Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

MIT Media Lab director Joi Ito recently published a thoughtful essay titled "Society-in-the-Loop Artificial Intelligence," and has kindly credited me with coining the term. Now that it is out there, I wanted to elaborate a little on what I mean by "society in the loop," and to highlight the gap that it bridges between the humanities and computing. What I call "society in the loop" is a scaled up version of an old idea that puts the "human in the loop" (HITL) of automated systems. In HITL systems, a human operator is a crucial component of a control system, handling challenging tasks of supervision, exception control, optimization and maintenance. Recently, a number of articles have been written about the importance of applying HITL thinking to Artificial Intelligence (AI) and machine learning systems (e.g. HITL AI has been going on for a while.

Vernor Vinge once stated in his book The Singularity, "We are on the edge of change comparable to the rise of human life on Earth." As AI now undoubtedly plays a pivotal role in many industries, its risks and repercussions simply cannot be ignored; and shining a light upon these has never been more imperative. That's why, in response to the White House's Notice Of Request For Information (RFI), IBM has created what seems to be an AI 101--covering the huge field of AI and its vast potential applications. "The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields," the RFI summary read. IBM's AI 101 consisted of a numbered list of topics in a somewhat re-ordered and slightly re-factored response to the RFI's questions.

This story originally appeared on Slate and is reproduced here as part of the Climate Desk collaboration. Every election cycle, science gets the short end of the stick. So a collective of scientists--56 scientific organizations representing 10 million scientists and engineers and spearheaded by the American Association for the Advancement of Science--tries to engage them in a debate by compiling a list of science-based questions, soliciting answers and publishing them. This year should be particularly interesting. As has been pointed out before, the two major presidential candidates this year hold vastly different views on science-related issues.

BAE Systems is looking for a Machine Learning and Signal Processing Research Engineer to join their Cognitive RF research group. The cognitive RF group works on programs and problems that involve machine learning, optimization, detection & estimation, information theory, deep learning, and adaptive decision and control. These technologies are applied to a number of different RF domains, such as cognitive communications, radar, electronic warfare (EW), spectrum sensing, SIGINT, to name a few. The Cognitive RF research group is part of BAE Systems' Technology Solutions division which is heavily involved in advanced research concepts primarily from the various government research labs and organizations, including DARPA and IARPA. Members of the group are involved in the entire cycle of research development, from the initial concept ideation and program shaping with potential customers and new business pursuits through the execution and transition of research concepts to BAE Systems' business areas and products.

In Munich, Germany, the technology conglomerate Siemens AG is betting 1.1 billion on digital technologies, such as the proposition that blockchain data can be leveraged by machine learning to improve the secure transmission of data used in energy trading. Siemens is welcoming its employees and independent firms to bid for the money if they are willing to research how Siemens can develop businesses that use artificial intelligence. Siemens is just one of a number of companies embracing machine learning--the combination of artificial intelligence and deep analytics that enables enterprises to make predictions on large amounts of data and allows developers to experiment by incorporating features like speech and pattern recognition, as well as statistical techniques, into their analysis. And HPE's recent announcement of "machine learning as a service" (MLaaS) is aimed at helping the process really take off. Speaking at HPE Discover Las Vegas 2016, HPE Executive Vice President Robert Youngjohns called machine learning and deep analytics the most fundamental change we're ever going to see.

Among other things, CareSkore wants to use machine learning to anticipate mortality. However, the newly endowed platform is more than just a Facebook poll that tells you how you'll meet your end this Christmas by being squashed by a falling piano. Storm ventures, Cota Capital, Rising Tide Fund and Liquid 2 Ventures are rallying behind the Y Combinator graduate with today's 4.5 million seed round. CareSkore is combining clinical, socio-economic, demographic, and behavioral data to paint a holistic picture of patients that doctors and insurance companies can use to provide better preventative care. The platform is leveraging Google's TensorFlow and Hadoop to cut through massive third party data sets and generate insights by finding relationships between environmental and clinical data.

The limits of your language are the limits of your world. There are three often overlooked dimensions of importance to be considered when attempting to define a concept used by humans. They are A: The Emotive-Evaluative Dimension. These terms range from expressive to dispassionate with either positive (celebratory) or negative (deprecatory) connotation.(For This focusses on the extent to which judgment is based on presumption as opposed to demonstration, e.g.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

In the past few years the wearables sector has been a hive of activity, but it feels like this year things have accelerated, particularly in the realm of health and wellness. A report by Forrester Research projected that 29 percent of Americans will use wearable devices, compared with 18 percent in 2015. It predicts that wearables sales will rise from 4.2 billion in 2015 to 9.8 billion in 2021. There has been more interest in wearables companies doing clinical validation. The AARP has been using its Project Catalyst initiative to validate the usability of connected devices and activity trackers for seniors.