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#439916 This Restaurant Robot Fries Your Food to ...
Four and a half years ago, a robot named Flippy made its burger-cooking debut at a fast food restaurant called CaliBurger. The bot consisted of a cart on wheels with an extending arm, complete with a pneumatic pump that let the machine swap between tools: tongs, scrapers, and spatulas. Flippy’s main jobs were pulling raw patties from a stack and placing them on the grill, tracking each burger’s cook time and temperature, and transferring cooked burgers to a plate.
This initial iteration of the fast-food robot—or robotic kitchen assistant, as its creators called it—was so successful that a commercial version launched last year. Its maker Miso Robotics put Flippy on the market for $30,000, and the bot was no longer limited to just flipping burgers; the new and improved Flippy could cook 19 different foods, including chicken wings, onion rings, french fries, and the Impossible Burger. It got sleeker, too: rather than sitting on a wheeled cart, the new Flippy was a “robot on a rail,” with the rail located along the hood of restaurant stoves.
This week, Miso Robotics announced an even newer, more improved Flippy robot called Flippy 2 (hey, they’re consistent). Most of the updates and improvements on the new bot are based on feedback the company received from restaurant chain White Castle, the first big restaurant chain to go all-in on the original Flippy.
So how is Flippy 2 different? The new robot can do the work of an entire fry station without any human assistance, and can do more than double the number of food preparation tasks its older sibling could do, including filling, emptying, and returning fry baskets.
These capabilities have made the robot more independent, eliminating the need for a human employee to step in at the beginning or end of the cooking process. When foods are placed in fry bins, the robot’s AI vision identifies the food, picks it up, and cooks it in a fry basket designated for that food specifically (i.e., onion rings won’t be cooked in the same basket as fish sticks). When cooking is complete, Flippy 2 moves the ready-to-go items to a hot-holding area.
Miso Robotics says the new robot’s throughput is 30 percent higher than that of its predecessor, which adds up to around 60 baskets of fried food per hour. So much fried food. Luckily, Americans can’t get enough fried food, in general and especially as the pandemic drags on. Even more importantly, the current labor shortages we’re seeing mean restaurant chains can’t hire enough people to cook fried food, making automated tools like Flippy not only helpful, but necessary.
“Since Flippy’s inception, our goal has always been to provide a customizable solution that can function harmoniously with any kitchen and without disruption,” said Mike Bell, CEO of Miso Robotics. “Flippy 2 has more than 120 configurations built into its technology and is the only robotic fry station currently being produced at scale.”
At the beginning of the pandemic, many foresaw that Covid-19 would push us into quicker adoption of many technologies that were already on the horizon, with automation of repetitive tasks being high on the list. They were right, and we’ve been lucky to have tools like Zoom to keep us collaborating and Flippy to keep us eating fast food (to whatever extent you consider eating fast food an essential activity; I mean, you can’t cook every day). Now if only there was a tech fix for inflation and housing shortages…
Seeing as how there’ve been three different versions of Flippy rolled out in the last four and a half years, there are doubtless more iterations coming, each with new skills and improved technology. But the burger robot is just one of many new developments in automation of food preparation and delivery. Take this pizzeria in Paris: there are no humans involved in the cooking, ordering, or pick-up process at all. And just this week, IBM and McDonald’s announced a collaboration to create drive-through lanes run by AI.
So it may not be long before you can order a meal from one computer, have that meal cooked by another computer, then have it delivered to your home or waiting vehicle by a third—you guessed it—computer.
Image Credit: Miso Robotics Continue reading
#439884 This Spooky, Bizarre Haunted House Was ...
AI is slowly getting more creative, and as it does it’s raising questions about the nature of creativity itself, who owns works of art made by computers, and whether conscious machines will make art humans can understand. In the spooky spirit of Halloween, one engineer used an AI to produce a very specific, seasonal kind of “art”: a haunted house.
It’s not a brick-and-mortar house you can walk through, unfortunately; like so many things these days, it’s virtual, and was created by research scientist and writer Janelle Shane. Shane runs a machine learning humor blog called AI Weirdness where she writes about the “sometimes hilarious, sometimes unsettling ways that machine learning algorithms get things wrong.”
For the virtual haunted house, Shane used CLIP, a neural network built by OpenAI, and VQGAN, a neural network architecture that combines convolutional neural networks (which are typically used for images) with transformers (which are typically used for language).
CLIP (short for Contrastive Language–Image Pre-training) learns visual concepts from natural language supervision, using images and their descriptions to rate how well a given image matches a phrase. The algorithm uses zero-shot learning, a training methodology that decreases reliance on labeled data and enables the model to eventually recognize objects or images it hasn’t seen before.
The phrase Shane focused on for this experiment was “haunted Victorian house,” starting with a photo of a regular Victorian house then letting the AI use its feedback to modify the image with details it associated with the word “haunted.”
Image Credit: Josephyurko, cc-by SA 4.0
The results are somewhat ghoulish, though also perplexing. In the first iteration, the home’s wood has turned to stone, the windows are covered in something that could be cobwebs, the cloudy sky has a dramatic tilt to it, and there appears to be fire on the house’s lower level.
Image Credit: Janelle Shane, AI Weirdness
Shane then upped the ante and instructed the model to create an “extremely haunted” Victorian house. The second iteration looks a little more haunted, but also a little less like a house in general, partly because there appears to be a piece of night sky under the house’s roof near its center.
Image Credit: Janelle Shane, AI Weirdness
Shane then tried taking the word “haunted” out of the instructions, and things just got more bizarre from there. She wrote in her blog post about the project, “Apparently CLIP has learned that if you want to make things less haunted, add flowers, street lights, and display counters full of snacks.”
Image Credit: Janelle Shane, AI Weirdness
“All the AI’s changes tend to make the house make less sense,” Shane said. “That’s because it’s easier for it to look at tiny details like mist than the big picture like how a house fits together. In a lot of what AI does, it’s working on the level of surface details rather than deeper meaning.”
Shane’s description matches up with where AI stands as a field. Despite impressive progress in fields like protein folding, RNA structure, natural language processing, and more, AI has not yet approached “general intelligence” and is still very much in the “narrow” domain. Researcher Melanie Mitchell argues that common fallacies in the field, like using human language to describe machine intelligence, are hampering its advancement; computers don’t really “learn” or “understand” in the way humans do, and adjusting the language we used to describe AI systems could help do away with some of the misunderstandings around their capabilities.
Shane’s haunted house is a clear example of this lack of understanding, and a playful reminder that we should move cautiously in allowing machines to make decisions with real-world impact.
Banner Image Credit: Janelle Shane, AI Weirdness Continue reading