Peggy Smedley: For you, what are the most interesting trends that you see? You and I have talked in the past about the IoT (Internet of Things) and I know that you have a lot of vision, a lot of examples that you look at when you think about cloud and edge and we talk about manufacturing and all these things in vertical markets, but for listeners right now, based on investments you guys [Microsoft] are making, what do you see are the most interesting trends? Sam George: Well, I think if you zoom the telescope way back out and look at the very big picture, what we're seeing across all of these vertical markets, whether it's manufacturing or agriculture, smart cites, smart energy. If you take a look at what's happening with all of these, there's a set of disruptive technologies that are fundamentally transforming how those industries function. Cloud was a big catalyst for that and I'd say, very well established at this point. And then IoT, a couple of years ago, really started hitting the scene, building on top of cloud and giving these businesses unprecedented visibility if they were able to take advantage of it back in the early days. Virtually all aspects of their business are able to sense things in the physical world, in realtime, that they weren't able to before. And then while the IoT was happening, edge computing started happening too, which was a normal and natural optimization, where as I connect and start collecting data from these billions of devices that are sensing across all of these different industries that are sensing things that are happening, it's natural to start taking some of the computing that you were doing in the cloud and some of the services that you were taking advantage of and pushing those right out and distributing those right out to the devices themselves for a variety of reasons, whether that's latency concerns or security concerns or anything else. We see this wonderful trend of AI that is powering really new breakthrough capabilities across all of these industries. AI is a great example, where as it takes advantage of those proceeding waves, edge computing and the IoT and cloud. AI can now run in a distributed fashion as well.

With software eating the world, it's time for the United States to create a cabinet-level position focused on digital. Whether it was the flawed rollout of the ACA website, the identified need for cybersecurity protection of our energy grid, or AI on the battlefield, the executive branch needs digital leadership and expertise to help guide the country. Here are 10 issues that require coordination, oversight, and/or financial support at the national level with links to relevant content. How do you ensure that machine learning algorithms aren't biased – by race, by age, or by gender? When can you trust an AI black box algorithm and when isn't it suitable?

A New Research on the Global Artificial Intelligence(AI) in Retail Market was conducted across a variety of industries in various regions to produce more than 150 page reports. This study is a perfect blend of qualitative and quantifiable information highlighting key market developments, industry and competitors' challenges in gap analysis and new opportunities and may be trending in the Artificial Intelligence(AI) in Retail market. Some are part of the coverage and are the core and emerging players being profiled IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, Intel, Google, Sentient Technologies, Salesforce, Visenze. Import and export policies that can have an immediate impact on the global Artificial Intelligence(AI) in Retail market. This study includes EXIM * related chapters for all relevant companies dealing with the Artificial Intelligence(AI) in Retail market and related profiles and provides valuable data in terms of finances, product portfolio, investment planning and marketing and business strategy. The study is a collection of primary and secondary data that contains valuable information from the major suppliers of the market.

The power of artificial intelligence to transform every field it touches is one of the unchallenged truths of our time -- but when it comes to healthcare, the technology's potential is often seen through a western lens. From hopes of faster drug discovery to systems that will help people manage chronic diseases better, the emphasis is frequently on how it can support, or otherwise make life easier, for those in the developed world. But attention is increasingly turning to how AI can address the pressing problems of poorer nations as they seek to move towards universal health coverage. In some cases, these are challenges that are distinct to the global south, but in others the problems faced in developing regions are to some extent shared by patients, physicians and payers across the globe. The Lancet's collaboration with the Financial Times on a commission on the convergence of digital health, AI and universal health coverage will concentrate on international governance and regulatory regimes.

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model- based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.

Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collapse of the embeddings, high sensitivity to sampling schemes and more importantly a lack of performance when compared to more modern methods. W e attribute this adoption to a lack of fair comparisons between various methods and the difficulty in adopting them for novel problem statements. In this paper, we perform an unbiased comparison of the most popular DML baseline methods under same conditions and more importantly, not obfuscating any hyper parameter tuning or adjustment needed to favor a particular method. W e find, that under equal conditions several older methods perform significantly better than previously believed. In fact, our unified implementation of 12 recently introduced DML algorithms achieve state-of-the art performance on CUB200, CAR196, and Stanford Online products datasets which establishes a new set of baselines for future DML research. The codebase and all tuned hyperparame-ters will be open-sourced for reproducibility and to serve as a source of benchmark.

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables. Our goal is to identify the best arm and the maximizer of the corresponding continuous function simultaneously. Our algorithm uses a Thompson sampling scheme that helps connecting both multi-arm bandit and BO in a unified framework. We extend our method to batch BO to allow parallel optimization when multiple resources are available. We theoretically analyze our method for convergence and prove sub-linear regret bounds. We perform a variety of experiments: optimization of several benchmark functions, hyper-parameter tuning of a neural network, and automatic selection of the best machine learning model along with its optimal hyper-parameters (a.k.a automated machine learning). Comparisons with other methods demonstrate the effectiveness of our proposed method.

This letter introduces an abstract learning problem called the ``set embedding'': The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy set embedding examples.

We apply methods from randomized numerical linear algebra (RandNLA) to develop improved algorithms for the analysis of large-scale time series data. We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. We show that the accuracy of approximations lies within $(1+\mathcal{O}(\varepsilon))$ of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Our proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach. To the best of our knowledge, this paper is the first attempt to establish a nexus between RandNLA and big time series data analysis.

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n).