A Google research scientist typed “I’m hungry” into a laptop connected to the one-armed bot. The robot considered the statement, rolled itself towards a nearby counter, and returned with a bag of chips.
For example, one Google research scientist typed “I’m hungry” into a laptop connected to Everyday Robots’ one-armed bot that vaguely resembles a large parking meter. The robot then considered the statement, rolled itself towards a nearby counter, and returned with a bag of chips—notably, this chosen solution wasn’t a preprogrammed one, but a decision based on copious conversational databases constructed from books, social media, Wikipedia articles, and other language-heavy online sources.
While the system still requires human training to learn myriad physical “solutions” to typed commands—other examples include cleaning up spilled liquid with a sponge and opening drawers to retrieve items—the ability for AI to determine which response works for nuanced human interactions is a huge step forward in more streamlined, accurate, and helpful robotics.
These systems are not without their faults, of course. Language modeling based on internet sourcing often produces. While it’s unlikely that Everyday Robots’ new prototypes will offend users with its solution to grabbing snacks or cleaning dishes, these inherent issues require careful attention and modification to ensure the best robo-future possible… whenever that comes around, of course.
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