Artificial intelligence is pushing scientists to fundamentally reconsider how they define life itself, moving beyond traditional definitions centered on cells and DNA. Science journalist Carl Zimmer recently challenged students at Elizabethtown College to think differently about life, technology, and the future of biology, arguing that AI systems like AlphaFold are not just solving problems but reshaping the very questions scientists ask about what makes something alive.

What Happens When AI Challenges Our Definition of Life?

Zimmer opened his lecture by asking ChatGPT, an artificial intelligence language model, a deceptively simple question: "What is life?" The response was detailed but ultimately unclear, revealing just how complicated the question really is. For centuries, scientists have debated what defines life, and those definitions have shifted dramatically over time. Early theories suggested that ocean slime held the key to life itself. Over time, the focus narrowed to cells and DNA as the fundamental units of life. Now, Zimmer explained, artificial intelligence is pushing those definitions in entirely new directions.

The challenge, according to Zimmer, stems from how modern AI systems actually work. Unlike traditional computer programs that follow explicit instructions, AI systems recognize patterns in massive amounts of data and adjust their predictions based on feedback. This approach is loosely inspired by how the human brain learns. The result is that AI can produce useful, accurate results without actually understanding the underlying biology. This distinction matters enormously when considering what AI reveals about life itself.

How Is AlphaFold Changing Protein Science?

One of the most striking examples Zimmer shared was AlphaFold, an AI system developed by DeepMind that can predict how proteins fold into three-dimensional shapes. Protein structures are key to understanding how proteins function in the body. For years, scientists struggled to solve these structures through experiments alone, a process that could take months or years of painstaking laboratory work. AI has now made that process dramatically faster, allowing researchers to predict protein structures in hours rather than years.

This breakthrough illustrates a crucial point: AI does not need to understand biology to help solve biological problems. AlphaFold produces accurate predictions about protein folding without knowing why those predictions are correct. It recognizes patterns in the data it was trained on and applies those patterns to new proteins. Yet the results are genuinely useful for scientists trying to understand disease, develop drugs, and advance fundamental biological knowledge.

Ways to Think Critically About AI's Role in Science

• Recognize the Limits: AI produces useful results without actually understanding biology, so scientists must still verify predictions through experiments and critical analysis rather than accepting AI outputs as truth.
• Consider the Broader Costs: Running powerful AI systems requires significant energy and water resources, raising important questions about environmental sustainability and the true cost of technological advancement.
• Distinguish Tools from Replacements: AI should be viewed as a tool that supports human research and judgment rather than as a technology that will replace scientists or eliminate jobs in medicine and science.

Zimmer emphasized that students should stay curious but also cautious when hearing bold claims about what AI might achieve. He warned against overestimating AI's capabilities or assuming that because AI can solve one problem, it understands the domain in which it operates.

What Are the Real-World Implications for Students and Scientists?

The lecture resonated with students who had not previously considered AI's positive applications in science. Emma Lohss, a first-year environmental and political science major, reflected on how the talk changed her perspective. "I had heard almost nothing about AI in biology or medicine," she said. "Most of what I hear about AI is that we should not use it for assignments. I did not realize it could actually be helpful. It changed how I think about what AI can be used for." Another first-year student, Lauren Vaughan, noted that connecting classroom learning to real-world applications made the concepts clearer. "I knew a little bit about proteins from biology class, but hearing how AI is used to predict their structures made it easier to understand," she explained. "It was interesting to see how technology is helping scientists solve problems that used to take years".

"AI should be seen as a tool that can support research rather than replace human judgment," said Carl Zimmer, science journalist and author.

Carl Zimmer, Science Journalist and Author

During the question-and-answer session, Zimmer addressed whether AI might replace jobs in science and medicine. He made an important distinction: technology itself does not replace people. Employers and institutions make those choices. AI, he added, should be understood as a tool that can support research rather than eliminate human involvement.

Zimmer also touched on concerns surrounding AI, including the possibility of misuse and the environmental impact of training large computer models. He explained that running powerful AI systems requires significant energy and water, which raises important questions about sustainability. He encouraged students to think critically about both the benefits and the costs of new technology, rather than accepting technological progress as inherently good or bad.

The lecture concluded with a lighter moment when the moderator asked about love and attraction. Zimmer, who has written about how particles and scents move through the air, joked about pheromones and reminded the audience to be skeptical of products claiming to bottle attraction. For many attendees, the event demonstrated the value of engaging with science outside their major field of study, showing how scientific thinking connects to everyday life and future careers.