In recent years, and even recent months, there have been rapid and dramatic advances in the technology known as generative artificial intelligence (AI). Generative AI models are trained on inconceivably massive collections of text, code, images, and other rich data. They are now able to produce, on demand, coherent and compelling stories, news summaries, poems, lyrics, paintings, and programs.
The potential practical uses of generative AI are only just beginning to be understood but are likely to be manifold and revolutionary – and include writing aids, creative content production and refinement, personal assistants, copywriting, code generation, and much more.
There is thus considerable excitement about the transformations and opportunities that generative AI may bring. 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). In this essay, I survey these concerns and how they might be addressed over time.
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. 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.
What is generative AI?
To understand what generative AI is and how it works, it is helpful to begin with the example of large language models (LLMs). Imagine the thought experiment in which we start with some sentence fragment like, “Once upon a time, there was a great …,” and we poll people on what word they would add next. Some might say wizard, others might say queen, monster, and so on. We would also expect that given the fairy tale nature of the fragment, words such as apricot or fork would be rather unlikely suggestions.
If we poll a large enough population, a probability distribution over next words would begin to emerge. We could then randomly pick a word from that distribution (say wizard), and now our sequence would be one word longer — “Once upon a time, there was a great wizard …” — and we could again poll for the next word. In this manner, we could theoretically generate entire stories, and if we restarted the whole process, the crowd would produce an entirely different narrative due to the inherent randomness.
Dramatic advances in machine learning have effectively made this thought experiment a reality. But instead of polling crowds of people, we use a model to predict likely next words, one trained on a massive collection of documents: public collections of fiction and nonfiction, Wikipedia entries and news articles, transcripts of human dialogue, open-source code, and much more.
Learn more in the full article.
ABOUT THE AUTHOR
Michael Kearns is a professor of computer and information science at the University of Pennsylvania, where he holds the National Center Chair, and an Amazon Scholar. With Amazon Scholar and Penn colleague Aaron Roth, he is the author of The Ethical Algorithm: The Science of Socially Aware Algorithm Design.