In the past, we've discussed the importance of adding quality metadata for several reasons. Of course, better metadata will improve all aspects of search performance. Also, the right identifiers and tags can provide you with business intelligence, audit support, ideas for extra revenue streams and more. The real gold mine is in converting unstructured data into structured data. Here's a quick one-minute primer on what exactly metadata is: Adding this additional information to describe your documents may seem like a big job, and that's certainly true if you don't use the best tools.

When it comes to adoption of artificial intelligence, the US Federal Government is moving at a rapid pace. On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative, the United States' national strategy on artificial intelligence. As part of this strategy the US took into consideration principles on Artificial Intelligence published by the Organization for Economic Cooperation and Development (OECD). The AI Today podcast interviewed Adam Murray, International Relations Officer from the US Department of State to discuss these principles in more detail and why it's important to have AI principles around responsible & trustworthy AI discussed and adopted on an international level. Adam Murray is a foreign relations officer and a US diplomat, and has been working with the Department of State for over 13 years.

This week we speak to Justin McLeod, founder and chief executive of dating app Hinge. When recovering alcoholic Justin McLeod set up his dating app, it was to help him get over his heartbreak. Five years earlier, his college sweetheart, the woman he thought was the love of his life, had split up with him because of his drink problem. He had subsequently gone to rehab and successfully sobered up, but he had not been able to move on romantically. Not comfortable going into bars because of his addiction issue, he started work on Hinge in 2011 to help him find a new partner.

The National Transportation Safety Board (NTSB) in its investigation of the March 2018 death of a pedestrian in a crash with an Uber test vehicle, the first attributed to a self-driving car, said in November that NHTSA should make self-driving vehicle safety assessments mandatory and ensure automated vehicles have appropriate safeguards.

The Computer & Communications Industry Association welcomes the European Commission's approach to Artificial Intelligence in comments filed today responding to the Commission's public consultation. CCIA sent a letter in January with its recommendations on how a proportionate and risk-based EU approach to AI framework can maximise the benefits of AI, while also mitigating risks. In February, we welcomed the European Commission's White Paper on AI. The Commission considers'prior conformity assessment [which] could include procedures for testing, inspection or certification.' CCIA's comments cautions against lengthy, bureaucratic approval processes. Companies should be responsible for the compliance and testing of so-called'high risk' applications, based on the EU requirements, prior to EU market introduction.

As the U.S. and countries around the world begin to ease--or at least think about easing--restrictions stemming from the COVID-19 pandemic, executives at leading software and services organizations are reflecting on the lasting impact we can expect. Greater use of cloud services, accelerated digital transformation, a need for the latest and greatest technology, and a generally stronger appreciation for knowledge management are among the key changes being seen. To help shed light on the lessons learned from the novel coronavirus and how it is impacting the way public and private organizations work internally and respond to customers, KMWorld asked KM leaders about the changes they expect in a post-pandemic world. Answers have been edited and condensed. Trustworty and easy-to-find information is critical during uncertain times. It appears that we are starting to flatten the COVID-19 curve.

As the world tries to grapple with the implications of 5G, researchers from China have already started looking into 6G. University, China, and others investigated the challenges of embracing 6G as the world moves towards ML heavy solutions. Their main objective is to find out how to make ML more feasible in a high-speed wireless environment. Federated learning, stated the authors, is an emerging distributed AI approach with privacy preservation nature is particularly attractive for various wireless applications, especially to achieve ubiquitous AI in 6G. Traditional Machine Learning techniques rely on a central server and are prone to critical security challenges, e.g., a single point of failure.

A big thank you to those who contributed to this exercise. You can see a visualisation of the results below. Have we missed anyone out? If so, please let us know! NB We were NOT looking for individual companies developing AI or individual researchers working on AI or organisations based outside Scotland.

COCIR is the European Trade Association representing the medical imaging, radiotherapy, health ICT and electromedical industries. Founded in 1959, COCIR is a non-profit association headquartered in Brussels (Belgium) with a China Desk based in Beijing since 2007.COCIR is unique as it brings together the healthcare, IT and telecommunications industries.

Where data is available, it is desirable in geostatistical modelling to make use of additional covariates, for example terrain data, in order to improve prediction accuracy in the modelling task. While elevation itself may be important, additional explanatory power for any given problem can be sought (but not necessarily found) by filtering digital elevation models to extract higher-order derivatives such as slope angles, curvatures, and roughness. In essence, it would be beneficial to extract as much task-relevant information as possible from the elevation grid. However, given the complexities of the natural world, chance dictates that the use of 'off-the-shelf' filters is unlikely to derive covariates that provide strong explanatory power to the target variable at hand, and any attempt to manually design informative covariates is likely to be a trial-and-error process -- not optimal. In this paper we present a solution to this problem in the form of a deep learning approach to automatically deriving optimal task-specific terrain texture covariates from a standard SRTM 90m gridded digital elevation model (DEM). For our target variables we use point-sampled geochemical data from the British Geological Survey: concentrations of potassium, calcium and arsenic in stream sediments. We find that our deep learning approach produces covariates for geostatistical modelling that have surprisingly strong explanatory power on their own, with R-squared values around 0.6 for all three elements (with arsenic on the log scale). These results are achieved without the neural network being provided with easting, northing, or absolute elevation as inputs, and purely reflect the capacity of our deep neural network to extract task-specific information from terrain texture. We hope that these results will inspire further investigation into the capabilities of deep learning within geostatistical applications.