When AI isn’t busy taking our jobs, it’s making brand new scientific discoveries that our clunky human brains somehow overlooked.
Researchers from Lawrence Berkeley National Laboratory trained an AI called Word2Vec on scientific papers to see if there was any “latent knowledge” that humans weren’t able to grock on first pass.
The study, published in Nature on July 3, reveals that the algorithm found predictions for potential thermoelectric materials which can convert heat into energy for various heating and cooling applications.
The algorithm didn’t know the definition of thermoelectric, though. It received no training in materials science. Using only word associations, the algorithm was able to provide candidates for future thermoelectric materials, some of which may be better than those we currently use. –Motherboard
“It can read any paper on material science, so can make connections that no scientists could,” said researcher Anubhav Jain. “Sometimes it does what a researcher would do; other times it makes these cross-discipline associations.“
The algorithm was designed to assess the language in 3.3 million abstracts from material sciences, and was able to build a vocabulary of around half-a-million words. Word2Vec used machine learning to analyze relationships between words.
“The way that this Word2vec algorithm works is that you train a neural network model to remove each word and predict what the words next to it will be,” said Jain, adding that “by training a neural network on a word, you get representations of words that can actually confer knowledge.“
Using just the