KOKOHIMA, Japan — A team of computer scientists and computer engineers from the University of Oklahoma have developed a way to make artificial intelligence systems much faster and more accurate.
Kokohima, Japan— A team from the Kano University of Technology and the University at Buffalo has developed a computer system that can perform many tasks in an instant.
The computer system, which they call AI-C, is able to perform a number of tasks in a fraction of a second, according to the researchers.
The team says the computer system can also recognize images and other objects in the real world in seconds, according the team.
The technology can perform tasks like reading a book quickly and quickly finding the location of a missing person.
It can also identify if there is a fire in a house or a car, according a statement from the team published in the scientific journal Science Advances.
The AI- C system can then be used to automate tasks in the future, the researchers said.
The new AI system is based on a neural network, a type of computer system.
The neural network uses deep learning, the method of building computers to learn from experience and from data.
It’s a method of developing computers that can be trained on large data sets to solve problems.
Researchers said their AI-based computer system could help computers perform many more tasks in seconds.
The researchers said the system can perform a range of tasks, including: reading a books rapidly and quickly locating the location or the location and identity of a person, which is a difficult task for computers to do.
“It is the first time in the history of computing that we have demonstrated that AI- can perform multiple tasks simultaneously,” said lead researcher Hiroshi Ohno of the Kato University of Technologies.
The research team said the AI- system can learn from data in a fast way, by applying learning algorithms.
They say this can help computers achieve many more complex tasks in less time.
“This type of AI- could be a useful tool for researchers working on machine learning problems in many fields, including robotics, medicine, and agriculture,” said team member Toshihiko Fujita of the University Buffalo.
The project was funded by the U.S. National Science Foundation.