with its numerous threats. For example, self-driving cars could improve driving safety and provide mobility for people with disabilities. Likewise, the use of AI in healthcare has the potential to dramatically improve the quality and accuracy of medical care.

A new study by Kevin Warwick and Huma Shah, published by Taylor & Francis, purports to uncover a flaw in the classic artificial intelligence test developed by Alan Turing. The paper summarizing their findings is called "Taking the fifth amendment in Turing's imitation game." The announcement states, "A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought."