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GAO-25-107651 1 (2024-10-22)

handle is hein.gao/gaoqyz0001 and id is 1 raw text is: Why This Matters Key Takeaways Generative artificial intelligence (Al) can create content such as text, images, audio, or video when prompted by a user. Generative Al differs from other Al systems in its ability to generate novel content, in the vast volumes of data it requires for training, and in the greater size and complexity of its models. Commercial developers have created a wide range of generative Al models that produce text, code, image, and video outputs, as well as products and services that enhance existing products or support customized development and refinement of models. Use of generative Al has exploded, with one commercial developer stating that it has reached more than 200 million weekly active users for one of its models. Commercial development of generative Al technologies has rapidly accelerated, with industry continually updating models with new features and capabilities. However, some stakeholders have raised trust, safety, and privacy concerns over the use of training data for models and the potential for harmful outputs. For this technology assessment, we were asked to describe commercial development of generative Al technologies. This report provides an overview of common generative Al development practices, limitations with these technologies and their susceptibility to attack, and processes commercial developers follow to collect, use, and store training data for generative Al technologies. This report is the second in a body of work looking at generative Al. In future reports, we plan to assess (1) societal and environmental effects of the use of generative Al and (2) federal research, development, and adoption of generative Al technologies. * The common practices developers use to facilitate responsible development and deployment of generative Al technologies include benchmark tests; development of trust, privacy, and safety policies; use of multi-disciplinary teams; and red teaming (testing efforts to identify flaws or vulnerabilities). * Commercial developers face several limitations when developing generative Al technologies. Commercial developers recognize that despite efforts to continuously monitor models after deployment, their models may be susceptible to attacks or may produce outputs that are factually incorrect or exhibit bias. * Developers collect data from a variety of sources to train their generative Al models, including publicly available information, data sourced from third parties, and user-provided data. However, specifics of the training data used by commercial developers are not entirely available to the public. Page 1 GAO-25-1 07651 ARTIFICIAL INTELLIGENCE Page 1 GAO-25-107651 ARTIFICIAL INTELLIGENCE

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