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    Home > Chemicals Industry > China Chemical > AI "turnover" from the side reflects the need to strengthen artificial machine vision

    AI "turnover" from the side reflects the need to strengthen artificial machine vision

    • Last Update: 2022-02-28
    • Source: Internet
    • Author: User
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    【Hot attention from Chemical Machinery Equipment Network ] In recent years, artificial intelligence has developed rapidly and its application fields have been continuously expanded.
    But while showing their talents in various fields, AI "rollover" cases are also frequently searched.

     
    Hot attention of Chemical Machinery Equipment NetworkChemical machinery and equipment
    At the end of October, Scottish fans experienced an "unforgettable" football match.
    In the Scottish Football League between Inverness and Ayr United, whether players pass or dribble the ball, the AI ​​camera on the sidelines turns a blind eye.
    Instead, they follow a lineman and come up with "C position" close-ups from time to time.
    .
    It turned out that the AI ​​camera mistakenly recognized the referee's bald head as a football, so * chased the whole game.

     

    Some technicians said that when training AI cameras for live ball games, not only the "this is the ball" data set, but also a "this is not the ball" data set is needed.
    The bald head, bright enough white shoes, lights, the ball on the training ground next to the playing field, and the ball used by the players to warm up are all interference factors that need to be considered when training AI.

     

    Deep learning is based on the training of image and video big data, and it is far from the biological vision that actively perceives the dynamic world, and it is still not out of the demand for computing power.
    For example, if you increase the video frame rate from 30 to 30,000, the computing power of deep learning needs to be increased by 1,000 times.

     

      The biological neural network is a pulse neural network, which is more suitable for processing visual information.
    Huang Tiejun believes that learning from the neural network structure and information processing mechanism of the biological vision system and establishing a new set of brain-like visual information processing theories and technologies is the hope for restarting machine vision.

     

      Experts said that there are currently two main technical routes for the development of artificial machine vision.
    One is to collect more data, increase the amount of data, and increase training to construct a powerful intelligent system; the other is to imitate the biological nervous system and follow the gourd painting.
    To clarify the structure and even mechanism of the biological nervous system, and develop future intelligence on this basis.

     

      Source: Science and Technology Daily
     

      Original title: AI "Rollover" from the side reflects the need to strengthen artificial machine vision
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