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    Intel has decided to contribute in U.S. military research wing’s new initiative, DARPA. Find all the details

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    The World’s largest manufacturer of microprocessors and chip, Intel, has decided to contribute to U.S. military research wing’s new initiative, DARPA.

    The main motive of this initiative is to improve cyber-security against deception hijack on various machine learning models. Machine learning is a subsidiary of AI, which means artificial intelligence, that is designed to improve the system with new information, experiences, and data.

    Object identification is one of its most common example today, such as taking a snap and exploring what’s in the photo. For those with weak visibility, it can help them identify what’s in the picture. Other computers can also use it, for example, autonomous vehicles that can detect objects on the road.

    Intel has decided to contribute in U.S. military research wing's new initiative, DARPA. Find all the details 5

    But machine learning algorithms can be altered due to deception attacks. Small alterations in real-world objects can have adverse consequences, in the case of automated vehicles.

    The World had recently seen the first example of changing and manipulating machine learning algorithms od the device when McAfee analysts mislead a Tesla into accelerating 80.46 kilometers per hour above its expected speed by adding some 5.08 centimeters piece of tape on a sign of speed limit.

    Intel has decided to contribute in U.S. military research wing's new initiative, DARPA. Find all the details 6

    That’s where the concept of DARPA arises. Intel revealed that for a four-year program, it’d serve as the prime contractor alongside Georgia Tech. Principle head engineer at Intel, who also leads Intel’s GARD team, said that Intel a d Georgia Tech will work as a partner for “improving object identification and enhancing the ability for artificial intelligence and machine learning to retaliate dangerous attacks.”

    The chipmaker said, throughout the first phase of the program, its main focus is centered on improving its object identification technologies’ temporal, spatial, and semantic coherence for both videos and static images. DRAPA said GARD could be implemented in various settings, for example, in biology.

    For further updates, stay glued to our website.

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