Import AI 475: Swarm scaling, Google DeepMind biology watermarking, AI science economy
Import AI 475 discusses swarm scaling, noting that swarms are useful for speed due to parallelization, though they require more total tokens than single agents. The issue also covers Google DeepMind's watermarking of biology and the emerging AI science economy.
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Import AI (Jack Clark)Jack Clark
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now When should you use swarms? When you are in a hurry: …How does swarm scaling work?... Toby Ord has a nice, short post about how to think about swarms in terms of AI capability development. “A good way to see AI swarms is as a new form of inference-scaling,” he says. He does a bit of analysis and his main conclusion is that swarms are useful if you’re in a hurry because though they need a ton of tokens relative to single agents, their parallelization lets you get things done in less wall clock time. “Why would you ever use swarms? The most important answer is speed. The 4-agent swarm needed about twice the total number of tokens to get the same performance, but in terms of tokens per agent, it only needed half as many. Since the agents are run in parallel, this means it can theoretically achieve the same tas
