Before the rise of ChatGPT sparked the current craze of using artificial intelligence for business, the technology was already transforming science. Though hiding under less-flashy names like “data science” or “machine learning,” the approaches we now call AI were helping astronomers, physicists, and biologists comb through massive datasets much more quickly than before and make unprecedented discoveries.
Science continues to be a pivotal testing ground for AI’s limits. This week, Kellogg’s Julio Ottino and Brian Uzzi ponder whether automated “end-to-end science” can succeed, and we hear what happened when AI turned a scientific field upside down overnight.
In both cases, the debates about AI in science today may foreshadow those in business tomorrow.
Progression without progress
Work in labs has long been moving towards automation, shifting from relying on research assistants to robots to conduct more experiments, faster. But the advent of new AI tools has inspired an even more ambitious vision where AI handles every step of research, from generating hypotheses and running experiments to publishing results.
This concept, of using AI for “end-to-end science,” is “the logical endpoint of current trajectories,” write Ottino and Uzzi in Science. But the two professors of management and organizations argue that fully delegating research to the machines risks losing a critical element of science: imperfection.
“The scientific system thrives on inefficiency: redundant efforts, failed attempts, and divergent paths,” Ottino and Uzzi write. “These are not costs to be eliminated but sources of discovery. By contrast, optimization pressures drive convergence—faster iteration within a constrained search space. The result may be more output but less exploration of the unexpected.”
Science is often a messy process, where the most-robust discoveries emerge from a battlefield of success and failure, criticism and competition. And while AI excels at recombining existing ideas into new ones, major scientific advances are often the result of forward-thinking conceptual shifts, which are less likely to emerge from models trained on the past.
Removing human error and creativity from the research process could thus stifle the search for new knowledge. And the same could be true for business applications of AI, where automation may create a trade-off between increasing efficiency and decreasing innovation.
Processes like end-to-end-science “may produce impressive results,” conclude Ottino and Uzzi. “But if it replaces an evolutionary process with an engineered one, it risks narrowing not only what we discover but what we are capable of discovering at all.”
Read more in Science.
