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For years, the world wanted "real" things in their products – like real milk, real cheese, real juice, and real bacon. But then the world changed – now people want more artificial things – artificial meat, artificial furs and, of course, artificial intelligence, also known as AI technology or simply AI. From virtual assistants like Apple's Siri, Amazon's Alexa, and Microsoft's Cortana, to the use of AI in home appliances to help consumers make the perfect dinner, to IBM's Watson to help develop new drugs, consumers and businesses alike seem to have an insatiable appetite for more products and services that have "AI Inside." For businesses, the use of AI is rapidly becoming not just a competitive advantage, but a must have business process. For consumers, AI brings the hope of an easier and more comfortable life.

In the first part of our blog series we have centered our focus on various operational challenges banking enterprises have been dealing with, how deploying virtual assistants will augment banking CX, finally the potential Enterprise Bot's BankAI holds in revamping contact center operations. Let's now move to the various applications of intelligent process automation in banking in the 2nd part of our blog: Generating suspicious activity reports ( SAR) is a regular requirement in banking institutions. However, reading manually through reports and updating details in the SAR form is time-consuming and involves high cost. Enterprise Bot's BankAI powered by NLP & RPA not only scans through high volumes of lengthy compliance documents in minutes but also extracts the required information for filing SAR. Most banks still use rule-based detection systems that often raise false positive alerts.

We never have been so closer to the future than we are now. There are news spreading across the media about the robots takeover of our jobs, driverless cars hitting the road with outstanding proficiency in driving standards, while at the same time, virtual assistants make us feel a bit less lonely telling us jokes and spending time with us. In fact, Siri, Alexa or Cortana have something machines didn't have before: a simulated human conscious capable of keep conversations with humans without being uncovered. AI is now at its most advanced development stage ever, but… do we need to worry about how smart are getting the robots? Will we ever need to?

Let's get this out of the way up front: the new Apple TV 4K looks exactly like the last model, and it's a bit faster. But what really stands out is the new Siri remote, which is practically an apology for what came before. Instead of being elegantly thin and easy to lose, it's a bit chunky and easier to hold. Instead of an infuriating touchpad, there's a directional pad that also has touchpad capabilities. The new Siri remote is so good, I'd recommend that existing Apple TV 4K owners just buy it separately for $60 and skip the new box entirely.

With a high user base comes a mighty recommendation system. You know me guys, I love to decode interesting algorithms. You can check out my articles on TikTok, Tinder, GPT-3, Google Hum! Homepage -- Give suggestions without any query/navigation. YouTube is a video-sharing website launched back in 2007 by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim. Once started above the small restaurant at California City, YouTube has detonated the market with its simplicity and a wide range of audiences.

A compact, HomeKit-compatible smart plug with plenty of automation options along with robust energy-monitoring features, Eve Energy might be pricier than its competitors, but it comes with an ace up its sleeve: support for Thread, an exciting wireless protocol that will--eventually--work hand-in-hand with Matter, the new smart home platform that promises to finally teach Alexa, Google Assistant, and Apple HomeKit to play nice with each other. Eve Energy checks most of the most important boxes when it comes with energy-monitoring smart plugs, but because it only works with HomeKit (for now, anyway), Android users will need to look elsewhere. The same is true for anyone who depends on Alexa or Google Assistant for smart home control. This review is part of TechHive's coverage of the best smart plugs, where you'll find reviews of the competition's offerings, plus a buyer's guide to the features you should consider when shopping for this type of product. Measuring roughly 2.5 x 1.5 x 1.3 inches (WxDxH, not including the prongs), the grounded Eve Energy plug is relatively compact compared to its competitors; while it is a tad wide, the plug is short enough to leave the second socket of a two-outlet plate free.

Digital transformation is no longer an inward tactic used to reform an organisation's operations; it's now a necessary undertaking sought out by CIOs and IT leaders. Recent developments have pushed organisations to embrace digitisation, causing the fourth industrial revolution and technologies such as artificial intelligence to go mainstream. Even though, according to Gartner, only 53 per cent of AI projects make it from prototype into production, companies can't ignore the benefits of successful AI implementation. Enhanced AI solutions such as the artificial intelligence of things (AIoT), conversational AI and machine learning (ML) are improving the future of digital transformation and offer more innovative ways than ever before to address business challenges. Today's AI solutions can be customised to address a company's unique set of challenges. But are these capabilities being used to their full advantage?

There are many sources which give similar answers to the question, "What is AI?". By the 1950's, there were many scientists, mathematicians and philosophers that were looking into the concept of Artificial Intelligence. One such person was Alan Turing, who to this day is considered by many to be the Father of Artificial Intelligence. He formed the idea and mathematical and logical reasoning behind the concept of machine intelligence wherein machines and computers would be able to replicate the behavior of humans and their intelligence. His paper Computing Machinery and Intelligence outlines his logic for the start of artificial intelligence.

In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. They affect the decision of RS by shifting the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures and computational time.