A multidisciplinary group of Stanford University professors and students wants to start a serious discussion about the increasing use of large, frighteningly smart, “foundation” AI models such as OpenAI’s GPT-3 (Generative Pretraining Transformer 3) natural language model.
GPT-3 is foundational because it was developed using huge quantities of training data and computer power to reach state-of-the-art, general-purpose performance. Developers, not wanting to reinvent the wheel, are using it as the basis for their software to tackle specific tasks.
But foundation models have some very real downsides, explains Stanford computer science professor Percy Liang. They create “a single point of failure, so any defects, any biases which these models have, any security vulnerabilities . . . are just blindly inherited by all the downstream tasks,” he says.
Liang leads a new group assembled by Stanford’s institute for Human-Centered Artificial Intelligence (HAI) called the Center for…
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Source : fastcompany.com
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