“The idea for EVA took shape a few years ago when my students and I began to notice that the robots in our lab were staring back at us through plastic, googly eyes.”

While this sounds simple, creating a convincing robotic face has been a formidable challenge for roboticists. For decades, robotic body parts have been made of metal or hard plastic, materials that were too stiff to flow and move the way human tissue does. Robotic hardware has been similarly crude and difficult to work with—circuits, sensors, and motors are heavy, power-intensive, and bulky.

The first phase of the project began in Lipson’s lab several years ago when undergraduate student Zanwar Faraj led a team of students in building the robot’s physical “machinery.” They constructed EVA as a disembodied bust that bears a strong resemblance to the silent but facially animated performers of the Blue Man Group. EVA can express the six basic emotions of anger, disgust, fear, joy, sadness, and surprise, as well as an array of more nuanced emotions, by using artificial “muscles” that pull on specific points on EVA’s face, mimicking the movements of the more than 42 tiny muscles attached at various points to the skin and bones of human faces.

To overcome this challenge, the team relied heavily on 3D printing to manufacture parts with complex shapes that integrated seamlessly and efficiently with EVA’s skull. After weeks of tugging cables to make EVA smile, frown, or look upset, the team noticed that EVA’s blue, disembodied face could elicit emotional responses from their lab mates.

While lifelike animatronic robots have been in use at theme parks and in movie studios for years, Lipson’s team made two technological advances. EVA uses deep learning artificial intelligence to “read” and then mirror the expressions on nearby human faces. And EVA’s ability to mimic a wide range of different human facial expressions is learned by trial and error from watching videos of itself.

The most difficult human activities to automate involve non-repetitive physical movements that take place in complicated social settings. Boyuan Chen, Lipson’s PhD student who led the software phase of the project, quickly realized that EVA’s facial movements were too complex a process to be governed by pre-defined sets of rules. To tackle this challenge, Chen and the second team of students created EVA’s brain using several Deep Learning neural networks. The robot’s brain needed to master two capabilities: First, to learn to use its own complex system of mechanical muscles to generate any particular facial expression, and, second, to know which faces to make by “reading” the faces of humans.

Chen and the team filmed hours of footage of EVA making a series of random faces. Then, like a human watching herself on Zoom, EVA’s internal neural networks learned to pair muscle motion with the video footage of its own face.

The researchers note that EVA is a laboratory experiment, and mimicry alone is still a far cry from the complex ways in which humans communicate using facial expressions. Such enabling technologies could someday have beneficial, real-world applications.

The military aims for this UAV to fuel collaboration among manned and unmanned aircraft. For its first test run, the Skyborg suite flew aboard a Kratos UTAP-22 Mako air vehicle in the first step of what’s known as the Autonomous Attritable Aircraft Experimentation Campaign.

By and large, the US Air Force Research Laboratory seeks a UAV solution that can carry out all of the functions of a manned aerial vehicle but also with the option of manned operation.

During its 130-minute test flight at Tyndall Air Force Base in Florida, the new aircraft exhibited fundamental behaviours needed to classify its system operation as safe. Indeed, Skyborg proved capable of staying within set “geo-fences,” responding to navigational commands and performing coordinated manoeuvres.

Overall, the Fighters and Advanced Aircraft program expects this Skyborg aircraft to represent the “brain” of all future Skyborg technology. Ultimately, the organisation hopes for this initial UAV to help build general confidence in unmanned aircraft, also known as the autonomy core system (ACS).

Going forward, the USAF Research Laboratory plans an upcoming series of tests involving multiple ACS-controlled unmanned aircraft. In fact, the Skyborg team intends full-mission autonomy with affordable and attractable UAVs, so that systems lost or damaged in combat can be feasibly replaced and reused.

The overarching mission of the Skyborg drone remains to develop an unmanned aircraft capable of making quick battle decisions at the rate of a computer while also ideally conserving the lives of human soldiers during combat.

The current updates are only available to about 100,000 owners of 2021 model year F-150s, Mustang Mach-Es and the upcoming Bronco, but Ford plans to spread the tech across its entire lineup as models are updated. It plans to make 33 million vehicles with the capability by 2028.

According to Alex Purdy, the company’s connectivity business director, updates can fix software glitches in the vehicles’ computers and will be able to make recall repairs, offer new features and cut warranty claims. In addition, there are about 80 different computers in Ford’s most sophisticated vehicles that control everything from the infotainment centre to brakes and transmission shifts.

Ford already has sent out an update to fix a lighting issue that drained batteries. One of the next updates will be a new version of Amazon Alexa that allows vehicles to communicate with the home assistant without the use of a smartphone. Ford also will roll out touchscreen drawing software and simple games for the Mach-E electric SUV.

Alex Purdy said software can identify potential problems in vehicles and fixes can be sent out if the software can solve the problem. “That fix can be done remotely, it doesn’t require you to go into a dealership,” he said.

Nowadays more than 6 million vehicles have the modems and collect vehicle data, but only those with the latest generation of electrical systems can get full over-the-internet updates. According to Ford, vehicles equipped for its new “Blue Cruise” hands-free driver-assist system will get the software needed to run it over the internet.

Arrival just became a publicly-traded company in March after merging with a special purpose acquisition company, or SPAC. Founded in 2015, Arrival is also developing electric delivery vans (with UPS as a customer) and buses. It also has backing from Hyundai and Kia.

Arrival and Uber released a handful of renderings of the new car’s interior and said a final design will be revealed by the end of the year. Between now and then, the companies plan to get some drivers involved in the design process, too.

From the images, we can see that there’s a large horizontal screen mounted to the dashboard, similar to what’s in Tesla’s Model 3 and Model Y (and the forthcoming Model S and Model X refresh), and the steering wheel is also quite similar to what’s found in a Tesla, with just two scroll wheels and no driver display.

Photo Credits: Reuters

However, there are a few subtle differences that Arrival says could enhance the ride-hailing experience for both drivers and passengers. The driver’s seat is ergonomically designed to ease the physical strain of sitting in a car for hours on end. The front passenger seat folds up to create more legroom. There’s a bench-style seat in the rear, which makes it easier to get in and out of the car. There are also small, lighted cubbies and handrails on the inside of each door.

According to Tom Elvidge, Arrival’s senior vice president of mobility, their goal is:

“to make hundreds of small improvements, changes, and tweaks to the design that perhaps haven’t been applied before.”

Electric vehicles tend to have fewer moving parts than internal combustion cars and therefore require less maintenance and upkeep. They’re also inherently compliant with the zero-emission zones that have been adopted in cities across Europe, as well as broader internal combustion bans that are on the horizon. Elvidge also added that their goal is to design an affordable vehicle from the outset.

Arrival has yet to prove that strategy can work. Like many of its SPAC peers, it is still in the developmental phase and has yet to actually sell any production vehicles. Uber has quietly poked at a number of electric vehicle startups over the last few years but never struck any deals, though it has recently cleaved a number of money-losing divisions, like the one focused on autonomous vehicles and the one dedicated to flying taxis.

Neuralink, Elon Musk’s brain implant company, has released a video showing how it has taught Pager, a nine-year-old macaque – to play the video game Pong with its mind.

The video follows Mr Musk’s assertion in February that the company has “a monkey with a wireless implant in their skull with tiny wires” that it uses to play video games.

Neuralink Corporation previously claimed to have implanted coin-sized wireless sensors inside the brains of two pigs, describing the sensors as “a Fitbit in your skull with tiny wires”.

At the time of the video, Pager was six weeks on from having had the devices installed – one in each side of his brain – and in that time has learned to interact with a computer for a “tasty banana smoothie” delivered through a straw. The implants work by recording signals in the monkey’s motor cortex that normally coordinates hand and arm movements.

“Our goal is to enable a person with paralysis to use a computer or phone with their brain activity alone.”

The billionaire told people on the call that an inspector from the US Department of Agriculture described the company’s laboratory as “the nicest monkey facility” they had ever examined.

“One of the things we’re trying to figure out is whether we can have the monkeys playing mind Pong with each other? That would be pretty cool.”

The tiny implant has more than 3,000 electrodes, attached to flexible threads measuring about a tenth of the size of a hair, capable of monitoring about 1,000 neurons. The aim is to create a brain-computer interface, and it is being trialled in order to treat people with brain and spinal injuries.

Neuralink unveils a pig with a computer chip in the brain. Photo Credit: Neuralink

Neuralink’s head surgeon, Dr Matthew MacDougall, said the first trials would focus on patients with paralysis or paraplegia. According to the company, the technology could contribute to finding a cure for neurological conditions such as Alzheimer’s, dementia and spinal cord injuries.

Some of the more conceptual ideas touted by Musk are not yet considered feasible by scientists, including a kind of “save state” in the brain.

Soft robots are a growing trend in the industry due to their versatility. Their soft parts can handle delicate objects and the soft bodies can help robots float or squeeze into tight spaces.

The soft robot is described online on March 25 in the following journal. The inspiration struck from the mind of Shyni Varghese, professor of biomedical engineering, mechanical engineering, materials science, and orthopaedic surgery at Duke. Vardhman Kumar, a PhD student in Varghese’s laboratory and first author of the paper commented:

“I got an email from Shyni from the airport saying she had an idea for a soft robot that uses a self-healing hydrogel that her group has invented in the past to react and move autonomously. But that was the extent of the email, and I didn’t hear from her again for days. So the idea sort of sat in limbo for a little while until I had enough free time to pursue it, and Shyni said to go for it.”

In 2012, Varghese created a self-healing hydrogel that reacts to changes in pH in a matter of seconds. Whether it be a crack in the hydrogel or two adjoining pieces “painted” with it, a change in acidity causes the hydrogel to form new bonds, which are completely reversible when the pH returns to its original levels.

Varghese’s idea was to find a way to use this hydrogel on a soft robot that could travel across water and indicate places where the pH changes. With the help of Ung Hyun Ko, a postdoctoral fellow, Kumar began designing a soft robot based on a fly. After several iterations, the shape of a dragonfly engineered with a network of interior microchannels was settled. This shape allows the soft robot to be controlled with air pressure.

The body of the robot was created to be about 2.25 inches long with a 1.4-inch wingspan—by pouring silicone into an aluminium mould and baking it. The team used soft lithography to create interior channels.

That’s how DraBot was born.

DraBot works by controlling the air pressure coming into its wings. Microchannels carry the air into the front wings, where it escapes through a series of holes pointed directly into the back wings. If both back wings are down DraBot goes nowhere but if both wings are up, it goes forward.

The team also designed balloon actuators under each of the back wings close to the robot’s body so that they can control it. When inflated, the balloons cause the wings to curl upward. By changing which wings are up or down, the researchers control the robot movements.

“We were happy when we were able to control DraBot, but it’s based on living things. And living things don’t just move around on their own, they react to their environment.”

Here comes the self-healing hydrogel. By painting one set of wings with the hydrogel, the researchers were able to make DraBot responsive to changes in the surrounding water’s pH. If the water becomes acidic, the front wing fuses with the back wing. Instead of travelling in a straight line, the imbalance causes the robot to spin in a circle. Once the pH returns to a normal level, the hydrogel “un-heals,” the fused wings separate, and DraBot once again becomes fully responsive to commands.

The researchers also created sponges under the wings and doped the wings with temperature-responsive materials. When the robot skims over the water with oil floating, the sponges will soak it up and change colour. And when the water becomes overly warm, DraBot’s wings change from red to yellow.

The team believe these types of measurements could play an important part in an environmental robotic sensor in the future. The team also sees many ways that they could improve their work in the future.

Reza Oftadeh, a doctoral student in the Department of Computer Science and Engineering at Texas A&M University developed an algorithm applicable to large datasets. This useful machine-learning tool can extract and directly order features from most salient to least. You can find the published paper here.

The abstract of R. Oftadeh’s Paper

“There are many ad hoc ways to extract these features using machine-learning algorithms, but we now have a fully rigorous theoretical proof that our model can find and extract these prominent features from the data simultaneously, doing so in one pass of the algorithm, ” commented Reza.

A subfield of machine learning deals with the problem of identifying a raw dataset’s features to help reduce its dimensionality. Once identified, the features are used to make annotated samples of the data for further analysis. Analyzing massive datasets is a very complicated, time-consuming process for programmers, so in recent years artificial neural networks (ANNs) have come to help.

ANNs are computational models that are designed to simulate how the human brain analyzes and processes information. They are typically made of dozens to millions of artificial neurons, called units. ANNs can be used in various ways, but they are most commonly used to identify the unique features that best represent the data and classify them into different categories based on that information. Oftadeh added:

“There are many ANNs that work very well, and we use them every day on our phones and computers. For example, applications like Alexa, Siri and Google Translate utilize ANNs that are trained to recognize what different speech patterns, accents and voices are saying.”

However, not all features are equally significant, and they can be classified. Previous approaches use a type of ANN called an autoencoder to extract them, but they cannot tell exactly where the features are located or which are more important.

To make a more intelligent algorithm, the researchers propose adding a new cost function to the network that provides the exact location of the features directly ordered by their relative importance. Once incorporated, their method results in more efficient processing that can be fed bigger datasets to perform classic data analysis.

To verify the effectiveness of their method, they trained their model for optical character recognition (OCR) experiment, which is the conversion of images of typed or handwritten text into the machine-encoded text from inside digital-physical documents, like a scanner produces. Once it’s trained for OCR using the proposed method, the model can tell which features are most important.

Currently, the algorithm can only be applied to one-dimensional data samples, but the team is interested in extending their algorithm’s abilities. The next step of their work is to generalize their method in a way that provides a unified framework to produce other machine-learning methods that can find the underlying structure of a dataset and extract its features by setting a small number of specifications.

Тhe first biological neuron was the “Halle Berry” one that proved its capability to recognize photographs and sketches of the actress and connecting those images with the name “Halle Berry.”

Now, OpenAI‘s multimodal vision system continues to outperform existing systems, namely with traits such as the “Spider-Man” neuron, an artificial neuron which can identify not only the image of the text “spider” but also the comic book character in both illustrated and live-action form.

This ability to recognize a single concept represented in various contexts demonstrates CLIP’s abstraction capabilities. The capacity for abstraction allows a vision system to tie a series of images and text to a central theme. However, a difference between biological and artificial neurons lies in semantics versus visual stimuli. Whereas neurons in the brain connect a cluster of visual input to a single concept, AI neurons respond to a cluster of ideas.

Photo Credits: Open AI

Research teams examine CLIP along two lines: 1) Feature visualization and 2) dataset examples. The teams have discovered that CLIP neurons seem to be immensely multi-faceted, meaning that they respond to many unique concepts at a high level of abstraction.

As a recognition system, CLIP also exhibits various forms of bias. For instance, the system’s “Middle East” neuron has been associated with terrorism, alongside an “immigration” neuron that responds to input involving Latin America.

In terms of limitations to these findings, scientists acknowledge that, despite CLIP’s ability to locate geographical regions, individual cities and landmarks, the system does not appear to exhibit a “San Francisco” neuron that ties a landmark such as Twin Peaks to the identifier San Francisco.

Mr Partovi joins Philips from Amazon Web Services (AWS), where he served as Worldwide Head of Business Development for Healthcare, Life Sciences and Medical Devices. In that role, he was responsible for the global AWS go-to-market strategy, charting the path for customer cloud transformation, and the adoption of AI and machine learning.

Prior to joining AWS, Mr Partovi spent 20 years at Dignity Health, the fifth-largest health system in the U.S. He started his career in 1998 as a neuroradiologist at the Barrow Neurological Institute and was in clinical practice until 2013. In addition to his medical training at McGill University in Montreal, he has post-graduate qualifications in computer science. Mr Partovi has a passion for learning and teaching. He helped launch the Biomedical Informatics Department at Arizona State University and taught there as a clinical professor for three years.

Mr Tas, who joined Philips in 2011 as Chief Information Officer and became Chief Innovation & Strategy Officer. He has made a personal decision to assume a part-time position in Philips focused on continuation of the company’s strategic business development until the end of 2022.

The CEO of Royal Philips, Mr Frans van Houten said:

“On behalf of Philips’ Executive Committee, I am very pleased to welcome Shez Partovi as our new Chief Innovation & Strategy Officer. Shez brings deep healthcare and informatics experience to Philips, which will be highly relevant as we accelerate the transition to becoming a solutions leader, leveraging deep clinical insights and big data analytics.”

Philips also today announced that Rob Cascella, currently Strategic Business Development Leader and formerly in charge of Philips’ Diagnosis & Treatment segment, will step down from the Executive Committee as planned. Mr Cascella has agreed to act as a special advisor to Philips’ strategic business development projects on a part-time basis.

The event will air live on NASA Television and the agency’s website and will live stream on the agency’s Twitter, Facebook,  YouTube, as well as the NASA app.

Jackson, the first African American female engineer at NASA, began her career with the agency in the segregated West Area Computing Unit of NASA’s Langley Research Center in Hampton, Virginia. The mathematician and aerospace engineer went on to lead programs influencing the hiring and promotion of women in NASA’s science, technology, engineering, and mathematics careers. In 2019, she posthumously received the Congressional Gold Medal.

The work of Jackson and others in the West Area Computing Unit caught widespread national attention in the 2016 Margot Lee Shetterly book “Hidden Figures: The American Dream and the Untold Story of the Black Women Mathematicians Who Helped Win the Space Race.” The book was made into a popular movie that same year, and award-winning actress Janelle Monáe portrayed Jackson.

Members of Jackson’s family and other guests will join Jurczyk for the ceremony, including the NASA Langley centre director Clayton Turner, the retired NASA engineer and “Hidden Figure” as a profiled in Shetterly’s book Christine Darden, the artist Tanbeete Solomon, and Wanda Jackson- granddaughter of Mary W. Jackson.

In addition to unveiling a building sign with Jackson’s name, the event will feature video tributes with reflections on Jackson’s career and legacy from a variety of individuals, including William R. Harvey, the president of Hampton University, Jackson’s alma mater, as well as family and friends, current and former NASA employees and astronauts, celebrities, elected officials and others. The event also will feature a video of poet Nikki Giovanni reading an excerpt from her poem “Quilting the Black-Eyed Pea,” which is about space and civil rights.

Because of the ongoing COVID-19 pandemic, on-site attendance will be limited to participants and invited guests, with no accreditation for in-person media. Members of the media are encouraged to attend the event remotely and take advantage of the resources available virtually.