Artificial intelligence's impact on the robotics industry

Researchers and manufacturers are looking to teach robots how to learn and handle complex tasks with artificial intelligence (AI), but their capabilities are quite a bit removed from what people believe robots are capable of achieving.

04/04/2018


Rodney Brooks says artificial intelligence (AI) is still in its infancy. There's no foreseeable competition between machine intelligence and human intelligence; humans remain smarter. Courtesy: Rethink Robotics/Robotic Industries Association (RIA) Researchers and entrepreneurs with decades of working in artificial intelligence (AI) are trying to help people better understand its elusive nature. They're working to reduce some of the confusion and misconceptions around AI and show how it's being used in robotics for industrial applications.

"I think the biggest misconception is how far along it is," said Rodney Brooks, chairman and CTO of Rethink Robotics. "We've been working on AI, calling it AI since 1956 (when the father of AI, John McCarthy, coined the term "artificial intelligence"), so roughly 62 years. But it's much more complicated than physics, and physics took a very long time. I think we're still in the infancy of AI."

Brooks believes much of the AI hype comes from recent press covering jaw-dropping demonstrations of anthropomorphic and animal-inspired robots, or spectator sports pitting AI systems against humans playing chess, Jeopardy!, ping-pong, and Go. AI is here, but it is taking baby steps.

Some of the misunderstanding stems from equating machine performance with competence. When we see a human perform a certain task, we can assume a general competence—skills and talent—the person must possess to perform that task. It's not the same with AI.

"An AI system can play chess fantastically, but it doesn't even know that it's playing a game," Brooks said. "We mistake the performance of machines for their competence. When you see how a program learned something that a human can learn, you make the mistake of thinking it has the richness of understanding that you would have."

Knowing what AI is and isn't

AI has become a marketing buzzword. Like "robot" before it, now everything is seemingly AI-powered. What is and isn't AI is sometimes difficult to pinpoint. Even the experts hesitate when it comes to identifying definitively what is and isn't AI. As Brooks noted, what was considered AI in the 1960s is now taught in the very first course on computer programming. But it's not called AI.

"It's called AI at some point," Brooks said. "Then later it just becomes computer science."

Machine learning, and all of its variations, including deep learning, reinforcement learning, and imitation learning, are subsets of AI.

"AI was a very narrow field for a while. Some people saw it very specifically around a set of search-based techniques," said Ken Goldberg, a professor and distinguished chair in industrial engineering and operations research at the University of California (UC) Berkeley. "Now AI is widely seen as an umbrella term over robotics and machine learning, so now it's being embraced as a whole range of subfields."

Advanced forms of computer vision are a form of AI.

"If you're just inspecting whether a screw is in the right place, we've had that since the '60s. It would be a stretch to call that AI," Goldberg said. "But at the same time, a computer vision system that can recognize the faces of workers, we generally do think of that as AI. That's a much more sophisticated challenge."

Pieter Abbeel is transitioning breakthrough research in machine learning into real world industrial applications for robots that can learn new skills on their own. Courtesy: Embodied Intelligence/RIA Lack of context

An important distinction between human intelligence and machine intelligence is context. As humans, we have a greater understanding of the world around us. AI does not.

"We've been working on context in AI for 60 years and we're nowhere near there," Brooks said. "That's why I'm not worried that we're going to have super intelligent AI. We've been successful in some very narrow ways and that's the revolution right now, those narrow ways. Certainly speech understanding is radically different from what we had a decade ago. I used to make the joke that speech understanding systems were set up so that you press or say '2' for frustration. That's no longer true."

He cited Amazon's Alexa as an example. Google's Assistant and Apple's Siri are two more.

"You say something to Alexa and it pretty much understands it, even when music is playing, even when other people in the room are talking," Brooks said. "It's amazing how good it is, and that came from deep learning. So some of these narrow fields have gotten way better. And we will use those narrow pieces to the best advantage we can to make better products.

"When I started Rethink Robotics, we looked at all the commercial speech understanding systems. We decided at that point it was ludicrous to have any speech recognition in robots in factories. I think that's changed now. It may make sense. It didn't in 2008."

Speech recognition compiles the right word strings. Brooks said accurate word strings are good enough to do a lot of things, but it's not as smart as a person.

"That's the difference," he said. "Getting the word strings is a narrow capability. And we're a long way from it being not so narrow."

These narrow capabilities have become the basis for many wildly optimistic AI predictions that are overly pessimistic about our role as humans in that future.

AI research in the real world

A robot manipulates objects it has never encountered before after researchers teach a neural network how to recognize objects from millions of 3-D models and images. Courtesy: University of California, Berkeley/RIA Goldberg stresses multiplicity over singularity, noting the importance of diverse combinations of people and machines working together to solve problems and innovate. This collaboration is especially important as AI's applications exit the lab and enter the real world.

Pieter Abbeel, a professor in the department of electrical engineering and computer sciences at UC Berkeley, who is working to bring AI to the industrial world as president and chief scientist of Embodied Intelligence, also stresses the importance of humans and machines working together.

"That's part of the challenge," Abbeel said. "How are humans able to use this technology and take advantage of it to make themselves smarter, rather than just have these machines be something separate from us? When the machines are part of our daily lives, what we can leverage to make ourselves more productive, that's when it gets really exciting."

While Abbeel is excited about AI's prospects, he thinks some caution is warranted."I think there is a lot of progress, and as a consequence, a lot of excitement about AI," he said. "In terms of fear, I think it's good to keep in mind that the most prominent progress like speech recognition, machine translation, and recognizing what's in an image are examples of what's called supervised learning."

Abbeel said it's important to understand the different types of AI being built. In machine learning, there are three main types of learning: supervised learning, unsupervised learning, and reinforcement learning.

"Supervised learning is just pattern recognition," Abbeel said. "It's a very difficult pattern to recognize when going from speech to text, or from one language to another language, but that AI doesn't really have any goal or any purpose. Give it something in English, and it will tell you what it is in Chinese. Give it a spoken sentence, and it will transcribe it into a sequence of letters. It's just pattern matching. You feed it data-images and labels-and it's supposed to learn the pattern of how you go from an image to a label.

"Unsupervised learning is when you feed it just the images, no labels," Abbeel continued. "You hope that from just seeing a lot of images that it starts to understand what the world tends to look like and then by building up that understanding, maybe in the future it can learn something else more quickly. Unsupervised learning doesn't have a task. Just feed it a lot of data.

"Then there's reinforcement learning, which is very different and more interesting, but much harder. (Reinforcement learning is credited for advancements in self-driving car technology.) It's when you give your system a goal. The goal could be a high score in a video game, or win a game of chess, or assemble two parts. That's where some of that fear can be justified. If AI has the wrong goal, what can happen? What should the goals be?"

It's important humans and artificial intelligence don't evolve in a vacuum from each other. As we build smarter and smarter machines, our capabilities as humans will be augmented.

"What makes me very excited about what we're doing right now at Embodied Intelligence is that the recent events in artificial intelligence have given AI the ability to understand what they are seeing in pictures," Abbeel said. "Not human-level understanding, but pretty good. If a computer can really understand what's in an image, then maybe it can pick up two objects and assemble them. Or maybe it can sort through packages. Or pick things from shelves. Where I see a big change in the near future are tasks that rely on understanding what a camera feed is giving you."


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