It is challenging for stochastic optimizations to handle large-scale sensitive data safely. Recently, Duchi et al. proposed private sampling strategy to solve privacy leakage in stochastic optimizations. However, this strategy leads to robustness degeneration, since this strategy is equal to the noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL). Specifically, the noise injection leads to the issue of label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields "private but robust" updates of the primal variable on the private curriculum, namely an reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that, PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.

Now do the same with tanks, helicopters and biped/quadruped robots. Welcome to the not-so-distant future of LAWs, or lethal autonomous weapon systems. A conclusion reached at the UN conference on regulating LAWs in warfare that took place this August in Geneva was that, instead of outright banning them, the topic should be revisited in November. The stall was initiated by the U.S., Russia, Israel, South Korea and Australia. Until the revision meeting one thing is sure -- AI-controlled robotic warfare isn't too far off.

There are significant, real-world benefits to having an accepted and recognized identity. That's why the concept of a digital identity is being pursued around the world, from Australia to India. From airports to health records systems, technologists and policy makers with good intentions are digitizing our identities, making modern life more efficient and streamlined. Governments seek to digitize their citizens in an effort to universalize government services, while the banking, travel, and insurance industries aim to create more seamless processes for their products and services. In places like Syria and Jordan, refugees are often displaced without an identity.

Instead it is about a thoroughly progressive, completely 360 degree view of the traveller and everything that goes into creating special, unique, memorable experiences." Did you get your tickets directly from the ticket office? In today's fast-paced world, finding time to travel to a ticket office and get your tickets is a luxury few can afford. Besides, why bother if you can get your tickets in just a couple of clicks via your laptop or even your smartphone? Indeed, digital travel sales grew rapidly over the last several years, totaling $564.87 billion in 2016. And the number is expected to reach $817.54 billion by 2020. Such explosive growth is fueled by recent technology advances, not the least of which is data science. We at AltexSoft are no strangers to successfully applying data science and machine learning technologies to the field of custom travel software development.

Microsoft wants the Australian government to close loopholes in proposed data sharing-and-release legislation that it believes could be used to shut down or limit access to data without explanation. The software giant used a submission [pdf] to a Prime Minister & Cabinet-led consultation to outline concerns that data could be too easily withheld or not offered in the first place, despite assertions that "much of the Australian government's data is not personal or sensitive". Microsoft suggested that Australian laws should, in part, mimic the EU's reuse of public sector information directive, which requires agencies to explain why they deny access to data. "We note that the proposed process for sharing data does not appear to require Commonwealth data custodians to provide an explanation either when denying a data access request, or if they decide not to provide open access to data in the first instance," Microsoft said. "[We] suggest that the bill require data custodians to provide such an explanation.

As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should be predicted from the training data and network parameters. In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap. Our measure is based on the concept of margin distribution, which are the distances of training points to the decision boundary. We find that it is necessary to use margin distributions at multiple layers of a deep network. On the CIFAR-10 and the CIFAR-100 datasets, our proposed measure correlates very strongly with the generalization gap. In addition, we find the following other factors to be of importance: normalizing margin values for scale independence, using characterizations of margin distribution rather than just the margin (closest distance to decision boundary), and working in log space instead of linear space (effectively using a product of margins rather than a sum). Our measure can be easily applied to feedforward deep networks with any architecture and may point towards new training loss functions that could enable better generalization.

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.

The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.

Today Microsoft employs over 8,000 researchers and engineers directly applied to artificial intelligence technologies. These individuals either fit centrally into Microsoft's AI & R (artificial intelligence & research) team or they fit into each business unit's own engineering pool. Unlike a number of its competitors, Microsoft isn't going to name its artificial intelligence with a human name. Think Einstein from Salesforce and Watson from IBM amongst others. "At Microsoft, we don't see Artificial Intelligence as a product, we see it as a strategy that all of our products and customers can benefit from. It's making its way into everything we do a Microsoft" said David Carmona, Cloud and Enterprise Artificial Intelligence General Manager at Microsoft.

Kai-Fu Lee is the chairman of Sinovation Ventures and the president of its Artificial Intelligence Institute. He was the founding president of Google China. He recently spoke about his new book, "AI Superpowers: China, Silicon Valley and the New World Order," with The WorldPost's editor in chief, Nathan Gardels. WorldPost: Artificial intelligence is surely the most consequential technological development of the 21st century. In your book "AI Superpowers," you've written the most comprehensive global account of artificial intelligence to date. What are the central themes of your book?