To operate in augmented and virtual reality, Facebook believes artificial intelligence will need to develop an "egocentric perspective." To that end, the company on Thursday announced Ego4D, a data set of 2,792 hours of first-person video, and a set of benchmark tests for neural nets, designed to encourage the development of AI that is savvier about what it's like to move through virtual worlds from a first-person perspective. The project is a collaboration between Facebook Reality Labs and scholars from 13 research institutions, including academic institutions and research labs. The details are laid out in a paper lead-authored by Facebook's Kristen Grauman, "Ego4D: Around the World in 2.8K Hours of Egocentric Video." Grauman is a scientist with the company's Facebook AI Research unit.
To operate in augmented and virtual reality, Facebook believes artificial intelligence will need to develop an "egocentric perspective." To that end, the company on Thursday announced Ego4D, a data set of 2,792 hours of first-person video, and a set of benchmark tests for neural nets, designed to encourage the development of AI that is savvier about what it's like to move through virtual worlds from a first-person perspective. The project is a collaboration between Facebook's Facebook Reality Labs, in conjunction with scholars from thirteen research institutions, including academic institutions and research labs. The details of the work are laid out in a paper lead authored by Facebook's Kristen Grauman, "Ego4D: Around the World in 2.8K Hours of Egocentric Video." Grauman is a research scientist with the company's Facebook AI Research unit.
Gil's thesis research was focused on 3D Computer Vision and has been published at CVPR, the top computer vision research conference in the world. Datagen is a pioneer in the new field of Simulated Data, a subset of synthetic data, which concentrates on photo-realistically recreating the world around us. The company launched from stealth with over $18M in funding in March 2021 and is now working with a number of Fortune 100 companies in augmented/virtual reality, robotics, and automotive, including the majority of the top U.S. tech giants. What initially attracted you to robotics and machine learning? Sci-Fi books, like Isaac Asimov's Foundation Series and iRobot always got me thinking about a future in which robots were an integral part of our day-to-day lives.
We have always wondered how life will transform in this world with the implementation of AI. Award-winning authors Chen Quifan and Kai-Fu Lee have presented the world with their new book AI 2041: Ten Visions for Our Future with the ten most interesting and enlightening chapters on September 14, 2021. All these chapters include an analysis of major disruptive technologies that are thriving in the tech-driven market like deep learning, big data, NLP, AI education, AI healthcare, virtual reality, augmented reality, autonomous vehicles, quantum computers, and other issues. AI 2041 book presents a ground-breaking blend of imaginative storytelling as well as scientific forecasting on the basis of the development of the 21st century. It opens the minds of readers about the applications of artificial intelligence in multiple industries across the world.
As industrial machines are becoming more connected and flexible, the process of building and commissioning the machine is also getting smarter. Machines are built now using artificial intelligence, digital twins, and augmented reality. We caught up with Rahul Garg, VP of industrial machinery and mid-market program at Siemens Digital Industries Software. Garg explained the process of creating smart industrial machines using advanced technology. Design News: Is artificial intelligence becoming a major factor in building industrial machines?
Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame. As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.
Li, Chengshu, Xia, Fei, Martín-Martín, Roberto, Lingelbach, Michael, Srivastava, Sanjana, Shen, Bokui, Vainio, Kent, Gokmen, Cem, Dharan, Gokul, Jain, Tanish, Kurenkov, Andrey, Liu, C. Karen, Gweon, Hyowon, Wu, Jiajun, Fei-Fei, Li, Savarese, Silvio
Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simulate: motion and physical contact. We present iGibson 2.0, an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations. As a result, we can collect demonstrations from humans on these new types of tasks, and use them for imitation learning. We evaluate the new capabilities of iGibson 2.0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI. iGibson 2.0 and its new dataset will be publicly available at http://svl.stanford.edu/igibson/.
The aim of brain-computer interfaces (BCIs), also called brain-machine interfaces (BMIs), is to improve the quality of life and restore capabilities to those who are physically disabled. Last week, researchers at the Georgia Institute of Technology and their global collaborators published a new study in Advanced Science that shows a wireless brain-computer interface that uses virtual reality (VR) and artificial intelligence (AI) deep learning to convert brain imagery into actions. The brain-computer interface industry is expected to reach USD 3.7 billion by 2027 with a compound annual growth rate of 15.5 percent during 2020-2027 according to Grandview Research. "Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain–machine interfaces," wrote Woon-Hong Yeo at the Georgia Institute of Technology whose laboratory led the study in collaboration with the University of Kent in the United Kingdom and Yonsei University in the Republic of Korea. The AI, VR with BCI system was assessed on four able-bodied human participants according to a statement released on Tuesday by the Georgia Institute of Technology.
A patent from Apple suggests the company is considering how machine learning can make augmented reality (AR) more useful. Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple's decision to introduce LiDAR in its recent devices has given AR a boost but it's clear that more needs to be done to make applications more useful. A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or "automagically," the company would probably say) detect objects in AR. The first proposed use of the technology would be for Apple's own Measure app. Measure's previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.