The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling...

The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling massive multi-modal datasets. Scientific AI agents can take on much of that busywork, acting as assistants that review literature, generate hypotheses, plan experiments, submit computational jobs, orchestrate lab operations, analyze results…

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Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety...

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing. But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind…

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AI is reshaping scientific research and innovation. Scientists can leverage AI to generate, summarize, combine, and analyze scientific data. AI models can find...

AI is reshaping scientific research and innovation. Scientists can leverage AI to generate, summarize, combine, and analyze scientific data. AI models can find patterns in data that human scientists have overlooked, find connections between seemingly unrelated fields and phenomena, and even propose new hypotheses to be tested. An AI co-scientist is a collaborative, multi-agent AI system…

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