Artificial intelligence (AI) is a widely heard term in today's world. The term was believed earlier to be a highly sophisticated advanced technology that could only be handled in high tech labs and the highly intelligent scientific community. Maybe that's another romanticized idea implanted by the Hollywood movie industry. But, with time, although many still have different opinions on what artificial intelligence is, people gradually realize the fact that AI is not only limited to high-end scientific researches. Willingly, or not people of the 21st century are already using numerous aspects of AI in their daily lives involuntarily.

Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic question-answer pairs that are used for training. However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size. This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated. In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective. By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to state-of-the-art baselines.

Google's Nest Audio has been one of our favorite smart speakers since it came out almost a year ago. When compared to other $100 devices, it packs a lot of value and will be especially attractive for those who already use the Google Assistant a lot. But now you can grab the speaker for even less because Best Buy and B&H Photo have the Nest Audio for only $80. While we did see the speaker drop to $75 ahead of Amazon Prime Day back in June, this is the best price we've seen since then. The Nest Audio is Google's answer to Amazon's Echo and Apple's HomePod mini and it holds its own against both of those devices. We like its attractive, minimalist design and you have five colors to choose from, so you'll likely find one that fits well with the rest of your home decor.

This is an inane hodgepodge of sci-fi, political thriller and perhaps some kind of ill-considered satire – of reality TV, venal politicians? It's hard to divine the target when the attack is so scattershot. It is supposed to take place in the US in 2040 where everyone is obsessed with watching a daily TV show about a buxom android housewife with an English accent named Ria (Jess Impiazzi); she spends every day nearly the same way with her husband Jack, from waking up and breakfasting to winding down with an evening soap opera and then sex if Jack so wishes. In other words, it's The Truman Show meets The Stepford Wives, except there's just the one wife – and the twist is that "Jack" is played by a different person in each episode. The first we meet is Luke Goss, who seems to be merely passing though before being replaced the next night while a recharged Ria gets rebooted with Jack number 2 (Amar Adatia), a coarser, crueller mate for a day, who is in turn replaced by many more Jacks – some of them women.

In the media and communication industry, writers are frequently confronted with huge volumes of textual material. They are having significant difficulty extracting structured knowledge from these papers, and the text is being underutilized, perhaps leaving critical information unknown. Machine learning techniques can assist, but they require a thorough understanding of the information required and manual annotation of the corpus. Before going further, let's understand what annotation, types, and how it is helping machine learning models to perform accurately. Annotation is the process of labeling data which are in the form of image, video, text annotation, or object in order to use Machine Learning to train a model.

Deepfake technology is on the rise in Hollywood, and here's how it will change the movie industry as time goes on. There has been an increased desire in Hollywood recently to use various forms of de-aging technology to bring back performers who are no longer alive or achieve realistic depictions of their younger selves. Rogue One: A Star Wars Story, for example, digitally recreated Peter Cushing in a controversial decision to bring Grand Moff Tarkin back to the franchise. Meanwhile, de-aging technology has become incredibly common as everything from the MCU to The Irishman has used it to recreate younger versions of their stars. Hollywood is now in the early stages of figuring out how to apply deepfake technology in various ways to achieve similar results.

Deep learning has wide application in artificial intelligence and computer vision backed programs. Across the world, machine learning has added more value to a range of tasks using key methodologies of artificial intelligence such as natural language processing, artificial neural networks and mathematical logics. Off lately, deep learning has become central to machine learning algorithms which are required to do highly complex computation and handle gigantic data. With a multi-layer neural architecture, deep learning has been solving multiple scenarios and presenting solutions that work. There are several deep learning methods which are actively applied in machine learning and AI.

While the skills of data scientists are increasingly in demand, there is currently no professional framework for those working in the field. These new industry-wide standards, which will be finalised by the autumn, look to address current issues, such as data breaches, the misuse of data in modelling and bias in artificial intelligence. They can give people confidence that their data is being used ethically, stored safely and analysed robustly. The Alliance members, who initially convened in July 2020, are the Royal Statistical Society, BCS, The Chartered Institute for IT, the Operational Research Society, the Institute of Mathematics and its Applications, the Alan Turing Institute and the National Physical Laboratory (NPL). They are supported by the Royal Academy of Engineering and the Royal Society.

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.

In the movie Guardians of the Galaxy, the Yaka arrow, when shot by the Yondu Udonta, goes through a bunch of people in a flash; the weapon is highly responsive to certain high-octave whistle commands, which cause it to change trajectory as needed, return to the holster promptly or even combust into a fiery explosion on command. Real life Artificial Intelligence drone technology is making possible the deployment of autonomous weapons that could do pretty much the same and more. It will take some time to see whether robots of the future steal all our jobs, but drones are already stealing the march on the future of war. Based on common understanding, it has almost become natural to visualise a robot as having a human-like form, fighting off enemies. But essentially, drones are robotic machines that are capable of executing specific tasks with little or no human intervention, with speed and precision.