Systems like ChatGPT have now been trained on the near-sum of all human knowledge. Yet they spend much of their time whipping up emails or regurgitating facts. What would it take for generative AI to spawn new ideas?
That’s a question that tech companies and research labs seem to be increasingly asking as they explore how GenAI might aid in the process of scientific discovery. New AI systems aim to give scientists tools to supercharge the hypothesis and experimentation flow.
- OpenAI is readying the release of new reasoning models—o3 and o4-mini—as soon as this week that are said to be better at drawing from different scientific fields to put forth novel ideas and devise experiments, which could be helpful for things like designing new drugs or materials, The Information reported. The company has been holding “AI jam sessions” at national labs to allow hundreds of scientists to explore how to put reasoning models and other AI offerings to use.
- Google said at its Cloud Next conference last week that it would be making scientific tools like AlphaFold 3 and WeatherNext available through Google Cloud. The systems are based on Google DeepMind’s research on protein structures and AI weather forecasting, respectively. Google previously released a multi-agent system called Co-Scientist in February, though some researchers told TechCrunch it fell short of Google’s hype.
- A startup called Lila Sciences raised $200 million in seed capital last month on its promise to build a “scientific superintelligence platform” and “fully autonomous labs” across biology, chemistry, and materials science. The company claims its tech can help scientists formulate hypotheses and design experiments.
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Many of these efforts are fueled by breakthroughs in AI reasoning that allow models to “think” through complex problems beyond just summarizing and amalgamating. GenAI has already been yielding progress in fields like life sciences—for which Google DeepMind won a Nobel Prize last year—and materials.
Yet some AI researchers also say that fully unlocking the potential of GenAI might require a rethink of how AI models are trained and tested. And some have cast doubt on the lofty promises of AI company leaders in this arena.
GenAI’s role in scientific discovery has been steadily growing in recent years, Vanessa Parli, research director at Stanford’s Human-centered AI Institute, told us in an interview last week. Parli said the institute added a chapter about scientific discovery to its annual AI Index for the first time last year and expanded it even further this year.
“AI’s role in scientific progress continues to expand,” the report says. “While 2022 and 2023 marked the early stages of AI-driven breakthroughs, 2024 brought even greater advancements.”