Machine Design recently interviewed Ron Di Carlantonio, CEO and founder of iNAGO, a Tokyo/Toronto-based company creating next-generation conversational digital assistants.
In this installment (the second of a three-part series), Di Carlantonio focuses on the human-machine interface and the opportunities for industrial applications.
The proliferation of robotics for industrial applications invites a broader set of implications for the way humans communicate or converse with machines. Robots can help extend services and provide new opportunities, improve efficiency and productivity, but all of these improvements require humans to adapt to the new functionality they derive from the technology.
The impact that robots and artificial intelligence (AI) technologies have on humans in industrial contexts extends well beyond basic prompts to get the robot to respond in a predictable manner, Di Carlantonio argued.
Mapping the Difference in Safety Levels in Industrial Contexts
He made the case that interaction with a physical robot in an industrial application is a more complex transaction than interacting with a chatbot on a bank site. For one, machine learning techniques demand more safety mechanisms that safeguard the people interacting with robots, he said.
“If a robot wants to talk, it has to be able to listen, has to be able to understand, has to be able to respond,” Di Carlantonio suggested. “It has a physical form. It has things it can do. Those things could be [manipulating] a large arm and it has a lot more context than, let’s say, a chatbot on a bank site. It could be a lathe cutting out a part. So, it has a lot of very important functionality. When trying to allow a robot to communicate, we have to take all of those things into consideration.”
READ MORE: For Better or For Worse, Multimodal AI Cultivates New Frontiers
The level of safety is significantly different when communicating with a chatbot on a bank site versus interacting with a robot on the factory floor. “If a chatbot doesn’t get the transaction right, the user can go to a call center,” Di Carlantonio said. “But if a robot [in a factory] doesn’t get it right, it could cut somebody’s arm off. So, the safety element is critical. Communication has to be accurate and has to be contextual to be valuable and still safe. The level of communication is much, much higher and much more complex.”
Bringing Industrial Displays Up to Speed
Di Carlantonio said that industry is at the point where the screens on machines are in dire need of updates. “They do look 20 years old and there are lots of numbers and only one person who usually knows how to program that machine,” he said. “I kind of think of it like the ’90s when websites came, and we had HTML. HTML was very complicated for the common person, so programmers had to be in there creating all these websites. What did that lead to?
“Very ugly websites that weren’t very user-friendly and, honestly, very difficult to attract the average person who was expecting a television experience,” he continued.
Today, Di Carlantonio pointed out, high-level tools are available for non-programmers and even the average person can create websites that are more functional than a programmer could. This presents an opportunity in manufacturing because more than one person on the plant floor could tell a machine what to do.
READ MORE: Making Conversation: Using AI to Extract Intel from Industrial Machinery and Equipment
At a more sophisticated level, Di Carlantonio speculated, “what if a designer could literally communicate with the machine to tell it the part it wants to make?” Moreover, he argued, that conversation ought to be conversational.
Another opportunity, he called out, is the use of AI for gathering data and processing it to determine improvements and patterns. This technology could become invaluable in helping manufacturers maintain knowledge about their plants.
“Every machine and every expert has an incredible amount of knowledge of that machine,” he said. “But you find one thing in manufacturing right now: People are older and they’re retiring, so no more experts. The person leaves and all of that knowledge leaves the organization. So how can we put that into something that can go to everybody? How can we retain that knowledge and make it available to everybody—even if they just started last week? So that’s another opportunity I think this technology can help solve.”
For more coverage of emergent technologies in the manufacturing space, be sure to check out the Nov./Dec. issue of Machine Design, out now.
