Global law firm Baker & McKenzie has paired with Euromoney Thought Leadership to reveal what is driving the global financial services industry towards a rapid embrace of Artificial Intelligence and Machine Learning, and the implications both for the industry and financial markets as a whole. Ghosts in the machine: Artificial intelligence, risks and regulation in financial markets shows an industry rapidly seeing the potential benefits of AI, while being increasingly concerned about risk and the ability of regulators to keep pace. The survey asked 424 senior executives from financial institutions and fintech companies around the world as well as leading experts in the field for their view on how AI will affect the financial sector, what risks and benefits AI will bring to the sector, what the associated regulatory and legal challenges will be and many more questions. Surprisingly similar views emerged from across the globe, with only a few notable variations, such as AI investment seen as lagging slightly in Asia Pacific compared to other regions and Latin American participants seeing AI as a regulatory tool to combat money laundering first rather than market misconduct, the top choice elsewhere. The results provide some insights into the likely future of the financial sector.

In popular books, academic articles, a TED talk, and countless lectures, the prominent Dutch primatologist Frans De Waal has spent his career showing just how many capacities and traits once thought to be distinctly human – from face-recognition to inequality aversion – are in fact broadly shared by many other species of primates. His new book, "Are We Smart Enough To Know How Smart Animals Are" argues that impressive forms of animal intelligence occur throughout the animal kingdom, not simply in the primate order. Our own preconceptions may be the main obstacle to recognizing animal intelligence. Until the 1980s researchers usually described animals with the terms "learning" and "instincts" but not "cognition." That's changed – now almost every week there's a new finding in animal cognition.

Researchers at Regenstrief Institute and Indiana University School of Informatics and Computing say they now can detect cancer cases using data from free-text pathology reports at least as well--and faster--than clinicians reviewing reports manually. The researchers used existing data algorithms and open source machine learning tools to create a breakthrough electronic approach that could significantly speed patient diagnoses and public health reporting. "We think that it's no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not," says Shaun Grannis, MD, senior study author and interim director of the Regenstrief Center of Biomedical Informatics. Machine learning, which in healthcare is most frequently associated with initiatives using IBM's Watson Health technology, uses established algorithms to find meaningful patterns in data automatically, and then uses these known patterns to uncover fundamental relationships, Grannis explains. At Regenstrief/IU, machine learning identified patterns of language in pathology reports, enabling algorithms to create a rule that if certain factors or findings are found in the automated pathology review, then a patient is likely to have cancer. Researchers sampled 7,000 free-text pathology reports from more than 30 hospital participants in the Indiana Health Information Exchange to predict if a report was positive or negative for cancer.

This paper offers some suggestions on how governments and businesses can manage the risks, whilst maximising the benefits, posed by Artificial Intelligence (AI) over the course of this century. It begins by outlining the history and present state of the art, summarising predictions made by experts in the field on how it might progress in the coming decades. Following this it provides an overview of the dangers that advanced AI applications may present across a range of industries. In its conclusion the paper provides a summary of the policy recommendations made across these areas. It continues with a discussion of the policy themes that have been developed, followed by some final remarks. Throughout we avoid discussion of the technical details of AI development, but focus on how states and private institutions can increase the likelihood that the social, economic and political impact of advanced AI will be positive.

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

Machine Learning for Data Science and Analytics is a free, self-paced online course conducted by the Columbia University. This course helps you learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics.

Diffbot, an artificial intelligence company that helps clients extract and combine data from multiple Web sources, announced today it raised 10 million from investors including Tencent and Felicis Ventures to expand its "knowledge-as-a-service" offerings to businesses and consumer apps. The Palo Alto, CA-based startup, founded in 2009, still has a tiny staff of 14. But Diffbot's ambition is huge: to catalog trillions of facts across the Web--many of them drawn from page elements such as comment forums, which can't be mined by traditional search engines. The startup says it has made a significant start on that goal, having indexed 1.2 billion entities such as people, products, and places since the middle of last year. Its Global Index also encompasses 10 to 20 times that number of facts, says Diffbot founder and CEO Mike Tung.

Hanson Robotics touted their android, Sophia, at this year's SWXW in Austin, Texas. The android is arguably the most human like robot to date, capable of masking 62 different facial expressions. Fundamentally, Sophia was designed to be "as conscious, creative and capable as any human," according to to CEO of Hanson Robotics, David Hanson. In addition to her various facial expressions, Sophia can recognize people, hold eye contact, carry on intelligent conversations with speech recognition software and is even capable of learning. Sophia is clothed in a patented silicon skin called Frubber, an elastic form of rubber used in robotics.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.