Credit: AI Trends
By Justin Sherman
We see the phrase everywhere — the United States and China are in an artificial intelligence “arms race.” It manifests in op-eds, news articles and television segments. It’s in books, think tank pieces and government documents. All this to capture the fear that another country might develop AI more powerful than our own.
But calling the AI competition an “arms race” is both wrong and dangerous. It suggests AI development is winner-take-all, in that two isolated national AI sectors struggle for total domination, leading to policies that cut off valuable interconnection. Simultaneously, it misrepresents AI research more generally by implying that this varied field is a single technology, almost inevitably focusing too heavily on AI’s military applications.
The premise that AI research is a zero-sum endeavor is especially easy to debunk. In reality, American firms invest billions of dollars in Chinese AI companies, and Chinese firms have invested tens of billions of dollars in the other direction. American firms also depend heavily on Chinese manufacturing, which will have an even greater impact on AI development, as artificial intelligence is increasingly deployed in hardware such as that of drones and robots.
The interconnections between U.S. and China AI development are also knowledge-related: To name just a few examples, China’s Tsinghua University opened in June an Institute for Artificial Intelligence, where Google’s AI Chief, Jeff Dean, is an adviser; Baidu, the Chinese search company, belongs to the U.S.-based Partnership for AI, which aims to develop best practices for AI technology; and China’s largest retailer has a research partnership with Stanford University’s Artificial Intelligence Lab to fund research areas such as computer vision, machine learning and forecasting. The open-source nature of some elements of AI research further contributes to a near-constant flow of information across borders.
To speak of AI as an arms race is also to ignore the many areas of AI development, such as the potential for improved public health outcomes, that may benefit both countries. Algorithms that better detect cancer, for instance, could notably reduce costs of care and increase the accuracy of early-stage cancer prediction. This could benefit the United States and China at once, not to mention other countries around the world. With a winner-takes-all “arms race” framing, though, U.S. policymakers may enact policies that hurt American AI development and foreclose opportunities by cutting off vital pipelines of funding, knowledge and other resources. Trump’s sweeping export controls on AI, for example, aim to limit the diffusion of certain knowledge and resources around AI to China. In the process, they might cut off beneficial relationships and exchanges and “substantially reduce” commercial opportunities for American companies.
The “arms race” metaphor is also misguided because it incorrectly treats “artificial intelligence” as a single technology. From recognizing a face to detecting skin cancer to assessing a convict’s likelihood of recidivism, different applications of AI have different properties and different sets of training data. These technologies also develop at different speeds, as they may require different data or computing power and may rely on different computer science techniques. Some (such as lethal autonomous weapons) may have wide-ranging effects on state power, while others (such as sophisticated chess programs) may function more as corporate showpieces. Equating these and other fields could easily lead us to prioritize the wrong things for the wrong reasons.
Justin Sherman is a cybersecurity policy fellow at New America and a student at Duke University.
Read the source article in The Washington Post.