Whether you're acquainted with devices such as the Amazon Fire Stick, Chromecast, Spotify, Youtube, or SlingTV, you presumably already have a general understanding of what over-the-top (OTT) television is. Numerous television viewers are rapidly turning away from traditional programming and toward over-the-top (OTT) services. Because this transformation seems to be unavoidable, the majority of media firms are publicly welcoming the transition. But what exactly is OTT? In this article, we'll go over all there is to know about over-the-top television, beginning with its description and rising significance in the television business.

Artificial Intelligence is becoming more important for the growth and development of businesses worldwide. Machines replacing people for the most intricate tasks is not the distant future; it's the reality. Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that humans ought to do accurately. When it comes to analyzing market and customer insight implementing AI in your business is a great option. Today, the amount of data companies have to work with is overwhelming.

Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Amazon's latest Echo device isn't designed to blend in with your surroundings. In fact, it's expected to be a center of attention. The Echo Show 15, available now for $249.99, is the tech giant's latest Alexa-enabled smart device, with features found on its vast line of smart speakers. Only this Echo has a touchscreen. It's 15.6 inches, which means it commands a notable presence in your home.

CES might look a little different this year, but that hasn't stopped Lenovo from doing what it does best: using the industry to event to launch many, many new products. In addition to the usual laptops, the company is showing off a new smart clock, the Smart Clock Essential with Alexa. Aside from a minimalist cloth design that clearly borrows from last year's Smart Clock 2, the new Essential improves on the old by adding a pogo docking pin at the bottom and, well, support for Amazon Alexa. The original Essential clock only worked with Google Assistant, and while I was hoping this new device could handle both, the new Essential with Alexa truly is Alexa-only, while the original remains available as a Google smart clock. As an object that's meant to sit on your bedside table, there really isn't much to the new Essential. The fabric comes in either a muted "Clay Red" or pale "Misty Blue," with the entire front face given over to the 4-inch LED display.

Have you ever used "SIRI" or "Google Assistant"? They both are popular voice assistants and are commonly used by smartphone users today. Both of these assistants are based on the technology of Artificial Intelligence Systems. Artificial intelligence, in short, known as AI, is a computer-related field that comes into contact with machine learning, natural language/speech processing, expert systems and robotics etc. The basic aim of developing AI is to have a machine or software that can meet the same intelligence as that of the human brain.

One day after work last month, Tom Otieno* went to a shopping centre in Nairobi to pick up groceries before heading home. He got a call from someone he had been chatting to for a week on Grindr, a social networking app for gay, bi, trans and queer people. The man had already tried ringing several times during the day while Otieno was with colleagues and was keen to meet. Otieno, 29, mentioned where he was but said that he did not want to see the man. Then, as he was heading to his car, he got another call.

Whether in the form of Robotic Process Automation, chatbots, or some other type of digital assistants, the presence of intelligent bots is substantially increasing across the data ecosystem … in more ways than one. The diversification of the number of tasks these bots can perform is multiplying, as is the intrinsic complexity of those jobs, which unambiguously benefits knowledge workers worldwide. Whether dynamically engaging in natural language interactions with contact center agents, for example, or issuing and answering queries from a certified knowledge base, intelligent bots are integral for not only automating these data exchanges, but also implementing the ensuing action required to complete workflows. "Over the next one to two years we'll see tens of thousands more knowledge workers deploy digital assistants to reduce complexity, achieve error-free work, help their customers by drastically reducing their'on-hold' times and, most importantly, eliminate the frustration that arises from performing repetitive, manual tasks," presaged Automation Anywhere CTO Prince Kohli. These capabilities, of course, are naturally augmented by coupling intelligent bots with the sundry of Artificial Intelligence manifestations that are more pervasive today than they ever were before. What will likely change in 2022, however, is the variety of AI that's invoked, which is subtly shifting from pure connectionist approaches involving machine learning to a return to AI's classical roots in symbolic reasoning.

In recent years, voice-based virtual assistants such as Google Assistant and Amazon Alexa have grown popular. This has presented both potential and challenges for natural language understanding (NLU) systems. These devices' production systems are often trained by supervised learning and rely significantly on annotated data. But, data annotation is costly and time-consuming. Furthermore, model updates using offline supervised learning can take long and miss trending requests.