Nvidia CEO Jensen Huang asserts that agentic AI systems are driving innovation across industries. At his annual NVIDIA GTC keynote, Huang highlighted OpenClaw, the personal AI assistant that went from a solo developer’s side project to one of the fastest-growing open-source projects in history. More researchers are customizing agents for specific use cases. GTC brought together life science leaders to discuss how these advances are transforming biology. Andrew Beam, PhD, chief technology officer at Lila Sciences, explains why AI advanced so rapidly in certain domains. The combination of internet-scale data and the rise of transformer architectures enabled the development of large language models. Progress then accelerated in areas that were “easy to verify,” such as mathematics, where proofs can be quickly and objectively evaluated. However, science is difficult to interrogate at scale. “When you’re talking about the discovery of new knowledge, you need verification,” Beam said. “In science, we call it an experiment.” Lila’s aims to build “scientific superintelligence” by scaling the scientific method through autonomous labs. Beam asserts that boosting the throughput of experiments describing the physical world will provide an invaluable data stream for the next generation of AI models. Marinka Zitnik, PhD, associate professor of biomedical informatics at Harvard Medical School, adds that agents must be tightly connected to the wet lab, particularly given strong biases in the literature. “95% of all life sciences publications focus on 5,000 of the most well-studied human genes,” she said. “If our AI agent just reads the literature, there are limitations to the hypotheses that can be generated.” Ensuring that agents have access…
