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How video game engines help create smarter AI

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Video game developers have longed used artificial intelligence to help create believable worlds. So it's not too surprising that researchers can now use some of those same game-making tools to train AI. During a talk at VentureBeat's Transform 2019 conference last week, Unity Technologies VP of AI and machine learning Danny Lange argued that game engines are perfect for creating what he called "real" computer intelligence -- self-learning systems capable of producing complex behaviors after a short amount of time. With game engines (like the company's own Unity engine), you can simulate the rules of the real world and test intelligent agents against it. "If you think about [it], the game engine has three dimensions, time, physics … it has everything you need to play around with the core elements that led to [human] intelligence," said Lange.


Google's DeepMind goes undercover to battle gamers

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Gamers in Europe are being invited to take on a bot developed by some of the world's leading artificial intelligence researchers. But there's a twist: players will not be told when they have been pitted against it. The tests are being carried out by DeepMind, the London-based AI company that previously created a program that defeated the world's top Go players. In this case, the challenge involves the sci-fi video game Starcraft II. It is seen as being a more complex task, since players can only get a partial overview of what their opponent is doing, unlike the Chinese board game Go where all the pieces are on show.


The Need Of AI For Balancing Customer Support Resources In 2019

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Customer expectations are rising in every sector and industry across the globe, which implies larger volume of incoming questions for organizations, and brand, as customers demand more meaningful interactions if they are to remain loyal nowadays. Hence the moot point is that being able to successfully respond to the customers is the golden-key to customer retention in 2019 and beyond. According to a survey conducted by Eptica (a leading European tech company specializing in intelligent platforms for digital customer experience) it states that more than 91 percent of consumers say that not replying to their questions satisfactorily gets them annoyed and thereby makes them become less loyal, while a little more than 94 percent affirms that getting a high-quality personalized response makes them more likely to do repeat purchases from that brand. Nevertheless, handling these ever-escalating volumes of customer support requests, across multiple channels, is not a straightforward easy objective to deal with, particularly as resources have not always been able to keep pace with the demands. Therefore, brands need to do more with less and are required to be more efficient, while still meeting their customer's needs.


Beyond sex robots: Erobotics explores erotic human-machine interactions

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Science fiction films such as Blade Runner (1982), Lars and the Real Girl (2007) and Her (2013) explore the advent of human-machine relationships. And in recent years, reality has met fiction. Powered by advancements in artificial intelligence (AI) and social robotics, artificial social agents are learning to communicate, learn and socialize, transforming our societies. Yet research on human-machine interaction is still in its early stages, particularly in the areas of intimacy and sexuality. In addition to our research on the topic, we have also been involved in spearheading initiatives to remedy the lack of knowledge on intimate human-machine relationships.


AI AND ML IN DAY TO DAY LIFE

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Virtual agents: You might be familier with the word Siri, Cortana and Google. These are the intelligent virtual agents of IOS, Windows and Android respectively. Almost every phone or laptop user has known its significance and uses it on daily basis. They assist you to find useful informations when you ask them through your voice. For example, "Where's the nearest Mobile Store?" or "Call David".


Moving Embodied AI forward, Facebook Open-Sources AI Habitat

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In a recent blog post, Facebook has announced they have open-sourced AI Habitat, an Artificial Intelligence (AI) simulation platform that is designed to train embodied agents, such as virtual robots. Using this technology, robots can learn how to grab an object from an adjacent room or assist a visually-impaired person in navigating an unfamiliar transit system. The technology leverages embodied AI which focuses on interactive environments to train real-world systems. This is a different approach than relying upon static data sets which other researchers have traditionally used. A team of Facebook researchers, including Manos Savva, Abhishek Kadian, Oleksandr Maksymets and Dhruv Batra, have released a research paper that demonstrates the capabilities of Al Habitat.


Machine Learning What is Machine Learning? - Machine Learning Carnegie Mellon University - Carnegie Mellon University

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Machine Learning (ML) is a fascinating field of artificial intelligence (AI) research and practice where we investigate how computer agents can improve their perception, cognition, and action with experience. Machine Learning is about machines improving from data, knowledge, experience, and interaction. Machine learning techniques to intelligently handle large and complex amounts of information build upon foundations in many disciplines, including statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural language processing. AI agents with their core ML aim at interacting with humans in a variety of ways, including providing estimates on phenomena, making recommendations for decisions, and being instructed and corrected. In our Machine Learning Department, we study and research the theoretical foundations of the field of machine learning, as well as on the contributions to the general intelligence goal of the field of artificial intelligence.


How is Machine Learning Being Used in Customer Service?

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Machine learning in customer service is used to provide a higher level of convenience for customers and efficiency for support agents. Support-focused tools enabled with machine learning are growing in popularity thanks to their increasing ease-of-use and successful applications across a variety of industries. Gartner predicts that by 2021, 15 percent of customer service interactions will be handled completely by artificial intelligence. Despite it's growing popularity, there's still a lot of confusion concerning how artificial intelligence, and more specifically machine learning, fits within our current understanding of customer service. So let's clear some of that up.


AI-led Chatbots - Your Next Best Friend

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Artificial Intelligence is increasingly changing the way we interact with the world. Chatbots or Virtual assistants are trending nowadays as they have simplified the interaction between humans and machines. Today, brands are using these chatbots to improve efficiency and productivity in all areas of business. Freshworks, a Chennai and San Mateo-based software unicorn is investing in developing chatbots. Brands and customers find themselves interacting online on apps and other platforms with artificial intelligence enabled conversations.


Researchers use biological evolution to inspire machine learning

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As Charles Darwin wrote in at the end of his seminal 1859 book On the Origin of the Species, "whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved." Scientists have since long believed that the diversity and range of forms of life on Earth provide evidence that biological evolution spontaneously innovates in an open-ended way, constantly inventing new things. However, attempts to construct artificial simulations of evolutionary systems tend to run into limits in the complexity and novelty which they can produce. This is sometimes referred to as "the problem of open-endedness." Because of this difficulty, to date, scientists can't easily make artificial systems capable of exhibiting the richness and diversity of biological systems.