AI 推动科学进步需推理而非仅靠数据

Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the feeling is in the air again—this time accompanied by a Nobel Prize.

In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes. This devilish problem had resisted systematic attacks for half a century; AlphaFold seemed to have solved it once and for all, and the world became fixated on the promise of its approach. Hassabis and his team called AlphaFold “the template for how AI can accelerate all of science to digital speed.” A wave of startups building foundation models for biology, chemistry, and materials discovery raised billions of dollars, buoyed by DeepMind’s success. AlphaFold had shown that the combination of AI and sufficient data could make groundbreaking discoveries (even if we did not understand the underlying mechanisms involved), and it seemed, once again, that a path through the rest of science was laid out before us.

To be sure, AI will bring extraordinary changes to science, but it has become increasingly clear that AlphaFold, and things like it, may not be the best template for that metamorphosis. Though it is a profound achievement, the conditions that produced the likes of AlphaFold are rare, and the time it will take to meet those conditions in other fields will be measured in decades, not years. Instead, the acceleration of science will come about thanks to another approach: AI agents.

The primary condition for AlphaFold’s success was the existence of the Protein Data Bank, a data set of roughly 170,000 experimentally validated protein structures on which DeepMind’s team could train its model. The creation of the Protein Data Bank was not simple: It took 53 years of international scientific cooperation and, by a recent estimate, roughly $21 billion worth of experimental work to assemble. Efforts of that scale are infamously difficult to fund, next to impossible to coordinate, and hugely time-consuming to execute; they have often been unsuccessful as a result.

But even in fields with the requisite cohesion and resources, and where the relevant data are not rendered inaccessible by commercial ownership, another barrier is too little discussed: the scientific impossibility of generating comparable data. In the case of protein structures, the key experimental technique—protein crystallography—is an unusually replicable and dependable tool, so much so that over 25 Nobel Prizes have relied on it. But in most of experimental science, results vary more often than not. Cell lines drift. Chemicals have trace contaminants. Lab humidity changes. The creation of measured datasets that will be consistent enough, accurate enough, precise enough, and scalable enough to train a modern neural network in biology or most of chemistry would require new kinds of measurement and new standardized approaches—none of which will be ready anytime soon.

Of course, there are a handful of fields where these requirements are met: weather forecasting, much of genomics, very limited areas of chemistry. These may see AlphaFold-style breakthroughs soon, if they haven’t already. Government support for the production and coordination of those datasets will be critical, as the US National Security Commission on Emerging Biotechnology has argued. But for most open questions in science, we will need a different plan, at least in the short term. Luckily, something quieter and more modest has begun to show promise.

Scientists have always reasoned under uncertainty. Biologists working to identify new drug targets have never had perfect datasets. Instead, they combine docking calculations and known structures, factor in molecular dynamics, run a handful of binding assays, and use their judgment to weigh each method according to its particular strengths and points of failure. The skill of science is not in any single tool; it is synthesizing what many tools produce, and revising the results as the evidence comes in. This is how most working research actually proceeds. But until very recently, no software could do it.

Agents now can. Simply put, an agent is an AI reasoning engine that has been given access to tools—digital or physical—and the capabilities to use them. Over the last few years, a fundamental architectural shift in AI has enabled the rapid proliferation of these programs, which are powered by large language models, dramatically reducing the need for scientifically specialized datasets. For science, this technological advancement represents a foundational change: it has allowed us to create digital tools that can mimic the iterative, highly contingent process of actual research. While tools like AlphaFold apply a powerful approach to a limited question, agents are inherently generalists. They do not represent a new way to do science—instead, they digitally model the human process of discovery.

Consider Google’s AI Co-Scientist, announced in May. Researchers gave it a one-page brief and a goal: Figure out how antibiotic resistance spreads between bacterial species, a key driver of drug-resistant infections. The system spun up sub-agents. One drafted hypotheses from the literature. Another picked them apart like a peer reviewer. A third ran tournaments to rank the strongest candidates. A fourth refined the winning hypothesis. The agent concluded that resistance genes were hitching rides on bacterial viruses, borrowing whichever virus could ferry them into a new host. The hypothesis was correct. Researchers at Imperial College London had spent a decade reaching the same conclusion through painstaking wet-lab work; their paper, previously unseen by Co-Scientist, was still in peer review.

Agents like Co-Scientist are still novel tools, and there are real challenges to overcome before they become a ubiquitous part of the scientific process: They are still liable to hallucinate, their judgment is not consistent, and they have memory and input constraints that limit the time they can run autonomously. But these technical barriers will fall away, and as they do we will begin to notice the compounding effects of scientific agents on the reliability, consistency, and velocity with which science is done.

Perhaps most notably, agents offer a structural fix for science’s “reproducibility crisis,” the widespread problem of researchers’ inability to replicate each other’s results. For decades, the scientific community has begged researchers to share their raw data and exact code in an effort to standardize experimental processes. But researchers have long resisted this tedious administrative work, which happens after the interesting science is already done. Agents, in contrast, automatically log every move they make, creating an exact record of the method that led to their results and allowing for precise replication.

A second consequence will be an amplification of scientific memory. The transfer of knowledge between researchers is a famously murky process; if it isn’t done over years of training and observation, graduate students are left to pore through the messy lab notebooks kept by decades of predecessors, looking for the details that will make or break their protocol. As agents become an increasingly large part of the scientific process, though, a lab’s entire scientific history will be recorded in a central, standardized repository of institutional knowledge.