One of the questions we get most frequently from officials in Washington is: “Who’s winning the U.S.-China AI race?” The answer is simple and unsettling: Artificial intelligence is winning, and we’re nowhere near ready for what it will bring.
In the past decade, cutting-edge AI systems moved from beating simple video games to solving decades-old scientific challenges such as protein folding, speeding up scientific discovery and accelerating the development of small-molecule drugs. The fastest-moving branch of AI is spawning large language models, such as OpenAI’s ChatGPT. Much progress in these models stems from a relatively simple engineering insight—the scaling hypothesis—that has been carefully implemented using specialized software and vast arrays of networked computers. The hypothesis predicts that the bigger an AI model is—the more data, computations, and parameters it incorporates—the better it will perform and the more it will be able to mimic or achieve intelligence irrespective of whether it is generating a draft of a speech, writing computer software, designing new weapons, or teaching kids math.
AI scientists are divided on where this is all headed. Some see the scaling hypothesis continuing to bear fruit as the relevant systems are refined by humans, and eventually by the machines themselves, until we build models that surpass human intelligence. Others are skeptical of large language models and doubt that scaling them up will yield anything comparable to human intelligence. If the scaling group is right, the risks from powerful ..
Support the originator by clicking the read the rest link below.
