As AI speeds ahead, we need more mathematicians, not fewer
A guest post on Terry Tao's blog argues that someone must still understand what AI discovers, and that takes a lot of people.
Outside your usual reading: this is a way to think about what happens to careful, slower experts when AI speeds up the top end of a field. The post is by Amit Sahai and sits on Terry Tao's blog. A note at the top says it was first written in another file format and converted using AI.
Sahai starts with a memory from his undergraduate years. Several students told him the top students understood new math far too fast for them. They could understand the ideas too, but it took them much longer. Almost all of them eventually gave up their dream of research mathematics and did something else.
He then writes that the AI systems he has worked with already produce beautiful new ideas. He says they do far more than impressive calculations or quickly carrying out arguments a strong human researcher would already understand.
He calls giving up on understanding a profound abdication of responsibility to humanity. He is careful about who owns that responsibility. Each person is entitled to choose a different life. The responsibility belongs to the community collectively: to build a future in which humans can understand and contribute to the discoveries that will change the world, a future with meaningful human agency.
His answer is that struggle can be shared. He asks us to picture a multitude of research groups, each with sustained support, each spending a term or a year trying to understand an extraordinary set of ideas produced by an AI system, with the help of AI systems.
Then he gives society a reason to care, using one example. Imagine a future AI system proposes a radically new design for a one terawatt (a huge unit of power) nuclear fusion power plant. Robots stand ready to manufacture the components and build it. Before approving construction, he would want communities of humans to understand why the design works and what justifies confidence in its safety.
He does not overclaim. Human involvement does not automatically improve a technical decision, and he sees no reason to insist that humans manually repeat work an AI system might do more reliably, even proving mathematical guarantees. But understanding the guarantee means understanding the model, the experimental evidence for it, and our uncertainties about the accuracy of the model.
His closing idea is a "deployable intellectual reserve": communities of mathematically sophisticated people that humanity can call upon to help understand consequential AI-enabled breakthroughs. He says human agency is a value of fundamental importance.
The source here is a set of selected passages, so parts of his argument are not covered.
Struggle is essential to understanding difficult concepts.via What's new (Terry Tao's blog) →