Eps 8: Virtual Environment Artificial Intelligence
Although artificial intelligence (AI) programs had been beating humans at chess for a number of years, Go is considered orders of magnitude more complex than chess, and many long considered it impossible for AI to defeat the best Go players.
It's one thing to train AI using simulations of computer games, it's another thing entirely to train AI so it can operate in the real world.
If the laws of physics aren't exactly incorporated into the training environment, whatever the AI learns about objects in that environment won't be accurate.
Host
Kyle Watts
Podcast Content
The main advantage is to obtain an emotional simulation with different configurations, which is intended to train assistant robots and avoid the complexity of working with real people. It involves machine learning to enable people to interact naturally. EIVE and how these factors are related to human emotions such as fear, sadness, anger and other emotions.
This tool can be used to create an immersive and realistic simulation that covers a wide range of environments, such as a city, a park or even a museum. This will allow the experience of creating a large number of intelligent and autonomous agents to be better replicated and these agents can be used to create immersive realistic simulations.
The world of Habitat is visual, which means that the A.I. agents will not be able to interact with objects in the virtual home. Depending on the training request of the a.i. agent, 3D modules of different rooms can be assembled. Once the AI has given the agent autonomy, he behaves in a predictable pattern that a soldier could potentially recognize after several training sessions.
They may not be able to grab a spoon, for example, but they could learn how to get from the bedroom to the kitchen.
Facebook researchers want to add this feature soon, but the lack of ability to interact with the environment is a glaring limitation of the platform. Ejacalive is designed to solve some of the problems that the project has overlooked by providing a platform and a robot that are able to address the problems and needs of the user.
The framework allows the creation of IoT and UC applications, and there are tools that enable the recognition and simulation of emotions.
The second advantage of VRKitchen is that the agents that navigate the virtual environment can be controlled by AI algorithms and human users. The first focuses on the integration of virtual reality and artificial intelligence into the real world.
This allows people to give AI agents demonstrations so they can gain a better understanding of how to complete a task through observation, rather than having to learn it on their own. The virtual environment that Gao and his colleagues at the University of California, San Francisco have developed could soon be used to train agents equipped with a wide range of machine learning to perform complex tasks that require fine - grainy - object manipulations, such as object recognition and manipulation in the real world.
To solve this problem, the Stanford researchers have developed a virtual environment for robot models to explore and perform these tasks. At VRKitchen, the researchers presented a prototype of their platform, which is intended to facilitate the use of the research platform in a research framework.
The iGibson trained robots have already demonstrated their abilities in real-world navigation, and their interactive features will soon be tested in the real world. Artificial intelligence in combination with VR and AR is an important step in educating the next generation of artificial intelligence researchers and the general public. The researchers trained their robot algorithms through the Sim2Real Challenge and iR2R Challenge with i Gibton and will test their robot's algorithms in a virtual environment to test the robot's intelligence in an environment similar to the virtual world of virtual reality.
Firefighters navigate through the worst of the flames, engines and fire engines, and doctors are given countless hours of virtual surgery time before experiencing complications once they reach the station.
Teaching and learning opportunities are rapidly expanding and university administrations are being given new and different ways to track students' results. By incorporating and comparing and contrasting different techniques, AI can improve simulated training to personalize training.
To learn more about the impact of this technology, join a panel discussion on this topic led by leading technology representatives in higher education at the American Association for the Advancement of Science 2017.
In this track, we examine how colleges and universities are using this new technology to conduct research, teach students, and create smarter campuses. We introduce a virtual environment based on the real world and testing active means of perception. It is virtually impossible for developers to train and test models of visual perception in the real world. Algorithms are not fast enough to learn in real time, the robots needed are prohibitively expensive and fragile, and it is impossible to teach in VR.
Gibson allows AI to explore its surroundings and take appropriate action without destroying anything. The key to learning an active ingredient in a simulation is to transfer the results into the real world.