How conversation (with context) will usher in the AI future
In the past few years, advances in artificial intelligence have captured the public imagination and led to widespread acceptance of AI-infused assistants. But this accelerating pace of innovation comes with increased uncertainty about where the technology is headed and how it will impact society.
One of the clearest areas of agreement, however, is that advancing the ability of computers to interact with us in a more natural way is critical for the AI-human relationship to reach its fullest potential. We spoke to 30 of AI’s most knowledgeable scientists and thought leaders about the future of the technology, and most agreed that advances in human-computer interaction (HCI) will be both dependent on AI—and essential to progressing its applications.
The consensus is that within three to five years, advances in AI will make the conversational capabilities of computers vastly more sophisticated, paving the way for a sea change in computing. And the key lies in helping machines master one critical element for effective conversation—context.
HCI shifting to conversation, but users expect more
While the move toward conversation might seem like a natural progression of HCI, AI thought leaders point out that talking to machines actually represents a tectonic shift in computing. It marks the first significant departure from the command-based, on-screen interaction we’ve used since the dawn of the modern computing age.
This shift, of course, has already begun. AI-powered assistants on our phones, and more recently in the home, allow us to interact with them conversationally through voice. And AI-infused chatbots let us ask a wide array of questions and receive answers via typed text. But user frustration levels with AI conversational agents are beginning to rise.
“Chatbots were super-hot and now not-quite-as-much,” says Shivon Zillis, partner at AI-focused venture capital firm Bloomberg Beta. “They’re seeing some early successes in a few narrow applications like customer support and smart appliances, but people are getting frustrated because they have overly high expectations.”
The source of such high expectations? Significant advances in machine learning have allowed conversational systems to better recognize speech and transform text into speech—two key elements in natural language processing (NLP). As a result, conversational agents can respond with human-like quickness via voice and text, leading users to wrongly assume these agents are also capable of unbound, back-and-forth exchanges. “Unfortunately I can’t yet really have a dialogue with Siri, for example. I can ask her, ‘what is the weather today?’ But I can’t then ask, ‘should I wear rain boots’ and get a proper response,” explains Satinder Singh, Director of the Artificial Intelligence Lab at the University of Michigan.
Nor can conversational agents yet meet user expectations related to sensing and responding with emotion. “People identify with and personify computers, and even more so, computer agents,” says David Konopnicki, an IBM Research Manager who studies affective computing. “Even when people know that they are having a conversation with a computer, it’s surprising to see that they not only appreciate that the computer has empathy— they expect it.”
These limitations exist because computers have not yet made the great strides in natural language understanding and dialogue that they’ve achieved in NLP. Without this, most computer responses are painstakingly scripted by engineers using if-then rules. “It’s really difficult to anticipate every way a conversation may go, and if you leave out some critical paths, then you end up with the system saying, ‘I don’t understand’,” says IBM Distinguished Researcher Murray Campbell, who was one of the architects of IBM’s AI chess master, DeepBlue.
How do we get beyond “I don’t understand” as a response to unexpected triggers? The key is to better embed a sense of context in conversational systems.
