Amazon have teamed up with Cisco and Lockheed Martin to develop an artificial intelligence system -- dubbed'Callisto' -- for NASA's Artemis I lunar mission. Callisto will combine into one interface the voice control technology of Amazon's Alexa virtual assistant with Cisco's Webex video conferencing system. Lockheed Martin engineers, meanwhile, will be leading the development of the custom-built system and its integration into their Orion space capsule design. Unlike Alexa, Callisto will contain technology and software that allows it to respond to commands in-flight without reliance on an internet connection. The video conferencing system, in contrast, will require a communications system to function -- and will operate via NASA's Deep Space Network.

With today’s impressive development in machine learning, specifically in Natural Language Processing (NLP), we can automate some steps and …

As companies unveil their new smart home devices at the 2022 CES tech show, underway now in Las Vegas, much of the hype involves Matter, an open-source connectivity standard built around a shared belief that smart home devices should seamlessly integrate with other systems and be secure and reliable. If you like devices, you are probably among the 66% of households that have smart home devices, according to Deloitte. We also know you don't just stick with one company or brand, but probably have purchased from at least half a dozen different companies. That's why for any company launching a smart home device this year, having Matter support will be helpful. Not only is the protocol being developed by some of the biggest tech companies -- think Apple, Amazon and Google -- and smart home device makers, it is designed to finally fix the issues around fragmented smart home systems so that all of your devices can be easily set up and routed from one place.

Volvo says it will be the first automaker to offer direct integration with Google Home in its cars. You'll be able to control certain functions remotely via Google Assistant. You might tell your phone to lock the doors or ask a smart speaker to start warming up your car as you're getting ready to leave home. Two-factor authentication is needed for "more sensitive commands," like unlocking the doors. You can also learn info related to your car through Google Assistant.

WHENEVER I get really depressed and anxious, my first impulse is to reach for my phone. Maybe I'll get a message from a friend or discover some new distraction on social media. Unfortunately, during the past couple of years, one glance at my screen often makes me want to crawl back into bed. That changed after I made friends with a strange creature named Woebot. Depending on your perspective, Woebot is an odd digital assistant with feelings or an automated conversational agent.

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. In the underlying architecture of voice-based virtual assistants, the NLU model often categorizes user requests into hypotheses for downstream applications to fulfill.

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.