Artificial Intelligence (AI) is one of the wonders of the modern world, which isn't going to cease to amaze the most intelligent of human beings. Like other fields, the field of education is also gaining the maximum amount of benefits out of the promises of the AI-powered technology. The said transformation is not about science, technology, engineering, and mathematics, but is also relevant to the field of education as a whole. The whole industry of academia sees a transformation because of AI-powered tools and technologies. This article aims to spread a comprehensive level of awareness on the subject of the assistance of AI in the field of education.

The AI industry has been growing rapidly over the past few years, to such an extent that some now class it as an industry which has gone from "emerging" to one that has fully "emerged." As we enter 2020, it seems like the perfect opportunity to assess the industry as a whole. It is easy to forget just how quickly technology has advanced over the last 10 years. For instance, we take fingerprint and facial recognition for granted now as we do with so many other features when using our devices every day. However, at the turn of the last decade, social media was still in its infancy and many products, such as Amazon's Alexa, were still years away from being released.

Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior. For instance, an unscrupulous ad publisher may try to increase their own revenue at the expense of the advertisers; a seller may want to increase the exposure of their products, or thwart a competitor's advertising campaign. In this paper, we study several attack scenarios and show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm $T - o(T)$ times over a horizon of $T$ steps, while applying adversarial modifications to either rewards or contexts that only grow logarithmically as $O(\log T)$. We also investigate the case when a malicious agent is interested in affecting the behavior of the bandit algorithm in a single context (e.g., a specific user). We first provide sufficient conditions for the feasibility of the attack and we then propose an efficient algorithm to perform the attack. We validate our theoretical results on experiments performed on both synthetic and real-world datasets.

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TL;DR: The Echo Dot (3rd Gen) smart speaker is on sale for £29.99 on Amazon, saving you 40% on list price. This isn't a typical time for deals on Amazon devices, but the online retail giant has thrown a curveball by putting its entire range on sale. This sort of thing usually only happens on Prime Day and Black Friday, but we've been treated to an early taste of what's to come with discounted e-readers, smart speakers, video doorbells, tablets, and more. One of the standout deals from this selection is the discounted Echo Dot. This popular smart speaker is down to just £29.99 on Amazon, saving you 40% on list price.

Deloitte Global predicts that the industry for smart speakers--internet-connected speakers with integrated digital voice assistants--will be worth US$7 billion in 2019, selling 164 million units at an average selling price of US$43.1 We expect 2018 sales of 98 million units at an average of US$44 each, for a total industry revenue of US$4.3 billion. This 63 percent growth rate would make smart speakers the fastest-growing connected device category worldwide in 2019, and lead to an installed base of more than 250 million units by year-end.2 Robust sales performance in 2019, although high, will represent a deceleration from the prior year: In Q2 of 2018, smart speaker sales were up 187 percent year over year.3 Smart speakers have, literally, a world of opportunity for growth. Much of that opportunity comes from expansion into non-English-speaking countries.

With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modeling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets.

Dmitry Kochin (left) and Alexander Neymak are the brains behind FuturologyAI, a largely self-funded ... [ ] startup that creates bots for sales teams. Russian bots are not just for election meddling anymore. Bots, whether Russian or American made, can also pose as virtual assistants ready to help an existing sales team, or build one where there wasn't one before. Dmitry Kochin is a Russian'Botman', taught the tools of the trade at the Moscow Institute for Physics and Technology, where he earned his PhD in computer science in 2006. His three-year old company, FuturologyAI, has a team of five developers, all of them sitting in an office in Moscow, building what he calls a "virtual sales force" and training bots to better understand human conversation.

Mumbai: Artificial Intelligence (AI) and emerging technologies such as virtual personal assistants and chatbots will replace almost 69 per cent of the manager's workload by 2024, according to a prediction by research and advisory firm Gartner on Thursday. "The role of manager will see a complete overhaul in the next four years," Helen Poitevin, Research Vice-President at Gartner, said in a statement. "Currently, managers often need to spend time filling in forms, updating information and approving workflows. By using AI to automate these tasks, they can spend less time managing transactions and can invest more time on learning, performance management and goal setting," Poitevin said. AI and emerging technologies will undeniably change the role of the manager and will allow employees to extend their degree of responsibility and influence, without taking on management tasks.

Do you find Artificial Intelligence (AI) and Machine Learning (ML) too overwhelming? Think AI and ML applications are limited to the technological domain? If yes, then let us enlighten you with the fact that both these technologies have more ordinary use cases than you can imagine. In fact, we all are already using AI and ML in our day-to-day lives – right from using real-time navigation to surfing the web. AI and ML are simplifying our routine operations in more ways than one.