What Are the Biggest Questions in Science Today? 100 Scientists Weigh In

A survey of the unknown

Science is often presented to the public as a catalogue of answers: the age of the universe, the structure of DNA, the mechanics of plate tectonics. But researchers tend to spend far less time admiring what is known than wrestling with what isn’t. A new project canvassing roughly 100 scientists about the biggest open questions in their fields is a reminder that the frontier is still vast — and that the most interesting parts of science are the parts not yet settled.

Asking working researchers what keeps them up at night produces a different map of science than the one found in textbooks. Textbooks organize knowledge by discipline. Scientists, when asked about mysteries, tend to converge on a handful of deep themes that cut across fields: origins, consciousness, complexity, and the limits of what humans can measure.

The perennial puzzles

Some questions have hovered over science for generations and show no sign of being retired. How did life begin on Earth, and is it common elsewhere in the universe? What is the dark matter and dark energy that appear to make up most of the cosmos, yet have never been directly identified? Why does the universe contain matter at all, rather than nothing? How do the firing of neurons and the chemistry of the brain give rise to subjective experience — the problem of consciousness that has frustrated philosophers and neuroscientists alike?

These are not idle riddles. Each one marks a place where current theories either run out of explanatory power or collide with one another. The long-standing mismatch between quantum mechanics and general relativity, for instance, is less a gap in trivia than a signal that something fundamental about reality remains misunderstood.

Newer frontiers

Other questions on scientists’ minds today would have been unrecognizable a few decades ago. Biologists now ask how the trillions of microbes living in and on the human body shape health, immunity and even behavior. Climate researchers want to know how quickly ice sheets can destabilize and how ecosystems will reorganize under rapid warming. Geneticists are probing why most of the human genome does not code for proteins, and what that vast remainder actually does.

Artificial intelligence has introduced a new category of uncertainty altogether. Machine learning systems are already being used to predict protein structures, sift astronomical data and search for new materials. But researchers are also asking harder questions: whether these tools can generate genuine scientific insight, how to verify results produced by systems no one fully understands, and what it means for discovery if the most powerful instruments in science become opaque.

Why the questions matter

There is value in collecting such questions in one place. Research funding, graduate training and public attention all tend to flow toward problems that have been clearly articulated. A well-posed question can organize a field for decades; the hunt for the Higgs boson and the mapping of the human genome both began as questions before they became projects.

There is also a lesson for readers outside the laboratory. Public debates about science often treat uncertainty as a weakness — evidence that experts cannot be trusted. Scientists themselves tend to see it the opposite way. Uncertainty is the raw material of the enterprise, and an honest inventory of what remains unknown is a measure of a discipline’s health, not its failure.

The answers, in most cases, will not arrive quickly. Some of the questions being asked today may take a generation to resolve, and a few may turn out to be the wrong questions entirely. That, too, is part of how the work proceeds. Read More