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Source: MIT Technology Review as of 15-05-2020

IBM’s and Google’s platforms work in similar ways. They make it easy for clients to spin up chat or voice-based agents that act a lot like Alexa or Siri but are tailored to different applications. When users text or call in, they are free to speak in open-ended sentences. The system then uses natural-language processing to parse their “intent” and responds with the appropriate scripted answer or reroutes them to a human agent. For queries that can’t be answered automatically, the algorithms group similar ones together to show the most commonly missed intents. “The nice thing about the technology is that it somewhat learns what types of questions are being asked, so we can plug them in; we can program it after the fact,” says Pokorny.

The platforms are proving popular among a range of organizations, especially those with limited technical resources. Small and midsize government organizations, including the city of Austin, Texas, and the Czech Ministry of Health, have used Watson to build chatbots that provide information about covid testing, prevention, and treatment. The Oklahoma Employment Security Commission has used Google’s virtual agent to help field over 60,000 daily calls related to unemployment claims. Health providers like the University of Arkansas for Medical Sciences and the University of Pennsylvania’s medical school have worked with both platforms to develop patient triage tools that help them administer timely care.

The goal of the systems is to get as many queries as possible answered by the virtual agents before any calls are handed over to humans. This reduces the burden on call centers and the wait times for users. It also reduces the need for expensive human agents. A week in, Pokorny was impressed. “I see us using this technology well beyond the crisis,” he says. Right now, his county is using the chatbot only to field covid-related questions, but he plans to deploy a voice-based version that becomes the first point of contact for anyone dialing in about any issue. Watson’s pricing after the free trial is over is also “inexpensive,” he says. He demurs about what this means for laid-off staff and whether they will regain their jobs after the pandemic. “We’re fighting this from two angles,” he says. “One is obviously the health crisis. But once that’s over, we’re going to have a tremendous economic crisis. I see some artificial intelligence maybe helping us.”

Many other organizations will be facing similar pressures to keep their operations lean, so it’s possible that call center jobs already lost may not come back. But perhaps the good news is such jobs won’t be eliminated entirely: algorithms are still far from understanding the full context and nuance of conversation, so rarer cases will have to be reserved for the more capable hands of humans. Some matters are also too sensitive to outsource to AI.

Around the same time Pokorny started using Watson, Children’s Healthcare of Atlanta in Georgia began working with IBM’s system to develop a chatbot for helping parents assess their children’s symptoms. The health center turned to automation after people began inundating their phone lines, cranking up wait times to 30 minutes or even an hour. Like Pokorny, CHOA was impressed by how fast it the bot was to deploy and how easy it was to customize so that it replicated the assessment flow that would’ve been performed by a pediatrician. In just its first week, the application logged 1,000 unique conversations a day. But as much as CHOA sees the technology’s role expanding beyond covid symptom assessment, the center doesn’t see it fully replacing call lines or becoming the first point of contact. “It’s worked really nicely during the pandemic time when people really just want some information,” says Daniel Hirsh, a physician there. “But I work in pediatric emergency medicine in the ER. When parents are nervous about their kids, people want to talk and have a conversation with a human.”

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