Video games have increasingly demonstrated a great deal of audiovisual realism, in par with the massive performance improvement of computer systems. At the.
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Category: robotics/AI – Page 1,976
The AI taught itself the skill through a technique called reinforcement learning — essentially, it picked up the rules of the game over thousands of matches in randomly generated environments.
A paper on their research was published today in Science.
“How you define teamwork is not something I want to tackle,” Max Jaderberg, a DeepMind researcher who worked on the project told The New York Times. “But one agent will sit in the opponent’s base camp, waiting for the flag to appear, and that is only possible if it is relying on its teammates.”
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Breaking: robot makes breakfast
Posted in robotics/AI
This post by Prof. Kevin Warwick originally appeared at OpenMind.
Article from the book There’s a Future: Visions for a Better World
If you could improve by implanting a chip in your brain to expand your nervous system through the Internet, ‘update yourself’ and partially become a machine, would you? What Kevin Warwick, professor of cybernetics at the University of Reading, poses may sound like science fiction but it is not; he has several implanted chips, which makes him a cyborg: half man, half machine. In this fascinating article, Warwick explains the various steps that have been taken to grow neurons in a laboratory that can then be used to control robots, and how chips implanted in our brains can also move muscles in our body at will. It won’t be long before we also have robots with brains created with human neurons that have the same types of skills as human brains. Should they, then, have the same rights as us?
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A growing number of devices are now connected to the internet and are capable of collecting, sending and receiving data. This interconnection between devices, referred to as the Internet of Things (IoT), poses serious security threats, as cyberattackers can now target computers and smartphones, but also a vast array of other devices, such as tablets, smart watches, smart home systems, transportation systems and so on.
For the time being, examples of large-scale IoT implementations (e.g. connected infrastructure, cities, etc.) are somewhat limited, yet they could soon become widespread, posing significant risks for businesses and public services that heavily rely on the internet in their daily operations. To mitigate these risks, researchers have been trying to develop security measures to protect devices connected to the internet from wireless network attacks.
To this end, two researchers at Baoji University of Arts and Sciences, in China, have recently developed a new method to defend devices in an IOT environment from wireless network attacks. Their approach, presented in a paper published in Springer’s International Journal of Wireless Information Networks, combines a deep neural network with a model based on game theory, a branch of mathematics that proposes strategies for dealing with situations that entail competition between different parties.
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Wearing a sensor-packed glove while handling a variety of objects, MIT researchers have compiled a massive dataset that enables an AI system to recognize objects through touch alone. The information could be leveraged to help robots identify and manipulate objects, and may aid in prosthetics design.
The researchers developed a low-cost knitted glove, called “scalable tactile glove” (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to “learn” a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed.
In a paper published in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects—including a soda can, scissors, tennis ball, spoon, pen, and mug. Using the dataset, the system predicted the objects’ identities with up to 76 percent accuracy. The system can also predict the correct weights of most objects within about 60 grams.
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Self-driving cars, voice assistants, and facial recognition technology are just a few of the advancements made possible by Hinton, LeCun, and Bengio’s work.
The award, named after British mathematician Alan Turing, carries a $1 million prize, which the trio will split. Previous Turing Award winners include Tim Berners-Lee, best known for inventing the World Wide Web.
Hinton is currently a top AI researcher at Google. LeCun is now at Facebook, working as the company’s chief AI scientist. Bengio has remained in academia but has worked with companies such as AT&T, Microsoft, and IBM.
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