From November 12 to 15, Munich became the epicenter of the global electronics industry as Electronica 2024 brought together 3,480 exhibitors and nearly 80,000 visitors from across the world. Renowned as the industry’s leading trade fair, this year’s event was more international than ever, offering a glimpse into how digital technologies are shaping the path to a carbon-neutral future. Spanning 18 exhibition halls, the event highlighted groundbreaking advancements in artificial intelligence (AI), sustainability, and the future of mobility, alongside the critical need to foster young talent in electronics.<h2>Key Topics Shaping the Electronics Landscape</h2><h3>Sustainability: Electronics Driving the Green Revolution</h3>At the heart of this year’s event was the drive to achieve carbon neutrality, with many exhibitors showcasing how electronics play a crucial role in environmental sustainability. From energy-efficient semiconductor solutions to IoT technologies that enable smarter energy management, it was clear that the industry is stepping up to address climate challenges.Sustainability discussions centered on:<ul><li>Smart Grids and Renewable Energy: Innovations in optimizing energy systems for greater efficiency.</li><li>Circular Economy: Efforts to design electronics for recyclability and longevity.</li><li>Green Electronics Manufacturing: Reducing the environmental footprint of production processes.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732193109394-electronica-infineon.jpg" style="width: 100%px;" class="fr-fic fr-dib">Smart and sustainable mobility was one of the main topics at this year's Electronica.<h3>Artificial Intelligence: Powering the Industry</h3>AI continued to dominate conversations, underscoring its influence across sectors. At Electronica 2024, AI’s transformative power was evident in:<ul><li>Smart Manufacturing: Machine learning-driven solutions to optimize production lines and reduce waste.</li><li>AI-powered Sensors: New sensor technologies with AI integration for predictive analytics in industrial and healthcare applications.</li><li>Autonomous Mobility: The role of AI in enhancing vehicle safety and energy efficiency.</li></ul><h3>Future of Mobility: Shifting Gears with Electronics</h3>The push toward sustainable transportation took center stage, with electronics enabling electric vehicles (EVs), autonomous systems, and advanced battery technologies. Key themes included:<ul><li>Electrification: Efficient power management and battery advancements for EVs.</li><li>Connectivity: V2X (vehicle-to-everything) communication for smarter traffic ecosystems.</li><li>Safety Systems: Innovations in sensor fusion and AI-enhanced driver-assistance technologies.</li></ul><h3>Fostering Talent: Investing in the Future</h3>With the global electronics industry facing a talent shortage, Electronica 2024 placed special emphasis on nurturing the next generation of engineers and innovators. Events, workshops, and mentorship programs were tailored to inspire students and young professionals to take up roles in this rapidly evolving field.<h2>Exhibitor Highlights: Innovations to Watch</h2><h3>Infineon Technologies: AI and Sustainability at the Core</h3><a href="https://www.infineon.com/cms/en/product/promopages/electronica/" target="_blank" rel="noopener noreferrer">Infineon</a> stole the spotlight with its dual focus on sustainability and digitalization. Showcasing AI-enabled semiconductor solutions, the company emphasized its commitment to green energy and smart mobility. Their technologies are pivotal in advancing applications such as solar energy systems, electric drives, and secure IoT solutions.<h3>Altium: Transforming Electronics Design</h3><a href="https://www.messe.tv/en/2024/electronica/altium-life-cycle-electronics-development" target="_blank" rel="noopener noreferrer">Altium</a> unveiled a trifecta of solutions—Altium Discover, Altium&nbsp;Develop, and Altium Lifecycle—aimed at streamlining the electronics design process. From conceptualization to product lifecycle management, these tools empower engineers to innovate faster and with greater efficiency.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732192928529-novelties_altium_booth_electronica_2024_fair_munich-large.webp" style="width: 100%px;" class="fr-fic fr-dib">Altium presented their three new solutions for solutions for the entire electronics development life cycle: Discover, Develop and Lifecycle. Source:&nbsp;<a href="https://www.messe.tv/en/2024/electronica/altium-life-cycle-electronics-development" target="_blank" rel="noopener noreferrer">Altium</a><h3>Nordic Semiconductor: The New Standard in Bluetooth LE</h3>Nordic Semiconductor introduced the <a href="https://www.nordicsemi.com/Nordic-news/2023/10/Nordic-announces-nRF54L-Series-expanding-industrys-most-efficient-Bluetooth-LE-portfolio" target="_blank" rel="noopener noreferrer">nRF54L Series,</a> expanding the world’s most energy-efficient Bluetooth Low Energy (LE) portfolio. With a focus on ultra-low power consumption, this series is ideal for applications like wearables, medical devices, and IoT solutions, pushing the boundaries of connectivity and efficiency.<h3>Analog Devices: Agile Manufacturing Robotics</h3><a href="https://www.analog.com/en/lp/001/electronica.html" target="_blank" rel="noopener noreferrer">Analog Devices</a> showcased robotics innovations for agile manufacturing, demonstrating how precision sensors and edge computing can revolutionize production lines. Their solutions highlighted the growing importance of flexibility and efficiency in Industry 4.0 environments.<h3>Ignion: IoT Innovation</h3>Ignion focused on smart antenna solutions designed to improve IoT device performance, showcasing their new <a href="https://ignion.io/landing-page/discover-omnia-mxtend/" target="_blank" rel="noopener noreferrer">OMNIA mXTEND™</a>, a one-of-a-kind three-port Virtual Antenna® component designed to simplify RF connectivity for IoT applications.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732194027984-ignion-omnia-mxtend-antenna.jpg" style="width: 100%px;" class="fr-fic fr-dib">Ignion unveiled their new OMNIA mXTEND™ Virtual Antenna.&nbsp;<h2>From Carbon Goals to Connectivity: Electronica’s Blueprint for the Future</h2>Electronica 2024 left attendees with a clear message: the future of electronics is deeply intertwined with global efforts to create a sustainable, connected, and intelligent world. Innovations showcased at the event demonstrated the industry’s resilience and ability to address pressing challenges, from climate change to supply chain disruptions.As we look ahead, several trends stand out:<ol><li>AI as the Catalyst: Expect deeper integration of AI across all electronics applications, driving efficiency and unlocking new capabilities.</li><li>Sustainability as a Mandate: Green manufacturing and circular design will become non-negotiable for businesses aiming to stay competitive.</li><li>Talent Development: The need for a robust talent pipeline will see continued investment in education and mentorship.</li></ol>For engineers, technologists, and industry stakeholders, Electronica 2024 served as both a showcase of current advancements and a roadmap for the exciting developments yet to come. As the industry evolves, platforms like Electronica will remain indispensable for fostering collaboration, innovation, and progress.

Over 80,000 visitors and 3,400 exhibitors attended this year's Electronica in Munich. Image source: Electronica.

From AI-powered manufacturing to sustainable mobility solutions, Electronica 2024 revealed how electronics are paving the way to a carbon-neutral world. This year’s record-breaking event featured game-changing technologies and fostered collaboration across the global industry.

Electronica 2024: Key Insights and Innovations Driving the Electronics Industry Forward

For roboticists, one challenge towers above all others: generalization – the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery. This process traditionally requires human oversight, with operators carefully challenging robots to expand their abilities. As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans' ability to provide it.Now, a team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments. The new system, called "LucidSim," uses recent advances in generative AI and physics simulators to create diverse and realistic virtual training environments, helping robots achieve expert-level performance in difficult tasks without any real-world data.<iframe width="640" height="360" src="https://www.youtube.com/embed/fgnJQrvTj70?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>LucidSim combines physics simulation with generative AI models, addressing one of the most persistent challenges in robotics: transferring skills learned in simulation to the real world. “A fundamental challenge in robot learning has long been the ‘sim-to-real gap’ – the disparity between simulated training environments and the complex, unpredictable real world,” says MIT CSAIL postdoctoral associate Ge Yang, a lead researcher on LucidSim. “Previous approaches often relied on depth sensors, which simplified the problem but missed crucial real-world complexities.”The multipronged system is a blend of different technologies. At its core, LucidSim uses large language models to generate various structured descriptions of environments. These descriptions are then transformed into images using generative models. To ensure that these images reflect real-world physics, an underlying physics simulator is used to guide the generation process.&nbsp;<h2>The Birth of an Idea: From Burritos to Breakthroughs</h2>The inspiration for LucidSim came from an unexpected place: a conversation outside Beantown Taqueria in Cambridge. ”We wanted to teach vision-equipped robots how to improve using human feedback. &nbsp;But then, we realized we didn't have a pure vision-based policy to begin with,” says Alan Yu, an undergraduate student at MIT and co-lead on LucidSim. "We kept talking about it as we walked down the street, and then we stopped outside the taqueria for about half an hour. That's where we had our moment."To cook up their data, the team generated realistic images by extracting depth maps, which provide geometric information, and semantic masks, which label different parts of an image, from the simulated scene. They quickly realized, however, that with tight control on the composition of the image content, the model would produce similar images that weren’t different from each other using the same prompt. So, they devised a way to source diverse text prompts from ChatGPT.&nbsp;This approach, however, only resulted in a single image. To make short, coherent videos which serve as little “experiences” for the robot, the scientists hacked together some image magic into another novel technique the team created, called “Dreams In Motion (DIM).” The system computes the movements of each pixel between frames, to warp a single generated image into a short, multi-frame video. Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.&nbsp;"We outperform domain randomization, a method developed in 2017 that applies random colors and patterns to objects in the environment, which is still considered the go-to method these days," says Yu. &nbsp;"While this technique generates diverse data, it lacks realism. LucidSim addresses both diversity and realism problems. It’s exciting that even without seeing the real world during training, the robot can recognize and navigate obstacles in real environments."&nbsp;The team is particularly excited about the potential of applying LucidSim to domains outside quadruped locomotion and parkour, their main testbed. One example is mobile manipulation, where a mobile robot is tasked to handle objects in an open area, and also, color perception is critical. “Today, these robots still learn from real-world demonstrations,” says Yang. “Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene. We hope to make this easier, thus qualitatively more scalable, by moving data collection into a virtual environment.”&nbsp;The team put LucidSim to the test against an alternative, where an expert teacher demonstrates the skill for the robot to learn from. The results were surprising: robots trained by the expert struggled, succeeding only 15 percent of the time – and even quadrupling the amount of expert training data barely moved the needle. But when robots collected their own training data through LucidSim, the story changed dramatically. Just doubling the dataset size catapulted success rates to 88 percent. "And giving our robot more data monotonically improves its performance – eventually, the student becomes the expert," says Yang.“One of the main challenges in sim-to-real transfer for robotics is achieving visual realism in simulated environments,” says Stanford University Assistant Professor of Electrical Engineering Shuran Song, who wasn’t involved in the research. “The LucidSim framework provides an elegant solution by using generative models to create diverse, highly realistic visual data for any simulation. This work could significantly accelerate the deployment of robots trained in virtual environments to real-world tasks.”From the streets of Cambridge to the cutting edge of robotics research, LucidSim is paving the way toward a new generation of intelligent, adaptable machines – ones that learn to navigate our complex world without ever setting foot in it.<hr>Yu and Yang wrote the paper with four fellow CSAIL affiliates: mechanical engineering postdoc Ran Choi; undergraduate researcher Yajvan Ravan; John Leonard, Samuel C. Collins Professor of Mechanical and Ocean Engineering in the MIT Department of Mechanical Engineering; and MIT Associate Professor Phillip Isola. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. The researchers will present their work at the Conference on Robot Learning (CoRL) in early November.

A team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments.

Can robots learn from machine dreams?

Every year, roughly 1.2 million patients experience adverse outcomes associated with injectable medications, and these errors are estimated to cost about $5 billion. A team including a researcher from Carnegie Mellon University has developed the first wearable camera that uses artificial intelligence to help prevent such errors.“The goal of our system is to catch these drug administration errors in real-time, before the injection, and provide an alert so the clinician has a chance to intervene before any patient harm,” said Justin Chan, an assistant professor in the School of Computer Science’s <a href="https://s3d.cmu.edu/" rel="noopener" target="_blank">Software and Societal Systems DepartmentOpens in new window</a> and the College of Engineering’s <a href="https://ece.cmu.edu/" rel="noopener" target="_blank">Department of&nbsp;Electrical and Computer EngineeringOpens in new window</a>.In addition to Chan, the team included researchers from the University of Washington, Makerere University and the Toyota Research Institute.The error rate for all drugs given in a hospital is about 5 to 10 percent, and they can happen at all levels of care. To design the wearable camera system, researchers focused on training deep learning algorithms that could detect errors when a clinician transfers a drug from a vial into a syringe. These could be vial swap errors, which occur when the wrong vial is used or the drug label on the syringe is incorrect; or syringe swaps, when the label is correct but the clinician administers the wrong drug. To prevent errors, hospitals use safeguards like requiring barcode scanning for syringes, but in high-pressure situations, clinicians can forget to scan the drug’s barcode or manually record its contents.<blockquote>The goal of our system is to catch these drug administration errors in real-time, before the injection, and provide an alert so the clinician has a chance to intervene before any patient harm.<cite>Justin Chan, Assistant Professor, Electrical and Computer Engineering</cite></blockquote>In their study, <a href="https://www.nature.com/articles/s41746-024-01295-2" rel="noopener" target="_blank">published in npj Digital MedicineOpens in new window</a>, researchers demonstrated how the AI-enabled wearable camera system could detect vial swap errors with a sensitivity of 99.6 percent and a specificity of 98.8 percent.Chan said creating the deep learning system to detect errors as they happen was challenging because syringes, vials and drug labels are small and clinicians can inadvertently obscure them when handling them. Researchers collected a large training dataset from different operating room environments with different backgrounds and lighting conditions. They collected footage using a small head-mounted camera strapped to physicians’ foreheads, and the camera was tilted down to film the providers’ hands. Over 55 days, they collected 4K video footage of drug preparation events from 13 anesthesiology providers and 17 operating rooms in two hospitals.“We designed the algorithm so that instead of reading the label text, which can be obscured, it only needs to catch a glimpse of visual cues like vial and syringe shape, label color, and font size for a short period of time to classify what the drug is,” Chan said.Now that researchers have shown the accuracy of the system, their next step is incorporating the system into smart eyewear that can provide visual or auditory warnings to clinicians before a drug is delivered to a patient.“This work demonstrates how AI-enabled systems can serve as a second set of ‘eyes’ to improve healthcare practices and patient safety,” Chan said. “When integrated into an electronic medical system, our system also opens up opportunities for automatic documentation of drug information and can reduce the overhead of manual record-keeping.”

Researchers develop the first wearable camera that uses artificial intelligence to help prevent medical administration errors.

A new set of eyes

<h2 dir="ltr" id="isPasted">Enabling Effortless AI Transition to the Edge</h2>The global market for edge AI is estimated to reach <a href="https://pages.edgeimpulse.com/5-rising-trends-for-ai-adoption-in-manufacturing">$143.6 billion by 2032</a>. This growing adoption emphasizes the need for businesses to adopt edge-based machine learning solutions. Companies that adopt edge AI will gain a competitive edge, especially those that invest in advanced neural network hardware.&nbsp;BrainChip has introduced its <a href="https://doc.brainchipinc.com/index.html">MetaTF</a> Software Tools to simplify the migration of ML models to the Akida event-based computing platform. MetaTF offers a set of tools for converting existing models so they can work with BrainChip's Akida processors.Key features of MetaTF include:<ul><li dir="ltr">Effortless Model Transition: It offers tools to convert, optimize, and deploy ML models for Akida processors.</li><li dir="ltr">Reduced Computational Load: Akida computing focuses only on relevant event data. This helps to reduce computational overhead as compared to traditional NPUs.</li><li dir="ltr">Familiar Development Environment: It allows developers to work in familiar environments like Jupyter Notebooks and experiment with ML on the edge. This can help to minimize the learning curve.</li><li dir="ltr">Enhanced Model Validation: It offers a simple pathway for companies to test and refine their models on advanced processors.</li></ul>With these capabilities, MetaTF helps companies to adopt edge AI quickly and efficiently. It also helps them find new opportunities for innovation and performance.<h2 dir="ltr">The Unique Challenges of Embedded Devices</h2>Deploying ML solutions on embedded devices comes with challenges that are different from traditional server. Embedded and IoT devices, ranging from sensors and smart phones, are highly diverse. They do not work like servers that follow standard architectures. These devices have their own architecture and operating systems. Also, they may sometimes have no OS.This diversity makes installing and packaging software more complex, especially when they have to scale it to multi-million devices. Software-build technologies are important to address these challenges. They:<ul><li dir="ltr">Simplify the management of complex hardware configurations.</li><li dir="ltr">Ensure repeatability in software deployments.</li><li dir="ltr">Enable the creation of consistent images throughout different devices.</li></ul>These tools are important for overcoming the unique challenges of deploying ML solutions in embedded environments.<h2 dir="ltr">Simplifying Edge Deployment with Edge Impulse and MetaTF</h2>Training ML models for compact, low-power devices is only part of the challenge. Packaging and deploying these models on edge devices present additional hurdles. BrainChip has significant experience with popular embedded systems like <a href="https://buildroot.org/">Buildroot&nbsp;</a>and <a href="https://www.yoctoproject.org/">Yocto</a>. BrainChip Akida System on Chip (SOC) integrates smoothly into different embedding environments of customers.By joining hands with <a href="https://edgeimpulse.com/">Edge Impulse</a>, BrainChip simplifies ML workflows to edge. It also simplify the deployment of event-based AI solutions. Together, they make it easier than ever to implement event-based ML solutions on embedded devices.One standout example of this collaboration is the <a href="https://shop.brainchipinc.com/products/akida%E2%84%A2-edge-ai-box">Akida™ Edge Box</a>. This was created by cooperation with Edge Impulse and <a href="https://www.vvdntech.com/vision/akida-edge-ai-box">VVDN</a>. This solution showcases BrainChip’s expertise in addressing the unique challenges of edge AI deployment by integrating advanced technologies. The key features of Edge Box include:<ul><li dir="ltr">Hardware Configuration: Built on an NXP i.MX 8M application processor. The Edge Box includes dual Akida 1000 chips integrated via a single M.2 PCI card.</li><li dir="ltr">Streamlined Deployment: MetaTF libraries and NXP’s i.MX Yocto Project Board Support Package help integrate models trained in Edge Impulse Studio.</li><li dir="ltr">Real-World Applications: It shows how event computing and embedded system tools can work together for better AI solutions.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732020180615-Akida_Examples-1200x627.jpg" style="width: 100%px;" class="fr-fic fr-dib"><a href="https://brainchip.com/contact/">Contact BrainChip's architecture team</a> for more information on integrating BrainChip’s event-based AI into embedded systems or to discover the potential of the Akida Edge Box.&nbsp;Bring your AI initiatives to the edge with BrainChip!

How BrainChip's Akida™ Edge Box Simplifies ML Deployment


Brainchip MetaTF Software Tools ease the Transition of ML Models to the Edge

Imagine being a passenger in a self-driving car as the vehicle starts veering off the road. It’s not a faulty sensor causing the dangerous situation — it’s a cyberattack. Hackers can access the deep learning (DL) neural networks at the heart of the vehicle’s computer system, compromising the safety of its passengers, as well as other drivers and pedestrians.&nbsp;Stopping such cyberattacks requires understanding them first, but this can be challenging. Finding a computing system’s exact deep neural network has many roadblocks. They are often proprietary and, therefore, inaccessible to investigators without considerable legal intervention. Another common problem is that they are updated frequently, making it difficult for investigating researchers to access the most current network iteration. But a new tool from Georgia Tech could unlock the mysterious malware on myriad neural networks in everything from self-driving cars to the IMDB entertainment database. AI Psychiatry (AiP) is a postmortem cybersecurity forensic tool that uses artificial intelligence to recover the exact models a compromised machine runs on and discover where the fatal error occurred.“We trust self-driving cars with our lives and ChatGPT with our careers, but when those systems fail, how are we going to investigate them?” said <a href="https://saltaformaggio.ece.gatech.edu/">Brendan Saltaformaggio</a>, an associate professor with joint appointments in the School of <a href="https://scp.cc.gatech.edu/">&nbsp;Cybersecurity and Privacy</a> and the School of <a href="https://www.ece.gatech.edu/">Electrical and Computer Engineering</a> (ECE).AiP can recover the original DL model on both the local network’s memory and the graphics processing unit that trains the network. It can accomplish this without any specific knowledge of the model’s framework, platform, or version. Instead, it recreates the model using what&nbsp;<a>Saltaformaggio</a> refers to as “clues,” or common components in all neural networks. These include weights, biases, shapes, and layers from the model’s memory image — a frozen set of the bits and bytes operating when the model is running normally. The memory image is crucial because it enables AiP to compare it with the model post-attack.“These models often refine their information as they go, based on their current environment, so an attack might happen as a result of an attacker poisoning the information a particular model is learning,” said David Oygenblik, an ECE Ph.D. student. “We determined that a memory image would capture all those changes that occur during a runtime.”\Once the model is recovered, AiP can run it on another device, letting investigators test it thoroughly to determine where the flaws lie. AiP has been tested with different versions of both popular machine learning frameworks (TensorFlow and PyTorch) and datasets (CIFAR-10, LISA, and IMDB). It successfully recovered and rehosted 30 models with 100% accuracy.&nbsp;“Before our research, you couldn't go to the cyber ‘crime scene’ and find clues because there was no technique available to do that,” Saltaformaggio said. “That's what we are pioneering in the cyber forensics lab right now — techniques to get that evidence out of a crime scene.”Tools like AiP will allow cyber investigators to see the whole picture immediately. Solving cybercrimes can help prevent future ones, from safeguarding a user’s data to keeping a car on the road.&nbsp;

New tool AI Psychiatry recovers compromised deep-learning models so researchers can understand what went wrong.

The Sherlock Holmes of AI

A less wasteful way to train large language models, such as the GPT series, finishes in the same amount of time for up to 30% less energy, according to a new study from the University of Michigan.The approach could save enough energy to power <a href="https://www.eia.gov/energyexplained/use-of-energy/electricity-use-in-homes.php" target="_blank" rel="noreferrer noopener">1.1 million U.S. homes</a> in 2026, based on Wells Fargo’s projections of <a href="https://www.marketwatch.com/story/ai-could-demand-a-shocking-amount-of-electricity-check-out-this-chart-e91e306d" target="_blank" rel="noreferrer noopener">AI power demand</a>. It could also take a bite out of the International Monetary Fund’s prediction that data centers could account for <a href="https://www.imf.org/en/Blogs/Articles/2024/08/15/carbon-emissions-from-ai-and-crypto-are-surging-and-tax-policy-can-help" target="_blank" rel="noreferrer noopener">1.2% of the world’s carbon emissions by 2027</a>—and the <a href="https://theconversation.com/ais-excessive-water-consumption-threatens-to-drown-out-its-environmental-contributions-225854">water demands</a> that come with that energy use.Some experts say that these costs could be outweighed by environmental benefits. They argue that AI could be a <a href="https://news.un.org/en/story/2023/11/1143187" target="_blank" rel="noreferrer noopener">“game changer” for fighting climate change</a> by identifying ways to optimize supply chains and the grid, manage our energy needs, and improve research on climate change. Still, that doesn’t excuse squandering energy, and some of the power used to train AI has zero impact on training time and model accuracy.“Why spend something when there’s no point?” said <a href="https://eecs.engin.umich.edu/people/chowdhury-mosharaf/" target="_blank" rel="noreferrer noopener">Mosharaf Chowdhury</a>, an associate professor of computer science and engineering and the corresponding author of the study presented at the <a href="https://sigops.org/s/conferences/sosp/2024/index.html" target="_blank" rel="noreferrer noopener">30th Symposium on Operating Systems Principles</a> last Monday.“We can’t keep building bigger and bigger data centers because we won’t have the power to run them,” said Chowdhury. “If we can reduce the energy consumed by AI, we can reduce AI’s carbon footprint and cooling requirements and allow for more computation to fit within our current energy constraints.”The energy waste is created when AI training is unequally divided between GPUs, which are computer processors specialized for large data and graphics applications. Although it opens the door for waste, splitting the work is necessary for processing huge datasets.“AI models today are so large, they cannot fit inside a single computer processor,” said <a href="https://jaewonchung.me/about" target="_blank" rel="noreferrer noopener">Jae-Won Chung</a>, a doctoral student in computer science and engineering and the first author of the study. “They need to be divided into tens of thousands of processors to be trained, but dividing the models in perfectly equal sizes across all processors is practically impossible.”The training jobs are so difficult to evenly split up because some tasks need to be grouped together on the same processor—like how each installment of a book series will be grouped together in an organized shelf. Depending on how the tasks are grouped, some processors might get stuck with the AI-training equivalent of the Encyclopedia Britannica while others get assigned a fantasy trilogy.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731630851359-AI_waste_Infographic-768x1470.jpg" style="width: 100%px;" class="fr-fic fr-dib">Infographic credit: Rachel Voigt, Michigan Engineering.Because current training methods run each processor at top speed, processors with a lighter load will finish their calculations before other processors. This doesn’t speed up training, which isn’t complete until every processor finishes its job—but it is wasteful because faster calculations require more energy. In addition, problems such as faulty hardware or network delays create energy waste by slowing down a single processor’s computing speed.To save energy, the researchers developed a software tool, called&nbsp;<a href="https://ml.energy/zeus/research_overview/perseus/" target="_blank" rel="noreferrer noopener">Perseus</a>, that identifies a critical path, or a series of subtasks that will take the longest time to complete. Then, Perseus slows down processors that aren’t on the critical path so that they all finish their jobs around the same time—eliminating unnecessary power use.“Reducing the power cost of AI can have important implications for equitable AI access,” said Chowdhury. “If a country doesn’t have enough power to run a big model, they might need to use services from far away, or be stuck running smaller, less accurate models. This gap could further perpetuate disparity between different communities.”The team tested Perseus by training GPT-3, three other large language models and one computer vision model.Perseus is an open-sourced tool available as part of&nbsp;<a href="https://ml.energy/zeus/" target="_blank" rel="noreferrer noopener">Zeus</a>, a tool for measuring and optimizing AI energy consumption.&nbsp;

Smarter use of processor speeds saves energy without compromising training speed and performance.

Up to 30 percent of the power used to train AI is wasted. Here's how to fix it.

A leap forward in artificial intelligence control from Georgia Tech engineers could one day make robotic assistance for everyday activities as easy as putting on a pair of pants.Researchers have developed a task-agnostic controller for robotic exoskeletons that’s capable of assisting users with all kinds of leg movements, including ones the AI has never seen before.It’s the first controller able to support a dozens of realistic human lower limb movements, including dynamic actions like lunging and jumping, as well as more typical unstructured movements like starting and stopping, twisting, and meandering.Paired with a slimmed down exoskeleton integrated into a pair of athletic pants that was designed by X, “The Moonshot Factory,” the system requires no calibration or training. Users can put on the device, activate the controller, and go.The study was led by researchers in the <a href="https://me.gatech.edu/">George W. Woodruff School of Mechanical Engineering</a> (ME) and the Georgia Tech <a href="https://research.gatech.edu/robotics">Institute for Robotics and Intelligent Machines</a>.Their system takes a first big step toward devices that could help people navigate the real world, not just the controlled environment of a lab. That could mean helping airline baggage handlers move hundreds of suitcases or factory workers with heavy, labor-intensive tasks. It could also mean improving mobility for older adults or stroke patients who can’t get around as well as they used to.“The idea is to provide real human augmentation across the high diversity of tasks that people do in their everyday lives, and that could be for clinical applications, industrial applications, recreation, or the military,” said <a href="https://me.gatech.edu/faculty/young">Aaron Young</a>, ME associate professor and the <a href="https://www.epic.gatech.edu/">senior researcher</a> on a <a href="https://doi.org/10.1038/s41586-024-08157-7">study describing the controller published Nov. 13 in the journal Nature</a>.What made it possible for the controller to accurately boost hip and knee joint movements was a whole new approach to the data that feeds the machine learning algorithms.Instead of predicting the task the user is trying to do — say, climbing stairs or lifting a heavy object — the researchers used sensors to instantaneously detect and estimate the human user’s internal joint efforts. That allowed the hip and knee exoskeleton device in the study to provide a 15% to 20% boost to those joints, making it easier for users to do those activities.“What's so cool about the system is, you put it on and now it's part of you. It's adapting to you. It's moving with you. There's no dependency on exactly what you're doing for the exoskeleton,” said Dean Molinaro, a lead author of the <a href="https://doi.org/10.1038/s41586-024-08157-7">Nature study</a> and a former robotics Ph.D. student. “This was kind of a big swing to say, we're willing to not only take a different crack at how we approach exoskeleton control, but also to bank on machine learning as that translator. All the pieces that had to align to realize this type of task-agnostic control felt like a bunch of tiny miracles.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731548452881-Exoskeleton-AI-Controller-Keaton-Scherpereel-Aaron-Young-Dean-Molinaro-Ethan-Schonhaut-9142-v.webp" style="width: 100%px;" class="fr-fic fr-dib">The research team included, left to right, Keaton Scherpereel, Associate Professor Aaron Young, Dean Molinaro, and Ethan Schonhaut.The controller described in Nature builds on previous work where the team <a href="https://coe.gatech.edu/news/2024/03/universal-controller-could-push-robotic-prostheses-exoskeletons-real-world-use">created a unified exoskeleton controller that could support walking, standing, and climbing stairs or ramps without user intervention.</a>Their new controller expands far beyond those activities and was able to offer seamless assistance across the range of natural human movement, including the transient and sometimes halting motions common in daily life. It also generalized, meaning it worked for activities that weren’t part of the data used to train the algorithms.<iframe width="640" height="360" src="https://www.youtube.com/embed/UVfo__lCNfo?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The team collected data on 28 different tasks. They included climbing stairs and ramps, running, walking backwards, jumping up or across, squatting, lifting and placing a weight, turning and twisting, tug-of-war, and calisthenics.Even with the added weight of the exoskeleton, users expended less energy, and their joints didn’t have to work as hard on most of the tasks the researchers tested. On the few activities with no measurable benefit from the exo, users still experienced significant compensation for the extra weight of wearing the device.Most work on exoskeleton devices has focused on a single joint. Devices for both the hip and knee typically are slow and show little metabolic benefit. Using biological data from the joints as the key driver overcomes those limitations.“Coordinating two joints, and being able to augment both simultaneously, is something engineers have struggled with because of the codependency on the joints,” Young said. “That's a beautiful part of our strategy that relies on internal human states: you can then coordinate the two joints naturally and rather effortlessly. That's huge from an engineering standpoint.”The exoskeleton involved in the group’s trials also is new. Instead of a bulky, robotic device worn on top of a user’s clothes, the device was integrated into a pair of athletic pants. It was designed by <a href="https://x.company/">X, "The Moonshot Factory,"</a> an initiative formerly known as Google X that aims to invent radical technologies to solve big problems. X helped fund the research and worked with Young’s team to create the “clothing-integrated” exoskeleton.Work on the “exo-pants” idea continues at <a href="https://www.skipwithjoy.com/">Skip, a company spun out of X</a> and where the study’s other lead author, Keaton Scherpereel, is now an exoskeleton control engineer. The idea is to move this kind of robotic assistance — in terms of both function and ease of use — toward the real world. Slipping on a pair of pants that can support the whole range of daily movement would be far more practical than the reality today.Next steps include finding ways to incorporate other physiological data that could further improve the controller and adapting the controller for different devices.“We’ve proved this approach works really well, but it requires a lot of data,” said Scherpereel, also a former robotics Ph.D. student. “What happens when we have a different exoskeleton, change the design of the exoskeleton, or move sensors? We want to handle those changes in a way that allows us to take advantage of the data we’ve already collected.”Along with Molinaro, Scherpereel, and Young, the research team included Ph.D. student Ethan Schonhaut, Georgios Evangelopoulos at Google, and <a href="https://bouve.northeastern.edu/directory/max-shepherd/">Max Shepherd</a> at Northeastern University.“This is the floor of how good we can do and still the very early phases of these kinds of controllers,” Young said. “Now that we have these amazing deep learning algorithms and this fundamental idea of using the internal human state, the capabilities are pretty endless for where this technology can go.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731548500304-Exoskeleton-AI-Controller-Pants-9137-v.webp" style="width: 100%px;" class="fr-fic fr-dib">The prototype robotic exoskeleton use in the research was integrated into a pair of athletic pants. X, The Moonshot Factory, worked with Young’s team to create the “clothing-integrated” exoskeleton and helped fund the team's AI controller research.<hr><h3 id="isPasted">About the Research<a title="Permalink to this item" href="https://coe.gatech.edu/news/2024/11/no-matter-task-new-exoskeleton-ai-controller-can-handle-it#about-the-research"></a></h3>This research was supported by X, The Moonshot Factory, Georgia Tech’s Partnership for an Advanced Computing Environment (PACE), and the U.S. National Science Foundation, grant Nos. 2233164 and 1830215. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.CITATION:&nbsp;Molinaro, D.D., Scherpereel, K.L., Schonhaut, E.B. et al. Task-agnostic exoskeleton control via biological joint moment estimation. Nature (2024).&nbsp;<a href="https://doi.org/10.1038/s41586-024-08157-7">https://doi.org/10.1038/s41586-024-08157-7</a>

A new exoskeleton controller developed by Georgia Tech engineers works for dozens of dozens of realistic human lower limb movements, including dynamic actions like tug-of-war and jumping, as well as more typical unstructured movements like starting and stopping, twisting, and meandering. Photos & Video: Candler Hobbs

Researchers created a deep learning-driven controller that helps users in real-world tasks, even those it wasn’t trained for.

No Matter the Task, This New Exoskeleton AI Controller Can Handle It

Precision, efficiency, and adaptability have always been essential in aerospace manufacturing. Today, simulations, digital twin technology, and real-time monitoring are not only helping companies meet these goals but also bringing them closer to carbon neutrality.To drive these innovations, companies require insights into every aspect of the manufacturing process. This is where Hexagon steps in, offering end-to-end workflow solutions to tackle key challenges across design, production, and quality control, with sustainability at the forefront.<h2 dir="ltr">Current Challenges and Opportunities in Aerospace Manufacturing</h2>Aerospace manufacturers face the challenge of creating complex parts while adhering to strict regulations. Additionally, they aim to reduce waste as part of their sustainability goals. To meet these demands, the industry requires advanced, integrated solutions to streamline production.<h3 dir="ltr">Design and engineering</h3>In aerospace design, simulations allow engineers to test for safety and performance without the repeated need for prototypes. This not only reduces costs but also makes the development process significantly less wasteful.&nbsp;However, running complex simulations requires powerful computing infrastructure. Without the right infrastructure and expertise, many design teams still rely on the more costly approach of prototype testing. This often leads to delays and added expenses in the development cycle.<h3 dir="ltr">Production</h3>One of the most prominent challenges is long setup times. Aerospace manufacturing involves intricate parts with each production run often requiring recalibrations and adjustments. Machine breakdowns is another well-known issue that can disrupt production.Additionally, maintaining consistent quality across multiple production sites is also a hurdle. Due to strict safety standards in aerospace, even minor deviations lead to additional quality checks, which can further slow down production. The industry also faces a shortage of skilled labor.<h3 dir="ltr">Quality control</h3>Coordinate Measuring Machines (CMM) are crucial for checking the accuracy of aerospace components.&nbsp;However, this process can be time-consuming, especially considering that parts need to be brought to these machines and may require multiple passes. The lack of real-time insights adds to this issue. Without immediate access to measurement data, it becomes challenging for engineers and technicians to detect and address deviations in production early on.<h2 dir="ltr">Manufacturing Made Intelligent&nbsp;</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731924823927-Hexagon_MI_OPTIV_M_Multisensor_CMMs_Family_1_Product_Photo_1680x885px.jpg" style="width: 100%px;" class="fr-fic fr-dib">Image: Hexagon's Multisensor CMMs Family use tactile probing and the high-speed measuring point capture of non-contact measurement on a single system and within a single measurement run for more efficient measurement of complex parts.Hexagon is a global leader in digital reality solutions, offering integrated manufacturing intelligence that spans from design to final product. The company’s goal is to make advanced technology accessible on the shop floor to ensure consistent quality throughout the process. Hexagon has supported top brands and organizations, including Formula 1’s Red Bull Racing, the cutting-edge research of CERN, and automotive giant Volkswagen. Its core offerings are as follows:<h3 dir="ltr">Complete integration</h3>Hexagon provides comprehensive integration across the entire aerospace production cycle, spanning design, manufacturing, and quality control. This seamless integration allows data to move freely across each stage and system, creating transparency that enables stakeholders to monitor progress at every step. With actionable insights coming from each phase, aerospace teams can make the right decisions that improve overall efficiency and product quality.<h3 dir="ltr">Simulation and optimization</h3>Hexagon’s Computational Fluid Dynamics (CFD) solutions help aerospace engineers simulate and analyze the aerodynamic forces acting on aircraft components. This allows companies to fine-tune designs without the need for physical prototypes at each iteration. Furthermore, Hexagon's data-driven approach enables teams to pinpoint bottlenecks and improve process efficiency, which leads to more sustainable production.<h3 dir="ltr">Real-time monitoring</h3>Hexagon’s real-time monitoring solutions bring machine and production data directly to engineers and operators through easy-to-read dashboards.&nbsp;This real-time accessibility enables better communication and collaboration between teams, while continuous monitoring enables predictive maintenance, reducing downtime by identifying potential issues before they escalate.<h2 dir="ltr">Ensuring Smart Design, Intelligent Production and Integrated Quality Assurance</h2><h3 dir="ltr">Smart Design for Aerospace Engineering</h3>Hexagon offers a cloud-based simulation tool Nexus Compute. Supporting various technologies including scFLOW, it enables engineers to test their designs for aerodynamic stress including hypersonic conditions. The cloud-based nature of the tool ensures that stakeholders from different teams can collaborate seamlessly. Hexagon's solutions also come with Integrated Computational Materials Engineering (ICME) for virtual material testing.<h3 dir="ltr">Intelligent Production for First-Time-Right Manufacturing</h3>Hexagon’s real-time monitoring solutions provide manufacturers with continuous insights into workflow efficiency, enabling engineers to catch potential issues before they lead to costly delays.With digital twin technology, Hexagon takes monitoring to another level by creating virtual replicas of physical equipment.&nbsp;It gives manufacturers a real-time view of machinery performance and its wear patterns paving the way for predictive maintenance.<h3 dir="ltr">Integrated Quality Assurance</h3>Hexagon offers in-process measurement, a system that measures and monitors parts or components during production. By integrating measurement tools directly into the production stage, manufacturers can detect and address defects early in the cycle, significantly reducing waste.Hexagon’s centralized data and documentation platform also ensures that information is easily accessible across teams. This facilitates traceability and better decision-making across the shop floor. Additionally, this approach streamlines processes for regulatory compliance and supports consistent quality across operations.<h2 dir="ltr">Real-World Application: Aeroplane Wing Nose Rib Case Study</h2>The aeroplane wing nose rib is a critical structural element that withstands intense aerodynamic forces while maintaining minimal weight. Getting it right with traditional methods is a time-consuming affair. However, with its end-to-end workflow solutions, Hexagon helped the client optimize every stage of the production cycle. Let’s find out how its advanced manufacturing intelligence helped streamline operations, reduce costs, and minimize waste to support sustainability.<h3 dir="ltr">Design Optimization</h3>Using Hexagon’s CFD and system dynamics, the team tested their designs in high-speed flow simulations. Through tools such as scFLOW, engineers were able to simulate high-speed flow conditions, including extreme phenomena like shockwaves. This virtual testing accurately gauged load behaviors and aerodynamic stresses, allowing engineers to evaluate multiple configurations and zero in on the one that meets stringent performance requirements before moving on to the prototyping phase.<h3 dir="ltr">Manufacturing Preparation and Verification</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMxNTc1NTY3MjU4LWhleGFnb24tbm9zZS1yaWItLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">In preparation for manufacturing, the client used Hexagon's digital twin technology to ensure precise alignment between design specifications and production processes. Through CAD modeling and direct "Send To CAM" interoperability, engineers ensured compatibility between design and manufacturability. Digital twins allowed for accurate simulation of material and process interactions, ensuring that each step adhered to high-quality standards with minimal operator intervention.<h3 dir="ltr">Shop-Floor Operations and Quality Inspection</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMxNTc1NjA5MjQyLWhleGFnb24td2luZy1ub3NlLXJpYi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">With in-machine measurement solutions and portable machines ready for the shop floor, the team was able to collect measurement data without delays. Hexagon's first-time-right machining approach minimized revisions and significantly reduced material waste. Hexagon’s in-machine inspection capabilities enabled real-time measurement of components, ensuring adherence to design tolerances. The integration of CMM allowed the client to capture high-precision measurements for geometric and free-form features. Portable machines equipped with various high-speed sensors enabled continuous quality assurance, minimizing waste and accelerating the process.<h3 dir="ltr">Reporting and Analytics</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMxNTc1NjUwOTc4LWhleGFnb24tdXNlci1zdG9yeS1xdWFsaXR5LWluc3BlY3Rpb24uanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">The team had easy access to real-time sensor data through Hexagon’s reporting tools. By centralizing all relevant documentation in the cloud, these tools made it easy for engineers and management to review process metrics and identify areas for improvement in both design and manufacturing. The digital records supported regulatory compliance with aerospace industry standards, which effectively reduced time spent on audits.<h2 dir="ltr">Key Benefits of Manufacturing Intelligence:</h2><ul><li dir="ltr">With its digital twins, simulation, and optimized workflows, Hexagon can help aerospace manufacturers accelerate product development. In the past, its clients have achieved up to a 30% reduction in time from concept to production.</li><li dir="ltr">With Hexagon’s virtual testing tools, the need for physical prototypes is minimized. This can mean up to a 50% reduction in cost for aerospace companies compared to traditional methods.</li><li dir="ltr">The company’s manufacturing intelligence solutions can improve “first-time-right” production rates. This reduces the need for rework saving time and resources.</li><li dir="ltr">Through real-time monitoring and predictive maintenance, Hexagon maximizes machine uptime and productivity.</li><li dir="ltr">A focus on quality control means Hexagon’s solutions help manufacturers comply with industry standards more reliably while also supporting sustainability by reducing waste.</li><li dir="ltr">Digital documentation offers complete traceability across the product lifecycle. This ensures compliance with regulatory requirements and industry standards with more confidence.</li></ul>The aerospace industry faces complex challenges in various production stages. For instance, access to advanced simulation tools is often limited. On the production floor, long setup times are common, and maintaining consistent quality across facilities is a struggle. Precision measurements CMMs is crucial but can often become bottlenecks in the workflow.<h2 dir="ltr">Step into the Manufacturing of Tomorrow at Hexagon LIVE</h2>Hexagon’s manufacturing intelligence suite addresses these challenges with its end-to-end solutions. Their cloud-based simulation tools enable engineers to run essential design tests faster and more collaboratively. By providing real-time monitoring throughout production, Hexagon helps manufacturers achieve consistent quality and reduce waste. Additionally, Hexagon speeds up the traditionally slow CMM measurement process by integrating measurement tools directly into the production stage.Curious about how Hexagon's manufacturing intelligence can transform your organization? Join the Hexagon LIVE event in Eindhoven on November 27 to explore their digital solutions firsthand.Don’t miss this opportunity to connect with industry experts, explore the latest innovations, and get your questions answered by Hexagon specialists.Secure your spot today!<a href="https://events.hexagon.com/Hexagon_LIVE_Eindhoven?creative=713450062%20759&keyword=hexagon%20live%20eindhoven&matchtype=e&network=g&de%20vice=c&gad_source=1&gclid=Cj0KCQjw99e4BhDiARIsAISE7P-jq9--%20x4A0TeKGXvQPXSR46iJ28I8bV7tNL3-yyfjZcLlvv_LBpE4aAsuEEALw_wcB" target="_blank" rel="noopener noreferrer" class="button-main-action">Read more and register</a>

Explore how advanced solutions in manufacturing intelligence are transforming aerospace manufacturing by addressing key challenges in design, production, and quality control while promoting sustainability. 

Harnessing Manufacturing Intelligence to Enhance Operational Excellence in Aerospace

In this episode, we explore how AI co-pilots are equipping doctors with powerful tools to enhance decision-making and patient care and discuss how it could impact you - a potential patient - in the not distant future.

Podcast: Will AI Assistants Make Your Doctor Better?

Edge AI Technology Report: Generative AI Edition
Chapter 1. Generative AI's demand for real-time insights is driving a shift from cloud to edge computing, enabling faster, local processing on devices and reducing cloud latency and bandwidth constraints.

Leveraging Edge Computing for Generative AI

Manufacturing defect rates have risen with metal fabrication facilities facing unprecedented challenges in maintaining quality control amid increasing operational complexity.&nbsp;As production lines become more sophisticated and quality standards more stringent, traditional inspection methods must be revised for modern manufacturing demands.Matroid is transforming this landscape by democratizing computer vision technology, making advanced automation accessible to industry professionals without coding expertise. Through its innovative platform, Matroid enables manufacturing teams to implement sophisticated defect detection and process monitoring solutions that can be deployed within minutes rather than months.The impact is significant in welding applications, where Matroid's computer vision technology can evaluate multiple quality parameters simultaneously - from porosity and spatter to burn-through - with the precision of a weld engineering expert.&nbsp;This scalable approach to quality control is proving essential as manufacturers face mounting pressure to maintain precision while managing costs.We will explore in detail how Matroid's computer vision technology is changing the landscape of metal manufacturing, examining its technical capabilities, practical applications, and the tangible benefits it brings to production environments. &nbsp;For decision-makers, we'll look at how this technology sets new standards in automated quality control and process optimization.<h2 dir="ltr">Industry Challenges and Opportunities in Metal Manufacturing</h2>The metal manufacturing industry faces unprecedented pressures, with many organizations' quality control costs consuming <a href="https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide">15-20%</a> of annual sales revenue. Traditional manual inspection methods, while common, are increasingly inadequate for modern manufacturing demands, showing inconsistency rates increase due to operator fatigue and subjective assessments.According to the US. Census Bureau <a href="https://www.census.gov/library/stories/2021/09/manufacturing-continues-to-be-among-top-five-largest-employment-sectors.html">study</a> shows that production facilities struggle with a critical shortage of skilled workers, with nearly one-fourth of the manufacturing workforce aged 55 or older. This demographic shift and early retirement trends post-pandemic have created significant gaps in quality control expertise and operational efficiency.<h3 dir="ltr">The Role of Automation and Computer Vision</h3>Computer vision technology is emerging as a transformative solution, capable of analyzing images and video from any imaging technology, spanning resolutions from low (VGA) to ultra-high (gigapixel) and across various spectra, including visible light, infrared, and x-ray. &nbsp;This enables the detection of the smallest and most challenging defects in manufacturing processes, enhancing quality control and operational efficiency.Unlike traditional rule-based inspection systems, modern computer vision &amp; AI solutions can detect and classify multiple defect types simultaneously, from surface imperfections to welding anomalies.Automated inspection systems are valuable in welding applications, where they can evaluate multiple quality parameters - including porosity, spatter, burn-through, edge welds, and cold welds - with consistent precision.&nbsp;Computer vision &amp; AI address the skilled labor shortage and significantly improve inspection accuracy, with some implementations showing up to <a href="https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai">10x</a> improvement in defect detection compared to conventional methods, which leads to dramatic cost savings in cost per part and rework efforts and also improves production throughput.<h2 dir="ltr">Matroid's Vision and Technological Focus</h2>Founded in 2016 by Stanford professor and founding member of Databricks Reza Zadeh, Matroid has established itself as a leader in enterprise-level computer vision &amp; AI software platform, backed by <a href="https://growjo.com/company/Matroid">$33.5M</a> in funding from prominent investors, including NEA, Energize Ventures, and Intel Capital.&nbsp;The Matroid <a href="https://www.matroid.com/">platform</a> domain experts from manufacturing to rapidly deploy and adapt custom detectors that can leverage camera systems' imagery/ video types to detect and classify defects, monitor manual assembly operations, verify SOPs are followed, and provide continuous cycle-time analysis with unprecedented precision across various production environments.<h3 dir="ltr">Custom Detectors and Real-Time Monitoring</h3>Matroid's platform distinguishes itself through its no-code software to build and deploy detector systems, allowing manufacturing teams to develop specialized detection models through an intuitive point-and-click interface.&nbsp;These vision detectors can simultaneously evaluate multiple quality parameters in metal processing applications - from weld porosity and spatter to surface defects and assembly errors - while maintaining consistent accuracy across varying conditions.The platform's camera-agnostic system integrates seamlessly with existing infrastructure, supporting everything from standard industrial cameras to specialized equipment like borescopes, microscopes, and X-ray systems.&nbsp;Real-time alerts and analytics are delivered directly with production systems (PLCs, SCADA, MES, ERP) and mobile devices. This enables immediate response to the right people and systems at the right time to detect anomalies and maintain digital traceability throughout manufacturing.<h2 dir="ltr">How Matroid’s Computer Vision Powers Welding Applications</h2><iframe src="https://www.youtube.com/embed/20FpAGGAqmk?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" style="width: 640px; height: 460px;"></iframe> <h3 dir="ltr">Detecting Defects in Metal Manufacturing</h3>Matroid's computer vision enables quality control oversight across the entire metal manufacturing process. The <a href="https://www.matroid.com/automated-visual-anomoly-detection/">system</a> identifies structural defects at the casting stage, like cracks, porosity, cold flow, and hot tears.&nbsp;Within the stamping processes, Matroid detectors are used to replace manual visual inspection by automating the inspection of stamped parts for tears and splits. &nbsp;This gives near real-time feedback to production engineers on how the presses are performing and gives insights into potential adjustments to prevent further defects.&nbsp;In the final inspection, the platform performs comprehensive checks to ensure consistently high quality, effectively reducing returns and warranty claims.<h3 dir="ltr">Computer Vision in Welding</h3>Matroid's technology monitors operations such as spot welding, MIG welding, and continuous seam welding through real-time monitoring and instant defect detection. The system evaluates multiple quality parameters simultaneously, identifying common issues like porosity, spatter, cold welds, edge welds, and burn-through, just as a weld engineering expert would.&nbsp;The platform's deep learning capabilities enable it to detect defects as a person could through visual media (images/video). The solutions are designed for rugged &amp; dynamic manufacturing environments.&nbsp;<h3 dir="ltr">Root Cause Analysis and Real-Time Adjustments</h3>Matroid's <a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">solution</a> provides detailed defect classification and analysis through its integrated system. The system immediately alerts operators when issues are detected through direct integration with production systems, QMS, and mobile devices. &nbsp;Production teams can take corrective action when they have near real-time classification information and analytics; for example, when burn-through is detected on welds, the welding engineer can make more effective adjustments to robotic welding systems faster. &nbsp;This reduces rework and defect risks and improves production throughput.&nbsp;The platform operates 24/7, providing continuous monitoring and real-time insights into production operations. Through its historical data analysis capabilities, manufacturing teams can capture actionable insights and implement zero-defect initiatives.<h2 dir="ltr">Real-World Applications and Benefits&nbsp;</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731927333487-1731927333487.png" id="isPasted" class="fr-fic fr-dii"><h3 dir="ltr" id="isPasted">Traditional Methods vs. Computer Vision &amp; AI for Welding</h3>Traditional welding inspection methods, relying on manual or basic automated systems, provide only binary pass/fail results. These systems need help to meet modern manufacturing demands, where quality teams need detailed defect analysis and real-time process adjustments.&nbsp;Manual inspections introduce delays between defect occurrence and detection, while inspector fatigue and varying experience levels lead to inconsistent results. Computer vision &amp; AI solutions powered by deep learning technology transform this landscape by enabling real-time identification and classification of specific weld defects.&nbsp;Matroid's system automatically detects and categorizes issues such as porosity, burn-through, and spatter, immediately notifying weld engineers for rapid response. This immediate feedback loop allows manufacturing teams to quickly identify root causes and implement real-time process adjustments.The deployment of edge or on-premises installations ensures minimal latency for real-time monitoring while maintaining data security. This approach enables manufacturing teams to shift from reactive quality control to proactive process optimization.&nbsp;Through consistent quality assessment and automated defect classification, companies can reduce inspection time, minimize waste, and improve overall welding quality. The system's integration with existing production processes creates a seamless transition from traditional inspection methods to advanced computer vision &amp; AI solutions.<h2 dir="ltr">The Deployment of Matroid’s Solution</h2><h3 dir="ltr">Easy Integration and Deployment</h3>Matroid's computer vision &amp; AI platform integrates seamlessly with existing manufacturing infrastructure through multiple deployment options. The system can be implemented as downloadable software for on-premises installation or as a Cloud software service, allowing manufacturers to choose the deployment method that best suits their operational needs.&nbsp;This flexibility enables manufacturing teams to maintain data security while leveraging advanced detection capabilities. &nbsp;Matroid’s platform makes integration into various control systems, MES, QMS, ERP, and databases easy so that production can work with real-time data and has enhanced traceability of the products produced.&nbsp;<h3 dir="ltr">Compatibility with Multiple Camera Technologies</h3>The platform's camera-agnostic approach sets it apart in the manufacturing sector. Matroid works with existing and new vision systems, supporting any image-or-video-based camera regardless of resolution (VGA - giga-pixel) or spectra (visible, infrared, X-ray, etc.).This allows manufacturers to reuse their existing machine vision cameras and use the right imaging technology for the application at hand while maintaining the ability to detect visual defects. The system connects effortlessly with industrial cameras, supporting continuous 24/7 monitoring and real-time insights into manufacturing processes.<h2 dir="ltr">Future of Welding with Computer Vision &amp; AI</h2>Integrating computer vision technology in welding operations represents a significant leap forward in manufacturing quality control. Matroid's platform transforms traditional inspection methods by enabling real-time defect detection and detailed classification, which enables immediate process adjustments.&nbsp;By combining deep learning capabilities with enterprise-grade deployment options, manufacturers can achieve consistent quality while reducing operational costs. The platform's ability to empower domain experts in manufacturing, work with existing infrastructure, and support multiple camera technologies makes it a practical solution for facilities of all sizes.<h2 dir="ltr">Ready to make your welding operations efficient and reliable with advanced computer vision?</h2>Discover how Matroid's platform can enhance your quality control processes and drive real-time improvements. Visit Matroid's <a href="https://www.matroid.com/">website</a> to explore their innovative solutions for metal manufacturing.<h2 dir="ltr">References</h2><ol><li dir="ltr"><a href="https://info.matroid.com/visual-inspection-computer-vision-software">https://info.matroid.com/visual-inspection-computer-vision-software</a></li><li dir="ltr"><a href="https://www.dayjob.com.au/metal-fabrication-challenges-with-sustainability/">https://www.dayjob.com.au/metal-fabrication-challenges-with-sustainability/</a></li><li dir="ltr"><a href="https://www.matroid.com/automated-visual-anomoly-detection/">https://www.matroid.com/automated-visual-anomoly-detection/</a></li><li dir="ltr"><a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control</a></li><li dir="ltr"><a href="https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide">https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide</a></li><li dir="ltr"><a href="https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai">https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai</a></li><li dir="ltr"><a href="https://www.matroid.com/automated-visual-anomoly-detection/">https://www.matroid.com/automated-visual-anomoly-detection/</a></li><li dir="ltr"><a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control</a></li></ol>

Discover how computer vision is solving quality challenges in metal manufacturing through enhanced defect detection, proactive process monitoring, and flexible integration solutions.

AI-Powered Welding: How Computer Vision Solutions Drive Precision and Efficiency in Metal Manufacturing

Large Language Model (LLM) training involves teaching AI models to understand and generate human-like text by processing vast amounts of data, significantly enhancing their language comprehension and production capabilities.

LLM Training: Mastering the Art of Language Model Development

In this episode, we explore how AI can help aerial vehicles adjust to extreme turbulence in real-time and enhance safety/stability, even in challenging driving conditions.

Scared of Turbulence? AI -Might- Fix That

In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.Typically, engineers collect data that are specific to a certain robot and task, which they use to train the robot in a controlled environment. However, gathering these data is costly and time-consuming, and the robot will likely struggle to adapt to environments or tasks it hasn’t seen before.To train better general-purpose robots, MIT researchers developed a versatile technique that combines a huge amount of heterogeneous&nbsp;data from many of sources into one system that can teach any robot a wide range of tasks.Their method involves aligning data from varied domains, like simulations and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process.By combining such an enormous amount of data, this approach can be used to train a robot to perform a variety of tasks without the need to start training it from scratch each time.This method could be faster and less expensive than traditional techniques because it requires far fewer task-specific data. In addition, it outperformed training from scratch by more than 20 percent in simulation and real-world experiments.“In robotics, people often claim that we don’t have enough training data. But in my view, another big problem is that the data come from so many different domains, modalities, and robot hardware. Our work shows how you’d be able to train a robot with all of them put together,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a <a href="https://arxiv.org/pdf/2409.20537" target="_blank">paper on this technique</a>.Wang’s co-authors include fellow EECS graduate student Jialiang Zhao; Xinlei Chen, a research scientist at Meta; and senior author Kaiming He, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented&nbsp;at the Conference on Neural Information Processing Systems.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730247515325-MIT-HeteroGenRobot-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">A figure shows how the new technique aligns data from varied domains, like simulation and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process. Image: Courtesy of the researchers<h2>Inspired by LLMs</h2>A robotic “policy” takes in sensor observations, like camera images or proprioceptive measurements that track the speed and position a robotic arm, and then tells a robot how and where to move.Policies are typically trained using imitation learning, meaning a human demonstrates actions or teleoperates a robot to generate data, which are fed into an AI model that learns the policy. Because this method uses a small amount of task-specific data, robots often fail when their environment or task changes.To develop a better approach, Wang and his collaborators drew inspiration from large language models like GPT-4.These models are pretrained using an enormous amount of diverse language data and then fine-tuned by feeding them a small amount of task-specific data. Pretraining on so much data helps the models adapt to perform well on a variety of tasks.“In the language domain, the data are all just sentences. In robotics, given all the heterogeneity in the data, if you want to pretrain in a similar manner, we need a different architecture,” he says.Robotic data take many forms, from camera images to language instructions to depth maps. At the same time, each robot is mechanically unique, with a different number and orientation of arms, grippers, and sensors. Plus, the environments where data are collected vary widely.The MIT researchers developed a new architecture called Heterogeneous Pretrained Transformers (HPT) that unifies data from these varied modalities and domains.They put a machine-learning model known as a transformer into the middle of their architecture, which processes vision and proprioception inputs. A transformer is the same type of model that forms the backbone of large language models.The researchers align data from vision and proprioception into the same type of input, called a token, which the transformer can process. Each input is represented with the same fixed number of tokens.Then the transformer maps all inputs into one shared space, growing into a huge, pretrained model as it processes and learns from more data. The larger the transformer becomes, the better it will perform.A user only needs to feed HPT a small amount of data on their robot’s design, setup, and the task they want it to perform. Then HPT transfers the knowledge the transformer grained during pretraining to learn the new task.<h2>Enabling dexterous motions</h2>One of the biggest challenges of developing HPT was building the massive dataset to pretrain the transformer, which included 52 datasets with more than 200,000 robot trajectories in four categories, including human demo videos and simulation.The researchers also needed to develop an efficient way to turn raw proprioception signals from an array of sensors into data the transformer could handle.“Proprioception is key to enable a lot of dexterous motions. Because the number of tokens is in our architecture always the same, we place the same importance on proprioception and vision,” Wang explains.When they tested HPT, it improved robot performance by more than 20 percent on simulation and real-world tasks, compared with training from scratch each time. Even when the task was very different from the pretraining data, HPT still improved performance.“This paper provides a novel approach to training a single policy across multiple robot embodiments. This enables training across diverse datasets, enabling robot learning methods to significantly scale up the size of datasets that they can train on.&nbsp;It also allows the model to quickly adapt to new&nbsp;robot embodiments, which is important as new robot designs are continuously being produced,” says David Held, associate professor at the Carnegie Mellon University Robotics Institute, who was not involved with this work.In the future, the researchers want to study how data diversity could boost the performance of HPT. They also want to enhance HPT so it can process unlabeled data like GPT-4 and other large language models.“Our dream is to have a universal robot brain that you could download and use for your robot without any training at all. While we are just in the early stages, we are going to keep pushing hard and hope scaling leads to a breakthrough in robotic policies, like it did with large language models,” he says.

Researchers filmed multiple instances of a robotic arm feeding co-author Jialiang Zhao's adorable dog, Momo. The videos were included in datasets to train the robot. Image: Courtesy of the researchers

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

A faster, better way to train general-purpose robots

In the future, buildings will provide enhanced, more personalized comfort to occupants while also achieving zero carbon emissions. This will be possible thanks to advances in sensors and AI, enabling new ways of interacting with our environments while leaving ultimate control in occupants’ hands. Yet, as users, we’ll also need to make lasting changes to our habits.The construction industry – the second-biggest greenhouse gas emitter and the biggest end user of energy – is shaping a new future, driven by technological innovation and the response to climate change. Buildings have long been viewed as passive structures, but they’re in the process of becoming smart, interactive and sustainable environments. Will it be possible to effect this transition and create more comfortable homes and offices while cutting buildings’ energy use? The answer is yes, but only if we also change our habits. Engineers are studying methods for reconciling the switch to clean energy with aspirations for more comfortable buildings that also improve occupants’ well-being and adapt to their behaviors. One project in this vein is SWICE, led by EPFL and carried out by a consortium of numerous Swiss universities together with partners from the private and public sectors. The project is being funded by the Swiss Federal Office of Energy as part of the country’s strategy to reach net zero by 2050.<h2>Control: a key element of comfort</h2>Indoor spaces are a prominent feature of our daily lives. According to an independent study, we spend 90% of our time indoors, whether at work, at home or during leisure activities – this figure also factors in commuting time. The well-being of building occupants is, therefore, an important issue, but it’s also a subjective one. “When we talk about occupants’ comfort, we need to make a distinction between their needs and their expectations,” says Marilyne Andersen, coordinator of the SWICE project and head of EPFL’s Laboratory of Integrated Performance in Design (LIPID). Whereas needs refer to the minimum conditions required for us to stay in good health, expectations are shaped by the society we live in, our personal preferences, outdoor climate and cultural factors.“It’s clear that lifestyles oriented towards needs rather than expectations would be better for the environment,” says Andersen. “But ironically, there are some cases where some of the occupants’ core needs aren’t met – for instance, sufficient access to daylight – while expectations linked to technological progress, such as the ability to control indoor lighting with voice commands, would be. Assuming the primary purpose of buildings remains to satisfy their occupants, architects should ultimately strive to meet both needs and expectations, while making sure resources are used economically. Comfort is a subjective, relatively personal concept, although we’ve identified certain trends that apply to most people.”For example, a study by Andersen’s research group found that occupants’ thermal comfort can be influenced by the color of the lighting in the room, where redder colors gave occupants the perception of a slightly higher ambient temperature compared to bluer ones. “Studies have also shown that occupants report a greater sense of well-being when they have agency over their environment, like if they know they’ll be able to open a window,” says Andersen. The impact of factors such as indoor air quality, natural light and noise levels can be very important, with increasingly documented effects on occupants’ health. “Not getting enough daylight exposure, for example – which often occurs when people spend a lot of time indoors, where there’s typically around 100 times less light than outdoors – can have negative effects on concentration, productivity, mood, the immune system and even quality of sleep,” says Andersen.<h2 id="isPasted">“Strong, almost empathetic ties with their building”</h2>Modern buildings are equipped with an array of sensors for measuring temperature, smoke, air quality, occupancy (to adjust the lighting) and more. Some buildings also have devices that automatically control the lighting and the heating, ventilating and air-conditioning (HVAC) systems for maximum efficiency. What’s more, these devices could soon be equipped with artificial intelligence (AI) to “learn” occupants’ habits and adjust ambient conditions accordingly in real time. For example, an AI-driven system could automatically lower a room’s temperature in the winter after confirming that it’s empty, leading to significant energy savings. Engineers at EPFL’s Laboratory of Integrated Comfort Engineering, headed by Dolaana Khovalyg, have developed these kinds of smart controllers and trained them with reinforcement learning. “Our devices can continuously and autonomously adjust the control settings for an indoor environment while aiming for several targets at once, such as lowering energy use and maximizing occupant comfort and safety,” says Khovalyg. Her team is now taking this one step further by developing models to predict the metabolic rate of individuals as they perform everyday indoor activities. These data will be used to develop control policies that automatically adjust heating or cooling in the immediate surroundings of a person.Recent advances in AI have made it a powerful tool for enabling personalized, efficient building control systems. But experts agree that humans must be the ones with the ultimate say on their indoor environments. If nothing else, the feeling of control psychologically improves the sense of comfort. Denis Lalanne, an expert in human-machine interaction at the University of Fribourg, explains: “With smart buildings, it’s almost as if occupants enter into a computer. They live and work inside an intelligent structure, and there needs to be strong, almost empathetic ties with it so that it can adjust ambient settings in exactly the right way for maximum comfort and minimal carbon emissions.” Human-Building Interaction (HBI) is a fast-growing field that puts the occupant back in the building as a player, not just a passive user.At EPFL, Andrew Sonta, the head of the Civil Engineering and Technology for Human-Oriented Sustainability Laboratory, is conducting important research in this area. “Indoor sensors not only collect data on ambient conditions, but also provide insight into how occupants interact with a building and how they affect the building’s performance,” he says. His research group has studied human-building interaction by mea-suring indicators such as power consumption and CO2 concentrations in different rooms. “People breathe in more oxygen as they speak, which could lead to an increase in the CO2 concentration of the air,” he says. “By mapping out these data, we can get an idea of the occupants’ social use of a building.”Innovative technology will go a long way towards reaching the twin goal of greater occupant comfort and less energy use. But as a society, we also need to adapt our expectations. Scientists in the SWICE project are thus trying out new approaches in a number of “living labs” – real-world experimentation spaces where they work directly with the occupants of residential and office buildings to see how new approaches fit in with occupants’ daily activities. “Interventions in living labs are co-created through an active collaboration with their residents and thus future users, thereby heightening awareness about these interventions,” says Andersen. “Their effects are then tested, measured and studied under real-world conditions to give us concrete data. This serves as a pilot project to design future interventions that would be relevant to more open contexts and could be conducted on a larger scale.” When it comes to human-building interaction, engineers can use different kinds of sensors to better understand occupants’ needs and improve buildings’ ability to meet them. Sensors can also enhance the way information about a building is transmitted to its occupants. “Input is required from both sides,” says Lalanne. “Machines can’t predict everything on their own, and work still needs to be done on how they can communicate their operating conditions to occupants.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730247330188-bef03cf4.jpg" style="width: 100%px;" class="fr-fic fr-dib">© 2024 EPFL - Illustration by Jeanne Guerard<h2 id="isPasted">Tomorrow’s housing will be collective and integrated into urban planning</h2>Is the clichéd house with a white picket fence on its way to extinction? According to 2022 figures from the Federal Statistical Office, single-family homes currently account for over half of Switzerland’s residences (although the size of these homes has shrunk considerably over the past 50 years owing to the high cost of land). Yet in view of the housing shortage in large Swiss cities, the push to stop land take and, especially, the goal of preserving biodiversity and cutting the country’s greenhouse gas emissions, the trend will probably be to shift towards collective housing designed according to a new, more ecological form of urban architecture that responds to the challenges of climate change.“As part of SWICE, we’re examining how the notion of energy savings can be applied to Switzerland’s urban centers,” says Andersen. “This involves reconciling the complex factors making up an individual’s well-being with the degree of behavior change that people are willing to accept. Our approach incorporates the concept of quality of life through designs for public spaces, vegetation – including the capacity for vegetation to lower urban temperatures – the spacing of buildings, and the potential dynamics of building use. We’re also looking at the energy footprint of urban transportation systems, notably pertaining to commuting.”

In the future, buildings will provide enhanced, more personalized comfort to occupants while also achieving zero carbon emissions. This will be possible thanks to advances in sensors and AI, enabling new ways of interacting with our environments while leaving ultimate control in occupants’ hands.

Towards comfortable, interactive and zero-carbon buildings

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making


Artificial Intelligence: A Comprehensive Guide to its Engineering Principles and Applications

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Introduction

Edge AI Market Analysis and Trends

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

Retail and Customer Experience

Energy Efficiency and Sustainability

Agriculture and Food Production 

Automotive and Transportation

Generative AI at the Edge

Edge AI Challenges and Real-World Mitigations

The Future of Edge AI

Exploring the Dynamic World of Edge AI Applications Across Industries


2024 State of Edge AI Report

A.I.

CUDA Cores and Tensor Cores are specialized units within NVIDIA GPUs; the former are designed for a wide range of general GPU tasks, while the latter are specifically optimized to accelerate AI and deep learning through efficient matrix operations.

Tensor Cores vs CUDA Cores: The Powerhouses of GPU Computing from Nvidia

Automation and Industry 4.0 are revolutionizing the manufacturing and industrial sectors.

Everything you need to know about  various types of automation and their role in modern manufacturing systems. 

6 Types of Automation: A Comprehensive Guide for Engineers

In today’s digital age, where information is massively booming, ordinary softwares cannot handle the data because it’s Big Data. In this article, we’ll understand the main characteristics of Big Data i.e., the 3V’s, its technological advancements, applications, challenges, and data security.

Big Data: The 3 V's of Data

An AI-generated depiction of a modern day chipset

Gaining a comprehensive understanding of the distinct capabilities and applications of TPUs and GPUs is crucial for developers and researchers aiming to navigate the complex and rapidly changing terrain of artificial intelligence.

TPU vs GPU in AI: A Comprehensive Guide to Their Roles and Impact on Artificial Intelligence

This article provides an in-depth exploration of the evolution, fundamental concepts, diverse types, operating principles, and practical applications of ASICs.

What is an ASIC: A Comprehensive Guide to Understanding Application-Specific Integrated Circuits

A.I.

2024 State of Edge AI Report

Introduction

1. Edge AI Market Analysis and Trends

2. Healthcare and Medical Applications

3. Industrial IoT and Manufacturing

4. Smart Cities and Urban Infrastructure

5. Retail and Customer Experience

6. Energy Efficiency and Sustainability

7. Agriculture and Food Production

8. Automotive and Transportation

9. Generative AI at the Edge

10. Edge AI Challenges and Real-World Mitigations

11. The Future of Edge AI

Artificial Intelligence: A Comprehensive Guide to its Engineering Principles and Applications

Profiles