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

<section id="isPasted">The European Championship is still in full swing but, at TU Eindhoven, they are already hard at work on the next ‘soccer tournament’, the RoboCup 2024 from July 17-21 in Eindhoven. This is the world championship for robotics and involves fully autonomous robots that play soccer matches, perform household tasks or locate difficult-to-reach places as part of rescue work.</section><section tabindex="-1">Thousands of people are expected to visit Eindhoven for RoboCup 2024, the world’s largest competition in robotics and AI. The best teams from around the world will compete for the world title in various robot competitions.Alongside René van de Molengraft, Professor of Robotics and co-founder of the successful TU/e team (and current robot soccer world champion) Tech United, we look ahead to the robot soccer tournament.A conversation about the latest wonders we can see there, the challenges on the way to achieving the grand goal of defeating human soccer players by 2050, and why soccer robots – and robots in general – perform even better when they are lazy.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721776291270-csm_51632710421_b5699b11ea_o_b7131df8d1.jpg" style="width: 100%px;" class="fr-fic fr-dib">Tech United’s soccer robots, with the goalkeeper on the left and a player on the right. In blue, you can see small omni wheels, which can roll both straight ahead and sideways. Photo: Bart van Overbeeke<section tabindex="-1" id="isPasted"><h3>Tech United team</h3>The soccer robots of TU Eindhoven’s team, Tech United, compete in the Middle Size League (MSL), the most spectacular robot soccer. Their healthcare robot, HERO, takes part in the @Home league.Soccer robots are attracting a lot of attention, especially in the MSL. Using a real soccer ball, they play five-on-five matches in two 15-minute halves on an 18 x 12-meter field. They are programmed in advance but, once the whistle blows, they play soccer completely autonomously and humans are simply spectators.They look like a sort of moving traffic cone on three omni wheels. These wheels cannot steer but can passively roll sideways. Thanks to these wheels, the robots are highly maneuverable and reliable.</section><section tabindex="-1"><h3>The latest wonders</h3>RoboCup primarily serves to stimulate the development of technology in mechatronics, computer science, electronics and AI, among other fields, with competition as the drive. After each tournament, the knowledge and progress are therefore shared with all participants. So, Tech United will have to come up with something new this year to go for its eighth (!) world title.“We’re introducing two new elements this year,” says Van de Molengraft. “The first improvement concerns the wheels. This year, we have a player with three steerable wheels. This allows the robot to accelerate and brake much faster. For now, it will be used as a substitute to see if the robot can handle an entire tournament.”“The other improvement is invisible as it relates to the software. We have completely recreated old code from the very beginning and have adapted it to the rest of the code. This code improves the robots’ movements and therefore basically all soccer actions.”<iframe width="640" height="360" src="https://www.youtube.com/embed/cfx4IONMwSU?&amp;wmode=opaque&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>In this episode of Tiki Taka Touzani, presentor Soufiane Touzani and soccer player Ismael Saibari (PSV) visit team Tech United and show that humans can still play the robots out now. Source: NPO3 / Tiki Taka Touzani<h3 id="isPasted">Defeating human soccer players</h3>Every few years, the RoboCup organization, in which Van de Molengraft is closely involved, draws up new rules for the various leagues. In doing so, they encourage innovations to get closer to the dot on the horizon: defeating a human soccer team by 2050. What will the next challenge be on the way to 2050?<blockquote>We need the switch from wheels to legs to win against people.René van de Molengraft, Professor Robotics</blockquote>“I’m thinking of the switch from wheels to legs. We will have to do that because we really cannot compete against humans using wheels yet. For that, we need to allow games with multi-legged robots in the MSL. That would be an enormous new challenge for Tech United,” he explains.“Of course, you will then see a huge drop-off in match results,” says Van de Molengraft. “But eventually, the robots on legs are going to win out over those on wheels. I’m really looking forward to that!”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721776377105-csm_51633359139_8465ebf61b_o_3b5396a5b7.jpg" style="width: 100%px;" class="fr-fic fr-dib">Tech United's @HOME care robot picks up a cup of tea. Photo: Bart van Overbeeke<h3 id="isPasted">Practical challenges</h3>In addition to the soccer leagues, the RoboCup has other categories in which to compete, such as autonomous healthcare robots and rescue robots. For example, Tech United is participating in the @Home league with their healthcare robot HERO.Within this competition, the focus lies in the development of robots that can provide service and assistance in home or care situations. In the future, the hope is that this will enable elderly people to live at home for longer, for example. Here too, the team is mainly a spectator during the matches.<blockquote>At the RoboCup, practical challenges are at the center of the care and rescue leagues.René van de Molengraft, Professor Robotics</blockquote>“You can see that highly practical challenges are at the center of the other leagues, such as picking up a cup of tea. The care robot has to know how big the cup is and how full it is, but also what material such a cup is made of. A plastic cup is easily crushed, resulting in hot tea everywhere. I see significant progress there, although the step to practice is still sizeable.”“It’s a different story for rescue robots. Robots are already being deployed in rescue work in which humans are difficult to reach or where it is too dangerous. Examples include the disaster at Fukushima or the earthquakes in Turkey and Syria.”“It’s really great to see rescue robots finding living people in incredibly difficult conditions, albeit usually still requiring a human operator to remotely control the robot.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721776418415-csm_51632932133_5d5131f4ce_o_f70bccc248.jpg" style="width: 100%px;" class="fr-fic fr-dib">Close-up of the part that the soccer robot uses to accept the ball and shoot it away. Photo: Bart van Overbeeke<h3 id="isPasted">At the heart of the research</h3>The TU/e team Tech United has been around for almost twenty years now. Van de Molengraft was involved in the foundation of the RoboCup soccer team, taking inspiration from Philips’ then-successful team. A little later, in 2009, followed the start of research into autonomous robotics at TU/e with the RoboEarth<a href="https://www.tue.nl/en/research/research-institutes/robotics-research/projects/roboearth" target="_blank">&nbsp;project</a>, which had European funding.“After that, we started carrying out more and more robotics projects and became a real research group. In 2019, as principal investigator,&nbsp;I was able to start my own robotics group,” he says.“We began with five people and are now a real section with about fifty employees and a number of robotics courses in the Mechanical Engineering bachelor’s program. And, starting next academic year, we will also have our own Robotics master’s track.”All of that research directly benefits the Tech United team as, in addition to students, the team consists of PhD candidates and staff.<h3 id="isPasted">Intelligent laziness</h3>As of this month, Van de Molengraft may call himself a Professor of Lazy Robotics. By ‘lazy robots’, he means robots that intelligently recognize which information they can safely ignore. “Normal robots, such as self-driving cars, consider all of the information around them to be equally important. It doesn’t work that way with humans,” explains Van de Molengraft.“Humans are perfectly capable of ignoring, for instance, visual information that is not important to a task.”“Imagine yourself on a bicycle. You perceive all of the information that you do not require for safe participation in traffic, but you don’t store that information. That person walking out of the supermarket you’re cycling past: unimportant!”“A self-driving car cannot do that yet. It has 20 cameras running simultaneously, viewing and analyzing each image. Everything is equally important, which takes a lot of computing power and a lot of energy. That needs to be improved.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721776461208-csm_52217918068_0eded10aec_k_e581fd5b89.jpg" style="width: 100%px;" class="fr-fic fr-dib">Tech United robots chase the ball. Photo: Tech UnitedHe gives another example: “We humans sit down or lean on a table when we don’t need to move. That costs us less energy. We want the same for robots. They have to become ‘intelligently lazy’, so to speak, and be able to assess which information is highly important for safety or their task and then focus on that.”“But we still have a lot of work to do to get robots to be as intelligently ‘lazy’ as humans are, both in terms of software and hardware. And that’s what we are working on at TU/e.”<h3>Lazy soccer robots</h3>Van de Molengraft laughs at the question of whether the best soccer robots should therefore be lazy. “Yes, that would certainly help. They wouldn’t always have to drive to the right spot down to the millimeter, for example. They only need to do that when receiving and shooting the ball.”“When a robot is simply moving freely, a few centimeters don’t really matter. We could make some gains there or do research on that. It does require some real rethinking on our part, but that’s what makes it such a great challenge that, to this day, never bores.”</section></section>

Tech United is the defending champion in the Middle Size League of the RoboCup, the most spectacular robot soccer. They have already been the world champion seven times. Photo: Bart van Overbeeke

Alongside René van de Molengraft, Professor of Lazy Robotics and founder of Tech United, we dive into the technology behind the RoboCup.

Why smart robots should be as lazy as possible

Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task. The traditional way to verify safety and stability is through techniques called Lyapunov functions. If you can find a Lyapunov function whose value consistently decreases, then you can know that unsafe or unstable situations associated with higher values will never happen. For robots controlled by neural networks, though, prior approaches for verifying Lyapunov conditions didn’t scale well to complex machines.Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and elsewhere have now developed new techniques that rigorously certify Lyapunov calculations in more elaborate systems. Their algorithm efficiently searches for and verifies a Lyapunov function, providing a stability guarantee for the system. This approach could potentially enable safer deployment of robots and autonomous vehicles, including aircraft and spacecraft.To outperform previous algorithms, the researchers found a frugal shortcut to the training and verification process. They generated cheaper counterexamples — for example, adversarial data from sensors that could’ve thrown off the controller — and then optimized the robotic system to account for them. Understanding these edge cases helped machines learn how to handle challenging circumstances, which enabled them to operate safely in a wider range of conditions than previously possible. Then, they developed a novel verification formulation that enables the use of a scalable neural network verifier, α,β-CROWN, to provide rigorous worst-case scenario guarantees beyond the counterexamples.“We’ve seen some impressive empirical performances in AI-controlled machines like humanoids and robotic dogs, but these AI controllers lack the formal guarantees that are crucial for safety-critical systems,” says Lujie Yang, MIT electrical engineering and computer science (EECS) PhD student and CSAIL affiliate who is a co-lead author of a new paper on the project alongside Toyota Research Institute researcher Hongkai Dai SM ’12, PhD ’16. “Our work bridges the gap between that level of performance from neural network controllers and the safety guarantees needed to deploy more complex neural network controllers in the real world,” notes Yang.For a digital demonstration, the team simulated how a quadrotor drone with lidar sensors would stabilize in a two-dimensional environment. Their algorithm successfully guided the drone to a stable hover position, using only the limited environmental information provided by the lidar sensors. In two other experiments, their approach enabled the stable operation of two simulated robotic systems over a wider range of conditions: an inverted pendulum and a path-tracking vehicle. These experiments, though modest, are relatively more complex than what the neural network verification community could have done before, especially because they included sensor models. “Unlike common machine learning problems, the rigorous use of neural networks as Lyapunov functions requires solving hard global optimization problems, and thus scalability is the key bottleneck,” says Sicun Gao, associate professor of computer science and engineering at the University of California at San Diego, who wasn’t involved in this work. “The current work makes an important contribution by developing algorithmic approaches that are much better tailored to the particular use of neural networks as Lyapunov functions in control problems. It achieves impressive improvement in scalability and the quality of solutions over existing approaches. The work opens up exciting directions for further development of optimization algorithms for neural Lyapunov methods and the rigorous use of deep learning in control and robotics in general.”Yang and her colleagues’ stability approach has potential wide-ranging applications where guaranteeing safety is crucial. It could help ensure a smoother ride for autonomous vehicles, like aircraft and spacecraft. Likewise, a drone delivering items or mapping out different terrains could benefit from such safety guarantees.The techniques developed here are very general and aren’t just specific to robotics; the same techniques could potentially assist with other applications, such as biomedicine and industrial processing, in the future. While the technique is an upgrade from prior works in terms of scalability, the researchers are exploring how it can perform better in systems with higher dimensions. They’d also like to account for data beyond lidar readings, like images and point clouds.As a future research direction, the team would like to provide the same stability guarantees for systems that are in uncertain environments and subject to disturbances. For instance, if a drone faces a strong gust of wind, Yang and her colleagues want to ensure it’ll still fly steadily and complete the desired task.&nbsp; Also, they intend to apply their method to optimization problems, where the goal would be to minimize the time and distance a robot needs to complete a task while remaining steady. They plan to extend their technique to humanoids and other real-world machines, where a robot needs to stay stable while making contact with its surroundings. Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering at MIT, vice president of robotics research at TRI, and CSAIL member, is a senior author of this research. The paper also credits University of California at Los Angeles PhD student Zhouxing Shi and associate professor Cho-Jui Hsieh, as well as University of Illinois Urbana-Champaign assistant professor Huan Zhang. Their work was supported, in part, by Amazon, the National Science Foundation, the Office of Naval Research, and the AI2050 program at Schmidt Sciences. The researchers’ paper will be presented at the 2024 International Conference on Machine Learning.

MIT CSAIL researchers helped design a new technique that can guarantee the stability of robots controlled by neural networks. This development could eventually lead to safer autonomous vehicles and industrial robots. Image: Alex Shipps/MIT CSAIL

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

Creating and verifying stable AI-controlled systems in a rigorous and flexible way

<h2 id="isPasted">The hidden waste</h2>At least 15 billion&nbsp;<a href="https://en.wikipedia.org/wiki/Primary_cell" target="_blank">primary batteries</a> are being disposed of yearly worldwide. The world is getting better at recycling, which is excellent news. However, the bad news is that we are still throwing batteries that are not fully utilized. Unused battery capacity is hidden waste that goes unnoticed for consumer electronics, but it is getting noticeably annoying for the businesses within the Internet of Things (IoT) and medical space. IoT and medical device suppliers need to deliver on promised battery life but don’t know how well their devices are using the batteries.<h2>The good or bad fit</h2>Batteries have specific performance characteristics. The electronic device needs to have a matching power consumption profile for the capacity to be fully used. The power consumption profile depends on the choice of hardware, layers of software and connectivity and the actual device usage. Suppose the power profile doesn’t match the battery characteristics. There will be energy that is never extracted, which is valid for primary batteries and rechargeable batteries for any application and industry. There could be a good or a bad fit for your small loT sensor, a smartwatch or the electrical vehicle (EV) that you are developing.Furthermore, the batteries can be of bad quality. For everyone working in development projects where second sourcing of components is essential for the reliability or cost-down activity (and batteries are one of the most expensive components), validating the battery performance is the key. You may have figured out your device’s power consumption performance but are still underutilizing the energy source due to a particular bad battery batch or an overall bad design and quality. The difference between brands is noticeable.<h2>Unused battery capacity</h2>One thing is for sure, there is no such thing as utilizing the battery to 100%, but you can get close. On the other hand, a bad match can be pretty bad. Our testing shows between 30-50%, on average, of capacity never being used.Let’s look at an example of an IoT device with a coin cell battery.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721608956514-connect-device-undertest-to-otii-1024x768.png" style="width: 100%px;" class="fr-fic fr-dib">Table 1. Capacity of batteries from two brands shown for an ideal, data sheet case and actual measured power consumption profile. Used capacity differs between the two batteries despite the same type.The battery data sheets usually show a typical capacity that you could expect if you match your device well with the battery and use it in a normal temperature type of use case. However, suppose the matching is not good or the battery is bad quality. You may be limited to utilizing only 16% of the available energy, as for Battery 1 in table 1 above.&nbsp;If you are interested, you can read the technical benchmark study where this example is from&nbsp;<a href="https://www.qoitech.com/blog/will-my-lorawan-iot-device-work-with-a-coin-cell-battery/" target="_blank">here</a>.&nbsp;<h2>Battery life impacts your business</h2>Unused battery capacity can make or break a business. As a device supplier, you can not guarantee battery life if you don’t understand how well your product and the energy source are matched. The hidden energy waste can mean countless and expensive maintenance and replacements of the batteries and costly IT downtime, either for you or your customer.Let’s play with an example of having 10 000 IoT devices deployed for tracking purposes. In general, we hear five years of battery life presented. Though not guaranteed, this is most likely only calculated by simply dividing battery capacity with an average current across use cases. Also, the battery characteristic is taken from the datasheet for a ‘typical’ use case. In this case, there will be a normal distribution of batteries running out, which means at least twice during the five years, there will be changes in batteries. At the worst, you may be looking at 10 000 x 2 x $300 (approx. cost per battery change) = $6M in maintenance cost per the life cycle. Who is paying for this, you or your customer?If the IoT solution that you are providing is for mission critical purposes, then you have to consider the IT downtime. According the Gartner the downtime can be very expensive, up to $5 600 per minutes are mentioned&nbsp;<a href="https://www.the20.com/blog/the-cost-of-it-downtime/" target="_blank">here</a>. Doing an software over the upgrade for the deployed IoT device and not checking that the upgrade doesn’t have negative impact on the battery life and thus your&nbsp;<a href="https://www.zdnet.com/article/google-nests-battery-drain-chilly-users-turn-up-heat-over-thermostat-software-glitch/" target="_blank">business</a> is really risky.<h2>What can be done?</h2>Start measuring! Gain insights into how your device behaves for different use cases and start characterizing and validating your batteries. It is simple as that. To understand your device, you can use&nbsp;<a href="https://www.qoitech.com/otii-arc-pro/" target="_blank">Otii Arc</a> and&nbsp;<a href="https://www.qoitech.com/automation-toolbox/" target="_blank">Otii Automation Toolbox</a> for software quality assurance. For battery validation and analytics,&nbsp;<a href="https://www.qoitech.com/battery-toolbox/" target="_blank">Otii Battery Toolbox</a> can help. You can&nbsp;<a href="https://www.qoitech.com/demo/" target="_blank">book a demo</a> to see what solution fits your organization the best for characterization, benchmark, and validation of batteries.&nbsp;

At least 15 billion primary batteries are being disposed of yearly worldwide. The world is getting better at recycling, which is excellent news. However, the bad news is that we are still throwing batteries that are not fully utilized.

How much of the IoT battery capacity is actually used?

Explore the future of connected communities in intelligent cities and underserved regions, highlighting the role of technologies like 5G, AI, and blockchain in enhancing connectivity and promoting inclusive development.

Connecting the Future: Bridging the Digital Divide in Intelligent Cities and Underserved Regions

The world of artificial intelligence (AI) has recently seen significant advancements in generative models, a type of machine-learning algorithms that “learn” patterns from set of data in order to generate new, similar sets of data. Generative models are often used for things like drawing images and natural language generation – a famous example are the models used to develop chatGPT.Generative models have had remarkable success in various applications, from image and video generation to composing music and to language modeling. The problem is that we are lacking in theory, when it comes to the capabilities and limitations of generative models; understandably, this gap can seriously affect how we develop and use them down the line.One of the main challenges has been the ability to effectively pick samples from complicated data patterns, especially given the limitations of traditional methods when dealing with the kind of high-dimensional and complex data commonly encountered in modern AI applications.Now, a team of scientists led by Florent Krzakala and Lenka Zdeborová at EPFL has investigated the efficiency of modern neural network-based generative models. The study, now published in&nbsp;PNAS, compares these contemporary methods against traditional sampling techniques, focusing on a specific class of probability distributions related to spin glasses and statistical inference problems.The researchers analyzed generative models that use neural networks in unique ways to learn data distributions and generate new data instances that mimic the original data.The team looked at flow-based generative models, which learn from a relatively simple distribution of data and “flow” to a more complex one; diffusion-based models, which remove noise from data; and generative autoregressive neural networks, which generate sequential data by predicting each new piece based on the previously generated ones.The researchers employed a theoretical framework to analyze the performance of the models in sampling from known probability distributions. This involved mapping the sampling process of these neural network methods to a Bayes optimal denoising problem – essentially, they compared how each model generates data by likening it to a problem of removing noise from information.The scientists drew inspiration from the complex world of spin glasses, materials with intriguing magnetic behavior, to analyze modern data generation techniques. This allowed them to explore how neural network-based generative models navigate the intricate landscapes of data.The approach allowed them to study the nuanced capabilities and limitations of the generative models against more traditional algorithms like Monte Carlo Markov Chains (algorithms used to generate samples from complex probability distributions) and Langevin Dynamics (a technique for sampling from complex distributions by simulating the motion of particles under thermal fluctuations).The study revealed that modern diffusion-based methods may face challenges in sampling due to a first-order phase transition in the algorithm’s denoising path. What this means is that they can run into problems because of sudden change in how they remove noise from the data they're working with. Despite identifying regions where traditional methods outperform, the research also highlighted scenarios where neural network-based models exhibit superior efficiency.This nuanced understanding offers a balanced perspective on the strengths and limitations of both traditional and contemporary sampling methods. The research is a guide to more robust and efficient generative models in AI; by providing a clearer theoretical foundation, it can help develop next-generation neural networks capable of handling complex data generation tasks with unprecedented efficiency and accuracy.<hr>ReferencesDavide Ghio, Yatin Dandi, Florent Krzakala, Lenka Zdeborovà. Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective. PNAS 24 June 2024. DOI:&nbsp;<a href="https://doi.org/10.1073/pnas.2311810121" target="_blank" rel="noopener">10.1073/pnas.2311810121</a>

Researchers at EPFL have made a breakthrough in understanding how neural network-based generative models perform against traditional data sampling techniques in complex systems, unveiling both challenges and opportunities for AI's future in data generation.

Navigating the labyrinth: How AI tackles complex data sampling

Proteins have evolved to excel at everything from contracting muscles to digesting food to recognizing viruses. To engineer better proteins, including antibodies, scientists often iteratively mutate the amino acids – the units that are arranged in a sequence to make up proteins – in different positions until the resulting protein has an improved function, like eliciting a stronger immune response or capturing carbon dioxide from the atmosphere more efficiently.But there are more possible amino acid sequences than there are grains of sand in the world. And finding the best protein and, therefore, the best potential drug, is often expensive or impossible.Stanford scientists have developed a new machine learning-based method to more quickly and accurately predict the molecular changes that will lead to better antibody drugs. Published in Science&nbsp;on July 4, the approach combines the 3D structure of the protein backbone with large language models based on amino acid sequence, and allows researchers to find, in minutes, rare and desirable mutations that would otherwise only be found with exhaustive experiments.Led by <a href="https://profiles.stanford.edu/peterkim" data-extlink="" target="_blank">Peter S. Kim</a>, professor of biochemistry and institute scholar at <a href="https://chemh.stanford.edu/" data-extlink="" target="_blank">Sarafan ChEM-H</a>, and <a href="https://profiles.stanford.edu/brian-hie" data-extlink="" target="_blank">Brian Hie</a>, assistant professor of chemical engineering, the team showed that they could improve a once FDA-approved SARS-CoV-2 antibody that had been discontinued due to its ineffectiveness against a new strain in November 2022. Their approach resulted in a 25-fold improvement against the virus.“A lot of effort in AI and drug development is centered around amassing tons of data about how well a certain molecule performs a certain task so that a computer can learn enough to design a better version,” said Kim. “What’s remarkable is that we’ve shown that structure can be used in lieu of a lot of that data, and the computer will still learn.”“Now, more antibodies actually get a shot at being optimized,” said Hie, who is also an innovation investigator at the<a href="https://arcinstitute.org/" data-extlink="" target="_blank">&nbsp;Arc Institute</a>.<h2>Bent into shape</h2>When faced with the challenge of finding the best amino acid sequence, scientists will often make millions and test them in miniaturized, simplified versions of biological systems. They hope that the best drug in a dish will also be the best drug in humans.“It’s a lot of guess and check,” said Hie. “The goal of a lot of intelligent algorithms is to remove the guesswork from this.”To speed up the process, scientists have developed ChatGPT-like machine learning algorithms that are trained on the amino acid sequences of millions of proteins to predict desirable mutations.These models, however, often point scientists toward sequences that, once produced in the lab, are unstable or worse than where they started.This is partially because protein function depends not only on the sequence of amino acids but also on the 3D structure of that sequence. For example, to trigger an immune response, antibodies must be the right shape to bind to molecules that sit atop the surface of viruses.The key, the team thought, to developing a better prediction algorithm was structure. So, they constrained the long list of possible beneficial mutations – as determined by the sequence-based large language model – to only those that would preserve the 3D shape of the starting protein.&nbsp;<h2>Testing ground</h2>In December 2022, the team put it to the test on a recently discontinued SARS-CoV-2 antibody therapy.“The prevailing theory was that trying to improve this antibody would fail,” said<a href="https://profiles.stanford.edu/varun-shanker" data-extlink="" target="_blank">&nbsp;Varun Shanker</a>, a medical student, graduate student in biophysics, and lead author on the study. “The virus was too smart. It evolved as it spread through millions of people to know exactly how to mutate to avoid these antibodies.”Using purely sequence-based models to optimize the protein resulted in a modest twofold increase in effectiveness. But with their structure-guided approach, the team saw a 25-fold increase.“We were finally catching up to the virus,” said Shanker, who is also a fellow in the Chemistry/Biology Interface Training Program at Sarafan ChEM-H.&nbsp;<h2>Teaching an old model new tricks</h2>Most efforts in using AI to build better drugs rely on “training” or “supervising” the model, which involves generating huge amounts of data about the function and performance of unique protein sequences. This approach takes a lot of time, and results in a model tailored to specific protein performing a specific task.This model does not require any input about what the protein does, how well it does it, or any lab experiments. Because structure is so closely tied to function, the protein’s coordinates become a proxy for performance. For the COVID antibody work, they constrained the structure not just to the antibody itself, but to the antibody when it is bound to the virus. From there, their model “learned” some rules of antibody binding without ever needing to be taught.Early experiments show that the approach is generalizable to other kinds of proteins, like enzymes, which help catalyze chemical reactions in our bodies. So far, the researchers have found that the model points scientists to tens of proteins, and, on average, half are better than the starting point.This tool could be useful to quickly respond to emerging or evolving diseases. It also lowers the barrier to making more effective medicines. Stronger medicines mean lower doses are necessary, which means that a given quantity could benefit more patients. For infectious diseases like HIV, where studies have shown that large but infrequent doses of an antibody can protect patients from infection, this could be transformational.The team is making their model and code freely available to anyone.“This is an exciting example of the power of deep learning to democratize the process of building better proteins,” said Shanker. “This not only allows people to develop new medicines, but also opens up new areas of scientific exploration that had been inaccessible.”Theodora Bruun, a&nbsp;<a href="https://knight-hennessy.stanford.edu/" data-extlink="" target="_blank">Knight-Hennessy Scholar</a>, is a co-author on the study. Kim is a member of&nbsp;<a href="http://biox.stanford.edu/" data-extlink="" target="_blank">Bio-X</a>, the&nbsp;<a href="https://humanperformance.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Human Performance Alliance</a>, the&nbsp;<a href="http://chri.stanford.edu/" data-extlink="" target="_blank">Maternal &amp; Child Health Research Institute</a>, and the&nbsp;<a href="https://neuroinstitute.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Neurosciences Institute</a>. Hie is a member of&nbsp;<a href="http://biox.stanford.edu/" data-extlink="" target="_blank">Bio-X</a>, the Dieter Schwarz Foundation SDS Faculty Fellow at&nbsp;<a href="https://datascience.stanford.edu/" data-extlink="" target="_blank">Stanford Data Science</a>, and an Innovation Investigator at the&nbsp;<a href="https://arcinstitute.org/" data-extlink="" target="_blank">Arc Institute</a>. Shanker is part of the Stanford&nbsp;<a href="https://med.stanford.edu/mstp.html" data-extlink="" target="_blank">Medical Scientist Training Program</a>.&nbsp;

Biochemistry Professor Peter S. Kim co-led research on a new machine learning-based method for designing antibody drugs. The method was shown to improve a once FDA-approved SARS-CoV-2 antibody that had been discontinued due to ineffectiveness. | Steve Fisch

The method, which combines a ChatGPT-like large language model with information about a protein’s 3D shape, could make it easier and faster to develop better medicines for infectious diseases, cancer, and other conditions.

New AI approach optimizes development of antibody drugs

The logistics industry today is grappling with multi-layered challenges, prominently among which is sustainability. Traditional methods of transporting goods, particularly perishable goods, rely heavily on energy-intensive refrigerated cargo trailers, resulting in substantial carbon emissions and operational costs. Researchers at <a href="https://www.tecnalia.com/en/" target="_blank" rel="noopener noreferrer">Tecnalia</a> have found a solution that can disrupt this space and overcome this longstanding challenge: their approach is to harness the potential of low-power IoT technology.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIwNTQwODA0NTM1LTE3MjA1NDA4MDQ1MzUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="401" class="fr-fic fr-dii">In 2023, Mouser Electronics, together with Nordic Semiconductor, Soracom, and Crowd Supply, initiated the <a href="https://www.wevolver.com/article/connect-for-good-low-power-wireless-sustainability-challenge" target="_blank" rel="noopener noreferrer">Connect for Good Challenge</a> with the goal of enabling innovation for social and environmental good. It rewarded the most innovative low-power wireless solutions aiming to encourage efforts towards achieving the UN’s Sustainable Development Goals (SDGs). The winner was a Tecnalia project led by researcher Iván Arakistain, a technologist and researcher at Tecnalia. It focused on sustainable logistics infrastructure, providing a solution for monitoring and detecting anomalies.&nbsp;Central to Arakistain's project is the seamless integration of advanced technology and embedded artificial intelligence (AI) to optimize the transport of perishable goods. Through sophisticated monitoring systems and anomaly detection algorithms, the solution ensures the integrity of the cold chain without the reliance on energy-intensive refrigeration. The incorporation of the <a href="https://www.wevolver.com/specs/nrf9160-development-kit" target="_blank">nRF9160 Development Kit</a>, renowned for its low energy consumption and robust cellular connectivity, underpinned the project's success.&nbsp;<h2 dir="ltr">Leveraging Embedded AI</h2>The Tecnalia solution revolves around a smart device connected to the Internet with multiple sensors and GPS capabilities. These features allow for effective monitoring and tracking of items. The core aspect of this solution lies in integrating AI directly into the device. This embedded AI is miniaturized and used within numerous everyday objects. By leveraging this technology, the project enhances the handling of perishable goods, ensuring that their required temperature levels are maintained throughout transportation. Additionally, the device incorporates a specialized sensor akin to an "electronic nose," which monitors, detects, and alerts users to the presence of harmful organic compounds. This functionality serves to identify spoiled food items, offering an added layer of quality control and safety.&nbsp;This innovative approach sets a new standard for eco-friendly transport in the logistics industry, with far-reaching implications for global sustainability initiatives. Moreover, the project's emphasis on operational efficiency underscores its potential to drive substantial economic benefits for many stakeholders.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIwNTQwODAzOTUxLTE3MjA1NDA4MDM5NTEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="344" class="fr-fic fr-dii"><h2 dir="ltr">Navigating Challenges, Embracing Opportunities</h2>As Arakistain's project gains traction, it is expected to encounter both challenges and opportunities on the path to widespread adoption. Convincing stakeholders to embrace innovative solutions necessitates not only technological prowess but also strategic communication and collaboration. However, initiatives like the <a href="https://www.wevolver.com/article/connect-for-good-low-power-wireless-sustainability-challenge" target="_blank">Connect for Good Challenge</a> and the <a href="https://www.wevolver.com/article/connect-for-good-challenge-winners-logistics-infrastructure-monitoring-smart-bees-and-bioreactors" target="_blank">innovative projects</a> it sparked serve as catalysts for change, fostering innovation and collaboration in pursuit of a more sustainable future.In envisioning the future of sustainable logistics, the future course from Arakistain’s point of view team is clear: a shift to electrified transport is a necessity. The logistics industry will continue to evolve, driven by innovation and collaboration, and with a focus on sustainability and climate action, the prospects for a more sustainable future can grow ever brighter. Arakistain's project explicitly addresses the seventh, eleventh, and thirteenth SDGs, which target accessibility to affordable and clean energy, sustainable cities, and climate action. The success of this project stands as a testament to our ability to innovate and effect change, paving the way for more sustainable logistics and inspiring generations to come to embrace the challenge of building a better world.&nbsp;The path ahead for this solution is laden with both promise and complexity. While the technology demonstrates its efficacy in controlled environments, scaling it across diverse regions and logistics networks presents unique challenges. The logistics industry's heterogeneity and reliance on established practices necessitate careful navigation of regulatory frameworks, infrastructure limitations, and stakeholder engagement.&nbsp;The Tecnalia team must strike a delicate balance between technological innovation and practical implementation. This involves not only refining the technical aspects of the solution but also addressing logistical, regulatory, and cultural barriers to adoption. Collaborating with industry stakeholders, policymakers, and regulatory bodies becomes imperative to ensure the seamless integration of the technology into existing logistics networks.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIwNTQwODA0MjM0LTE3MjA1NDA4MDQyMzQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="401" class="fr-fic fr-dii"><h2 dir="ltr">Economic Implications, Market Dynamics, &amp; New Applications</h2>Beyond its environmental benefits, this project carries significant economic implications for logistics companies and end-users alike. By reducing reliance on energy-intensive refrigeration and optimizing transportation routes, the solution offers substantial cost savings for stakeholders. In fact, by minimizing the transport of refrigerated cargo, the proposed solution promises a 10% reduction in cost per kilometer and no less than 64 tons of annual carbon emissions per truck. Adopting such eco-friendly transport methods would also enhance brand reputation and market competitiveness, positioning companies as leaders in sustainability.&nbsp;This Tecnalia project opens doors to new market opportunities and revenue streams. As consumers increasingly prioritize sustainability, companies that embrace eco-friendly practices stand to gain a competitive edge. The technology not only addresses current market demands but also anticipates future trends, positioning stakeholders for long-term success in an evolving industry that is awaiting change.&nbsp;Furthermore, while this project focuses on revolutionizing cold chain logistics, its impact extends far beyond this niche. The integration of low-power IoT solutions and advanced monitoring systems paves the way for innovation across diverse industries and applications. From structural health monitoring to audio detection and classification, the technology holds promise for addressing a wide range of societal challenges.&nbsp;The Tecnalia team continues to explore novel applications of their technology, leveraging embedded AI and sensor networks to tackle pressing issues in fields such as infrastructure, healthcare, and environmental monitoring. By harnessing the power of data and connectivity, the project exemplifies the transformative potential of technology in addressing complex global challenges. It represents more than just a technological innovation; it embodies a vision for a more sustainable and interconnected world. By leveraging cutting-edge technology and collaborative partnerships, Arakistain and his team pave the way for a future where logistics is not only efficient but also environmentally responsible.&nbsp;For more information, check out our <a href="https://www.wevolver.com/article/towards-sustainable-logistics-infrastructure-an-interview-with-tecnalia" target="_blank">interview with Iván Arakistain</a> about his winning project.

The logistics industry today is grappling with multi-layered challenges, prominently among which is sustainability.

Pioneering Greener Logistics: The Impact of Low-Power IoT Solutions

Explore how IoT revolutionizes remote infrastructure management, enhancing efficiency and resilience with solutions like Particle's M-Series for future-ready connectivity.



Bridges to the Future: Revolutionizing Remote Infrastructure Management with IoT

In a world where connectivity is king, the choice of SIM form factor plays a crucial role in the success of any deployment, from consumer electronics to IoT applications. As a versatile platform, Monogoto supports an array of SIM types, each tailored to meet specific operational needs and strategic goals. Whether you’re prioritizing security, seeking cost-efficiency, or navigating device constraints, we provide a solution to match. This comprehensive comparison of Physical SIM, MFF2 SIM, iSIM, and SoftSIM technologies aims to illuminate their unique advantages, guiding you toward the ideal selection for your project.&nbsp;<h2>A Comparative Look at SIM Form Factors</h2>Delving into the specifics of Physical SIM, MFF2 SIM, iSIM, and SoftSIM, we evaluate critical aspects such as security, OTA updates, SIM storage capabilities, and price. This comparison offers a foundation for understanding which SIM form factor aligns best with your project’s requirements.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1719853142970-Network-Settings-Storage-e.g.-FPLMNPLMN-1536x1086.png" style="width: 100%px;" class="fr-fic fr-dib"><h2>A Note from Monogoto</h2>At Monogoto, we’re thrilled by the shift from Physical SIMs to innovative software-based solutions like SoftSIM. This transition not only reflects our drive for cutting-edge technology but also our commitment to providing flexible and scalable connectivity solutions. As the telecom landscape evolves, our platform continues to support all SIM types, ensuring robust, secure, and adaptable options for every use case.&nbsp;<h2>Conclusion: Tailoring the Choice to Your Needs</h2>Monogoto’s horizontal platform embraces the diversity of SIM technologies, ensuring you have the freedom to choose the form factor that best suits your project’s demands. Whether you’re navigating the complexities of global IoT deployments or launching consumer tech, we are here to support you with state-of-the-art connectivity solutions that meet today’s needs and anticipate tomorrow’s challenges.Special thanks to ChatGPT for assisting in crafting this blog post. The integration of AI-driven insights has been invaluable in presenting a clear and comprehensive perspective on the diverse world of SIM technologies.This addition not only thanks ChatGPT but also underscores the innovative approach Monogoto takes in leveraging advanced technology tools like AI to enhance communication and insight sharing.

In a world where connectivity is king, the choice of SIM form factor plays a crucial role in the success of any deployment from consumer electronics to IoT applications. As a versatile platform Monogoto supports an array of SIM types, tailored to meet specific operational needs and strategic goals. 

The Right Approach for My Use Case. SIM Form Factor.

The micromobility industry is at an exciting crossroads. As cities worldwide strive for smarter, cleaner, and more efficient modes of transportation, the importance of reliable connectivity in the micromobility sector is paramount.&nbsp;Let’s explore the impact of connectivity on micromobility, offering insights into how businesses like&nbsp;<a href="https://getwhizz.com/" data-wpel-link="external" target="_blank" rel="external noopener noreferrer">Whizz</a>, a New York City-based company that offers flexible e-bike rentals tailored for delivery riders, leverage software-defined connectivity to enhance customer satisfaction.<h2>The Right Connectivity Partner is the Backbone of Micromobility</h2>A trustworthy connectivity partner is the backbone of a micromobility provider. It enables real-time data collection and analysis, and, with the right monitoring software, can provide invaluable insights into device performance, user behavior, and operational efficiency.&nbsp;Micromobility device providers continue to be faced with &nbsp;two crucial challenges: maintaining profitability, and ensuring seamless operational integration. Innovators within the industry are pioneering solutions to these obstacles, leveraging technologies such as GPS tracking, AI, and machine learning to enhance user experience and foster brand loyalty, which in turn boosts revenue through repeat customers. Whizz, for example, uses Monogoto’s connectivity solutions to track their bicycles, monitoring operational health—including preventing device theft. &nbsp;<h2>Enhancing Customer Experience and Fostering Brand Loyalty</h2>Software-defined connectivity is revolutionizing how micromobility services operate, providing the agility to adapt to changing environments and user needs. From enhancing service reliability with private networks to automating maintenance through predictive algorithms, this approach is setting new standards for operational excellence and cost efficiency in the micromobility sector.&nbsp;At the heart of micromobility’s future is the imperative to deliver exceptional customer experiences and build lasting brand loyalty. Reducing failure rates and ensuring successful user engagements are critical to achieving this goal. Software-defined connectivity, like that offered by Monogoto, enables service providers like Whizz to develop reliable and user-friendly solutions that align with the high expectations of modern consumers. With Monogoto as their connectivity partner, Whizz can manage and scale their e-bike connectivity effortlessly, ensuring optimal service availability and performance. This technology also allows for better network management, security features, and seamless integration across different communication platforms, enhancing both operational efficiency and customer satisfaction.Watch this webinar featuring Tanya Kashina, Head of Supply Chain at Whizz, to discover more about how software-defined connectivity solves salient challenges in the micromobility industry.&nbsp;WATCH THE WEBINAR&nbsp;<a href="https://www.youtube.com/watch?v=leR1ztubJuE&feature=youtu.be" data-wpel-link="external" target="_blank" rel="external noopener noreferrer">HERE</a>As the micromobility industry evolves, embracing connectivity advancements will be key to unlocking new opportunities for growth and customer engagement. Monogoto stands at the forefront of this journey, offering cutting-edge connectivity solutions that empower micromobility providers to redefine urban transportation.&nbsp;<a href="https://lp.monogoto.io/book-a-demo" data-wpel-link="external" target="_blank" rel="external noopener noreferrer">Book a demo</a> with Monogoto today and take the first step towards a revolutionary experience.

The micromobility industry is at an exciting crossroads. As cities worldwide strive for smarter, cleaner, and more efficient modes of transportation, the importance of reliable connectivity in the micromobility sector is paramount. 

Revolutionizing Transportation: How Connectivity Shapes the Future of Micromobility

In our digitally connected world, seamless integration and communication between devices are not just conveniences but necessities to ensure the success of businesses, whether just starting out or firmly established.The collaboration between Monogoto and Xyte introduces a groundbreaking solution in device management and connectivity. This partnership leverages Monogoto’s robust software-defined connectivity and Xyte’s comprehensive device management capabilities to create a unified platform—Xyte Anywhere. This platform is designed not only to streamline device operations but to enrich the end-user experience with reliability and innovation at its core. As detailed in&nbsp;<a href="https://www.youtube.com/watch?v=u-K44sh9ax8" data-wpel-link="external" target="_blank" rel="external noopener noreferrer">our recent webinar</a>, this collaboration isn’t just about enhancing product capabilities—it’s about transforming devices into service platforms.<h2>Enhancing User Experiences Across Industries</h2>The real-world advantages of the Monogoto and Xyte partnership are demonstrated through its diverse applications across multiple industry sectors. Here are specific examples of how their combined capabilities transform everyday operations and user interactions:<h3>Corporate Digital Signage</h3>In corporate settings, digital signage is crucial for internal communications. With Xyte Anywhere, companies can deploy signage solutions that are instantly connected and managed remotely, bypassing traditional IT setup challenges. This not only speeds up deployment but also ensures that content updates are swift and seamless, enhancing the way information is shared across campuses.<h3>Professional Audio and Video</h3>For the professional audio and video industry, reliable device management is vital. Xyte Anywhere enables remote monitoring and updates, which are essential for events and installations that depend on the flawless operation of audiovisual equipment. This reduces downtime and improves the delivery of media content, directly benefiting event managers and attendees by providing a smoother, uninterrupted experience.<h3>Industrial Automation</h3>In the field of industrial automation, connectivity and remote management are key to ensuring machinery and devices operate without interruption. Xyte Anywhere’s capabilities allow for continuous monitoring and immediate troubleshooting from remote locations, significantly reducing response times to malfunctions and maintaining high levels of productivity.<h2>Driving Business Innovation and Revenue</h2>Beyond improving operational efficiencies, the partnership between Monogoto and Xyte facilitates new business models for device manufacturers. By transforming devices into service platforms, manufacturers can unlock recurring revenue streams through subscriptions and as-a-service models. This shift not only increases profitability but also fosters long-term customer relationships by continuously providing value beyond the initial sale.<h2>A Future-Ready Connectivity Solution</h2>As industries continue to evolve, the demand for integrated and efficient device connectivity solutions grows. Monogoto and Xyte’s partnership is a proactive response to this demand, providing a scalable and robust platform that supports the future needs of businesses and their customers.<h2>Embrace the Connectivity Revolution</h2>For stakeholders in industries ranging from manufacturing to corporate communication and beyond, adopting the Monogoto and Xyte solution means not just keeping pace with technological advancements but leading the charge. Businesses are encouraged to leverage these innovations to enhance their product offerings, improve customer satisfaction, and explore new markets. In essence, the partnership between Monogoto and Xyte isn’t just about technological integration—it’s about redefining how businesses interact with technology to create more dynamic, responsive, and user-centric environments. As we look to a future where connectivity is increasingly synonymous with productivity and innovation, embracing this partnership could be your strategic step towards a more connected and successful enterprise.Device manufacturers and businesses looking to stay ahead in the digital curve are invited to explore the possibilities that this partnership offers. Embrace the future of connectivity with Monogoto and Xyte, and unlock new growth opportunities for your business. Contact us today to learn more about how our solutions can empower your devices and drive your business forward.

In our digitally connected world, seamless integration and communication between devices are not just conveniences but necessities to ensure the success of businesses, whether just starting out or firmly established.

How Transforming Devices into Service Platforms Creates More Revenue for Your Business

Hidden occupational health risks impact both employees and businesses across sectors such as manufacturing, logistics, retail, and construction. By addressing these risks early and promoting safer working conditions, companies can achieve a 46% increase in efficiency!&nbsp;&nbsp;To discuss the critical role of safe work environments, Xsens is hosting a crash course webinar series that consists of two chapters. The first chapter,&nbsp;<a href="https://www.movella.com/resources/live-stream/workplace-ergonomics-maximising-workplace-health-and-efficiency" target="_blank" rel="noreferrer noopener">Workplace Ergonomics: Maximize Employee Well-being and Productivity</a>, was held on April 30, and you can&nbsp;now&nbsp;<a href="https://bit.ly/3W7ePEn" target="_blank" rel="noopener noreferrer">register for the second session</a>, which takes place on July 9.&nbsp;&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/NxUrHnd9xmQ?&amp;wmode=opaque&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">&nbsp;&nbsp;</iframe>&nbsp;&nbsp;Here are the topics we discussed in the first chapter: &nbsp;&nbsp;<ol start="1"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">What is workplace ergonomics, and how does it impact occupational health and safety?&nbsp;&nbsp;</li></ol>Workplace ergonomics is a critical aspect of occupational health &amp; safety. It involves tailoring workspaces and processes in line with human physical and cognitive abilities. It aims to reduce the risk of musculoskeletal disorders (MSDs) and enhance a facility's safety and productivity. Poor ergonomics can lead to significant financial impacts, including high injury costs and operational disruptions. On average, workplace injuries cost businesses over $20,000 per incident and result in about 20 days (about 3 weeks) of lost work time.&nbsp;&nbsp;<ol start="2"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="2" data-aria-level="1">Why is workplace ergonomics so important for organizations? &nbsp;&nbsp;</li></ol>Poor ergonomics in workplaces can result in high injury costs and challenges in maintaining operational continuity. Furthermore, companies need to abide by international standards, such as ISO, OSHA, and EN norms, that serve as objective tools for ergonomic assessments and imply legal consequences for non-compliant facilities.&nbsp;&nbsp;<ol start="3"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="3" data-aria-level="1">What are some of the different ergonomic assessment methods and tools often used by organizations?&nbsp;&nbsp;</li></ol>There are several options for conducting ergonomic assessment today:&nbsp;<ul><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">Traditional ergonomic assessments rely on manual methods such as checklists and video recordings to observe worker postures and movements based on international standards such as ISO and OSHA.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="2" data-aria-level="1">Advanced software tools, including Siemens Process Simulate and CATIA, offer digital workspace design and ergonomic assessment capabilities.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="3" data-aria-level="1">AI-powered video analysis tools automate assessments by tracking joint angles and movements but may not fully capture dynamic work environments.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="4" data-aria-level="1">Motion capture, allowing for rapid data collection and&nbsp;objective&nbsp;analysis, offers a quick return on investment by reducing injury rates and associated costs.&nbsp;&nbsp;</li></ul>Prioritizing improved ergonomics can profoundly impact worker well-being and satisfaction. Businesses can lower costs and boost productivity by reducing injuries and creating safer work environments. &nbsp;&nbsp;&nbsp;Do not miss the second chapter of our webinar series, where we will talk about the role of motion capture in promoting occupational health &amp; safety, real-life use cases, and ROI! Register today:&nbsp;<a href="https://bit.ly/3W7ePEn" target="_blank" rel="noopener noreferrer">https://bit.ly/3W7ePEn</a>&nbsp;&nbsp;

A woman wearing Xsens Awinda suit sorting our fruits in the supermarket environment. The pose analytics are visualized using Scalefit

Improved occupational health and safety can enhance productivity by 46% and boost employee well-being. To address these opportunities, Xsens is launching a crash course on topics like injury risk reduction, regulation compliance, and the positive influence of safer workspaces.

A crash course on occupational health & safety: safer workplaces for improved productivity

IoT devices&nbsp;capable of&nbsp;providing fast and power-efficient location details are in high demand, and nowhere more so than in the $9 trillion global logistics and transportation sector[1].&nbsp;Driven by&nbsp;an e-commerce boom and a shift in consumer preference&nbsp;towards online purchases,&nbsp;logistics&nbsp;companies are&nbsp;shipping&nbsp;a staggering volume of parcels. The biggest e-commerce company of the lot, Amazon, processed 4.79 billion U.S.&nbsp;delivery&nbsp;orders in 2022,&nbsp;equivalent&nbsp;to 13.13 million a&nbsp;day[2].While not every delivery is tracked in ‘real time’ at the parcel level yet, that day will come, particularly for high value or perishable cargoes. As such, logistics companies looking to improve efficiencies and reduce costs are rapidly adopting cellular IoT and Wi-Fi solutions that support real time location.&nbsp;&nbsp;<h2>Cellular IoT and GNSS&nbsp;deliver&nbsp;RTLS</h2>To provide real time location, such an IoT device can employ cellular IoT wireless connectivity in concert with GNSS capability—for example Nordic Semiconductor’s&nbsp;<a href="https://www.nordicsemi.com/Products/Wireless/Low-power-cellular-IoT/Products?lang=en#infotabs" target="_blank">nRF91 Series</a>&nbsp;SiPs—which is generally accurate enough (within 10 meters) for many asset tracking applications.&nbsp;&nbsp;GNSS alone, however, is far from perfect. It won’t work indoors, and even outdoors the relatively weak signal can easily be disrupted by tall buildings, as well as other radio sources. Another challenge for GNSS-based asset tracking is the time it can take to secure a fix, and the battery drain that causes on an already resource-constrained device.&nbsp;<h2>Location and&nbsp;low-power</h2>One solution to the issue of battery drain is to use Assisted- and Predicted-GPS (A-GPS and P-GPS), that employ satellite assistance data stored in a Cloud database, relayed to the IoT device via an LTE-M or NB-IoT network, saving significant power compared to an extended first fix. Another option is to use the known location of cellular base stations to narrow down the position of the receiver. Single-cell (SCELL) and multi-cell (MCELL) location methods rely on identifying in which cell the tracked device is situated, and then referencing the cell ID against a database of known base station locations.&nbsp;&nbsp;A further locationing technique which complements cellular IoT and GNSS is Wi-Fi Service Set Identifier (SSID) scanning. Every Wi-Fi access point network is identified with an SSID, and with knowledge of the SSID, it’s possible to cross reference against Cloud databases to approximately locate the position of an IoT device.&nbsp;&nbsp;<h2>The power of Cloud&nbsp;services</h2>To take advantage of these various locationing technologies, IoT asset tracking devices require not only LTE-M and NB-IoT cellular connectivity and GNSS, but also Wi-Fi (if using SSID locationing), and the Cloud services to support them.&nbsp;&nbsp;This is why Nordic Semiconductor has backed its cellular IoT hardware offering—the nRF91 Series SiPs—and it’s Wi-Fi solutions—the&nbsp;<a href="https://www.nordicsemi.com/Products/Wireless/WiFi/Products?lang=en#infotabs" target="_blank">nRF70 Series</a>&nbsp;Companion ICs—with&nbsp;<a href="https://nrfcloud.com/?__hstc=8439722.02bd342c1db7d9a6a838dac1f5f506c2.1719564172438.1719564172438.1719564172438.1&__hssc=8439722.3.1719564172438&__hsfp=3048804036#/" target="_blank">nRF Cloud</a>&nbsp;and&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services" target="_blank">Cloud Services</a>. By providing hardware, software, and Cloud services, Nordic offers a complete solution.&nbsp;&nbsp;For real time asset tracking, Nordic’s&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services/Location-Services?lang=en#infotabs" target="_blank">Cloud Location Services</a>&nbsp;can be used to assist devices and customer applications that need SCELL, MCELL, A-GPS, P-GPS and/or Wi-Fi location features, guaranteeing they will last longer in the field while also sending accurate location data to the Cloud. These features can all be implemented in the same device and used when needed, depending on accuracy requirements at any given time.&nbsp;&nbsp;For example, if you want to track an asset moving between two locations, precise location is likely not needed, as long as it can be determined that the asset is moving towards the destination. That means an asset tracker powered by an nR91 Series SiP can use SCELL or MCELL location, while also ensuring minimal power consumption.&nbsp;&nbsp;As the asset gets closer to delivery, more precise location data might be necessary, at which point Nordic’s Cloud Location Services can dynamically enable GNSS-based location or even Wi-Fi location (assuming the asset tracker employs Wi-Fi), to determine a more precise location and track the asset all the way to its destination.&nbsp;&nbsp;<h2>Cloud-based security services</h2>In addition to location support, these devices also need security credentials to connect to the Cloud. Securely provisioning devices can be complex and costly, and typically involves creating proprietary infrastructure and processes. Nordic’s Cloud Security Services solve these problems by providing a secure and unique identity for devices that can be used for authentication with Nordic Root of Trust, as well as secure provisioning to configure devices remotely with the required credentials.&nbsp;For the developer this means they can ensure only authentic Nordic devices are used in their supply chain, and that their application firmware only runs on authentic Nordic silicon. It also protects against counterfeiting and cloning, and verifies the devices are what they claim to be, before they connect to the Cloud.&nbsp;&nbsp;This combination of Cloud location and security services will spur more innovative applications by relieving companies of the burden of managing and integrating their devices, allowing them to focus their efforts on developing new and innovative solutions.&nbsp;<hr><h3>References</h3>1. Logistics Market Size and Growth 2024 to 2033. Precedence Research, June 2024.&nbsp; 2. Amazon Logistics Statistics. CapitalOne Shopping Research, October 2023.&nbsp;

While not every delivery is tracked in ‘real time’ at the parcel level yet, that day will come, particularly for high value or perishable cargoes. Logistics companies looking to improve efficiencies and reduce costs are rapidly adopting cellular IoT and WiFi solutions that support real time location

The Power of Cloud Services

<table style="width: 100%;"><tbody><tr><td style="width: 100%;">SAE will host a two-day training course, "Machine Learning for Safety Experts," with their partner Fraunhofer IKS on October 22-23, 2024, in Munich. The course aims to equip safety engineers with the knowledge to integrate Machine Learning into safety-critical applications while adhering to standards like ISO 26262 and ISO 21448. Using established methods, participants will learn to develop safety lifecycles, derive safety requirements, and address Machine Learning insufficiencies. The training is essential for engineers looking to enhance their skills in ensuring the safety of Machine Learning based functions. <a href="https://www.sae.org/learn/content/c2208/?utm_source=https://www.sae.org/learn/content/c2208/&utm_medium=facebook,_email,_instagram,_linkedin_etc&utm_campaign=sae_ml_training" target="_blank" class="button-main-action">Read more or register for the event here</a>.</td></tr></tbody></table>Machine Learning (ML) is increasingly pivotal across various engineering disciplines, transforming problem-solving and innovation capabilities. In the automotive sector, Machine Learning drives advancements in autonomous driving systems, enhancing vehicle safety and functionality. Healthcare benefits from Machine Learning through improved diagnostic accuracy and patient care personalization, while industrial automation optimizes efficiency and predictive maintenance, reducing downtime and costs.However, as Machine Learning becomes integral to engineering solutions, the imperative for engineers to continuously advance their understanding of Machine Learning safety grows. Integrating Machine Learning into safety-critical applications presents a unique challenge, especially in adhering to stringent safety standards.The ISO 26262 and ISO 21448 standards are particularly pertinent, focusing on automotive safety and the safety of intended functionality, respectively. These standards guide engineers in developing, validating, and certifying Machine Learning based systems to ensure they meet rigorous safety requirements.&nbsp;The upcoming "Machine Learning for Safety Experts" training by SAE and Fraunhofer IKS in Munich is a timely initiative. It will address this crucial skill gap and ensure engineers are well-equipped to handle the intricacies of safe Machine Learning applications.<h2 dir="ltr">Ensuring Safety in Machine Learning Applications</h2>Integrating Machine Learning with established safety standards, particularly in sectors like automotive, where safety is paramount, presents significant challenges. The complexity of Machine Learning systems, which often behave unpredictably, necessitates adherence to robust safety protocols to mitigate risks not traditionally accounted for in older standards.<h3 dir="ltr">Overview of Relevant Safety Standards</h3>Both ISO 26262 and ISO 21448 are critical in guiding the integration of Machine Learning into safety-critical applications. They provide a structured framework for evaluating and managing the risks associated with Machine Learning applications in automotive and other engineering fields. This ensures that vehicles and other engineered systems become more autonomous and remain safe for end-users under all conditions.The interaction between these standards and Machine Learning development is a growing area of focus as engineers seek to align cutting-edge technological advancements with rigorous safety requirements. Ensuring the safety of Machine Learning applications requires a balance between innovation and stringent compliance with these evolving safety standards.<h3 dir="ltr">Challenges of Integration with Safety Standards</h3>One of the principal standards, ISO 26262, focuses on functional safety for all road vehicles, highlighting the need for risk assessment and mitigation strategies throughout the product lifecycle. This standard has been crucial in addressing risks associated with electrical and electronic systems in vehicles. However, the integration of Machine Learning brings new dimensions to these challenges because Machine Learning systems can fail in ways that are hard to predict with traditional safety analysis techniques.ISO 21448, also known as Safety of the Intended Functionality (SOTIF), complements ISO 26262 by addressing safety risks in vehicle operations that occur without system failures. This includes scenarios where vehicles perform as designed but fail to handle unexpected real-world conditions, such as unusual weather or road obstacles. SOTIF guides how to design, verify, and validate Machine Learning systems to ensure they behave safely under various operating conditions.<h2 dir="ltr">SAE Course</h2>The "Machine Learning for Safety Experts" course by SAE and Fraunhofer IKS is designed to train safety engineers in integrating machine learning into safety-critical applications.&nbsp;Scheduled for October 22-23, 2024, in Munich, this training focuses on applying fundamental safety principles of Machine Learning across various applications. Participants will learn to develop Machine Learning safety-related functions, derive safety requirements, and address Machine Learning insufficiencies through a comprehensive toolkit of methods. This course is crucial for engineers aiming to enhance the safety of Machine Learning functions in automotive and other critical sectors.&nbsp;For more details, <a href="https://www.sae.org/learn/content/c2208/?utm_source=https://www.sae.org/learn/content/c2208/&utm_medium=facebook,_email,_instagram,_linkedin_etc&utm_campaign=sae_ml_training" target="_blank">Check the course details here.</a><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE5NDk0MjIwMTMxLXJvYWQgY3Jvc3MuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Course participants will learn to develop Machine Learning-based safety functions, establish safety requirements, and address Machine Learning deficiencies using a comprehensive set of methods. Image credit: City of Bellevue, the UW and Microsoft.<h3 dir="ltr" id="isPasted">Key Learning Objectives of the Training</h3>The Machine Learning for Safety Experts training is designed to provide safety engineers with the essential knowledge and skills to integrate Machine Learning (ML) into safety-critical applications, ensuring compliance with standards like ISO 26262 and ISO 21448.<h4 dir="ltr">Understanding the Impact of Machine Learning on Functional Safety&nbsp;</h4>One of the primary learning outcomes is recognizing the profound impact of Machine Learning on functional safety. Participants will gain insights into how Machine Learning can influence the safety lifecycle of systems, especially in the context of automotive safety. This includes understanding the potential risks and the necessity of robust safety arguments to mitigate these risks effectively.<h4 dir="ltr">Applying Machine LearningSpecific Safety Measures&nbsp;</h4>The course emphasizes the application of Machine Learningspecific safety measures. This includes learning about the methodologies and tools tailored to address the unique challenges of Machine Learning algorithms. Participants will be trained on runtime monitoring, robustness testing, and other techniques to ensure that Machine Learning systems perform reliably under diverse conditions, aligning with the stringent requirements of ISO 26262 and SOTIF.<h4 dir="ltr">Developing Safety Requirements for Machine LearningBased Systems</h4>A crucial part of the training is the development of safety requirements specific to Machine Learningbased systems. Engineers will learn to derive and implement safety requirements from a system-level perspective, ensuring that Machine Learning components meet the necessary safety standards. This involves comprehensive training on validating and verifying Machine Learning systems to ensure they adhere to the safety protocols outlined in ISO 26262 and ISO 21448.This training equips engineers with the expertise to navigate the complexities of integrating Machine Learning into safety-critical systems, making it an essential program for those looking to stay ahead in the rapidly evolving field of safety engineering.<h2 dir="ltr">Practical Benefits of the Training</h2>The "Machine Learning for Safety Experts" training is invaluable for engineers addressing prevalent Machine Learning safety challenges in real-world applications. This training equips participants with the necessary skills and knowledge to effectively integrate Machine Learning into safety-critical systems, ensuring adherence to stringent safety standards like ISO 26262 and ISO 21448. The course will discuss Machine Learning in three core application areas of the Automotive Industry, Healthcare, and Manufacturing.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE5NDk0MjQ2MjkzLWNyb3NzIDMuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Image credit:&nbsp;<a href="https://link.springer.com/chapter/10.1007/978-981-15-6728-5_2#auth-Zhanxiang-Chai" target="_blank" id="isPasted">Zhanxiang Chai</a> et al.&nbsp;<h2 dir="ltr" id="isPasted">Who should attend? &nbsp;</h2>The "Machine Learning for Safety Experts" training course is tailored for automotive safety engineers at all experience levels who must assess and ensure the safety of functions and systems incorporating Machine Learning (ML). This course is especially relevant for professionals working with safety-critical applications in the automotive industry, where compliance with standards such as ISO 26262 and ISO 21448 is crucial.<h3 dir="ltr">Course prerequisites:</h3><ul><li dir="ltr">Pre-existing Safety Knowledge: Participants should have a solid understanding of general safety principles and practices.</li><li dir="ltr">Experience with Safety Standards: Familiarity with applying ISO 26262 (functional safety for road vehicles) and ISO 21448 (safety of the intended functionality) is essential.</li><li dir="ltr">Basic Understanding of Machine Learning Techniques: A fundamental grasp of Machine Learning concepts and techniques is required to benefit fully from the training.</li></ul><h2 dir="ltr">Expert Instructors and Their Credentials</h2>The training is led by two distinguished experts from Fraunhofer IKS:<h3 dir="ltr">Karsten Roscher</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE5NDk0MDg4MTMzLWthcnN0ZW4uanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">With over a decade of research experience in connected mobility, advanced driver assistance systems, and Machine Learning for computer vision and communication systems, Karsten is a leading advocate for safe intelligence. He heads the Dependable Perception &amp; Imaging department at Fraunhofer IKS and is actively involved in German and European standardization bodies. His extensive work developing reliable perception systems across automotive, rail, industrial automation, and medical domains highlights his deep expertise in integrating Machine Learning with safety standards.<h3 dir="ltr">Philipp Schleiss</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE5NDk0MTEzMzM4LXBoaWxpcHAtc2NobGVpc3MuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Philipp has been a pivotal figure at Fraunhofer since 2013, focusing on increasing the dependability of embedded and autonomous systems. He leads the department in systems safety engineering and specializes in tool-based design automation, continuous safety assurance, and real-time guarantees for automated driving. Philipp's contributions to advancing safety standards and his role in developing methods for the safe implementation of Machine Learning in autonomous systems make him an invaluable resource for course participants.These instructors bring a wealth of knowledge and practical experience, ensuring participants gain theoretical insights and actionable skills to apply Machine Learning safety measures in their professional practices effectively.&nbsp; <a href="https://www.sae.org/learn/content/c2208/?utm_source=https://www.sae.org/learn/content/c2208/&utm_medium=facebook,_email,_instagram,_linkedin_etc&utm_campaign=sae_ml_training" target="_blank" class="button-main-action">Find out more information here.&nbsp;</a><h2 dir="ltr">Conclusion</h2>Staying current in Machine Learning safety practices is crucial in today's rapidly evolving technological landscape. The "Machine Learning for Safety Experts" training by Fraunhofer IKS provides engineers with the tools and knowledge needed to integrate Machine Learning into safety-critical applications effectively. Continuous learning and professional training are essential to keep pace with technological advancements and ensure the safe deployment of Machine Learning systems in various industries. This course equips participants to lead their teams confidently, ensuring that safety remains a top priority as they leverage the power of advanced Machine Learning techniques.<h2 dir="ltr">References</h2><ol><li dir="ltr"><a href="https://www.iks.fraunhofer.de/en/events/machine-learning-for-safety-experts1.html" target="_blank">https://www.iks.fraunhofer.de/en/events/machine-learning-for-safety-experts1.html</a></li><li dir="ltr"><a href="https://www.sae.org/learn/content/c2208/" target="_blank">https://www.sae.org/learn/content/c2208/</a></li><li dir="ltr"><a href="https://saemobilus.sae.org/courses/machine-learning-safety-experts-c2208" target="_blank">https://saemobilus.sae.org/courses/machine-learning-safety-experts-c2208</a></li><li dir="ltr"><a href="https://www.iks.fraunhofer.de/en/services/ai-act-high-risk-systems.html" target="_blank">https://www.iks.fraunhofer.de/en/services/ai-act-high-risk-systems.html</a></li><li dir="ltr"><a href="https://www.automate.org/ai/industry-insights/ai-in-the-real-world-4-case-studies-of-success-in-industrial-manufacturing" target="_blank">https://www.automate.org/ai/industry-insights/ai-in-the-real-world-4-case-studies-of-success-in-industrial-manufacturing</a></li><li dir="ltr"><a href="https://integranxt.com/blog/success-stories-how-ml-solutions-transformed-businesses-worldwide/" target="_blank">https://integranxt.com/blog/success-stories-how-Machine Learningsolutions-transformed-businesses-worldwide/</a></li><li dir="ltr"><a href="https://www.omdena.com/blog/machine-learning-examples" target="_blank">https://www.omdena.com/blog/machine-learning-examples</a></li><li dir="ltr"><a href="https://learntube.ai/blog/data-science/machine-learning/real-world-applications-of-machine-learning-case-studies-and-success-stories/" target="_blank">https://learntube.ai/blog/data-science/machine-learning/real-world-applications-of-machine-learning-case-studies-and-success-stories/</a></li><li dir="ltr"><a href="https://pg-p.ctme.caltech.edu/blog/ai-ml/machine-learning-in-healthcare-applications-use-cases-careers" target="_blank">https://pg-p.ctme.caltech.edu/blog/ai-ml/machine-learning-in-healthcare-applications-use-cases-careers</a></li><li dir="ltr"><a href="https://ar5iv.org/pdf/1908.04674" target="_blank">https://ar5iv.org/pdf/1908.04674</a></li><li dir="ltr"><a href="https://www.frontiersin.org/articles/10.3389/fcomp.2023.1132580/full" target="_blank">https://www.frontiersin.org/articles/10.3389/fcomp.2023.1132580/full</a></li><li dir="ltr"><a href="https://www.ptc.com/en/blogs/alm/iso-26262-vs-sotif-iso-pas-21448-whats-the-difference" target="_blank">https://www.ptc.com/en/blogs/alm/iso-26262-vs-sotif-iso-pas-21448-whats-the-difference</a></li></ol>

The upcoming "Machine Learning for Safety Experts" training by SAE and Fraunhofer IKS in Munich on October 22-23, 2024 is a timely initiative. It will address this crucial skill gap and ensure engineers are well-equipped to handle the intricacies of safe Machine Learning applications.

Mastering Machine Learning for Critical 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

Robotics Software

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

Born in the ‘80s, the CAN bus helps in carrying reliable electronic communication within your vehicles. This article delves into the basic principles, architecture, protocols, applications, and limitations of the CAN bus.

Understanding CAN Bus: A Comprehensive Guide

An engineer using SCADA to monitor and control industrial processes via PLC.

PLCs bring precision control to critical processes, while SCADA empowers real-time data visualization and decision-making. In today's fast-paced industrial landscape, the ability to distinguish between PLC and SCADA is vital to ensure optimized production and efficient operations.


PLC and SCADA: Understanding the Differences in Industrial Automation Systems

Sensors are ubiquitous in our modern world, playing pivotal roles in numerous sectors. This article delves into their fundamental principles, diverse types, and their significant impact across industries.

What is a Sensor? An In-Depth Exploration and Comprehensive Guide to Engineering Principles and Applications

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Half Duplex vs Full Duplex: Understand how these protocols affect data transmission, learn their advantages and limitations, and discover how they are utilized in various networking technologies.

Half Duplex vs Full Duplex: The Role of Transmission Modes in Networking

A Comprehensive Guide to Understanding Factors, Challenges, Limitations, and Real-World Applications for Optimized Communication Speed and Reliability


Understanding Baud Rate: Why is it Important?

Robotics Software

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

Profiles