Physicist Sabine Hossenfelder says science may need to accept descriptions of nature too complicated for humans to understand, and that AI is the way forward
If we want to continue progress in science, we must be prepared to acknowledge that the correct description of nature will eventually simply be too complicated for humans to understand. I do not think it is wise to make human understanding a requirement. AI is the way forward.
Context
A well known physicist arguing that understanding should not be required for scientific progress challenges a core idea of how science works.
This is an opinion post from Hossenfelder, a German physicist and the author of the books Lost in Math and Existential Physics. She did not give examples or cite research.
Deep dive
On October 19, Sabine Hossenfelder posted that if we want to continue progress in science, we must be prepared to acknowledge that the correct description of nature will eventually simply be too complicated for humans to understand.
She wrote that she does not think it is wise to make human understanding a requirement, and ended: AI is the way forward.
The post did not say which fields she meant, what such descriptions might look like or how scientists would check results they cannot follow. Her argument implies that AI systems could find models of nature that predict well even if no person can grasp why they work.
The view cuts against a common expectation that scientific theories should explain as well as predict, and that people should be able to follow the reasoning behind a result. Supporters of AI driven research say models can find patterns in data that are too complex for people, while critics worry about trusting results that cannot be explained or independently checked.
Hossenfelder is known for criticizing parts of theoretical physics, arguing in Lost in Math that some physicists rely too heavily on ideas of beauty and elegance. Her post passed 110,000 views.
What to expect next
Watch how other scientists respond, especially on how results produced by AI could be verified if humans cannot follow them.
Also watch for AI driven discoveries in physics, biology and mathematics that come with predictions but little explanation, and how journals and funders treat them. The question of whether understanding is needed will grow as AI systems take a larger role in research.