Early last month, a member of the US Air Force donned a virtual reality headset and scanned a 3D map of a desert landscape. He saw a speeding object that algorithms warned was likely a cruise missile. The airman considered the data, then used a hand controller to send out an order. This was no video game: The command led to a real projectile taking down a mock cruise missile over White Sands Missile Range in New Mexico. The episode was a demonstration of the future of war as seen by Anduril, the defense company cofounded by Palmer Luckey, the politically contentious cofounder of Oculus, the VR company acquired by Facebook in 2014.

You might have read the news about how our mail-sorting machines have been dismantled and how our boss replaced our mailbags with a "Flintstones"-style prehistoric pelican that carries your letters in its mouth pouch and says, "Eh, it's a living" every time we put a letter into its bill. And, don't worry, they're all true. We just want to officially state that "voting by mail is easy!" is what we would have said every election up till now. This special year, we've designed a handy guide to help you participate in democracy via mail. You can access this on the Internet.

In the present scenario, techniques like AI and machine learning are involved in almost all sectors. These techniques help organisations by various means, starting from getting insights from raw data to predicting future outcomes, and more. Focussing all the benefits of AI and ML, the utilisation of machine learning techniques in cybersecurity has been started only a few years ago and still at a niche stage. AI in cybersecurity can help in various ways, such as identifying malicious codes, self-training and other such. Here is a list of top eight machine learning tools, in alphabetical order for cybersecurity.

Three cybersecurity experts explained how artificial intelligence and machine learning can be used to evade cybersecurity defenses and make breaches faster and more efficient during a NCSA and Nasdaq cybersecurity summit. Kevin Coleman, the executive director of the National Cyber Security Alliance, hosted the conversation as part of Usable Security: Effecting and Measuring Change in Human Behavior on Tuesday, Oct. 6. Elham Tabassi, chief of staff information technology laboratory, National Institute of Standards and Technology, was one of the panelists in the "Artificial Intelligence and Machine Learning for Cybersecurity: The Good, the Bad, and the Ugly" session.text "Attackers can use AI to evade detections, to hide where they can't be found, and automatically adapt to counter measures," Tabassi said. Tim Bandos, chief information security officer at Digital Guardian, said that cybersecurity will always need human minds to build strong defenses and stop attacks.

Artificial Intelligence (AI) is red hot, but as we talk about its potential, the space is also engulfed with apprehensions such as loss of jobs, unethical use of the technology, and the biggest worry is - what if AI takes over humanity? Jaideep Mishra, Joint Secretary, MeitY, said, trust issues, lack of clarity on development and deployment processes are the main barriers for the adoption of AI in India. Commenting on this, some of the experts at the summit said there is possibility for'responsible' AI if best practices such as ethical framework, redressal system, open data, collaborations, and others are followed by the ecosystem. Jaideep was speaking at a panel discussion on'Education and Awareness for Responsible AI' at RAISE 2020, an AI summit organised by the Ministry of Electronics and Information Technology (MeitY) in partnership with NITI Aayog. The mega virtual summit on artificial intelligence was inaugurated on October 5 by Prime Minister Narendra Modi, who called for India to be a global hub for AI.

Inaugurating a five-day global virtual summit on artificial intelligence (AI), Responsible AI for Social Empowerment or RAISE 2020, Prime Minister Narendra Modi highlighted how the use of AI will empower India and also warned against the pitfalls. In June, India along with Australia, the United States, the United Kingdom, Canada, France, Germany, New Zealand and others joined together to create the Global Partnership on Artificial Intelligence (GPAI) for the responsible development and use of AI. Algorithm transparency is key to establishing this trust. This will create an e-education unit to boost the digital infrastructure, digital content and capacity. Under this programme, more than 11,000 students from schools completed the basic course.

Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph. Although such approaches have shown advances in downstream node classification tasks, they are ineffective in jointly representing larger $k$-node sets, $k{>}2$. We propose MHM-GNN, an inductive unsupervised graph representation approach that combines joint $k$-node representations with energy-based models (hypergraph Markov networks) and GNNs. To address the intractability of the loss that arises from this combination, we endow our optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator. Our experiments show that the unsupervised MHM-GNN representations of MHM-GNN produce better unsupervised representations than existing approaches from the literature.

Decisions made nowadays by Artificial Intelligence powered systems are usually hard for users to understand. One of the more important issues faced by developers is exposed as how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more important role. This technique could also be applied successfully for explaining the results of Association Rule Mining.This Chapter focuses on two issues: (1) How to discover the relevant association rules, and (2) How to express relations between more attributes visually. For the solution of the first issue, the proposed method uses Differential Evolution, while Sankey diagrams are adopted to solve the second one. This method was applied to a transaction database containing data generated by an amateur cyclist in past seasons, using a mobile device worn during the realization of training sessions that is divided into four time periods. The results of visualization showed that a trend in improving performance of an athlete can be indicated by changing the attributes appearing in the selected association rules in different time periods.

Post-processing immunity is a fundamental property of differential privacy: it enables the application of arbitrary data-independent transformations to the results of differentially private outputs without affecting their privacy guarantees. When query outputs must satisfy domain constraints, post-processing can be used to project the privacy-preserving outputs onto the feasible region. Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy. Post-processing has been applied successfully in many applications including census data-release, energy systems, and mobility. However, its effects on the noise distribution is poorly understood: It is often argued that post-processing may introduce bias and increase variance. This paper takes a first step towards understanding the properties of post-processing. It considers the release of census data and examines, both theoretically and empirically, the behavior of a widely adopted class of post-processing functions.

In this work, we study the problem of learning partially observed linear dynamical systems from a single sample trajectory. A major practical challenge in the existing system identification methods is the undesirable dependency of their required sample size on the system dimension: roughly speaking, they presume and rely on sample sizes that scale linearly with respect to the system dimension. Evidently, in high-dimensional regime where the system dimension is large, it may be costly, if not impossible, to collect as many samples from the unknown system. In this paper, we will remedy this undesirable dependency on the system dimension by introducing an $\ell_1$-regularized estimation method that can accurately estimate the Markov parameters of the system, provided that the number of samples scale logarithmically with the system dimension. Our result significantly improves the sample complexity of learning partially observed linear dynamical systems: it shows that the Markov parameters of the system can be learned in the high-dimensional setting, where the number of samples is significantly smaller than the system dimension. Traditionally, the $\ell_1$-regularized estimators have been used to promote sparsity in the estimated parameters. By resorting to the notion of "weak sparsity", we show that, irrespective of the true sparsity of the system, a similar regularized estimator can be used to reduce the sample complexity of learning partially observed linear systems, provided that the true system is inherently stable.