PARIS (Reuters) - French President Emmanuel Macron promised 1.5 billion euros ($1.85 billion) of public funding into artificial intelligence by 2022 in a bid to reverse a brain drain and catch up with the dominant U.S. and Chinese tech giants. The investment is part of an AI strategy laid out by the centrist leader at the elite College de France research institute in Paris and builds on a report that points to the assets and drawbacks of France in the field. Business-friendly Macron wants to turn France into a "start up nation" and bets that easing labor laws and higher investments technology will create jobs, alleviate the domination of Alphabet's Google, Facebook and lay out the seeds for Europe-based champions. "There's no chance of controlling any effects (of these technologies) or having a say on any adverse effect if we've missed the start of the war," the president said on Thursday in front of a row of ministers and top executives, including BNP Paribas Jean-Laurent Bonnafé. He spoke between two black boards covered with complex equations in the main amphitheatre of the institute, founded in the 16th century.

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Swiss engineering company ABB is considering acquisitions to increase its capabilities in artificial intelligence, CEO Ulrich Spiesshofer said March 29. "We will continually further expand the portfolio of ABB," he said, speaking on the sidelines of the company's annual general meeting in Zurich. This would include investment in organic growth in artificial intelligence (AI), and partnerships with other companies to accelerate areas such as linking AI to industrial robots. ABB, whose products also include charging stations for electric cars and massive converters for continent-spanning transmission systems, would also invest selectively in start-up companies, Spiesshofer said. He was speaking after ABB gave a slightly more upbeat assessment about the development of its markets for 2018, saying conditions had brightened.

Swiss engineering company ABB is considering acquisitions to increase its capabilities in artificial intelligence, CEO Ulrich Spiesshofer said March 29. "We will continually further expand the portfolio of ABB," he said, speaking on the sidelines of the company's annual general meeting in Zurich. This would include investment in organic growth in artificial intelligence (AI), and partnerships with other companies to accelerate areas such as linking AI to industrial robots. ABB, whose products also include charging stations for electric cars and massive converters for continent-spanning transmission systems, would also invest selectively in start-up companies, Spiesshofer said. He was speaking after ABB gave a slightly more upbeat assessment about the development of its markets for 2018, saying conditions had brightened.

Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests. According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said co-author Peter Fedichev, Science Director at GERO. "Inspired by these examples, we explored AI potential for'Health Risks Assessment' based on human physical activity," Fedichev added. For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES). They trained neural network to predict biological age and mortality risk of the participants from one-week long stream of activity measurements.

Two major UK-based law firms, Allen & Overy (A&O) and Addleshaw Goddard (AG), have both announced new career paths to help them to adapt to legal tech's impact. Both initiatives are a clear indication of the growing importance of legal technology, in terms of showing the need for law firms to have the right skillsets internally and that legal tech capabilities have moved far beyond'operational' needs of just'keeping the lights on' and have now moved front and centre in terms of strategic growth planning for law firms. Machine learning/NLP tools are clearly part of this movement given that they can help in the direct production of legal work, such as via review, but legal tech's impact also includes a whole new wave of technology that connects to risk and compliance analysis, litigation prediction, contracting automation tools, smart contract and blockchain technology, and a range of incremental changes to more well-developed tech such as DMSs and collaboration platforms. In short, there is now so much new legal technology having an impact on how lawyers operate on a day to day basis and most importantly how they actually produce work that the more forward thinking firms are adapting their recruitment and career paths to meet these needs. This is all the more important when one considers that the clients are becoming increasingly savvy to the benefits of this'new means of production', leaving law firms that want to retain market position little option other than to adapt, while this market change is also offering early adopters the chance to compete more effectively against rivals in the legal market.

To avoid misuse of artificial intelligence, French President Emmanuel Macron proposed setting up a panel akin to the Intergovernmental Panel on Climate Change. France is going big on artificial intelligence (AI). President Emmanuel Macron yesterday announced a €1.5 billion plan to turn his country into a world leader for AI research and innovation, a field dominated by the United States and China. It calls for a hefty investment, a handful of specialized institutes, a focus on ethics and open data, and a call to recruit foreign researchers and French scientists working abroad to the country, not unlike Macron's 2017 "Make Our Planet Great Again" climate initiative. Macron presented his plans in a lengthy speech peppered with erudite references and touches of humor at the end of the"AI for Humanity" conference in Paris.

Deep learning is the technology driving today's artificial intelligence revolution. Dimensionality reduction is one of the most crucial tools in a data scientists' toolbox, and modern tools can yield truly magical results. ODSC Europe 2017 is a unique collection of over 70 insightful presentations on data science modeling, tools, and languages, and topics delivered by top experts in the field. Topics include deep learning, quant finance and AI for business and more. Data visualisation offers a brilliant way of bringing the raw numbers to life.

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: background and handwritten annotation. The best model achieves a mean Intersection over Union (IoU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.

Deep learning techniques may pave the way for a more accurate outcome prediction in colorectal cancer patients as compared to evaluations currently performed by an experienced human observer. Researchers at the University of Helsinki are reporting in Scientific Reports that they have created a deep learning algorithm that appears to help clinicians better predict patient outcomes based on colorectal cancer tissue samples. "In our study we hypothesized whether a deep learning–based algorithm can be trained to extract prognostic features from cancer tissue images without any expert-defined supervision. It appeared exciting that almost no domain expertise is needed to build accurate classifiers," said study investigator Johan Lundin, MD, PhD, who is the Research Director of FIMM-Institute for Molecular Medicine Finland, at the University of Helsinki. The researchers combined convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based simply on images of tumor tissue samples.