The model might generate Once upon a time, there was a great storm based on occurrences of tremendous storm in the training data, combined with the learned synonymy of great and tremendous. But LLMs can generalize and create as well, and not always in ways that humans might expect. If the training data contains enough sentences beginning Once upon a time, there was a great …, it will be easy to sample plausible next words for our initial fragment. We would also expect that given the fairy tale nature of the fragment, words such as apricot or fork would be rather unlikely suggestions.Īn example of how a language model uses context to predict the next word in a sentence. Some might say wizard, others might say queen, monster, and so on. , and we poll people on what word they would add next. Imagine the thought experiment in which we start with some sentence fragment like Once upon a time, there was a great. To understand what generative AI is and how it works, it is helpful to begin with the example of large language models (LLMs). At Amazon, our hope is that such a balanced approach can significantly reduce the risks, while still preserving much of the excitement and usefulness of generative AI. I will focus primarily on technical approaches to the risks, while acknowledging that social, legal, regulatory, and policy mechanisms will also have important roles to play. In this essay, I survey these concerns and how they might be addressed over time. There are also understandable concerns - some of them new twists on those of traditional responsible AI (such as fairness and privacy) and some of them genuinely new (such as the mimicry of artistic or literary styles). There is thus considerable excitement about the transformations and new opportunities that generative AI may bring. Michael Kearns, a professor of computer and information science at the University of Pennsylvania and an Amazon Scholar.
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