Google’s Pathways Language Model (PaLM) is a relatively new 540-billion parameter network built to complete a variety of complex language-based tasks. It’s said to be intelligent enough to describe how it solved a math problem and annoy you by explaining your own jokes. Rather than focusing on one area of “expertise” and starting fresh every time it learns a new skill, PaLM can “stack” previously-learned knowledge to devise solutions to novel problems, similar to how humans assess new situations. This is important if a robot is meant to help humans in their jobs or day-to-day personal lives.
It just so happens that Google’s parent company, Alphabet, has been working on a new robotics firm called Everyday Robots. As its name suggests, the firm’s goal is to build robots that learn on their own and take care of “time-consuming, everyday tasks.” Combined with PaLM, Everyday Robots’ SayCan robot becomes the PaLM-SayCan, a bot capable of assessing its own capabilities, its environment, and the task at hand, then breaking that task into smaller sub-tasks to achieve the desired goal.
(Photo: Everyday Robots, Google)
While most robots are tested within the confines of a lab, the whole point of PaLM-SayCan is to assist with diverse and often unplanned problems and tasks. This inspired the Google and Everyday Robots teams to take PaLM-SayCan into an office kitchen instead. Here, PaLM-SayCan can be asked to fetch a bag of chips from a drawer, pick between Coke and Pepsi, and even help humans tackle the problem of a spilled beverage.
Upon hearing “I spilled my Coke, can you bring me something to clean it up?” PaLM-SayCan planned and executed the steps necessary to find a sponge, pick it up, and bring the sponge to the person who’d spilled the drink, according to a post on GitHub. A slight alteration to the robot’s instructions successfully changed the outcome: “I spilled my Coke, can you bring me a replacement?” prompted PaLM-SayCan to locate a Coke can and bring it over. Similarly successful outcomes occurred when humans told PaLM-SayCan they’d just completed a workout and wanted a drink and a snack to recover, or that they wanted the robot to clean up some left-behind items on the kitchen counter. PaLM-SayCan could even solve these problems when they were presented in Spanish, French, and Chinese.
What’s both impressive and terrifying about the PaLM-SayCan combination is that once one robot learns a skill, other PaLM-equipped robots can forgo the learning process by having the skill quickly transferred to themselves. That being said, this process appears to require human intervention, so it’s unlikely that a fleet of PaLM-SayCan bots will learn and spread how to take over the world anytime soon.
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