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."

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. One of the key themes for the event emerged from a survey of the attendees.

Last week 300 planners were packed into Google HQ in London to talk all things AI, robotics and machine learning. This whole area, despite being with us for a long time, has suddenly become a huge area of focus for the industry not least because of the huge shifts in behaviour, advantage and opportunity that it will bring. We wanted to take as broad a perspective as possible with this event, and so we had five exceptional speakers, each with a unique point of view on the theme. Author and designer Tom Chatfield kicked us off by talking about how much of the recent focus on AI has been around the'usurpation narrative of human-machine interactions…a creation is pitted against its creators, aspiring ultimately to supplant them'. Science fiction is full of machines that are out to eliminate or hoodwink us, and the application of AI in game scenarios (Go, Chess) always means that there's a winner (usually the machine) and a loser (the human creator).

We are excited to announce that the third eBook in our technical blog book series, Lessons for Large-Scale Machine Learning Deployments on Apache Spark, has been released today! This eBook, the third of a series, picks up where the second book left off on the topic of advanced analytics, and jumps straight into practical tips for performance tuning and powerful integrations with other machine learning tools – including the popular deep learning framework TensorFlow and the python library scikit-learn. The second section of the book is devoted to addressing the roadblocks in developing machine learning algorithms on Apache Spark – from simple visualizations to modeling audiences with Apache Spark machine learning pipelines. As with the past eBooks, we've augmented the blogs with code examples in Databricks notebooks, which are complimentary with the eBook download. Download the eBook to get started on your next advanced analytics project today.