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Try these useful Siri commands when you watch a movie on Apple TV

USATODAY - Tech Top Stories

I love my Apple TV, but I can't stand the old Siri remote that came with it. Its touchpad is too sensitive, and I never know if it's in the right orientation when I grab it. Apple did fix all that with its second-generation Siri remote, but why buy another one when the one I currently have still works--especially when I can use a few handy Siri commands to do more than what the touchpad can do. On the Apple TV and accompanying Siri remote there's a handy microphone button that lets you chat directly with Siri to get your TV to do things like search for movies in a specific genre or year, ping other devices, and even fast forward by a specific amount of time. Not every command is intuitive, though, so it's helpful to know a few of them off-hand before you binge another series.


26 Must Watch AI Movies

#artificialintelligence

I am a binge-watcher of movies and series. I love comedy, thrillers, romantic everything. Anyway, here are some sci-fi movie lists related to technology and AI I could think of the name. I have tried to write as much short I could write about them so that I don't give any spoilers. So for a better description, you might try watching Google.


Hands-on Content Based Recommender System using Python

#artificialintelligence

One of the most surprising and fascinating applications of Artificial Intelligence is for sure recommender systems. In a nutshell, a recommender system is a tool that suggests you the next content given what you have already seen and liked. Companies like Spotify, Netflix or Youtube use recommender systems to suggest you the next video or song to watch given what you have already seen or listened to. The idea of build recommender system has surely not been developed yesterday. In 2006 Netflix announced a 1 million dollar reward to the research team able to build the best recommender system possible given some test data.


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

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.


Est-ce que vous compute? Code-switching, cultural identity, and AI

arXiv.org Artificial Intelligence

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.


Conversational Recommendation: Theoretical Model and Complexity Analysis

arXiv.org Artificial Intelligence

Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialog. A common approach in the literature to drive such dialogs is to incrementally ask users about their preferences regarding desired and undesired item features or regarding individual items. A central research goal in this context is efficiency, evaluated with respect to the number of required interactions until a satisfying item is found. This is usually accomplished by making inferences about the best next question to ask to the user. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions is better than another one in a given application. With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation. This model, which is designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, in particular with respect to the computational complexity of devising optimal interaction strategies. Through such a theoretical analysis we show that finding an efficient conversational strategy is NP-hard, and in PSPACE in general, but for particular kinds of catalogs the upper bound lowers to POLYLOGSPACE. From a practical point of view, this result implies that catalog characteristics can strongly influence the efficiency of individual conversational strategies and should therefore be considered when designing new strategies. A preliminary empirical analysis on datasets derived from a real-world one aligns with our findings.


Top 30 Machine Learning Projects Ideas for Beginners in 2021

#artificialintelligence

"What projects can I do with machine learning?" We often get asked this question a lot from beginners getting started with machine learning. ProjectPro industry experts recommend that you explore some exciting, cool, fun, and easy machine learning project ideas across diverse business domains to get hands-on experience on the machine learning skills you've learned.


Reason first, then respond: Modular Generation for Knowledge-infused Dialogue

arXiv.org Artificial Intelligence

Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficult challenge of both reasoning to provide correct knowledge and generating conversation simultaneously. In this work, we propose a modular model, Knowledge to Response (K2R), for incorporating knowledge into conversational agents, which breaks down this problem into two easier steps. K2R first generates a knowledge sequence, given a dialogue context, as an intermediate step. After this "reasoning step", the model then attends to its own generated knowledge sequence, as well as the dialogue context, to produce a final response. In detailed experiments, we find that such a model hallucinates less in knowledge-grounded dialogue tasks, and has advantages in terms of interpretability and modularity. In particular, it can be used to fuse QA and dialogue systems together to enable dialogue agents to give knowledgeable answers, or QA models to give conversational responses in a zero-shot setting.


Turing test in science fiction - 🤖 ChatBot Pack

#artificialintelligence

The decade isn't over yet, but we've seen some remarkable advancements in the field of artificial intelligence. We've marveled at the invention of the first self-driving car in 1995. We've witnessed Deep Blue beat Garry Kasparov in 1997. Lastly and more recently we've had the chance to enjoy the company of Apple's Siri, Google's Assistant, Microsoft's Cortana, and Amazon's Alexa. While much advancement in artificial intelligence came about relatively recently, the idea of a machine-based artificial intelligence actually existed even before the computer. Its theoretical basis came about in the 1950s, introduced by British mathematician Alan Turing.


Amazon made Disney a 'Hey, Disney!' voice assistant

Engadget

Amazon and Disney has just announced a new voice assistant called "Hey, Disney!". Built on Amazon's Alexa technology, this new Disney assistant will be available in your home Echo as well as in Echo devices located in Walt Disney World Resort hotel rooms. You can use it to interact with characters from Disney, Pixar, Marvel, Star Wars and more. It is the first time an Alexa custom assistant will be available on Echo devices. According to Amazon, this voice assistant will give you access to interactive storytelling experiences and entertainment featuring Disney characters. You can also play games and access jokes set in the Disney world.