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The manipulation of existing, or the fabrication of new digital imagery is not new – in fact, AI-generated pornographic content first surfaced in late 2017. This technology can also be used to make real people appear to say or do things that they never said or did, or to replace anyone in an existing video, or create video content with either completely non-existent characters, celebrities, or important political figures. Deepfake technology uses someone’s behaviour – like your voice, imagery or typical facial expressions, or body movements – to create entirely new content that is virtually indistinguishable from authentic content. We know that deepfakes are enabled by deep learning technology, a machine learning technique that teaches computers to learn by example. The term deepfake combines the words deep (from deep learning) and fake. It’s not easy to agree on a comprehensive definition of deepfakes, but let’s give it a try. What exactly are deepfakes again? A refresher On the whole, the ability to use artificial intelligence to create realistic simulations might even be a positive thing for humanity. For instance, deepfake tech will enable entirely new types of content and democratise access to creation tools that have, until now, been either too expensive or too complicated to use for the average person.
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But aside from the negative press deepfakes have been getting, there seem to be many reasons to be excited about this technology and its many positive applications as well. Whether we like it or not, and no matter how troubling the negative implications of the use of deepfakes may be, they’re not going anywhere. We’re bombarded with content detailing the incredible speed at which this deep learning technology is being developed, how deepfakes are becoming increasingly sophisticated and more easily accessible, and what the risks are when this tech falls into the wrong hands.
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