Soon you could be chatting with your computer about the morning news. An AI has learned to read and answer questions about a news article with unprecedented accuracy.
Creating AI systems that can learn in the background from humanity’s existing stores of information is one of the big goals of computer science. “Computers don’t have the kind of general knowledge and common sense of how the world works [from reading] about things in novels or watch[ing] sitcoms,” says Chris Manning at Stanford University.
To get a step closer to this, last year, Google’s DeepMind team used articles from the Daily Mail website and CNN to help train an algorithm to read and understand a short story. The team used the bulleted summaries at the top of these articles to create simple interpretive questions that trained the algorithm to search for key points.
Now a group led by Manning has designed an algorithm that beat DeepMind’s results by an impressive 10 per cent on the CNN articles and 8 per cent for Daily Mail stories. It scored 70 per cent overall.
The improvement came through streamlining the DeepMind model. “Some of the stuff they had just causes needless complications,” says Manning. “You get rid of that and the numbers go up.”
“It makes sense,” says Robert Frederking of Carnegie Mellon University in Pittsburgh. “Making something more complicated doesn’t make it better.”
There’s a trade-off in AI design: if an algorithm is complex, it’s more powerful, but to perform well it needs more data to learn from, says Frederking. Simpler AI can train quickly with smaller amounts of data.