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Lastpass family discount9/4/2023 As programmers train their AIs, the generator learns how to produce increasingly realistic images that fool the discriminator into believing they are real. The first network, called the generator, is trained to create new data that resembles existing content, while the second network, called the discriminator, is trained to distinguish between real and generated data. GANs are a type of generative AI that uses two deep-learning neural networks to generate new data. And because the data set to train AI models like ChatGPT are so large, it can mix and match elements from multiple sources to deliver something that is both unique and recognizable as the whatever the prompt asked for. When an AI has a large enough sample size to draw from (its training set) it can recreate pretty much anything it can recognize. AI programmers use machine learning to build models that can recognize patterns and trends in existing data, while content generation allows for the creation of unique items like a composition or an image. Generative AI combines two powerful AI technologies: machine learning and the ability to create new content. Other AI products can create uncanny voice recreations, and there are even services waiting in the wings that can make video content based on text prompts. And it’s not just text and pictures that generative AI can create. In ChatGPT’s case, a prompt could be “write a sonnet about bagels in the style of HL Mencken.” The resulting text and images are wholly unique and generated by the AI. For example, the prompt that you give Lensa to make those cool AI profile pics a selection of selfies. To put it as simply as possible: Generative AI is an AI (so-called “artificial intelligence”) that creates unique content based on a prompt from a user. Potential Risks and Ethical Considerations
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