LG TVs add'delete' option for Copilot The company's tools will allow Netflix subscribers to have avatars that can be used across games. Netflix is acquiring Estonian startup Ready Player Me, a company creating cross-game avatar tech that allows players to bring their digital personas with them to different games, the company's CEO Timmu Tõke shared in a LinkedIn post . The acquisition is part of Netflix's new games strategy, which puts an emphasis on approachable multiplayer titles and adaptations of the streaming service's IP. Ready Player Me's team of around 20 employees will be incorporated into Netflix's staff, writes, though Tõke is the only one of the startup's four founders who will continue on after the acquisition. Neither company has shared when the avatar tech will be incorporated into Netflix's games or what games will support the feature when they do.

Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which in turn constrain significantly the implementation of such environments. In particular, these environments are implemented either as separate processes or as state machines, leading to synchronization and communication overheads in the first case, and to unstructured programming in the second. We propose a new domain-specific, co-routine-based, compiled language, called Rulebook, designed to automatically generate the state machine required to interact with machine learning (ML) algorithms and similar applications, with no performance overhead. Rulebook allows users to express programs without needing to be aware of the specific interface required by the ML components. By decoupling the execution model of the program from the syntactical encoding of the program, and thus without the need for manual state management, Rulebook allows to create larger and more sophisticated environments at a lower development cost.

Remember how it felt the first time you played Dungeons & Dragons? The first time you felt that creative spark of being part of a collective storytelling experience? You and your friends were each equal parts author and reader of a living, breathing story that existed only at that table, and only in those moments. There's no other word that quite does that feeling justice. Watching people play D&D in shows like Dimension 20 is definitely fun, but you're always part of the audience, not a participant.

Artificial intelligence has already beaten chess. Hell, the most sophisticated AI systems have a very good chance against top players in the incredibly complicated game of Go. But, in the uber-complicated car-based soccer game of Rocket League, can an AI do a boosted 360 aerial bicycle kick power shot from the midline? Can it pinch a ball off the side ramp so precisely it sails into the goal at 90 MPH? No, at least not yet, but AI can apparently dribble like a madman. For more than a week, players have been driven up the wall (sometimes literally, in game) by machine learning-based AI that's been hacked into games of Rocket League.

Developers and teams building avatars and virtual assistants can now register to join the early-access program for NVIDIA Omniverse Avatar Cloud Engine (ACE), a suite of cloud-native AI microservices that make it easier to build and deploy intelligent virtual assistants and digital humans at scale. Omniverse ACE eases avatar development, delivering the AI building blocks necessary to add intelligence and animation to any avatar, built on virtually any engine and deployed on any cloud. These AI assistants can be designed for organizations across industries, enabling organizations to enhance existing workflows and unlock new business opportunities. ACE is one of several generative AI applications that will help creators accelerate the development of 3D worlds and the metaverse. Members who join the program will receive access to the prerelease versions of NVIDIA's AI microservices, as well as the tooling and documentation needed to develop cloud-native AI workflows for interactive avatar applications.

This project aims to compose background music in real-time for tabletop role-playing games. To accomplish this goal, we propose a system called MTG that listens to players' speeches in order to recognize the context of the current scene and generate background music to match the scene. A speech recognition system is used to transcribe players' speeches to text and a supervised learning algorithm detects when scene transitions take place. In its current version, a scene transition occurs whenever the emotional state of the narrative changes. Moreover, the background music is not generated, but selected based on its emotion from a library of hand-authored pieces. As future work, we plan to generate the background music considering the current scene context and the probability of scene transition. We also consider to retrieve more information from the narrative to detect scene transitions, such as the scene's location and time of the day as well as actions taken by characters.

Artificial Intelligence offers a wide variety of services to streamline jobs such as mass production and data gathering. The main purpose of an AI in industry is to make certain tasks automatically so there’s no need to hire skilled personnel.

A touch-sensitive glove made from stretchable fibre-optic sensors could be used in robotics, sport and medicine. "We made a sensor that can sense haptic interactions, in the same way that our own skin sensors interact with [the] environment," says Hedan Bai at Cornell University in Ithaca, New York. Bai and her team created the glove using optical fibres made from thin elastomeric polyurethane cables that transmit light from an LED. The light is interrupted when the cables are bent, stretched or put under pressure. The team dyed parts of the fibres with different colours, meaning that as they are distorted, the colour of light coming out of the fibres changes.

Earlier this month, I crawled into Dr. Wendy Ju's autonomous car simulator to explore the future of human-machine interfaces at CornellTech's Tata Innovation Center. Dr. Ju recently moved to the Roosevelt Island campus from Stanford University. While in California, the roboticist was famous for making videos capturing people's reactions to self-driving cars using students disguised as "ghost-drivers" in seat costumes. Professor Ju's work raises serious questions of the metaphysical impact of docility.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.