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#439404 Walker X by UBTECH

Walker X, is the latest version by UBTECH Robotics of its groundbreaking bipedal humanoid robot.

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#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

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#439441 Bipedal robot makes history by learning ...

Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Continue reading

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#439382 An approach to achieve compliant robotic ...

Over the past few decades, roboticists have created increasingly advanced and sophisticated robotics systems. While some of these systems are highly efficient and achieved remarkable results, they still perform far poorly than humans on several tasks, including those that involve grasping and manipulating objects. Continue reading

Posted in Human Robots

#439263 Somehow This Robot Sticks to Ceilings by ...

Just when I think I’ve seen every possible iteration of climbing robot, someone comes up with a new way of getting robots to stick to things. The latest technique comes from the Bioinspired Robotics and Design Lab at UCSD, where they’ve managed to get a robot to stick to smooth surfaces using a vibrating motor attached to a flexible disk. How the heck does it work?

According to a paper just published in Advanced Intelligent Systems, it’s due to “the fluid mediated adhesive force between an oscillatory plate and a surface” rather than black magic. Obviously.

Weird, right? In the paper, the researchers explain that what’s going on here: As the 14cm diameter flexible disk vibrates at 200 Hz, it generates a thin layer of low pressure air in between itself and the surface that it’s vibrating against. Although the layer of low pressure air is less than 1 mm thick, the disk can resist 5 N of force pulling on it. You can sort of think of this as a suction effect, except that it doesn’t require the disk to be constantly sealed against a surface, meaning that the robot can move around without breaking adhesion.

Image: UCSD

The big advantage here is that this is about as simple and cheap as a smooth-surface climbing robot gets, especially at small(ish) scales. There are a couple of downsides too, though. The biggest one could be that 200 Hz is a frequency that’s well within human hearing, which probably explains that soundtrack in the video—the robot is, as the researchers put it, “inherently quite noisy.” And in contrast to some other controllable adhesion techniques, this system must be turned on at all times or it will immediately plunge to its doom.

The robot you’re looking at in the video (with a 14cm disk) seems to be the sweet spot when it comes to size—going smaller means that the motor starts taking up a disproportionate amount of weight, while going larger would likely not scale well either, with the overall system mass increasing faster than the amount of adhesion that you get. The researchers suggest that “it could be advantageous to combine several disk geometries to achieve the desired load capacity and resilience to disturbances,” but that’s one of a number of things that the researchers need to figure out to properly characterize this novel adhesion technique.

Gas-Lubricated Vibration-Based Adhesion for Robotics, by William P. Weston-Dawkes, Iman Adibnazari, Yi-Wen Hu, Michael Everman, Nick Gravish, and Michael T. Tolley, is available here. Continue reading

Posted in Human Robots