Leisure & Entertainment


From anime to reality: Mobile 25-ton Gundam robot to be built in Yokohama

The Japan Times

What was once thought limited to the realm of animation is set to become reality in Yokohama this fall when an 18-meter mobile Gundam robot steps into action. Fans of the iconic anime series will be able to get an up-close look at the 25-ton machine at Gundam Factory Yokohama, a 9,000 sq.-meter facility set to open at Yamashita Pier on Oct. 1 for a year. Tickets for the facility will go on sale in July, though the price has not been disclosed. Other details remain a mystery, such as the exact movements the robot will be able to perform using its 24 fully functional joints. Gundam Factory Yokohama will consist of two areas: a 25-meter-tall Gundam-Dock that will serve as its maintenance site, and a two-story building with shops and event space.


AI Year in Review: Highlights of Papers from IBM Research in 2019

#artificialintelligence

January 17, 2020 Written by: John R. Smith IBM Research has a long history as a leader in the field of Artificial Intelligence (AI). IBM's pioneering work in AI dates back to the field's inception in the 1950s, when IBM developed one of the first instances of machine learning, which was applied to the game of checkers. Since then, IBM has been responsible for achieving major milestones in AI, ranging from Deep Blue – the first chess-playing computer to defeat a reigning world champion, to Watson – the first natural language question and answering system able to win at Jeopardy!, to last year's Project Debater – the first AI system that can build persuasive arguments on its own and effectively engage in debates on complex topics. IBM's leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities in language, speech, vision, knowledge & reasoning, human-centered AI, and more. While recent years have seen incredible progress in "narrow AI," built on technologies like deep learning, IBM Research pushed its AI research in 2019 towards developing a new foundational underpinning of AI for enterprise applications by addressing important problems like learning more from less, enabling trusted AI by ensuring the fairness, explainability, adversarial robustness, and transparency of AI systems, and integrating learning and reasoning as a way to understand more in order to do more.


10. Introduction to Learning, Nearest Neighbors

#artificialintelligence

Sign in to report inappropriate content. Instructor: Patrick Winston This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. We then discuss how to learn motor skills such as bouncing a tennis ball, and consider the effects of sleep deprivation.


The Future of Deep Learning - DATAVERSITY

#artificialintelligence

Deep learning (DL) became an overnight "star" when a robot player beat a human player in the famed game of AlphaGo. Deep learning training and learning methods have been widely acknowledged for "humanizing" machines. Many of the advanced automation capabilities now found in enterprise AI platforms are due to the rapid growth of machine learning (ML) and deep learning technologies. A Deep Dive into Deep Learning in 2019 comments on the "ubiquitous" presence of DL in many facets of AI -- be it NLP or computer vision applications. Gradually, AI and DL-enabled automated systems, tools, and solutions are penetrating and taking over all business sectors --from marketing to customer experience, from virtual reality to natural language processing (NLP) -- the digital impact is everywhere.


Making Friends on the Fly: Advances in Ad Hoc Teamwork - Programmer Books

#artificialintelligence

It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates. PLASTIC is instantiated in both a model-based approach, PLASTIC-Model and a policy-based approach, PLASTIC-Policy. In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new Two Stage Transfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.


Making Friends on the Fly: Advances in Ad Hoc Teamwork - Programmer Books

#artificialintelligence

It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates. PLASTIC is instantiated in both a model-based approach, PLASTIC-Model and a policy-based approach, PLASTIC-Policy. In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new Two Stage Transfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.


Optimising Utilisation Forecasting with AI and Machine Learning

#artificialintelligence

What IT team wouldn't like to have a crystal ball that could predict the IT future, letting them fix application and infrastructure performance problems before they arise? Well, the current shortage of crystal balls makes the union of artificial intelligence (AI), machine learning (ML), and utilisation forecasting the next best thing for anticipating and avoiding issues that threaten the overall health and performance of all IT infrastructure components. The significance of AI has not been lost to organisations in the United Kingdom, with 43 per cent of them believing that AI will play a big role in their operations. Utilisation forecasting is a technique that applies machine learning algorithms to produce daily usage forecasts for all utilisation volumes across CPUs, physical and virtual servers, disks, storage, bandwidth, and other network elements, enabling networking teams to manage resources proactively. This technique helps IT engineers and network admins prevent downtime caused by over-utilisation.


Computer Algorithm Can Identify Unique Dancing Characteristics

#artificialintelligence

Over the past few months, Microsoft and other companies researching machine learning challenged teams of AI developers to create an AI system that could play Minecraft and find a diamond within the game. As reported by the BBC, while AI platforms have managed to dominate chess and go, but it has struggled to master a task in Minecraft. Microsoft's Minecraft-based AI challenge was called MineRL, and the competition results were formally announced at the recent NeurIPS conference. The competition's intention was to train an AI through an "imitation learning" approach. Imitation learning is a method where an AI is trained through the use of observation.


New product launches to bring new dawn for industrial robotics

#artificialintelligence

The advent of robotics has been a boon for the industrial sector and new innovations have been offering improved efficiency, less downtime, and better products. From implementing robots for mundane tasks to enhancing precision of existing tasks, industrial robotics has come a long way. The advancements in technologies have enabled manufacturers to utilize robots that are lightweight, perform multiple tasks, and improve efficiency. Moreover, they have been aiming for safety, reliability, and improved environment for workers. Emerging technologies such as artificial intelligence, machine learning, and others have been incorporated in robots for mimicking human intelligence and facilitating some of the tasks of humans.


Building An AI-Empowered Music Library with TensorFlow

#artificialintelligence

A guest post by Tencent QQ Music Audio Engineering Team Introduction QQ Music is a music streaming service owned by the Tencent Music Entertainment Group (TME) and we serve 800M users globally. We have a massive music library with an extensive collection of albums and live music available in both audio and video formats.