One might say The Singularity Is Near:
AI Pores Over Old Scientific Papers, Makes Discoveries Overlooked By Humans
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.
“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.“
As one example, researchers fed publications from before 2009 into the algorithm and were able to predict one of the most effective modern-day thermoelectric materials four years before it was actually discovered in 2012.
The technology isn’t restricted to materials science either – as it can be trained on a wide variety of disciplines by retraining it on literature from whichever subject for which one wants to provide a deeper analysis.
“This algorithm is unsupervised and it builds its own connections,” said the study’s lead author, Vahe Tshitoyan, adding “You could use this for things like medical research or drug discovery. The information is out there. We just haven’t made these connections yet because you can’t read every article.”
One could also say, with a similar amount of justification, Skynet smiles.