MagniLEARN, an Ed-Tech company using artificial intelligence and Natural Language Processing, received second prize for innovation in Artificial Intelligence and was recognized as a promising AI Startup in the third annual China – Israel Innoweek Conference held in Beijing, China. "Combining Natural Language Processing (NLP) with Artificial Intelligence (AI) allowed us to turn the computer into a language-aware personal tutor for each student," said MagniLEARN CEO Howard Cooper in accepting the award. "Our difference lies in presenting personalized exercises that the student answers with free-form responses. Just like a personal tutor teaching language to a child, we understand what is correct, what is nearly correct, and provide feedback and then reinforcement as the student learns proper English. We gave the computer enough language awareness to become an intelligent and efficient language tutor for each student," he concluded.

The "LegalTech Artificial Intelligence Market" is evolving at an exciting pace driven by changing dynamics and risk ecosystem, an analysis of which forms the crux of the report. The study on the global LegalTech Artificial Intelligence Market takes a closer look at several regional trends and the emerging regulatory landscape to assess its prospects. The critical evaluation of the various growth factors and opportunities in the global LegalTech Artificial Intelligence Market offered in the analyses helps in assessing the lucrativeness of its key segments. Summary of Market: The global LegalTech Artificial Intelligence market is valued at xx million US$ in 2019 is expected to reach xx million US$ by the end of 2025, growing at a CAGR of xx% during 2019-2025. Legal technology, also known asLegal Tech, refers to the use oftechnologyandsoftwareto providelegal services.

In 2020, the nature of customer engagement will change as personalisation - how marketing and customer value management actually engage with customers - rapidly matures. This means a change will be required in legacy campaign and loyalty programme management solution architectures (i.e. a move from relational databases of static customer data and batch processes to a real-time online customer profiling and engagement triggering). Those Communications Service Providers (CSPs) who lead the way will tap the real benefits that can be achieved by moving to CE 3.0. Net Promoter Scores in the telecoms industry are low; yet to date there's been relatively little analysis of why. One change lies in clearer answers to the question "Does my operator give me value for my money?".

The U.S. will step up its crackdown on China's surveillance industry this coming week, with further restrictions on the supply of American technology that will be in effect from January 6. The new regulations target the use of artificial intelligence in geospatial applications--essentially the detection and classification of objects from planes, drones and satellites. While many such applications are civilian in nature--geographical surveys, construction, town planning, the real focus is military and surveillance. Essentially, the U.S. does not want these technologies in enemy hands. The restrictions cover U.S. exports to all countries bar Canada, but the primary target is of course China and its world-leading AI surveillance industry.

One video shows Barack Obama using an obscenity to refer to U.S. President Donald Trump. Another features a different former president, Richard Nixon, performing a comedy routine. But neither video is real: The first was created by filmmaker Jordan Peele, the second by Jigsaw, a technology incubator within Alphabet, Inc. Both are examples of deepfakes, videos or audios that use artificial intelligence to make someone appear to do or say something they didn't. The technology is a few years old and getting better.

Increased rollouts of 5G in 2020 will unlock the development of more advanced technologies, said Gianfranco Lanci, corporate president and chief operating officer at Lenovo. With potential speeds of up to 10Gbps, under ideal conditions, 5G is set to be as much as 20-times faster than 4G, Lanci said. It's the additional benefits of greater stability and lower latency though that make it an all-round win. "The reason everyone is so keen for 5G to roll out is because those combining factors mean it is the key to unlocking the development of more advanced technologies such as artificial intelligence, machine learning, edge computing and others." South Korea, the United Kingdom, Germany, and the United States are currently leading the 5G rollout race.

President Trump continued issuing threatening warnings Sunday that more action would come if Iran retaliates against the U.S. for the killing of Iranian Gen. Qassem Soleimani, which critics have been calling an illegal action taken without consulting Congress. "These Media Posts will serve as notification to the United States Congress that should Iran strike any U.S. person or target, the United States will quickly & fully strike back, & perhaps in a disproportionate manner," he tweeted Sunday afternoon. "Such legal notice is not required, but is given nevertheless!" Many Democrats in Congress had said the Trump administration failed to consult with legislative leaders before conducting the drone attack Friday against Soleimani, the head of the Islamic Revolutionary Guard Corps' elite Quds Force, and the White House faced a barrage of questions about the killing's legality. "I really worry that the actions the president took will get us into what he calls another endless war in the Middle East. He promised we wouldn't have that," said Chuck Schumer of New York, the Senate's top Democrat.

We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu search, and the others are a genetic algorithm and ant colony optimization. The methods are implemented, performing the proper calibration of parameters in the case of heuristics, to ensure good results. From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM. Simulated annealing perform very well, especially compared to classical methods.

We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter. We show on both simulated data and the Norwegian wind farm dataset that the proposed feature importance statistic yields statistically significant improvements relative to similar feature importance measures in both variable selection power and predictive performance on a real world dataset.

Federated learning is an emerging research paradigm to train models on private data distributed over multiple devices. A key challenge involves keeping private all the data on each device and training a global model only by communicating parameters and updates. Overcoming this problem relies on the global model being sufficiently compact so that the parameters can be efficiently sent over communication channels such as wireless internet. Given the recent trend towards building deeper and larger neural networks, deploying such models in federated settings on real-world tasks is becoming increasingly difficult. To this end, we propose to augment federated learning with local representation learning on each device to learn useful and compact features from raw data. As a result, the global model can be smaller since it only operates on higher-level local representations. We show that our proposed method achieves superior or competitive results when compared to traditional federated approaches on a suite of publicly available real-world datasets spanning image recognition (MNIST, CIFAR) and multimodal learning (VQA). Our choice of local representation learning also reduces the number of parameters and updates that need to be communicated to and from the global model, thereby reducing the bottleneck in terms of communication cost. Finally, we show that our local models provide flexibility in dealing with online heterogeneous data and can be easily modified to learn fair representations that obfuscate protected attributes such as race, age, and gender, a feature crucial to preserving the privacy of on-device data.