Ladies and Gentlemen, I am happy to be here today at the inauguration of the Wadhwani Institute of Artificial Intelligence. Let me begin by congratulating Romesh Wadhwani ji and Sunil Wadhwani ji,The Government of Maharashtra, and Mumbai University for coming together to make this Institute a reality. This is a prime example of how the public sector and the private sector can combine with good intention to build a world-class institute aimed at benefiting the poor. I have had various interactions with Indian diaspora across the globe during the last three and a half years or so. I have felt an intense desire, to make a contribution to India.

Biometrics and Biostatistics are disciplines of biological sciences concerned with the application of mathematical-statistical theory, principles, and practices to the observation, measurement, and analysis of biological data and phenomena. Journal of Biometrics and Biostatistics is a leading peer reviewed journal, promoting open access publishing in the collection of major scientific journals available in the scientific society. This promotes the application of statistical methods to the solution of biological problems. Journal of Biometrics and Biostatistics is a academic journal and aims to publish most complete and reliable source of information on the discoveries and current developments in the mode of original articles, review articles, case reports, short communications, etc. in all areas related to Biometrics, Medical statistics and making them freely available through online without any restrictions or any other subscriptions to researchers worldwide. It is an online manuscript submission, review and managing systems.

Modi made it clear that "the march of technology cannot be at the expense of further increasing the difference between societies over access to technology". "The evolution of technology has to be rooted in the ethic of'Sabka Saath, Sabka Vikaas'. Technology opens entirely new spheres and sectors for growth, and entirely new paradigms for more opportunities. "The road ahead for AI depends on and will be driven by human intentions'. It is our intention that will determine the outcomes of artificial intelligence," Modi said after inaugurating the Wadhwani Institute of Artificial Intelligence at the University of Mumbai's Kalina Campus here. "Can AI help us predict natural calamities?

Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our $A^4NT$ to preserve the semantics of the input text. $A^4NT$ learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.

Guest blog post by Xiu-Shen Wei, originally posted here. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it might lack a recent and comprehensive summary about the details of how to implement an excellent deep convolutional neural networks from scratch. Thus, we collected and concluded many implementation details for DCNNs. Here we will introduce these extensive implementation details, i.e., tricks or tips, for building and training your own deep networks. We assume you already know the basic knowledge of deep learning, and here we will present the implementation details (tricks or tips) in Deep Neural Networks, especially CNN for image-related tasks, mainly in eight aspects: 1) data augmentation; 2) pre-processing on images; 3) initializations of Networks; 4) some tips during training; 5) selections of activation functions; 6) diverse regularizations; 7)some insights found from figures and finally 8) methods of ensemble multiple deep networks. Additionally, the corresponding slides are available at [slide].

A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nature of real environments, which allows to produce more accurate, situational (e.g. context-aware, criteria-driven) recommendations. Despite the promising results, tensor-based methods are poorly covered in existing recommender systems surveys. This survey aims to complement previous works and provide a comprehensive overview on the subject. To the best of our knowledge, this is the first attempt to consolidate studies from various application domains in an easily readable, digestible format, which helps to get a notion of the current state of the field. We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems.

Beyond the technical and engineering limitations we're faced with, AGI also brings many moral and societal questions that haven't yet been answered. Would it be right to create and control a new sentient being? For a great dive into this, listen to Sam Harris interview Max Tegmark, an MIT Physicist and AI thinker. This talk reflects on the nature of intelligence, the risks of superhuman AI, the idea of a non biological definition of life, the substrate independence of minds, the relevance and irrelevance of consciousness for the future of AI, near-term breakthroughs in AI, and other topics.

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. In this article, we provide an overview of distributed software architectures and libraries as well as machine learning models for online learning. We highlight the most important ideas for classification, regression, recommendation, and unsupervised modeling from streaming data, and we show how they are implemented in various distributed data stream processing systems. This article is a reference material and not a survey. We do not attempt to be comprehensive in describing all existing methods and solutions; rather, we give pointers to the most important resources in the field. All related sub-fields, online algorithms, online learning, and distributed data processing are hugely dominant in current research and development with conceptually new research results and software components emerging at the time of writing. In this article, we refer to several survey results, both for distributed data processing and for online machine learning. Compared to past surveys, our article is different because we discuss recommender systems in extended detail.

For many, artificial intelligence (AI) stirs images of a futuristic cyborg (like the one pictured above), but business people know that the groundbreaking technology is here today. The smartest ones have already put it to work. Forrester reports that 63 percent of business technology decision makers – mostly chief data officers and chief analytics officers – are implementing, have implemented, or are expanding AI in their businesses. Nearly 60 percent say changing their business with AI over the next 12 months is a high, or critical, priority. Along these lines, a PwC report found that 72 percent of business executives believe AI will be the business advantage of the future.

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