Perceptron: AI mixes concrete, designs molecules, and thinks with space lasers

This is called “slump testing.”

What the Meta/UIUC team did was train a model on over a thousand concrete formulas, which differed in proportions of sand, slag, ground glass, and other materials (you can see a sample chunk of more photogenic concrete up top). Finding the subtle trends in this dataset, it was able to output a number of new formulas optimizing for both strength and low emissions. The winning formula turned out to have 40 percent fewer emissions than the regional standard, and met… well, some of the strength requirements. It’s extremely promising, and follow-up studies in the field should move the ball again soon.

The second Meta study has to do with changing how language models work. The company wants to work with neural imaging experts and other researchers to compare how language models compare to actual brain activity during similar tasks.

In particular, they’re interested in the human capability of anticipating words far ahead of the current one while speaking or listening — like knowing a sentence will end in a certain way, or that there’s a “but” coming. AI models are getting very good, but they still mainly work by adding words one by one like Lego bricks, occasionally looking backwards to see if it makes sense. They’re just getting started but they already have some interesting results.

Back on the materials tip, researchers at Oak Ridge National Lab are getting in on the AI formulation fun. Using a dataset of quantum chemistry calculations, whatever those are, the team created a neural network that could predict material properties — but then inverted it so that they could input properties and have it suggest materials.

“Instead of taking a material and predicting its given properties, we wanted to choose the ideal properties for our purpose and work backward to design for those properties quickly and efficiently with a high degree of confidence. That’s known as inverse design,” said ORNL’s Victor Fung. It seems to have worked — but you can check for yourself by running the code on Github.