Are we replacing science with an AI oracle?

In ancient Greece, people who had trouble with the answers offered by Aristotle, Pythagoras, and Archimedes could turn to the Pythia. She was the high priest at the temple of Apollo in Delphi — more widely known as the Oracle of Delphi.

The oracle had access to a mighty source of predictions: she could ask Apollo what was going to happen.

But Pythia disappeared together with the rest of the ancients. This was certainly a great cultural loss, but she probably never worked that well anyway. At least, I have never seen experimental evidence for the correctness of her predictions.

In the absence of an oracle, scientists since Galileo have turned to another method: careful data collection, thinking hard and proposing theoretical explanations, and finally validating or falsifying these theories with experiments. This is the scientific method, and it has served humanity quite well, in my opinion.

But if we had had Pythia-in-a-box, we could have skipped all that, couldn’t we? No Newton’s laws. No Darwin’s theory of evolution, and no Watson-Crick base pairing. In fact, no reason to create theories at all. Want to know where Mars will be in its orbit in 30 days? Ask the Oracle! Want to know if a drug will be toxic to humans? Ask the oracle! Want to know the final 3D structure of a protein? (Believe me, this is really something we want to know). Ask the oracle!

And behold: Today, we have figured out how to build oracles! Individual oracles go by names such as Deep Neural Networks, Recurrent Neural Networks, Gradient Boosting, Random Forests, and so on.

Actually, these methods are not in themselves oracles, but we have discovered that if we gather millions of observations, they can be trained to be reliable oracles in a specific domain. Combining Big Data and AI has allowed us to build very powerful oracles without ever understanding the tiniest bit about how they work. This is why such methods are called black-box.

Here’s an example: protein folding. For decades scientists have meticulously tried to understand the mechanisms that govern how a chain of amino acids will fold into the complex structures we call proteins. Concepts and theories such as hydrophobic interactions, hydrogen bonds, and van der Waals forces have been developed. These are powerful and useful theories with applications far beyond the protein folding problem itself.

Then, in November 2020, Alphabet’s DeepMind built an oracle called AlphaFold. Based on millions of observed proteins, they trained a Deep Neural Network to predict the final shape of the protein from the amino chain itself — with unrivaled reliability. No longer do we need to bother with understanding the process that drives the folding. No more research into the different forces and interactions in the process. Just ask the machine your questions, and the answer pops right out! And if the answers are not correct enough, we just collect a few more million proteins and train the oracle some more.

Actually, I am in awe of AlphaFold. The genius that went into the design of the system is inspiring. Sometimes I think that AI researchers are the only people who get to do real science nowadays.

But my point is that if the availability of black-box methods and big data tempts us to give up on theory in science, we lose in the long run. The flow of novel scientific understanding will dwindle and eventually come to a stop.