|By Le Williams | 2 years ago|
A new artificial intelligence (AI) program developed by Stanford physicists organized the periodic table of elements within a few hours, contrasting a century-long development by human scientists.
The program named Atom2Vec successfully learned to distinguish between different atoms after analyzing a list of chemical compound names from an online database.
The unsupervised AI then used concepts borrowed from the field of natural language processing, in particular, the idea that the properties of words can be understood by looking at other words surrounding them to cluster the elements according to their chemical properties.
“We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can,” said study leader Shou-Cheng Zhang, the J. G. Jackson and C. J. Wood Professor of Physics at Stanford’s School of Humanities and Sciences.
Zhang says the research, published in the June 25 issue of Proceedings of the National Academy of Sciences, is an important first step toward a more aggressive goal, which is designing a replacement to the Turing test; the current gold standard for gauging machine intelligence.
The new AI must be capable of responding to written questions in ways that are indistinguishable from a human, in order to pass the Turning Test.
Notably, Zhang deems the test as flawed because it is subjective.
“Humans are the product of evolution and our minds are cluttered with all sorts of irrationalities. For an AI to pass the Turing test, it would need to reproduce all of our human irrationalities,” Zhang said. “That’s very difficult to do, and not a particularly good use of programmers’ time.”
Zhang would instead like to propose a new benchmark of machine intelligence. By recreating the periodic table of elements, Atom2Vec has achieved this secondary goal, Zhang says.