Artificial intelligence (AI) assistants are becoming indispensable in today’s society. You see them everywhere, from smartphones to medical institutions. The number of AI assistants on the market is only increasing, and they will become even more integrated into our lives.  What defines an AI assistant? What is an AI Assistant? An AI assistant can be […]

"Is Artificial Intelligence better than Human Intelligence?" is a popular question. Artificial intelligence's negative reputation stems from its apparent overmatching of human intellect. Compared to a brilliant individual, a computer is quicker in various fields. What would take years for a person to complete will only take minutes for AI, and that is the case. In other words, comparing the two types of intelligence may prove that they are significantly different from one another. But how distinct are they? Artificial Intelligence has advanced a long way from being a science fiction component to reality. We have self-driving cars, smart virtual assistants, chatbots, and surgical robots, among other intelligent machines these days.

As we approach the end of the first quarter, what does the future hold for AI? We already know that artificial intelligence (AI) has an impact on every industry around the globe. These are the areas where AI will be more important in our lives in 2022. AI is a data-hungry beast and has created new avenues for data collection that have increased the value of data as an asset to businesses and governments. There are also initiatives to educate the general public about how data can be used.

Contrary to press-propagated blames on rapid industry changes, unforeseen circumstances and uncontrollable crises, most business failures boil down to poor corporate culture. Interestingly, how corporate culture is perceived has changed just as rapidly as industries have evolved in recent times. In the 20th and early 21st centuries, assessment of corporate culture focused almost entirely on how businesses treated their customers. For instance, the dent in Blackberry's culture was caused by the company prioritizing its smartphone technology over customers' needs. Meanwhile, how customers interact with technology was changing. More recently, corporate culture has more to do with how companies manage communication internally than with their public relations.

We searched the risks and benefits of artificial intelligence and tried to decide is it evil or not? Humans have long desired to construct machines that can make decisions. It was thought of as a possibility that seemed too good to be true for a long time, and it was only seen in science-fiction films. Self-driving automobiles and apps like SIRI made this far-fetched fantasy come true. In 1950, John McCarthy coined the term "Artificial Intelligence." He added, "Every aspect of learning or any other feature of intelligence may in principle be so precisely described that a machine can be made to mimic it. It will be attempted to discover how to program machines to use language; create abstractions and concepts; solve problems that humans now only solve, and develop themselves."

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Ford and Lincoln drivers will be able to set custom Alexa voice commands for their thanks toa new updated to the Alexa Built-In system. This upgraded'Car Control' lets drivers teach Alexa how they prefer to issue commands, even if it's an indirect comment on an issue that the car voice Ai can address. The hands-free version of Alexa includes all of the voice assistant's standard and automotive-specific features, including control over the car's locks and remote control over smart home devices. The new Alexa arrangement comes just a few months after Ford unveiled plans for building Google Assistant and Android into millions of Ford cars starting in 2023. The idea behind Car Control is the Teachable AI for cars Alexa feature added at the end of 2021 that lets users decide what phrases will activate Alexa tasks, much like the alternative phrase triggers for Alexa Routines.

Figure 2 illustrates the distribution of research on various aspects of fairness in recommender systems.111The We may observe a division/split in the research on fairness-aware recommendation algorithms, with around 49.1% of papers concentrating on consumer fairness and somewhat fewer on producer fairness (41.8%). Few studies (less than 10%) address consumer and producer fairness concerns simultaneously. However, the underlying user-item interaction coexists on (and can impact) both sides of beneficiary stakeholders. For example, there may be disparities in the consumption of item groups (defined by protected attributes) between active and inactive users in specific domains.

Lenovo launched a new smart clock at CES earlier this year, and it improved upon its previous models by giving it a pogo docking pin at the bottom and support for Amazon's Alexa. If you've been thinking of picking it up but haven't gotten the chance to until now, you may want to head over to the device's listing at Best Buy. Lenovo's Smart Clock Essential with Alexa is currently on sale for $45 on the retailer's website only for today -- there's less than 20 hours left for the deal as of this writing. The Smart Clock Essential with Alexa retains the brand's Smart Clock 2 cloth design. While its predecessors only supported Google Assistant, though, this model only supports the Alexa voice assistant.

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with fixed-size caches or elastic leased caches subject to time-average budget constraints. The predictions are provided by a content recommendation system that influences the users viewing activity, and hence can naturally reduce the caching network's uncertainty about future requests. We prove that the proposed optimistic learning caching policies can achieve sub-zero performance loss (regret) for perfect predictions, and maintain the best achievable regret bound $O(\sqrt T)$ even for arbitrary-bad predictions. The performance of the proposed algorithms is evaluated with detailed trace-driven numerical tests.