Why Technology (and AI) Won’t Save Biology

There seems to be no end to biology's explosive progress. Genomes can now be read, edited, and rewritten with unprecedented scope. Individual neurons can now be studied in both space and time. Mathematical models help us understand the dynamics of virus spread and ecological populations, and vaccines for deadly diseases like HIV and Ebola seem to hold more promise than ever. Many say the twentieth century belonged to physics and the twenty-first belongs to biology, and indeed, this sentiment seems to be borne out by the astonishing advances in the biological sciences.

Six Major Revolutions in Biology

Over the past five hundred years, biology has undergone six major revolutions that transformed our understanding of life. The first was Linnaeus’s classification of organisms into binomial nomenclature. The second was the invention of the microscope by pioneers like Hooke and Leeuwenhoek. The third was the discovery of cells' composition in health and disease by Schwann and Schleiden, thanks to advancements in microscopy. The fourth was Darwin’s formulation of evolution by natural selection. The fifth was Mendel’s discovery of the laws of heredity. And the sixth was the discovery of the structure of DNA by Watson, Crick, and others. An ongoing seventh revolution centers around mapping genomes and understanding their implications for disease and ecology. This has been aided by breakthroughs in statistics and new imaging techniques like MRI and CT scans.

These revolutions were driven not just by new ideas, but also by new tools—a pattern that underscores the intertwined nature of scientific progress. Scientific revolutions are a two-pronged affair: breakthroughs come both from new paradigms of thinking and new technological advances.

The Role of Technology in Biological Discoveries

From the microscope to electron microscopy, x-ray diffraction, and modern imaging techniques, four of the six revolutions in biology hinged on technological advancements. The invention of tools that enabled deeper observation revealed new truths that theory could then catch up with. In genomics, for example, rapid sequencing methods paired with powerful computers and statistical techniques for identifying rare events have unlocked a flood of genetic data. However, this bounty of data brings its own challenges: it hides gems of understanding within mountains of information. Systems biology seeks to make sense of this deluge by piecing together the puzzle at different levels, but even this approach highlights the gaps between what we can observe and what we can truly understand.

The Promise and Peril of Artificial Intelligence in Biology

This is where artificial intelligence (AI) enters the conversation. AI promises to address biology’s complexity by analyzing vast datasets and finding patterns that elude human comprehension. Indeed, AI and machine learning have already shown promise in areas like genomics, protein folding, and drug discovery. For instance, AlphaFold, a machine learning algorithm, achieved a breakthrough in predicting protein structures, which had remained a tough challenge in biology for decades. AI models also excel at recognizing anomalies in medical imaging, predicting the spread of pandemics, and optimizing experimental designs.

However, while AI offers immense potential, it also amplifies existing challenges. The major limitation lies in the fundamental difference between recognizing patterns and understanding causation. AI systems excel at correlating data and identifying intricate relationships, but these correlations do not necessarily reveal underlying biological mechanisms. In fields like cancer genomics, sequencing technologies have revealed hundreds of mutated genes, and AI can help sift through these to find correlations between certain mutations and cancer progression. But beyond identifying these correlations, biologists must still build causal frameworks to explain why and how these mutations impact cancer growth. Without such theoretical underpinnings, AI runs the risk of creating a black box where patterns are identified without leading to genuine understanding. Even in terms of strict utility, these patterns may be inadequate since prediction without understanding can lead to blind spots; for instance witness "activity cliffs" in medicinal chemistry or missed interaction partners in cancer biology, both of which can thwart true prediction.

The very nature of AI models may also reveal their limitations. For instance, LLMs and other leading AI models are trained and based on discrete, binary data, while biology is full of continuous data; something as simple as dose-response curves testifies to this fact. The difference between digital and analog systems comes to mind: computers are digital, but life is a hybrid of analog and digital, combining digital outputs built on analog substrates and vice versa. In a previous post, I argued that the relative thermodynamic inefficiency of the brain might point to its analog workings. If AI were to truly transform drug discovery and biology, its discrete digital models would have to learn to deal with continuous, analog data.

This challenge is not new. Sydney Brenner once pointed out that biology in the 1950s used to be "low input, low throughput, high output," whereas today it's often "low input, high throughput, no output." While AI has the potential to plug gaps in improving output, it can also exacerbate this trend by focusing on high throughput without significant output. Just as we once mistook data accumulation for understanding, we now risk mistaking AI-generated correlations for genuine scientific insight. AI-driven discoveries must therefore be complemented with theoretical and experimental models that go beyond pattern recognition.

Reductionism and Emergence in the Era of AI

One of the key reasons why technology—and now AI—hasn’t “saved” biology lies in the fundamental philosophy of reductionism. Reductionism, the idea of breaking down complex systems into simpler parts, has been the great legacy of twentieth-century science. However, as complexity theorists like Philip Anderson and Stuart Kauffman pointed out, complex systems often display emergent properties that can’t be deduced from their individual components. AI tools, which mostly operate on reductionist principles by breaking down biological data into discrete elements, are thus poorly equipped to handle emergent phenomena.

For example, while AI can analyze neuronal firing patterns and model brain networks, it struggles to explain higher-order cognitive functions and consciousness. Mirror neurons are a case in point: AI can track their activation across brain regions, but understanding their role in human empathy or social behavior remains elusive. Neuroscientists like John Krakauer argue that such limitations reflect the broader challenge of understanding emergent properties within biological systems.

The Future: An Integrated Approach

For AI to truly revolutionize biology, it must not merely amplify reductionist methods but enable a more integrated approach to biological understanding. This involves studying biological systems at multiple levels of organization - both digital and analog - and creating AI models that can reconcile these levels. For example, in neuroscience, we must bridge low-level recordings of single neurons with models of neuronal clusters and behavioral observations of entire organisms. Similarly, in fields like genomics, AI must work alongside biologists to develop causal models that connect molecular changes with physiological and ecological consequences. This is why organizations that center their entire product or discovery pipeline around AI need to be careful and avoid the proverbial pitfall of having everything look like a nail when they have a hammer.

The promise of AI in biology also depends on fostering closer interdisciplinary collaborations. Physicists, computer scientists, biologists, and psychologists need to work together to build models that combine reductionist and holistic perspectives. AI should serve as a tool that integrates data from different levels, facilitating not just more precise measurements, but more comprehensive theories.

Final thoughts

As the late biologist Carl Woese observed, living systems are not simply collections of molecular machines, but resilient patterns in a turbulent flow. In his elegant essay titled “A New Biology for A New Century,” Woese argued for a broader perspective that sees organisms as complex, dynamic organizations. AI must help us move toward this vision by integrating the patterns it finds with deeper theoretical insights, enabling a biology that goes beyond the sum of its parts.

The era of AI in biology holds great promise, but it also poses new risks of mistaking data accumulation for understanding. AI must be seen not as a savior of biological sciences, but as a vital partner in building bridges between different levels of biological understanding. By combining AI’s pattern recognition with theoretical frameworks that address emergence and historical contingency, we can move toward a more integrated, holistic view of life.

As technology advances, we must remember the limitations of reductionism and the dangers of technological solutionism. AI’s role should not be to replace human understanding, but to augment and expand it. Just as we harness the tools of the past revolutions in biology, we must now harness AI to not just process data, but to deepen our understanding of the flow of life itself.

Image link: https://scitechdaily.com/ai-reveals-previously-unknown-biology-we-might-not-know-half-of-whats-in-our-cells/