AI’s Black Box Comes into View Thanks to Cell Biology

Even though humans designed AI systems, they’re gobsmacked trying to figure out how the networks AI builds actually work.

They refer to the inner workings of AI as black boxes.  Now, scientists are working on a way to understand these black boxes, and cell biology is the key to that discovery.


Cracking Open the Black Box of AI with Cell Biology

The deep neural networks that power today’s artificial intelligence systems work in mysterious ways.

They’re black boxes: A question goes in (“Is this a photo of a cat?” “What’s the best next move in this game of Go?” “Should this self-driving car accelerate at this yellow light?”), and an answer comes out the other side. We may not know exactly how a black box AI system works, but we know that it doeswork.

But a new study that mapped a neural network to the components within a simple yeast cell allowed researchers to watch the AI system at work. And it gave them insights into cell biology in the process. The resulting tech could help in the quest for new cancer drugs and personalized treatments.

First, let’s cover the basics of the neural networks used in today’s machine learning systems.

……Although they’re called neural networks, these systems are only very roughly inspired by human neural systems, explains Trey Ideker, a professor of bioengineering and medicine at UC San Diego.

“Look at AlphaGo [the program that beat the Go grandmaster]. The inner workings of the system are a complete jumble; it looks nothing like the human brain,” Ideker says. “They’ve evolved a completely new thing that just happens to make good predictions.”

Ideker, who led the new research on the AI for cell biology, set out to do something different. He wanted to use a neural network not just to spit out answers, but to show researchers how it reached those conclusions. And by mapping a neural network to the components of a yeast cell, his team could learn about the way life works. “We’re interested in a particular structure that was optimized not by computer scientists, but by evolution,” he tells IEEE Spectrum.

This project was doable because brewer’s yeast, a single-cell organism, has been studied since the 1850s as a basic biological system. “It was convenient because we had a lot of knowledge about cell biology that could be brought to the table,” Ideker says. “We actually know an enormous amount about the structure of a yeast cell.”

So his team mapped the layers of a neural network to the components of a yeast cell, starting with the most microscopic elements (the nucleotides that make up its DNA), moving upward to larger structures such as ribosomes (which take instructions from the DNA and make proteins), and finally to organelles like the mitochondrion and nucleus (which run the cell’s operations). Overall, their neural network, which they call DCell, makes use of 2,526 subsystems from the yeast cell.

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