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#437992 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
This Chinese Lab Is Aiming for Big AI Breakthroughs
Will Knight | Wired
“China produces as many artificial intelligence researchers as the US, but it lags in key fields like machine learning. The government hopes to make up ground. …It set AI researchers the goal of making ‘fundamental breakthroughs by 2025’ and called for the country to be ‘the world’s primary innovation center by 2030.’ BAAI opened a year later, in Zhongguancun, a neighborhood of Beijing designed to replicate US innovation hubs such as Boston and Silicon Valley.”

ENVIRONMENT
What Elon Musk’s $100 Million Carbon Capture Prize Could Mean
James Temple | MIT Technology Review
“[Elon Musk] announced on Twitter that he plans to give away $100 million of [his $180 billion net worth] as a prize for the ‘best carbon capture technology.’ …Another $100 million could certainly help whatever venture, or ventures, clinch Musk’s prize. But it’s a tiny fraction of his wealth and will also only go so far. …Money aside, however, one thing Musk has a particular knack for is generating attention. And this is a space in need of it.”

HEALTH
Synthetic Cornea Helped a Legally Blind Man Regain His Sight
Steve Dent | Engadget
“While the implant doesn’t contain any electronics, it could help more people than any robotic eye. ‘After years of hard work, seeing a colleague implant the CorNeat KPro with ease and witnessing a fellow human being regain his sight the following day was electrifying and emotionally moving, there were a lot of tears in the room,’ said CorNeat Vision co-founder Dr. Gilad Litvin.”

BIOTECH
MIT Develops Method for Lab-Grown Plants That May Eventually Lead to Alternatives to Forestry and Farming
Darrell Etherington | TechCrunch
“If the work of these researchers can eventually be used to create a way to produce lab-grown wood for use in construction and fabrication in a way that’s scalable and efficient, then there’s tremendous potential in terms of reducing the impact on forestry globally. Eventually, the team even theorizes you could coax the growth of plant-based materials into specific target shapes, so you could also do some of the manufacturing in the lab, by growing a wood table directly for instance.”

AUTOMATION
FAA Approves First Fully Automated Commercial Drone Flights
Andy Pasztor and Katy Stech Ferek | The Wall Street Journal
“US aviation regulators have approved the first fully automated commercial drone flights, granting a small Massachusetts-based company permission to operate drones without hands-on piloting or direct observation by human controllers or observers. …The company’s Scout drones operate under predetermined flight programs and use acoustic technology to detect and avoid drones, birds, and other obstacles.”

SPACE
China’s Surging Private Space Industry Is Out to Challenge the US
Neel V. Patel | MIT Technology Review
“[The Ceres-1] was a commercial rocket—only the second from a Chinese company ever to go into space. And the launch happened less than three years after the company was founded. The achievement is a milestone for China’s fledgling—but rapidly growing—private space industry, an increasingly critical part of the country’s quest to dethrone the US as the world’s preeminent space power.”

CRYPTOCURRENCY
Janet Yellen Will Consider Limiting Use of Cryptocurrency
Timothy B. Lee | Ars Technica
“Cryptocurrencies could come under renewed regulatory scrutiny over the next four years if Janet Yellen, Joe Biden’s pick to lead the Treasury Department, gets her way. During Yellen’s Tuesday confirmation hearing before the Senate Finance Committee, Sen. Maggie Hassan (D-N.H.) asked Yellen about the use of cryptocurrency by terrorists and other criminals. ‘Cryptocurrencies are a particular concern,’ Yellen responded. ‘I think many are used—at least in a transactions sense—mainly for illicit financing.’i”

SCIENCE
Secret Ingredient Found to Power Supernovas
Thomas Lewton | Quanta
“…Only in the last few years, with the growth of supercomputers, have theorists had enough computing power to model massive stars with the complexity needed to achieve explosions. …These new simulations are giving researchers a better understanding of exactly how supernovas have shaped the universe we see today.”

Image Credit: Ricardo Gomez Angel / Unsplash Continue reading

Posted in Human Robots

#437990 Video Friday: Record-Breaking Drone Show ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.

A new parent STAR robot is presented. The parent robot has a tail on which the child robot can climb. By collaborating together, the two robots can reach locations that neither can reach on its own.

The parent robot can also supply the child robot with energy by recharging its batteries. The parent STAR can dispatch and recuperate the child STAR automatically (when aligned). The robots are fitted with sensors and controllers and have automatic capabilities but make no decisions on their own.

[ Bio-Inspired and Medical Robotics Lab ]

How TRI trains its robots.

[ TRI ]

The only thing more satisfying than one SCARA robot is two SCARA robots working together.

[ Fanuc ]

I'm not sure that this is strictly robotics, but it's so cool that it's worth a watch anyway.

[ Shinoda & Makino Lab ]

Flying insects heavily rely on optical flow for visual navigation and flight control. Roboticists have endowed small flying robots with optical flow control as well, since it requires just a tiny vision sensor. However, when using optical flow, the robots run into two problems that insects appear to have overcome. Firstly, since optical flow only provides mixed information on distances and velocities, using it for control leads to oscillations when getting closer to obstacles. Secondly, since optical flow provides very little information on obstacles in the direction of motion, it is hardest to detect obstacles that the robot is actually going to collide with! We propose a solution to these problems by means of a learning process.

[ Nature ]

A new Guinness World Record was set on Friday in north China for the longest animation performed by 600 unmanned aerial vehicles (UAVs).

[ Xinhua ]

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering.

[ University of Michigan ] via [ Fetch Robotics ]

This paper reports the technological progress and performance of team “CERBERUS” after participating in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge.

And here's a video report on the SubT Urban Beta Course performance:

[ CERBERUS ]

Congrats to Energy Robotics on 2 million euros in seed funding!

[ Energy Robotics ]

Thanks Stefan!

In just 2 minutes, watch HEBI robotics spending 23 minutes assembling a robot arm.

HEBI Robotics is hosting a webinar called 'Redefining the Robotic Arm' next week, which you can check out at the link below.

[ HEBI Robotics ]

Thanks Hardik!

Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialised by a distinct set of pre-trained experts, each in a separate deep neural network (DNN). Then by learning the combination of these DNNs using a Gating Neural Network (GNN), MELA can acquire more specialised experts and transitional skills across various locomotion modes.

[ Paper ]

Since the dawn of history, advances in science and technology have pursued “power” and “accuracy.” Initially, “hardness” in machines and materials was sought for reliable operations. In our area of Science of Soft Robots, we have combined emerging academic fields aimed at “softness” to increase the exposure and collaboration of researchers in different fields.

[ Science of Soft Robots ]

A team from the Laboratory of Robotics and IoT for Smart Precision Agriculture and Forestry at INESC TEC – Technology and Science are creating a ROS stack solution using Husky UGV for precision field crop agriculture.

[ Clearpath Robotics ]

Associate Professor Christopher J. Hasson in the Department of Physical Therapy is the director Neuromotor Systems Laboratory at Northeastern University. There he is working with a robotic arm to provide enhanced assistance to physical therapy patients, while maintaining the intimate therapist and patient relationship.

[ Northeastern ]

Mobile Robotic telePresence (MRP) systems aim to support enhanced collaboration between remote and local members of a given setting. But MRP systems also put the remote user in positions where they frequently rely on the help of local partners. Getting or ‘recruiting’ such help can be done with various verbal and embodied actions ranging in explicitness. In this paper, we look at how such recruitment occurs in video data drawn from an experiment where pairs of participants (one local, one remote) performed a timed searching task.

[ Microsoft Research ]

A presentation [from Team COSTAR] for the American Geophysical Union annual fall meeting on the application of robotic multi-sensor 3D Mapping for scientific exploration of caves. Lidar-based 3D maps are combined with visual/thermal/spectral/gas sensors to provide rich 3D context for scientific measurements map.

[ COSTAR ] Continue reading

Posted in Human Robots

#437978 How Mirroring the Architecture of the ...

While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.

The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.

Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.

It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.

There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.

This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.

“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”

Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).

“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.

The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.

Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.

First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.

They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.

While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.

“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.

As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.

Image Credit: Gerd Altmann from Pixabay Continue reading

Posted in Human Robots

#437971 Video Friday: Teleport Yourself Into ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.

Samsung announced some new prototype robots at CES this week. It's a fancy video, but my guess is that the actual autonomy here is minimal at best.

[ Samsung ]

Some very impressive reactive agility from Ghost Robotics' little quadruped.

[ Ghost Robotics ]

Toyota Research Institute (TRI) is researching how to bring together the instinctive reflexes of professional drivers and automated driving technology that uses the calculated foresight of a supercomputer. Using a Toyota GR Supra, TRI will learn from some of the most skilled drivers in the world to develop sophisticated vehicle control algorithms. The project’s goal is to design a new level of active safety technology for the Toyota Guardian™ approach of amplifying human driving abilities and helping keep people safe.

[ TRI ]

The end of this video features one of the most satisfying-sounding drone outtakes I've ever heard,

[ ASL ]

Reachy can now run the first humanoid VR teleoperation app available on the market. This app allows you to place yourself in the body of a humanoid robot, in VR, wherever you are in the world, to remotely operate it and carry out complex tasks. With this new functionality, Reachy is able to learn from the demonstration of the humans who control it, which makes application development even easier.

[ Pollen Robotics ]

Thanks Elsa!

Boston Dynamics has inspired some dancing robot videos recently, including this from Marco Tempest.

[ Marco Tempest ]

MOFLIN is an AI Pet created from a totally new concept. It possesses emotional capabilities that evolve like living animals. With its warm soft fur, cute sounds, and adorable movement, you’d want to love it forever. We took a nature inspired approach and developed a unique algorithm that allows MOFLIN to learn and grow by constantly using its interactions to determine patterns and evaluate its surroundings from its sensors. MOFLIN will choose from an infinite number of mobile and sound pattern combinations to respond and express its feelings. To put it in simple terms, it’s like you’re interacting with a living pet.

I like the minimalist approach. I dislike the “it’s like you’re interacting with a living pet” bit.

[ Kickstarter ]

There's a short gif of these warehouse robots going around, but here's the full video.

[ BionicHIVE ]

Vstone's Robovie-Z proves that you don't need fancy hardware for effective teleworking.

[ Vstone ]

All dual-arm robots are required, at some point, to play pool.

[ ABB ]

Volkswagen Group Components gives us a first glimpse of the real prototypes. This is one of the visionary charging concepts that Volkswagen hopes will expand the charging infrastructure over the next few years. Its task: fully autonomous charging of vehicles in restricted parking areas, like underground car parks.

To charge several vehicles at the same time, the mobile robot moves a trailer, essentially a mobile energy storage unit, to the vehicle, connects it up and then uses this energy storage unit to charge the battery of the electric vehicle. The energy storage unit stays with the vehicle during the charging process. In the meantime, the robot charges other electric vehicles.

[ Volkswagen ]

I've got a lot of questions about Moley Robotics' kitchen. But I would immediately point out that the system appears to do no prep work, which (at least for me) is the time-consuming and stressful part of cooking.

[ Moley Robotics ]

Blueswarm is a collective of fish-inspired miniature underwater robots that can achieve a wide variety of 3D collective behaviors – synchrony, aggregation/dispersion, milling, search – using only implicit communication mediated through the production and sensing of blue light. We envision this platform for investigating collective AI, underwater coordination, and fish-inspired locomotion and sensing.

[ Science Robotics ]

A team of Malaysian researchers are transforming pineapple leaves into strong materials that can be used to build frames for unmanned aircraft or drones.

[ Reuters ]

The future of facility disinfecting is here, protect your customers, and create peace of mind. Our drone sanitization spraying technology is up to 100% more efficient and effective than conventional manual spray sterilization processes.

[ Draganfly ]

Robots are no long a future technology, as small robots can be purchased today to be utilized for educational purposes. See what goes into making a modern robot come to life.

[ Huggbees ]

How does a robot dog learn how to dance? Adam and the Tested team examine and dive into Boston Dynamics' Choreographer software that was behind Spot's recent viral dancing video.

[ Tested ]

For years, engineers have had to deal with “the tyranny of the fairing,” that anything you want to send into space has to fit into the protective nosecone on top of the rocket. A field of advanced design has been looking for new ways to improve our engineering, using the centuries-old artform to dream bigger.

[ JPL ] Continue reading

Posted in Human Robots

#437964 How Explainable Artificial Intelligence ...

The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.

However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.

Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.

This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.

Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.

Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”

AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.

The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.

The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.

My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.

Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.

Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.

A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND

Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.

Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.

People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Serg Antonov / Unsplash Continue reading

Posted in Human Robots