Tesla is on the cusp of finishing its Optimus project. The company pushed back its annual technology day, Tesla’s AI Day, by six weeks, hoping to have a working prototype of its Optimus Humanoid Robot. Initially scheduled for 19 August, the event’s second edition focusing on artificial intelligence will witness Tesla’s AI ventures and innovations again.
Elon Musk, Tesla’s co-founder and CEO, tweeted, “Tesla AI Day pushed to Sept 30, as we may have an Optimus prototype working by then.” At the previous event, as a part of the Optimus project, Musk surprised the audience with a hint of employing Dojo to train a humanoid robot. However, no working prototype has been revealed at the moment.
Musk presented only a three-dimensional version with basic specifications. The humanoid robot had a height of five feet and eight inches, weighing 125 pounds. The final rendering would be equipped with the Tesla FSD system for intelligence, powered by around 40 actuators.
Musk seems to have a record of repeatedly overpromising and underdelivering, given his short-spanned interest in one project. Any delay in the Optimus project would put the company further behind its spotty track record of delayed launches. Like it happened at the beginning of this year, Musk announced a delay in Cybertruck, shifting the company’s focus to the robot. Hopefully, Optimus will be ready before any other projects are delayed.
Meta has developed three new AI models – Visual-Acoustic Matching, Visually-Informed Dereverberation, and VisualVoice – to make the sound more realistic in mixed and virtual reality (VR) experiences.
The three AI models focus on human speech and sound in the video. They are designed to push the industry faster toward the immersive reality, the company said in a statement.
The AI models were built in collaboration with the University of Texas at Austin. The company is also making the audio-visual understanding models open to developers.
Acoustics play a role in how sound will be experienced in the metaverse. According to Meta’s AI researchers and audio specialists, AI will be core to delivering realistic sound quality.
AViTAR, the self-supervised Visual-Acoustic Matching model, adjusts audio to match the space of a target image.
Despite the lack of acoustically mismatched audio and unlabelled data, the self-supervised training objective learns acoustic matching from in-the-wild web videos, said Meta. VisualVoice learns by learning visual and auditory cues from unlabelled videos to achieve audio-visual speech separation.
VisualVoice generalizes well to the challenging real-world videos of diverse scenarios, said Meta AI researchers.
For instance, consider attending a group meeting in the metaverse with colleagues worldwide. However, instead of people having fewer conversations and talking over one another, the acoustics and reverberation would adjust accordingly as they join smaller groups and moved around the virtual spaces.
George Sugihara and his four international colleagues have discovered the machine learning-based empirical dynamic modeling (EDM) approach to forecast and manage Lake Geneva’s ecological response to the threat of phosphorus pollution. Sugihara is a biological oceanographer at Scripps Institution of Oceanography.
Phosphorus inputs from detergents and fertilizers have degraded the water quality of Switzerland’s Lake Geneva throughout the middle of the 20th century. This led the officials to take action to remediate pollution in the 1970s.
The authors explain that their machine learning-based approach leads to substantially better predictions and a more actionable description of the biogeochemical and ecological processes that sustain water quality.
The hybrid model suggests that the impact of raised air temperature by 3°C (5.4°F) on water quality would be the same as the phosphorus pollution of the previous century. It also implies that the best management practices may no longer involve single control lever approaches like reducing phosphorus inputs alone.
The team of researchers also includes Damien Bouffard of the Swiss Federal Institute of Aquatic Sciences and Technology. The new hybrid empirical dynamic modeling (EDM) approach was published in the journal Proceedings of the National Academy of Sciences.
EDM can also help as a supervised machine learning tool, a way for computers to learn patterns and educate researchers about the mechanisms involved in data.
Several malicious Python packages accessible through the PyPI module were discovered, taking confidential data, including AWS keys and credentials, and sending it to openly accessible destinations.
PyPI is an open-source repository of Python packages that developers use for their Python-based projects. The widely used PyPI package “ctx” was recently compromised and might release versions that leak your environment variables to an external server. “Ctx” is a simple Python package that enables programmers to manipulate their “dictionary” or “dict” objects.
Companies like Sonatype, specialize in software supply-chain security and employ specific automated malware detection methods to find them. Sonatype identified several more packages to be malicious. These include:
loglib-modules
pyg-modules
pygrata
hkg-sol-utils
Pygrata-utils
J. Cardona and C. Fernandez, Sonatype analysts, identified that ‘loglib-modules’ and ‘pygrata-utils’ were used for exfiltration and snatching AWS credentials and other essential information.
The two analysts contacted the domain owners to alert them to the public exposure and to provide an explanation under the assumption that they might be missing anything. The endpoint was quickly made inaccessible to the public without any other response, likely indicating illegitimacy.
PyPI often responds quickly to reports of harmful packages on the platform, but because there is no actual filtering before submission, risky packages may remain for some time. It’s interesting to note that “pygrata” requires “pygrata-utils” as a dependency because it lacks the data-stealing functionality. Because of this, even though four malicious packages were swiftly detected and deleted from PyPI, “pygrata” stayed there for a more extended period despite its limited autonomy.
Software developers are recommended to examine package descriptions, upload dates, release histories, and upload dates in addition to package names. These factors tell whether a Python package is authentic or a risky imitation.
In the most recent development, OpenAI claims its new AI model can play Minecraft, having trained on 70,000 hours of in-game visuals. The new AI uses standard keyboard-and-mouse inputs to play in the same world as humans, unlike many earlier Minecraft algorithms that function in far simpler “sandbox” versions of the game.
In recent years, numerous neural networks, like DeepMind’s MuZero for chess, have triumphed in various games with reinforcement learning. For the more complicated “open-world” game environment of Minecraft, Bowen Baker and his team sought to create a neural network.
They broke ground in releasing “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”. They used a large dataset to train the neural network to mimic human keystrokes in solving different tasks in the game.
After some tweaking, OpenAI discovered that the model could carry out a wide range of complex tasks, from swimming to tracking down prey and eating it. The AI also mastered the “pillar jump,” in which the player deposits a block of material beneath itself mid-jump to boost height. It learned to construct a diamond pickaxe after further fine-tuning with reinforcement learning—a feat that requires typically human gamers 24,000 actions and 20 minutes to complete.
Baker and his team said, “While we only experiment in Minecraft, we believe that VPT provides a general recipe for training behavioral priors in hard, yet generic, action spaces in any domain that has a large amount of freely available unlabeled data, such as computer usage.”
OpenAI has been doing wonders in training large datasets for AI-based tasks since its GPT-3 success in 2020. It blew people away by ingesting billions of words into the algorithm and receiving well-crafted sentences. VPT is yet another addition to the company’s outstanding AI portfolio.
According to researchers, babies have the ability to unlock the next generation of artificial intelligence (AI). The research paper by neuroscientists at Trinity College, Dublin, was published in the journal Nature Machine Intelligence.
The study examines the psychology and neuroscience of infant learning and distills the principles to direct the next generation of AI. This approach can help overcome the most prevalent limitations of machine learning.
Dr. Zaadnoordijk and Prof. Cusack of Trinity College Institute of Neuroscience, and Dr. Besold of TU Eindhoven, in their article ‘Lessons from infant learning for unsupervised machine learning’, discuss that better ways to learn from unstructured data are required.
They also make solid proposals about the fact that certain insights from infant learning can be efficiently applied in machine learning and how exactly to apply them.
Machines will require in-built preferences for their learning from the beginning. They will also need to learn from richer datasets that capture how the real world looks and feels, the researchers added. Like infants, the machines will require a developmental trajectory where experiences and networks change as they grow up.
According to Dr. Besold, AI researchers often draw metaphorical parallels between ML systems and the mental development of human babies. The researchers are adamant about looking at the knowledge of infant development from the perspective of psychology and neuroscience, as it may help overcome the limitations of machine learning.
According to a research paper published in ACS Nano, researchers have developed an artificial intelligence (AI) device that mimics the retina of a human eye. The advanced AI device can rapidly recognize images captured by a smartphone or a camera and has potential applications in robotics and self-driving cars.
The device outperforms the human eye by seeing wavelengths, including ultraviolet (UV) and infrared (IR) light, which are beyond optical frequencies. The device works on a model developed in previous research that mimics the human brain.
The device can integrate three different operations of sensing, memorization, and data processing into one. Present intelligent imaging technology used in self-driving cars requires all three operations to be separate. The versatility of the device can make self-driving cars safer by enabling them to navigate in a wide range of situations.
According to principal investigator Tania Roy, researchers have added image sensing ability to devices that behave like the human brain’s synapses. They now have synapse-like devices that act like intelligent pixels in a camera by sensing, recognizing, and prcessing images simultaneously.
Molla Manjurul Islam, the lead author of the study, said that the device could see in all conditions. There is no reported evidence of a device that can operate simultaneously in the ultraviolet range, visible wavelength, and infrared wavelength, they added.
The engineering of unique nanoscale surfaces made of platinum ditelluride and molybdenum disulfide has enabled the device’s memory functionality and multi-wavelength sensing. Researchers said the device would be available in the market within 5-10 years.
Powered by the NVIDIA DRIVE Orin, Pony.ai autonomous driving controller (ADC) is set for mass production by the end of this year. Currently, the ADC has been sampled to only a few customers.
Pony.ai has been performing road tests since January 2022 to optimize the program for low latency and real-time performance. To achieve this, the NVIDIA DRIVE Orin system-on-chip (SoC) will serve as the centralized computation and AI engine for autonomous fleets.
DRIVE Orin, the world’s highest-performance automotive-grade SoC handles numerous apps and deep neural networks simultaneously. It also meets all the strict safety regulations to enable intelligent driving that is highly advanced. NVIDIA DRIVE Orin also powered JIDU’s Robo-01 Concept Vehicle recently.
Vice President of Automotive at NVIDIA, Rishi Dhall, said that NVIDIA’s sophisticated AI would be essential for Pony.ai’s self-driving systems and make them more reliable as robotaxis/robotrucks.
The ADC will enable vehicles to intelligently maneuver on busy roads and highways while following the traffic rules. It will allow automatic merging during congestion, lane-changing for navigation, passing cars, handling U-turns, identifying unprotected intersections, nudging around construction zones, and handling on-and-off roads.
James Peng, Pony.ai’s co-founder and CEO said, “By co-developing a turnkey computing platform with NVIDIA, Pony.ai has gained a deep understanding of the characteristics and advantages of NVIDIA DRIVE to rapidly advance our time to market.”
Tianchneg Lou, co-founder and CTO of Pony.ai, said, “As our NVIDIA-powered ADC is routinely tested and further integrated at Pony.ai, we’ve entered into a fast technical iteration phase to meet customer demand.”
He confirmed the mass production, saying, “We are delighted to begin mass production of our proprietary ADC soon — delivering to a wide range of autonomous driving customers.”
Daniel Patt, a software engineer, now working for Google, developed an AI ‘From Number to Names (N2N)’ to help people find the photos of the victims and survivors of WWII. N2N helps to identify the people in Holocaust photos by scanning and linking them to people living today.
N2N is an AI facial recognition platform that scans through prewar Europe and Holocaust images. It only displays the top 10 probable matches it can locate in the database. The non-profit endeavor has already experienced tremendous success, despite not being perfect.
Patt is working for N2N to access 700,000 more images from pre and Holocaust eras in addition to the images and movies already made available on the platform.
Incredible use of technology for good! From Numbers to Names (N2N) is an artificial intelligence-driven facial recognition platform that can scan through photos from prewar Europe and the Holocaust, linking them to people living today.https://t.co/KGKPZMVIsj
The 40-year-old engineer found the inspiration for creating such an AI in 2016 while visiting Warsaw’s POLIN Museum of the History of Polish Jews. Realizing the possibility of walking past the photos of several loved ones and not reconciling them as relatives, Patt wanted to help his family and others to find their murdered loved ones’ photos.
Patt has a personal stake in the venture. Three of Patt’s grandparents are Holocaust survivors from Poland, and he wants to assist his grandmother in locating photographs of the members of her family who the Nazis killed.
Patt revealed the AI and its purpose on June 26, Holocaust Survivor Day, to celebrate the Holocaust survivors. He said, “I started this project after visiting the POLIN Museum of the History of Polish Jews in Warsaw, Poland, in 2016. I couldn’t shake the feeling that I had potentially walked past a photo of a family member without even knowing it.” He added, “Looking ahead, we’d like for N2N to become a vehicle for Holocaust education, giving students an opportunity to directly contribute to the historical record.”
Robotics is among the world’s fastest-growing industries as technology advances over the years. Experts suggest that the robotics sector is expected to develop rapidly in 2022 as new tactics and technology improvements spark a surge in new activities. According to a recent report, the global robotics market is set to rise in value from $25 billion in 2021 to nearly $260 billion by 2030. Robotics is a crowded industry with over 500 companies producing products that fall into four categories: traditional industrial robots and cobots, stationary professional services, mobile professional services, and automated guided vehicles (AGVs) for transporting loads in logistics or assembly lines. Professional services robots, which are currently only a fragment of the market, might have sales that are more than twice those of conventional and logistics robots. This article will provide information regarding the top robotics companies in 2022 across the world. A point to be noted is that this article does not rank the companies according to the positions they are placed in.
Boston Dynamics
Boston Dynamics is a United States-based robotics company founded in 1992 by Marc Raibert. The firm was founded as a spin-off from the Massachusetts Institute of Technology, where National Academy of Engineering member Marc Raibert and his colleagues pioneered the development of animal-like robots. Last year the company released the third version of the software for its dog-like robot named the Spot robot. The latest version focuses on allowing Spot to complete tasks without the need for human participation, pushing the limits of automation. Furthermore, Boston Dynamics is the home of DI-Guy, a real-time Human Simulation, and Artificial Intelligence program that is the industry leader in the VizSim market. These capabilities and offerings of Boston Dynamics will make it one of the best robotics companies in the world in 2022.
Nuro
United States-based electric self-driving vehicles developing firm Nuro specializes in developing and operating a fleet of autonomous electric vehicles that can be used for delivering assorted local goods. Nuro’s R2 robot is the US Department of Transportation’s first entirely autonomous, zero-occupant on-road vehicle licensed for commercial delivery. The newly launched R2 autonomous delivery car comes with two times the cargo space compared to its older generation. Nuro has a unique feature in that it has front airbags, which serve to save the car from serious damage if it is hit accidentally while in transit and also reduce the risks of causing any damage to pedestrians. The company was founded by Dave Ferguson and Jiajun Zhu in 2016 and has received total funding of more than $2 billion over the past. The company’s highly competent and green autonomous vehicles will make it a notable robotics company in 2022.
Mobile Industrial Robots
Mobile Industrial Robots is a top Denmark-based robotics company that was founded by Niels Jul Jacobsen in 2013. According to the company, it is committed to developing user-friendly, versatile, and safe robots to assist businesses in increasing the efficiency of their operations. Internal transportation and material handling play a fundamental part in logistics success, and MIR offers multiple robots such as MIR 100, MIR 250, MIR EU Pallet Lift 1350, and several more that help companies in optimizing their internal transportation networks. The robotic company’s AMRs target a variety of workflows for small and mid-sized firms as well as a major production and distribution hubs. Its robots have been deployed in various parts of the world, including Finland, China, Slovakia, and the United Kingdom, where they have been serving their duties successfully.
Miso Robotics
One of the leading robotics companies working for the restaurant industry, Miso Robotics, was founded by Buck Jordan, David D. Zito, Robert Anderson, and Ryan Sinnet in 2016. The company develops and manufactures artificial intelligence-powered robots that help chefs across the world in the preparation of food in restaurants. Recently the company deployed its Flippy 2 frying robots at Wimpy in Dubai Mall in collaboration with Americana Restaurants. Miso Robotics claims that its Flippy 2 can accomplish the duty of a complete fry station on its own, streamlining operations in quick-service restaurants (QSRs). Its capabilities include grabbing burger patties, placing them on a heated grill, checking each burger’s cook time and temperature, and then sending notifications to chefs. Apart from the Flippy 2 robot, the company plans to station its arm-based robot named Chippy to make tortilla chips at fast-food chain Chipotle. These capable robots offered by Miso Robotics make it one of the best robotics companies in 2022 globally.
Seergrid
Seergrid is yet another top robotics company that develops and manufactures Autonomous Mobile Robots. United States-based Seergrid was founded by Hans Moravec and Scott Friedman in 2003. It combines AMRs, enterprise software, and best-in-class services, according to the firm, to provide a full, integrated material handling automation system. Some of its offerings include Palion AMR, Palion Lift, and Pallet Truck. Seegrid’s Fleet Central business software package delivers critical information regarding Palion AMR fleet’s state and performance, along with the flow of materials. Furthermore, Seergrid’s Fleet Geek platform gives real-time actionable analytics that supports facility decision-making. Palion AMRs have traveled over 5 million autonomous kilometers in client sites without a single incident involving employees, making it a highly reliable and secure robotics solution. Earlier this month, the company also had been identified as a Representative Vendor in Gartner “Market Guide for Intralogistics Smart Robotics.”
COCO
United States-based COCO, also known as Cyan Robotics, is a last-mile delivery service that deploys human-operated sidewalk robots to transport goods to end consumers. The company was founded recently in 2020 by Brad Squicciarini and Zach Rash. COCO currently employs 1,000 robots to make deliveries for more than 50 restaurant brands, ghost kitchens, convenience and liquor stores, supermarkets, and others. Coco travels to the restaurant to pick up the order, then merchant staff pack Coco as soon as the food is ready, and the robot eventually arrives at the customer in 15 minutes or less. Moreover, COCO claims that its solution can considerably increase the margins of merchants, making it a reliable and effective robot in 2022.
Advanced Navigation
Australia-based robotics firm Advanced Navigation was founded by Chris Shaw and Xavier Orr in 2010. Advanced Navigation, being a top robotics company in 2022, has a strong team specializing in sensors, GNSS, inertial navigation, RF technologies, acoustics, robotics, AI, and algorithms. Recently, the company launched its new artificial intelligence (AI)-powered undersea autonomous drone called Hydrus. It has been developed primarily for applications that demand high-resolution pictures and video along with precise geo-referenced data. According to the company, its new underwater drone will drastically improve underwater research capabilities by simplifying the data collection process more than traditional methods. Hydrus can uncover the causes of some of the ocean’s most significant climate change phenomena, such as CO2 absorption, reef bleaching, new diseases, loss of marine life and biodiversity, coastal erosion, fishery decline, and others.
Nimble
Stanford and Carnegie Mellon’s artificial intelligence research centers served as the foundation for Nimble. The company specializes in building AI-powered robots that pick and pack anything to provide the most efficient, cost-effective, and long-term on-demand order fulfillment. Its high-end technology makes it one of the top robotics companies in 2022. AutoStore, Opex, Bastian, Swisslog, TGW, and Kuecker Pulse Integration are among the main system integrators and providers using Nimble’s robots in their systems. The Nimble robots have picked over 15 million objects from 500,000 different goods, including eyeliners, belts, body wash, and loofahs, along with electronics like USB drives, keyboards, etc. Nimble had also secured $50 million in Series A funding in March 2021, headed by DNS Capital and GSR Ventures. Nimble has grown its workforce from 25 to 75 individuals since its Series A fundraising round and has rapidly expanded its customer base.
Bharati Robotics Systems
Bharati Robotics Systems is a homegrown robotics company that specializes in developing robotic solutions for the service, defense, industrial, agricultural, and other industries. According to the Indian robotics company, robots will not only boost efficiency across processes in companies but will also reshape markets and sectors. This would result in a more robust economy with more quality and well-paying employment. One of the most famous offerings of Bharati Robotics Systems is the August 1.0 robot. It is an Industrial floor cleaning robot that is utilized to keep floors clean while potentially saving money, labor, and time. August 1.0 is also equipped with an obstacle avoidance system with a safety bumper, and instant braking with an emergency switch is standard. The firm’s vision and its offerings make it one of the top robotics companies in India.
Tesla
Last but not least, it is Elon Musk’s Tesla. Though their robot has not yet hit the market, the company has been developing a humanoid robot for years. According to recent reports, Tesla’s humanoid robot will be showcased during the company’s second Tesla AI day, which is scheduled to be held in the month of September this year. Tesla’s Optimus is a human-sized robot that can handle necessary daily duties such as cleaning, food shopping, and so on. Therefore, it can be expected that it would make the lives of people simpler when launched in the market. Optimus will be about 173 cm (5ft 8 inches) tall, weigh around 57 kgs, and be capable of carrying up to 20 kgs of weight. If the promises are kept, then Optimus will be the world’s one of the most high-tech robots to be ever created for commercial purposes.
