AI & Robotics News and Discussions

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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Turtle Robot Can Travel on Land and Water
October 13, 2022

Introduction:
(Futurity) Researchers have created a robot that can morph its legs into flippers through a process they dubbed “adaptive morphogenesis.”

The robot, ART (Amphibious Robotic Turtle), takes inspiration from water and land turtles, a group whose fossil record spans over 110 million years.

A paper on the project appears in Nature: https://www.nature.com/articles/s41586-022-05188-w
Read more of the Futurity article here: https://www.futurity.org/turtle-robot-2813932/
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Spotting Frankensteins: Why Humans Beat AI at Shape Detection
by Sara Goudarzi
October 11, 2022

Introduction:
(Bulletin of Atomic Scientists) In 2020, a Tesla vehicle on autopilot mode crashed into an overturned truck on a busy highway in Taiwan. The crash was just one example of several well-documented artificial intelligence (AI) system failures in autonomous vehicles. But still, one wonders how the vehicle’s autopilot system missed such a large object on the road. It turns out that the answer isn’t much of a mystery to those in the field.

Computer vision systems can only recognize objects they have been trained on. The Tesla AI system is likely to have been trained to identify a truck but only if the truck is upright. The semi-trailer lying on its side was in an unfamiliar orientation and did not match the trained experience of the network. Once the driver of the vehicle realized what was happening, they put on the brakes, but it was too late to avoid the crash.

To a human driver, it didn’t matter how the truck was orientated, because we are capable of recognizing objects by relying on configural relations between local features. But deep convolutional neural networks—a popular form of artificial intelligence for “seeing,” that uses the patterns found in images and videos to recognize objects—are incapable of seeing like us, which can be dangerous in some applications.

To better understand why deep AI models fail at configural shape perception, the Bulletin’s Sara Goudarzi spoke with James H. Elder, professor and research chair in Human and Computer Vision at York University, who co-authored a recent study detailed in iScience which found that deep convolutional neural networks are not sensitive to objects taken apart and put back together wrong. Such understanding could help avoid accidents like the one in Taiwan.
Read more here: https://thebulletin.org/2022/10/spotti ... t-heading
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New Walking Robot May Be the Future of Space Construction
by Alanna Madden
October 13, 2022

Introduction:
(Courthouse News) — Certain tasks are necessary but dangerous, such as fixing as a wind turbine, washing a skyscraper window or even constructing the skyscraper itself. Yet the field of robotics is moving in the direction of developing technology that can alleviate dangerous jobs for humans both on Earth and in outer space.

According to a new study from Frontiers in Robotics and AI, researchers have designed a new type of robot to revolutionize large construction projects in space: The E-Walker.

“The research is about the future of space which would involve a lot of in-orbit activities,” lead researcher Manu Nair, a doctoral candidate at the University of Lincoln in England, said. Such activities range from assembly missions to manufacturing, which are quite difficult and dangerous for astronauts to do in the extreme conditions of space.

But while several types of advanced robots already exist, Nair’s research suggests the E-Walker is unique. Instead of handling limited tasks, the robot has the potential to walk, learn different modules and assemble complex equipment with seven degrees of motion. Additionally, the robot has a larger payload capacity, making it an ideal candidate for assembling large-scale projects in-orbit.

Nair’s team is currently focusing on having the robot assemble a 25-meter large aperture telescope — larger than NASA’s James Webb Space Telescope — so that researchers can use it for both for astronomical purposes and to observe Earth to detect seismic vibrations, weather systems or even climate change. But before Nair’s team can test the E-Walker’s functionality in space, they still need to test its design at the University of Lincoln.
Read more here: https://www.courthousenews.com/new-wal ... truction/


Image
This is a 30-second exposure shot of Earth captured by an astronaut aboard the International Space Station.
ESA/NASA
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A new AI model can accurately predict human response to novel drug compounds
https://phys.org/news/2022-10-ai-accura ... -drug.html
by Graduate Center, CUNY
The journey between identifying a potential therapeutic compound and Food and Drug Administration approval of a new drug can take well over a decade and cost upward of a billion dollars. A research team at the CUNY Graduate Center has created an artificial intelligence model that could significantly improve the accuracy and reduce the time and cost of the drug development process.

Described in a newly published paper in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions. Researchers expect the technique to significantly accelerate drug discovery and precision medicine.

Accurate and robust prediction of patient-specific responses to a new chemical compound is critical to discover safe and effective therapeutics and select an existing drug for a specific patient. However, it is unethical and infeasible to do early efficacy testing of a drug in humans directly. Cell or tissue models are often used as a surrogate of the human body to evaluate the therapeutic effect of a drug molecule. Unfortunately, the drug effect in a disease model often does not correlate with the drug efficacy and toxicity in human patients. This knowledge gap is a major factor in the high costs and low productivity rates of drug discovery.

"Our new machine learning model can address the translational challenge from disease models to humans," said Lei Xie, a professor of computer science, biology and biochemistry at the CUNY Graduate Center and Hunter College and the paper's senior author. "CODE-AE uses biology-inspired design and takes advantage of several recent advances in machine learning. For example, one of its components uses similar techniques in Deepfake image generation."
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Jasper’s Robots Assemble Fresh Meals for Nearby Apartment Dwellers
by Kyle Wiggers
October 21, 2022

Introduction:
(TechCrunch) After attempting to sell its tech to large food service companies, cooking automation startup Jasper has shifted to direct-to-consumer. In a recent conversation, CEO Gunnar Froh told TechCrunch about the pivot and gave a general update on the company, a member of this year’s Battlefield 200 at Disrupt 2022.

When Gunnar founded Jasper several years ago (as YPC Technologies) with human-robot interaction expert Camilo Perez Quintero, their motivation was primarily to save time on cooking. After developing robotics technologies to automate cooking processes, they opted for a business-to-business go-to-market approach, hoping to sell their platform to food suppliers and service vendors. But the company never gained the corporate traction Gunnar and Quintero hoped it would.

The company pivoted a few months ago, rebranding to Jasper and adopting what Gunnar calls a “cooking as a service” model. Jasper now runs robotic kitchens in or next to residential high-rises, charging residents a subscription fee plus the cost of ingredients for meals.

“Having good meals at home is expensive or time consuming. Food delivery is highly inefficient — restaurants or ghost kitchens prepare meals worth a few dollars and then pay someone to ship them across town. While most customers aren’t aware of this, about half of their dollars are spent on platform fees and delivery costs,” Gunnar told TechCrunch. “By running robotic kitchens in or next to residential high-rises, Jasper eliminates labor and delivery inefficiencies to offer residents freshly prepared gourmet meals at the cost of home cooking. Jasper meals are plated on porcelain, which allows its clients to cut up to a third of their household waste.”

Food automation startups are having a moment, as recently evidenced by Chipotle’s investment in Miso Robotics’ tortilla chip–making robot. It’s no surprise — labor shortages and increasingly costly ingredients make food-prepping robots an attractive proposition. In 2020, Karakuri landed $8.4 million for its automated canteen to make meals. Last May, Chef Robotics raised $7.7 million with the goal of helping automate certain aspects of food preparation. A few months later, salad chain Sweetgreen bought kitchen robotics startup Spyce, and this past summer Makeline secured $24 million for its robot that automatically assembles bowl lunches.
Read more here: https://techcrunch.com/2022/10/21/jasp ... dwellers/
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AlphaFold’s New Rival? Meta AI Predicts Shape of 600 Million Proteins
by Ewen Callaway
November 1, 2022

Introduction:
(Nature) When London-based Deep Mind unveiled predicted structures for some 220 million proteins this year, it covered nearly every protein from known organisms in DNA databases. Now, another tech giant is filling in the dark matter of our protein universe.

Researchers at Meta (formerly Facebook, headquartered in Menlo Park, California) have used artificial intelligence (AI) to predict the structures of some 600 million proteins from bacteria, viruses and other microbes that haven’t been characterized.

“These are the structures we know the least about. These are incredibly mysterious proteins. I think they offer the potential for great insight into biology,” says Alexander Rives, the research lead for Meta AI’s protein team.

The team generated the predictions — described in a 1 November preprint1 — using a ‘large language model’, a type of AI that are the basis for tools that can predict text from just a few letters or words.

Normally language models are trained on large volumes of text. To apply them to proteins, Rives and his colleagues fed them sequences to known proteins, which can be expressed by a chains of 20 different amino acids, each represented by a letter. The network then learned to ‘autocomplete’ proteins with a proportion of amino acids obscured.
Read more here: https://www.nature.com/articles/d41586-022-03539-1
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To know is essentially the same as not knowing. The only thing that occurs is the rearrangement of atoms in your brain.
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Soft Skills: Researchers Invent Robotic Droplet Manipulators for Hazardous Liquid Cleanup
November 18, 2022

Introduction:
(EurekAlert) CSU researchers have created the first successful soft robotic gripper capable of manipulating individual droplets of liquid, according to a recent article in the Royal Society of Chemistry journal Materials Horizons.

The breakthrough is the product of a collaboration between two different laboratories in CSU’s Department of Mechanical Engineering. It was accomplished by combining two applied technologies, soft robotics and super-omniphobic coatings.

The soft robotic manipulator is made of inexpensive materials like nylon fibers and adhesive tape. It’s powered by an electrically activated artificial muscle. The combination can be used to produce lightweight, inexpensive grippers capable of delicate work, yet 100x stronger than human muscle for the same weight.

The result is something that flies in the face of our cultural concept of what a robot is, and what it can do.

Conventional robots are made of components that are heavy, rigid, and expensive. That makes them poorly suited for some tasks.
Read more here: https://www.eurekalert.org/news-releases/971877
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