Besides being a major player in the earthquake prediction method discussed here, the ionosphere is important because it's the layer of the atmosphere that reflects electromagnetic waves back to Earth and enables radio communication. There was increased ionization over Japan before the 2011 Tohoku earthquake and a spike in radio wave emissions near Haiti before the 2010 quake there. Enough historical data linking ionospheric activity to earthquakes needs to be collected in order to generate patterns, and the patterns then need to be matched to real-time data. When the Tohoku earthquake hit, Tokyo residents received a one-minute warning via Japan's earthquake early warning system.

DeepMind, an artificial intelligence firm that was acquired by Google in 2014 and is now under the Alphabet umbrella, has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions. DeepMind says its new AI model, called a differentiable neural computer (DNC), can be fed with things like a family tree and a map of the London Underground network, and can answer complex questions about the relationships between items in those data structures. For example, you could get responses to questions like, "Starting at Bond street, and taking the Central line in a direction one stop, the Circle line in a direction for four stops, and the Jubilee line in a direction for two stops, at what stop do you wind up?" It's these networks that helped DeepMind's AlphaGo AI defeat world champions at the complex game of Go.

Three key guiding philosophies were presented across the reports: AI needs to augment humanity instead of replacing it, AI needs to be ethical, and there must be an equal opportunity for everyone to develop these systems. Human-machine collaboration in its many forms is major theme in the reports, titled "Preparing for the Future of Artificial Intelligence" and "National Artificial Intelligence Research and Development Strategic Plan." "The walls between humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing human capabilities," the Strategic Plan report says. The White House imagines virtual personal assistants housed in smart glasses, automated factories that assist humans in complex building tasks, and systems that provide better data for farmers, all in the context that these could be job creators and not job stealers.

These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon's AI team. Once they've been trained, they can be incredibly efficient at tasks like recognizing cats or playing Atari games, says Google DeepMind research scientist Raia Hadsell. A solution to this might be something called progressive neural networks -- this means connecting separate deep learning systems together so that they can pass on certain bits of information. One way of doing this is revisiting an old, unfashionable strand of artificial intelligence known as symbolic AI or Good Old-Fashioned Artificial Intelligence (GOFAI), says Murray Shanahan, a professor of cognitive robotics at Imperial College London (and also the scientific advisor on Ex Machina).

In 1996 the Health Insurance Portability and Accountability Act (HIPAA) was enacted. The Accountability portion of the law requires that healthcare providers protect the privacy of patient health information and includes security measures that must be followed. Provider success has been mixed and has recently come under intense scrutiny due to the number and size of reportable breaches of health information.

Washington, D.C. - IBM, a pioneer in the advancement of artificial intelligence, today welcomed the release of a White House report on the future and promise of this exciting technology: "We commend the Administration, and the Office of Science and Technology Policy (OSTP) in particular, for launching an open and inclusive dialogue that helped shape today's report. The document recognizes what IBM has believed all along, that artificial intelligence (AI), or cognitive computing systems like IBM Watson, will jump start economic opportunity and help solve some of humanity's biggest challenges. Embedding ethical training into computer science education, as the report recommends, is a positive way to prepare the next generation of technology experts to appropriately guide the advancement of AI systems. Cybersecurity is one area in particular where IBM agrees that AI can be a true game-changer, and one where we are actively preparing IBM Watson to make a real and tangible difference in the push to better defend America's digital networks.

The latest AI-powered upgrade for Google Photos brings four tricks. In a blog post today, we learned about four new features for the service, including the simplest one, which autodetects sideways pictures and prompts you to correct them. Two others dig into its talent for facial recognition, as it will detect people in your new photos, and offer to "rediscover old memories" of those same people in older pics, or, it can pop up a highlight reel showcasing the best pictures of a frequent subject.

"Long-term concerns about super-intelligent General AI should have little impact on current policy," the report Preparing for the Future of Artificial Intelligence reads. The administration is exploring how AI can bolster existing initiatives such as the Data Driven Justice and Police Data Initiative, both of which attempt to "provide law enforcement and the public with data that can better inform decision-making in the criminal justice system, while also taking care to minimize the possibility that AI might introduce bias or inaccuracies due to deficiencies in the available data," the report reads. The government should also explore ways to improve the understanding of and uses of AI in key agencies, the report says: "For example, Federal agencies should explore the potential to create DARPA-like organizations to support high-risk, high-reward AI research and its application, much as the Department of Education has done." Along with a call for AI training for federal employees, the proposal suggests an exchange model that would allow experts from federal and state governments to rotate among departments, "colleges and universities, Indian tribal governments, federally funded research and development centers, and other eligible organizations."

As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. The team tried several different methods, including various kinds of neural networks, as well as software-generated decision trees that produced clear, human-readable rules. It was hospital policy to send asthma sufferers with pneumonia to intensive care, and this policy worked so well that asthma sufferers almost never developed severe complications. He and other computer scientists are importing techniques from biological research that peer inside networks after the fashion of neuroscientists peering into brains: probing individual components, cataloguing how their internals respond to small changes in inputs, and even removing pieces to see how others compensate.

Yet there are several important data points involved, and the Alexa assistant that runs on my Amazon Echo speaker doesn't know what to do. Using the Google Assistant bot on the new Google Allo messaging app, things are a little different. Google has spent the last 18 years parsing the data for search queries. One of the key differences between the Assistant and Alexa is that Google understands context.