CASTRO VALLEY, CALIFORNIA – A man was killed when the Tesla automobile he was driving veered off a road, crashed through a fence and plunged into a pond, authorities said Monday. California Highway Patrol spokesman Daniel Jacowitz said rescuers pulled the Tesla Model S from the pond early Monday and found the man's body inside. The driver was identified as Keith Leung, 34, of Danville, California, said Sgt. Ray Kelly, spokesman for the Alameda County Sheriff's office. Kelly said it was too soon to know if the vehicle's semi-autonomous Autopilot mode was engaged when the crash occurred or whether the driver may have been speeding or intoxicated. Photographs of the car show that its back-end was destroyed, its hood crumpled and windows shattered.

Very important breakthroughs in data centric deep learning algorithms led to impressive performance in transactional point applications of Artificial Intelligence (AI) such as Face Recognition, or EKG classification. With all due appreciation, however, knowledge blind data only machine learning algorithms have severe limitations for non-transactional AI applications, such as medical diagnosis beyond the EKG results. Such applications require deeper and broader knowledge in their problem solving capabilities, e.g. integrating anatomy and physiology knowledge with EKG results and other patient findings. Following a review and illustrations of such limitations for several real life AI applications, we point at ways to overcome them. The proposed Wikipedia for Smart Machines initiative aims at building repositories of software structures that represent humanity science & technology knowledge in various parts of life; knowledge that we all learn in schools, universities and during our professional life. Target readers for these repositories are smart machines; not human. AI software developers will have these Reusable Knowledge structures readily available, hence, the proposed name ReKopedia. Big Data is by now a mature technology, it is time to focus on Big Knowledge. Some will be derived from data, some will be obtained from mankind gigantic repository of knowledge. Wikipedia for smart machines along with the new Double Deep Learning approach offer a paradigm for integrating datacentric deep learning algorithms with algorithms that leverage deep knowledge, e.g. evidential reasoning and causality reasoning. For illustration, a project is described to produce ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders. Data is important, but knowledge deep, basic, and commonsense is equally important.

In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. Based on a detailed data analysis, this paper introduces a novel automatic fake news credibility inference model, namely FakeDetector. Based on a set of explicit and latent features extracted from the textual information, FakeDetector builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FakeDetector with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.

We present a novel inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical error covariance in $M$-estimation, both for unregularized convex learning problems and high-dimensional LASSO regression, without using exact second order information, or resampling the entire data set. In practice, we demonstrate the effectiveness of our framework on large-scale machine learning problems, that go even beyond convexity: as a highlight, our work can be used to detect certain adversarial attacks on neural networks.

Speaking in Macclesfield, the Prime Minister will use a speech to challenge the NHS, Artificial Intelligence (AI) sector and health charities to use data and AI to transform the diagnosis of chronic diseases.

Google has pressed forward with its effort to provide artificial intelligence solutions to the Department of Defense, despite an internal employee petition against the company's involvement in a pilot program that analyzes drone footage using AI and the resignations of around a dozen employees who objected to the program. But Google isn't the only company partnering with the Department of Defense on Project Maven--the artificial intelligence pilot program at the heart of the controversy--and the Pentagon has explored the possibility of working with other major tech firms on Project Maven. The involvement of other tech companies in Project Maven makes the project seem more like a bakeoff between several leaders in the field of artificial intelligence and less like a Google-led effort. It also raises questions about whether employees at other companies will raise the same ethical objections to the program that Google employees have. DigitalGlobe, a Colorado-based firm that specializes in geospatial imagery, reportedly provides images and algorithms to Project Maven. IBM has been approached about participating in the project by using artificial intelligence to analyze streaming video, a person familiar with the exchange told Gizmodo.

It wasn't too long ago that the concept of carrying a sophisticated computer, camera and phone, all rolled into one gadget fitting in your pocket, was the stuff of science fiction. Now smartphones are everywhere and they're getting smarter all the time. Imagine when your phone will be able to diagnose most of your medical problems for you based on artificial intelligence (AI) in the cloud, saving you a trip to the doctor. The app could issue a diagnosis and a prescription, and your local pharmacy could 3D-print your medicine. This exciting new frontier is part of the Fourth Industrial Revolution (4IR), a period of rapid change driven by progress in science and technology.

Most people visit the Swiss Alps to ski or hike, maybe to launder money. British photographer Chloe Dewe Mathews went to find Frankenstein. Author Mary Shelley dreamed up her legendary science fiction tale while staying near the Alps, and their snowy peaks serve as a backdrop for the story. Mathews, a fan, brought along her old copy to read, letting the text guide her journey through the landscape. "My eyes scanned the barren white lands for Frankenstein's creature, crossing the glacier at'super-human speed'," she writes in the introduction to her new photo book, In Search of Frankenstein - Mary Shelley's Nightmare. "I imagined catching a darting figure in my peripheral vision or coming across a makeshift cabin that had sheltered the fugitive for the night."

In a speech in Macclesfield today, Mrs May is to outline plans that will prevent 22,000 cancer deaths every year by 2033 through the deployment of advanced technologies extracting knowledge from NHS data, cross-referencing patients' habits and genetics. "Late diagnosis of otherwise treatable illnesses is one of the biggest causes of avoidable deaths. "And the development of smart technologies to analyse great quantities of data quickly and with a higher degree of accuracy than is possible by human beings opens up a whole new field of medical research and gives us a new weapon in our armoury in the fight against disease." This will create a new industry offering high-skilled science jobs, drawing on'existing centres of excellence' such as Oxford or Leeds, Mrs May will pledge.

Ian Bremmer warns the audience about the dangers of automation at an Intelligence Squared U.S. debate. Can we all agree that Google Duplex demo was eerie? A robot, posing as a human being, scheduled a reservation over the phone. We all knew artificial intelligence was coming, but it was breathtaking to hear software come to life. Before we go any further, let's get our terms straight.