Today, AI is being increasingly integrated into scientific discovery to accelerate research, helping scientists generate hypotheses, design experiments, gather and interpret large datasets, and write papers. But the reality is that science and AI have little in common and AI is unlikely to make science obsolete. The core of science is theoretical models that anyone can use to make reliable descriptions and predictions.
The core of AI, in contrast, is, as Anderson noted, data mining: ransacking large databases for statistical patterns.
The hype around AI replacing science is getting a bit out of hand. This article does a cracking job of puncturing that bubble a bit.
The core argument is spot on: science is about building theoretical models that anyone can use to make reliable predictions. AI, on the other hand, is just glorified data mining - finding patterns without necessarily understanding why they exist.
It’s not that AI isn’t useful in science - it clearly is. But it’s a tool, not a replacement for the scientific method. The real test is whether AI actually leads to new products and services being developed faster and cheaper. So far, the evidence is pretty thin on the ground.
The most telling quote comes from the CEO of an AI-powered drug company: “People are saying, AI will solve everything. They give you fancy words. We’ll ingest all of this longitudinal data and we’ll do latitudinal analysis. It’s all garbage. It’s just hype.”
AI might be changing the world, but let’s not get carried away. Science isn’t going anywhere.