With OpenAI’s ChatGPT now a constant presence both on social media and in the news, generative artificial intelligence (AI) models have taken hold of the public’s imagination. Policymakers have taken note too, with statements from Members addressing risks and AI-generated text read on the floor of the House of Representatives. While they are still emerging technologies, generative AI models have been around long enough to consider what we know now, and what regulatory interventions might best tackle both legitimate commercial use and malicious use.
WHAT ARE GENERATIVE AI MODELS?
ChatGPT is just one of a new generation of generative models—its fame is a result of how accessible it is to the public, not necessarily its extraordinary function. Other examples include text generation models like DeepMind’s Sparrow and the collaborative open-science model Bloom; image generation models such as StabilityAI’s Stable Diffusion and OpenAI’s DALL-E 2; as well as audio-generating models like Microsoft’s VALL-E and Google’s MusicLM.
While any algorithm can generate output, generative AI systems are typically thought of as those which focus on aesthetically pleasing imagery, compelling text, or coherent audio outputs. These are different goals than more traditional AI systems, which often try to estimate a specific number or choose between a set of options. More traditional AI systems might identify which advertisement would lead to the highest chance that an individual will click on it. Generative AI is different—it is instead doing its best to match aesthetic patterns in its underlying data to create convincing content.
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