"Artificial intelligence (AI) is becoming increasingly important for society and the economy. However, modern AI research requires expensive resources that are often only available to a small group of for-profit firms. Consequently, there is a growing concern that only a handful of companies will be able to perform cutting-edge research, leading to a worrisome concentration of power in AI development and its future.
AI has enormous potential: to make possible new discoveries in medicine and other fields; to reduce the cost of goods and services (for example, machine translation); and to improve lives. But in AI, unlike in most technologies, basic and applied research overlap—the so-called “Pasteur’s Quadrant” (Stokes 2011). An example is the Transformer architecture (Vaswani et al. 2017), an important deep-learning innovation developed by Google Brain researchers in 2017 and a crucial piece of ChatGPT. But while this invention was the product of basic research, it was almost immediately incorporated into industry models. Because of this close relationship between basic and applied research in AI, we are unlikely to see a division of labor emerge between academia and industry in AI where academia does basic research and industry focuses on commercialization. Instead, industry’s dominance will likely continue to give it an outsize impact in both basic and applied AI research going forward. Therefore, for policymakers, the key will be to ensure that academics continue to be able to participate in cutting-edge research."
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