Facebook Inc. suspended another company that harvested data from its site and said it was investigating whether the analytics firm's contracts with the U.S. government and a Russian nonprofit tied to the Kremlin violate the platform's policies. Crimson Hexagon, based in Boston, has had contracts in recent years to analyze public Facebook data for those and other clients, according to people familiar with the matter and federal procurement data. Crimson Hexagon says it has the largest repository of public social media posts, totaling more than one trillion, from sites that also include Twitter Inc. TWTR -0.05% and Instagram. Crimson Hexagon operates with little oversight from Facebook once it pulls public data from the social-media platform, according to more than a dozen people familiar with the business. The government contracts weren't approved by Facebook in advance, for example, the people said.

CSG, a business support solutions (BSS) provider said that telecom carriers are increasingly leveraging the cloud to bring down the recurring operational costs, and with India's top service provider Bharti Airtel as one of the telcos to deploy revenue management platform, the US-headquartered company feels that the IoT and AI would fundamentally the change the way we live, work and play. How have you been supporting businesses to digitally transform? Almost every industry is faced with digital disruption and the need to transform to survive and thrive. Among our primary client base of communications service providers, digital transformation encompasses every aspect of their business, from rolling out new 5G networks to launching new services designed to attract consumers on-the-go. CSG supports the digital transformation of companies in ways such as investments in our solution portfolio that enable our customers to meet these increased demands, and through the deep expertise of our people across digital strategy, processes, and technology domains.

Artificial intelligence (AI) and machine learning (ML) are the most widely chosen domains for reskilling among working tech professionals in India, according to the findings of education technology company Simplilearn. The firm's'Career Impact Survey 2018' which was aimed at analyzing the impact of professional certifications and reskilling among working professionals revealed that AI and ML domains were chosen by 25% of respondents. This was followed by big data and data science domains chosen by 20% of the participants. Other new age categories such as'digital marketing, cloud computing, cybersecurity, DevOps and Agile and Scrum' together saw 55% uptake in reskilling among professionals. The certification courses helped 31% of professionals to enhance their performance, gain manager and peer appreciation, according to the survey.

This work proposes an adaptive trace lasso regularized L1-norm based graph cut method for dimensionality reduction of Hyperspectral images, called as `Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is to generate the optimal projection matrix by considering both the sparsity as well as the correlation of the data samples. The conventional L2-norm used in the objective function is sensitive to noise and outliers. Therefore, in this work L1-norm is utilized as a robust alternative to L2-norm. Besides, for further improvement of the results, we use a penalty function of trace lasso with the L1GC method. It adaptively balances the L2-norm and L1-norm simultaneously by considering the data correlation along with the sparsity. We obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm with trace lasso as the penalty. Furthermore, an iterative procedure for this TL-L1GC method is proposed to solve the optimization function. The effectiveness of this proposed method is evaluated on two benchmark HSI datasets.

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.

Ready or not, autonomous robots are leaving laboratories to be tested in real-world contexts. With more and more people living in cities, these technologies offer ways to cope with ageing populations and poorly maintained infrastructures, while promoting safer transport, productive manufacturing and secure energy supplies. Urban "living labs" are one way scientists are trying to understand how autonomous robots – or Robotics and Autonomous Systems (RAS), to give them their full title – will affect our everyday lives. Autonomous robots are interconnected, interactive, cognitive and physical tools, which can perceive their environments, reason about events, make or revise plans and control their own actions. These technologies are designed to draw on big data and connect with the Internet of Things, to make our lives easier by increasing accuracy and efficiency.

During the AI boom of the 1980s, the field also enjoyed a great deal of hype and rapid investment. Rather than considering the value of individual startups' ideas, investors were looking for interesting technologies to fund. This is why most of the first generation of AI companies have already disappeared. Companies like Symbolics, Intellicorp, and Gensym -- AI companies founded in the '80s -- have all transformed or gone defunct. And here we are again, nearly 40 years later, facing the same issues.

Does all the hype about AI sound just a little too familiar? If you're old enough to remember the first beginnings of the Internet and the dotcom bubble, you might also remember the tsunami of hype that attended these events as they unfolded. Wired magazine made endlessly breathless predictions about how the Internet would transform humanity and bring about a technologically-driven utopia. Now we're wrestling with how such a promising technology devolved into a netherworld of hacking, hate speech, exploitation of personal data, "dark webs", misinformation, political chicanery, and citizen surveillance despite these glowing promises. In the latest twist, AI is being sold in a similar way by similar players and the cultural amnesia is impressive.

Xilinx this week announced that it had taken over DeepPhi Technology, a machine learning startup from China. Deep Phi has been using Xilinx FPGA for its ML projects since its inception in 2016 and is therefore seen as a good fit for Xilinx to expand further into machine learning. DeepPhi's key product is Xilinx FPGA-based Aristotle architecture that is used to compute convolutional neural networks (CNN). The product is now used for video and image recognition tasks on surveillance cameras and NVR/DVR solutions, but the architecture itself is flexible and scalable for everything from smartphones to servers. For example, right now DeepPhi's NVR/DVR solution can analyze up to nine channels of 1080p videos in real time and can model for over 30 human faces in a single frame.