AI & Robotics News and Discussions

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^^^Just noticed this same video in The Verge. Not all that impressive in comparison to robots of other companies. I have seen videos of such robots doing summersaults. Plus...the...Tesla...robot...moved...so...slow...across...the...stage.

Still, in the interest of seeing a competition in the construction of humanoid robots, I guess this is a positive development. Especially when you consider that Musk is also a key executive in SpaceX. One can imagine such a robot doing a spacewalk, or walking upon the surface of the Moon, Mars, etc.

[raklian]

^^^Just noticed this same video in The Verge. Not all that impressive in comparison to robots of other companies. I have seen videos of such robots doing summersaults. Plus...the...Tesla...robot...moved...so...slow...across...the...stage.

Musk did point out they're going to mass produce them at a cost per unit that is far, far cheaper than any robot out there. That is one of the biggest takeaways from the event.

[caltrek]

^^^Just noticed this same video in The Verge. Not all that impressive in comparison to robots of other companies. I have seen videos of such robots doing summersaults. Plus...the...Tesla...robot...moved...so...slow...across...the...stage.

Musk did point out they're going to mass produce them at a cost per unit that is far, far cheaper than any robot out there. That is one of the biggest takeaways from the event.

Ok, I missed that comment.

[caltrek]

Gecko Robotics to Establish International Headquarters in UAE
by Babu Das Augustine
October 2, 2022

Introduction:

(The National) Gecko Robotics, a US company, announced on Sunday that it will set up a new international headquarters in the UAE under the Ministry of Economy’s NextGenFDI programme.

The company has developed robots capable of inspecting oil and gas, and power infrastructure.

As part of its investment in the country, the Pittsburgh-based company is planning to establish a manufacturing plant in Khalifa Industrial Zone Abu Dhabi that will create around 300 jobs over the next few years.

“Once again, we are seeing world-leading technology make its home in the UAE. Our industrial base is seeking to integrate the tools of the Fourth Industrial revolution to improve efficiencies and boost productivity, and Gecko Robotics will be a key partner on that journey,” said Dr Thani Al Zeyoudi, UAE Minister of State for Foreign Trade.

Gecko’s wall-climbing robots use specially designed sensor payloads to detect otherwise invisible damage, allowing for precision repairs and predictive maintenance. Many companies and government agencies use Gecko’s software to extend the lifespan and efficiency of critical infrastructure, including power plants, oil refineries, manufacturing facilities, and other assets.

Read more here: https://www.thenationalnews.com/busine ... -the-uae/

[raklian]

[caltrek]

New Algorithms Help Four-legged Robots Run in the Wild
October 4, 2022

Introduction:

(EurekAlert) A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles.

In tests, the system guided a robot to maneuver autonomously and swiftly across sandy surfaces, gravel, grass, and bumpy dirt hills covered with branches and fallen leaves without bumping into poles, trees, shrubs, boulders, benches or people. The robot also navigated a busy office space without bumping into boxes, desks or chairs.

The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.

The team will present its work at the 2022 International Conference on Intelligent Robots and Systems (IROS), which will take place from Oct. 23 to 27 in Kyoto, Japan.

The system provides a legged robot more versatility because of the way it combines the robot’s sense of sight with another sensing modality called proprioception, which involves the robot’s sense of movement, direction, speed, location and touch—in this case, the feel of the ground beneath its feet.

Read more here: https://www.eurekalert.org/news-releases/966200

[weatheriscool]

Dictador hires "world's first" AI robot CEO in a global company
Source: FoodBev Media, Ltd

Colombian premium rum brand, Dictador, has announced the hiring of the “first world-ever” artificial intelligence (AI) robot as a CEO of a global company.

The new CEO is a female robot called Mika with human-like attributes, incorporating AI. The brand says the move underlines its passion for new technology and offers “positive disruption” in the sector.

Mika is said to be more advanced than its sister prototype, Sophia, developed in 2015 by Hanson Robotics in Hong Kong. Mika will become a board member of Dictador and will be responsible for its Arthouse Spirits DAO project and communications.

President of Dictador Europe, Marek Szoldrowski, said: “Dictator’s board decision is revolutionary and bold at the same time. This first human-like robot, with AI, in a company structure, will change the world as we know it, forever.”

Read more: https://www.foodbev.com/news/dictador-h ... l-company/

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Self-Teaching AI Uses Pathology Images to Find Similar Cases and Diagnose Rare Diseases
October 10, 2022

Introduction:

(EurekAlert) Rare diseases are often difficult to diagnose and predicting the best course of treatment can be challenging for clinicians. Investigators from the Mahmood Lab at Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, have developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories. Known as SISH (Self-Supervised Image search for Histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies. A paper introducing the self-teaching algorithm is published in Nature Biomedical Engineering: https://www.nature.com/articles/s41551-022-00929-8

“We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training,” said senior author Faisal Mahmood, PhD, in the Brigham’s Department of Pathology. “This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification.”

Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However, the gigapixel size of each individual WSI and the ever-increasing number of images in large repositories, means that search and retrieval of WSIs can be slow and complicated. As a result, scalability remains a pertinent roadblock for efficient use.

To solve this issue, researchers at the Brigham developed SISH, which teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database.

In their study, the researchers tested the speed and ability of SISH to retrieve interpretable disease subtype information for common and rare cancers.

Read more here: https://www.eurekalert.org/news-releases/967241

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AI predicts physics of future fault slip in laboratory earthquakes
https://phys.org/news/2022-10-ai-physic ... atory.html
by Los Alamos National Laboratory

An artificial intelligence approach borrowed from natural language processing—much like language translation and autofill for text on your smart phone—can predict future fault friction and the next failure time with high resolution in laboratory earthquakes. The technique, applying AI to the fault's acoustic signals, advances previous work and goes beyond by predicting aspects of the future state of the fault's physical system.

"Simply put, we predict future friction. That's never been done, and it provides a potential path to near-term forecasting of earthquake timing in Earth," said Chris Johnson, co-lead author of a paper on the findings in Geophysical Research Letters.

Paul Johnson, corresponding author of the paper, geophysicist and Laboratory fellow at Los Alamos National Laboratory, leads a team that has made steady advances in applying various machine learning techniques to the challenge of forecasting earthquakes in the laboratory and in the field.

"The acoustic signals emitted by the laboratory fault contain foreshadowing information about the future fundamental physics of the system through the entire earthquake cycle and beyond, as we now show," Paul Johnson said. "That's never been seen before."

In a novel approach, the Los Alamos team applied a deep-learning transformer model to acoustic emissions broadcast from the laboratory fault to predict the frictional state.

"The deep-learning transformer model we used is synonymous with a language translation model, such as Google Translate, using a codebook to translate a sentence to a different language," said Chris Johnson. "You can think about this as writing an email in English and having the AI translate the English to Japanese while also anticipating your words and autofilling the end of the sentence."

[Yuli Ban]

[Yuli Ban]

[caltrek]

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/



This is a 30-second exposure shot of Earth captured by an astronaut aboard the International Space Station.
ESA/NASA

[weatheriscool]

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

[raklian]

[Yuli Ban]

[caltrek]

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