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.

A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, and a neural network. Data assimilation is used to optimally combine a surrogate model with sparse noisy data. The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model. The two steps are then repeated iteratively. Numerical experiments have been carried out using the chaotic 40-variables Lorenz 96 model, proving both convergence and statistical skills of the proposed hybrid approach. The surrogate model shows short-term forecast skills up to two Lyapunov times, the retrieval of positive Lyapunov exponents as well as the more energetic frequencies of the power density spectrum. The sensitivity of the method to critical setup parameters is also presented: forecast skills decrease smoothly with increased observational noise but drops abruptly if less than half of the model domain is observed. The successful synergy between data assimilation and machine learning, proven here with a low-dimensional system, encourages further investigation of such hybrids with more sophisticated dynamics.

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

THE THAMES seems to draw people who work on intelligence-gathering. The spooks of MI6 are housed in a funky-looking building overlooking the river. Two miles downstream, in a shared office space near Blackfriars Bridge, lives Arkera, a firm that uses machine-learning technology to sort intelligence from newspapers, websites and other public sources for emerging-market investors. London has the right time zone, between the Americas and Asia. It is a nice place to live.

BAGHDAD – Iraq's Parliament called for the expulsion of U.S. forces from the country in reaction to the American drone attack that killed a top Iranian general, raising the prospect of a troop withdrawal that could cripple the battle against the Islamic State group and allow a resurgence of the extremists. Lawmakers approved a resolution asking the Iraqi government to end the agreement under which Washington sent troops more than four years ago to help fight ISIS. The bill is nonbinding and subject to approval by the Iraqi government but has the backing of the outgoing prime minister. But the vote was another sign of the blowback from the U.S. airstrike Friday that killed Iranian Gen. Qassem Soleimani and a number of top Iraqi officials at the Baghdad airport. Soleimani was the architect of Iran's proxy wars across the Mideast and was blamed for the deaths of hundreds of Americans in roadside bombings and other attacks.

April 2, 2019; Dubai, United Arab Emirates – UAE businesses show a significant lead in both maturity and proactiveness when it comes to the adoption of artificial intelligence solutions, according to a Microsoft report titled'AI Pulse' which was released today. 'AI Pulse' is a global Microsoft initiative designed to establish the attitudes and intentions of senior executives around the world towards artificial intelligence. The report is the result of widespread research across the US and EMEA of senior-level decision makers from dozens of industries. The study also involved inputs from many renowned experts in the fields of leadership, including Susan Etlinger, Industry Analyst with Altimeter Group and Heike Bruch, Professor of Leadership at Switzerland's University of St. Gallen among other Microsoft Data Scientists. "UAE AI strategy 2031 is marking a new level of innovation and the government is significantly investing in the latest AI technologies and tools to enhance performance, efficiency and fuel growth. Microsoft strongly believes that AI technologies will have significant impact on what good leadership will mean for future generations, and that sparking conversation now about smart solutions will allow current private and public organisations to take proper stock of the implications of various technologies," said Sayed Hashish, Regional General Manager, Microsoft Gulf.