NEW DELHI: Barun Gorain joined Hindustan Zinc Ltd as chief technology and innovation officer two months ago, moving from Barrick Gold in Canada, one of the many CXO-level hires that Indian companies have been making in the buzzing areas of artificial intelligence (AI), machine learning, the Internet of things (IoT) and robotic process automation (RPA). Other recent instances of Indians returning home with domain knowledge in these emerging technologies include Raghuram Velega, who left his job at a San Francisco-based cognitive computing company to join Reliance Jio Infocomm as vice-president, head, big data and analytics. Former National Aeronautics and Space Administration (NASA) executive Santanu Bhattacharya joined Bharti Airtel as chief data scientist and Ayush Sharma moved from Silicon Valley to join Reliance Jio as senior vicepresident of engineering and technology. Search firms like Korn Ferry, EMA Partners, Transearch and Hunt Partners say there's a paucity of experts in these fields, leading to a jump in salaries of new hires by as much as 50%, most of them from overseas. Salaries for such executives are at Rs 1-2 crore annually but can be even higher.

Written on 06 April 2018. But what would a good AI plan actually look like? Politicians worldwide are stealing one of the US government's best ideas by drawing up ambitious plans to make the most of advances in artificial intelligence. These AI manifestos, penned in Paris, Beijing, and elsewhere, follow the example of the Obama administration, which released a report on the technology toward the end of its tenure. This report did not include funding, but it made it clear that AI should be a key focus of government strategy.

Most companies are not very well prepared for the weaponizing of cyberspace by foreign governments. That was part of an assessment of the current state of cyberspace by Eric Schmidt, former chairman of Alphabet, Inc. and CEO of Google. Schmidt gave the remarks during his keynote address at the two-day annual conference of the Yale Johnson Center for the Study of American Diplomacy, which kicked off on April 6. This year's conference, "Understanding Cyberwarfare and Artificial Intelligence," drew both academics and practitioners. This was the seventh annual conference of the Johnson Center, which was made possible by Dr. Henry Kissinger's donation of his papers to Yale and a generous gift from Charles B. Johnson '54 and Nicholas F. Brady '52.

In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is based on the recently proposed tensor-tensor product (or t-product) [13]. Induced by the t-product, we first rigorously deduce the tensor spectral norm, tensor nuclear norm, and tensor average rank, and show that the tensor nuclear norm is the convex envelope of the tensor average rank within the unit ball of the tensor spectral norm. These definitions, their relationships and properties are consistent with matrix cases. Equipped with the new tensor nuclear norm, we then solve the TRPCA problem by solving a convex program and provide the theoretical guarantee for the exact recovery. Our TRPCA model and recovery guarantee include matrix RPCA as a special case. Numerical experiments verify our results, and the applications to image recovery and background modeling problems demonstrate the effectiveness of our method.

Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this paper presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm (FISTA), and the features of every subspace can be extracted using the smooth multi-channel functional PCA. Numerical studies together with case studies demonstrate the efficiency and applicability of the proposed methodology.

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We propose two alternative divergence minimization approaches to learn the generative model and recognition model jointly. The first one treats all variables as a whole, while the second one utilizes the structural information by checking the individual local factors defined by the generative model and works better in practice. Finally, we present two important instances of Graphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN), which can successfully learn the discrete and temporal structures on visual datasets, respectively.

The Chinese government's plans for mass surveillance using facial recognition have received a boost from one of the country's tech powerhouses, after Alibaba led a $600m investment in SenseTime, which develops technology for tracking individuals. The company is working on facial and object recognition technology that accurately can spot people using cameras, recently demonstrated on CCTV in Beijing. Honda is using SenseTime for its driverless car research and development and it is also being used at shopping counters that allows customers to check-out using their faces. SenseTime already smashed the record for AI funding, beating British competitor DeepMind which was bought by Google for an...

In my last blog post, I introduced readers to William Grey Walter (1910 – 1977), a renowned neurophysiologist, cybernetician, and robotician. His futuristic aim was to construct mechanical models--robots--that were capable of realistically simulating the behavior of living beings. Grey Walter's most famous robotic creations were his Cybernetic Tortoises. Elmer and Elsie, his first two robots, were constructed between 1948 and 1949. They appeared to exhibit intelligent action: they were goal-directed (they moved toward light and stopped doing so when they reached the light) and they avoided obstacles that blocked their way to the goal. In a truly remarkable coincidence, robotic tortoises have very recently made the news!

Facebook founder Mark Zuckerberg has been receiving special coaching on how to present himself when he appears before US politicians demanding to know what he is doing to protect users' data, and how Russia was able to use his platform to allegedly meddle in the 2016 presidential election. Amid continuing controversy over the inappropriate harvesting of the data of up to 87 million Facebook users by British political consulting firm Cambridge Analytica, Mr Zuckerberg will try and reassure Congress he is taking the concerns of them and the general public seriously. He will also try to deflect the efforts of those who favour more stringent government regulation. "It's clear now that we didn't do enough to prevent these tools from being used for harm," he is expected to tell the House Committee on Energy and Commerce, according to written testimony released ahead of his appearance. "We didn't take a broad enough view of our responsibility, and that was a big mistake. It was my mistake, and I'm sorry. I started Facebook, I run it, and I'm responsible for what happens here."

As an industry observer, I've watched the development of artificial intelligence (AI) with fascination. While the technology itself is developing at a breakneck pace and offers plenty of opportunity for analysis and prediction, it is the evolving industry reaction that I find most interesting. But until very recently it was primarily the purview of theoretical scientists and science fiction writers. It is only within the last few years that AI, in even modest forms, has encroached upon our everyday reality. As it has gone from fantasy to the first inklings of reality - and that has begun to sink in - the reaction has been instructive.