In a new article by MIT Technology Review lays bare the shortcomings of the current AI landscape: AI Assistants Say Dumb Things, and We’re About to Find Out Why
The goal is to inform a new approach to machine learning that yields better AI experiences. Basically, looking for ways to teach machines common sense and worldly experiences, rather than limiting them to one area of competence and one narrow set of performance benchmarks that create less-than-desirable experiences for end users.
From the article:
Language systems that rely on machine learning can often provide convincing answers to questions if they have seen lots of similar examples before. A program trained on many thousands of IT support chats, for instance, might be able to pass itself off as a tech support helper in limited situations. But such a system would fail if asked something that required broader knowledge.
“We need to use our common sense to fill in the gaps around the language we see to form a coherent picture of what is being stated,” says Peter Clark, the lead researcher on the ARC project. “Machines do not have this common sense, and thus only see what is explicitly written, and miss the many implications and assumptions that underlie a piece of text.”
Here’s one question: “Which item below is not made from a material grown in nature? (A) a cotton shirt (B) a wooden chair © a plastic spoon (D) a grass basket”
Such a question is easy for anyone who knows plastic is not something that grows. The answer taps into a common-sense picture of the world that even young children possess.
It is this common sense that the AI behind voice assistants, chatbots, and translation software lacks. And it’s one reason they are so easily confused.
Why it’s hot: This new approach to testing AI voice command tools like Alexa, Siri, and Google may help lead to breakthroughs and improvements in the space that open up new possibilities in communication.