Popular expressions, for example, "artificial intelligence", "machine learning" and "Big Data" have without question become a significant topic in the present tech scene and they are digging in for the long-term. However, the advancement power behind Artificial Intelligence and its related perspectives have additionally discovered its way to the core phase of our society. Can Artificial Intelligence be utilized for the more noteworthy benefit of society? Also, what job should organizations play in it? While AI is certainly not a silver bullet, it can help handle a lot of our general society's most challenging issues on a social, economic and environmental level.

With an imperative to empower the youngest minds of the country with the latest technologies, NITI Aayog, Atal Innovation Mission (AIM) in collaboration with the National Association of Software and Services Companies (NASSCOM) today launched an AI based Module rolled out for students in Indian schools. The AI-Base Module has been introduced with an objective for students to leverage the full potential of AIM's Atal Tinkering Lab (ATL) and further empowers them to innovate and create valuable solutions benefiting societies at large. The module contains activities, videos and experiments that enable students to work through and learn the various concepts of AI. Sharing his thoughts, CEO, NITI Aayog Amitabh Kant said that India can add 1.3% to its GDP on an annual basis through the use of machine learning and artificial intelligence. "Indians can find solutions to the challenges of a shared connected zero emission world, improving learning outcomes, disease like tuberculosis, cancer etc. If we are able to find solutions to these challenges for the 1.3 billion people of India, we can find solutions for the 7.5 billion people of the world too," he said.

Virtual reality has burst onto the stage, and because of how well-supported it has become as a hardware solution, its practical applications--from enterprise training to video games--are expanding. VR is a powerful tool for a multitude of industries and even consumers. Real-time medical education through projects such as Immertec--in which physicians can aid and train, in real time, during an active surgery--is by far the most valuable use of VR that has real-world impact. But many other examples exist within healthcare, such as VR gamification of medical rehabilitation as done by Verapy and others. The future of medicine will be the ability to practice everywhere through VR. - José Morey, Liberty BioSecurity We are seeing some incredible uses for virtual reality in healthcare.

I was talking with a global consulting company leader the other day and he told me that they had close to a thousand "Top AI Experts".

More than 1,000 students are vying for places at the world's first dedicated artificial intelligence university in Abu Dhabi. The Mohamed bin Zayed University of Artificial Intelligence will swing open its doors in August, with demand high from those eager to be part of the inaugural class of 2020. The graduate-level institute revealed the bumper number of applicants are currently being put through a stringent vetting process ahead of the landmark opening term. Masters and PhD courses will be held at the forward-thinking seat of learning, which has cast the net far and wide across the globe in search of top talent. World's first artificial intelligence university to open in Abu Dhabi Artificial intelligence isn't coming to the UAE - it is already here During the university's first advisory board meeting, Dr Sultan Al Jaber, Minister of State, said the first wave of students would be at the forefront of a new era of innovation in the country.

A new course, Artificial Intelligence for Business, will be offered by Wharton to undergraduate and MBA students in 2021. An online version of the course launched on Feb. 20. Artificial Intelligence for Business will be the first course to be fully dedicated to studying AI in a business context, said Kartik Hosanagar, a John C. Hower Professor of Technology and Digital Business. Hosanagar, who will be teaching the course, said he believes the new program will allow students who were not previously experienced with AI to become familiar with the field. He said the curriculum will cover the importance of big data, the use of machine learning, and other forms of AI in business.

With modern requirements, there is an increasing tendancy of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue --- how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto non-dominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multiobjective optimization may still be relatively new to SE/SBSE researchers, who may not be able to identify right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multiobjective optimisation problems may not be appropriate for specific SE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly available. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading uses of evaluation methods. In this paper, we carry out a critical review of quality evaluation for multiobjective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in 7 repositories, and select 97 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators and general situations in SBSE, which, together with the identified issues, enables us to provide a methodological guidance to selecting and using evaluation methods in different SBSE scenarios.

We study Online Convex Optimization in the unbounded setting where neither predictions nor gradient are constrained. The goal is to simultaneously adapt to both the sequence of gradients and the comparator. We first develop parameter-free and scale-free algorithms for a simplified setting with hints. We present two versions: the first adapts to the squared norms of both comparator and gradients separately using $O(d)$ time per round, the second adapts to their squared inner products (which measure variance only in the comparator direction) in time $O(d^3)$ per round. We then generalize two prior reductions to the unbounded setting; one to not need hints, and a second to deal with the range ratio problem (which already arises in prior work). We discuss their optimality in light of prior and new lower bounds. We apply our methods to obtain sharper regret bounds for scale-invariant online prediction with linear models.

Existing multi-armed bandit (MAB) models make two implicit assumptions: an arm generates a payoff only when it is played, and the agent observes every payoff that is generated. This paper introduces synchronization bandits, a MAB variant where all arms generate costs at all times, but the agent observes an arm's instantaneous cost only when the arm is played. Synchronization MABs are inspired by online caching scenarios such as Web crawling, where an arm corresponds to a cached item and playing the arm means downloading its fresh copy from a server. We present MirrorSync, an online learning algorithm for synchronization bandits, establish an adversarial regret of $O(T^{2/3})$ for it, and show how to make it efficient in practice.

Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estimating the accuracies of multiple noisy labeling sources (e.g., heuristics, crowd workers). Existing approaches use latent variable estimation to model the noisy sources, but these methods can be computationally expensive, scaling superlinearly in the data. In this work, we show that, for a class of latent variable models highly applicable to weak supervision, we can find a closed-form solution to model parameters, obviating the need for iterative solutions like stochastic gradient descent (SGD). We use this insight to build FlyingSquid, a weak supervision framework that runs orders of magnitude faster than previous weak supervision approaches and requires fewer assumptions. In particular, we prove bounds on generalization error without assuming that the latent variable model can exactly parameterize the underlying data distribution. Empirically, we validate FlyingSquid on benchmark weak supervision datasets and find that it achieves the same or higher quality compared to previous approaches without the need to tune an SGD procedure, recovers model parameters 170 times faster on average, and enables new video analysis and online learning applications.