Altair’s Future.Industry 2025, a premier global virtual event, united professionals, innovators, and technology enthusiasts to explore the convergence of advanced technologies and sustainability. Over two days, participants delved into how cutting-edge innovations can drive sustainable development across various sectors, including engineering, high-performance computing (HPC), and product design. Featuring panel discussions, keynote presentations, and networking opportunities, Future. Industry 2025 highlighted the transformative role of technology in fostering a more sustainable and inclusive future. The event underscored the potential of these advancements to address global challenges and create meaningful progress toward a greener tomorrow.<h1>Setting the Stage: The Role of AI in Advancing Sustainability</h1>As global efforts to combat climate change and optimize resource consumption intensify, the timing of this event could not have been more critical. Advanced technologies, which can analyze vast amounts of data and automate complex processes, have become powerful tools for enabling sustainable practices. From reducing energy consumption in data centers to improving product lifecycles and minimizing waste, these technologies offer organizations a wide range of opportunities to align innovation with environmental responsibility.Future. Industry 2025 expanded on these themes by hosting sessions that explored the environmental, societal, and economic impacts of technological advancements. One attendee captured the event's spirit, stating, “Wonderful platform to see how many people are using AI and real-world examples helped provide some good insights.” This sentiment reflected the enthusiasm shared by participants, many of whom attended to learn best practices and discover the latest breakthroughs in sustainable computing and product design.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743078560733-CORP_Event_Digital_Wevolver_FI_1200x675-01.jpg" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Key Panels: Exploring the Frontiers of Innovation</h2><h3>1. Sustainable Computing for HPC and AI</h3>One of the most highly anticipated sessions was the panel on Sustainable Computing for HPC and AI, where industry experts explored strategies to reduce energy consumption and carbon footprints in large-scale computing environments. The discussion delved into emerging trends, including advancements in cooling systems, AI-driven workload optimization, and innovative hardware solutions designed to align with sustainability objectives.The session received widespread acclaim from attendees for its depth and clarity. Participants shared feedback such as, “The sessions were informative and thought-provoking” and “This program gave me good insight on integrating AI with mechanical engineering perspectives.” Another attendee remarked, “The session is very good and helpful,” emphasizing the practical value of the frameworks and insights presented, which could be readily implemented within their organizations.Watch the full session here: <a href="https://altair.com/resource/sustainable-computing-for-hpc-and-ai-navigating-the-future-of-high-performance-and-ai-workloads?lang=en">Sustainable Computing for HPC and AI</a><h3>2. Engineering 2030: AI’s Impact on Teams, Tools, and Innovation</h3>In the Engineering 2030 session, speakers explored how AI transforms product development, manufacturing, and design processes. The discussion highlighted the convergence of predictive analytics, machine learning algorithms, and simulation tools, showcasing how these technologies streamline workflows and enable engineers to innovate at an unprecedented pace. Attendees found the insights particularly enlightening, especially in fields traditionally dominated by mechanical processes. One participant shared, “It also opened up my views as to how powerful Altair products can be, not just in mechanical engineering aspects,” and another expressed hope for “some demo on how to integrate AI to simulations” in future events. These comments underscored the importance of detailed demonstrations in bridging the gap between AI theory and its real-world application, highlighting the demand for actionable insights that participants can directly apply in their work.Watch the full session here: <a href="https://altair.com/resource/engineering-2030-how-ai-will-reshape-teams-tools-and-innovation?lang=en">Engineering 2030: How AI Will Reshape Teams, Tools, and Innovation</a><h3>3. Accelerating Action: Empowering Women to Lead the Change</h3>Diversity and inclusion were front and centre in this inspiring session, where panellists explored how organizations can cultivate environments that support women in technology and encourage them to step into leadership roles. The conversation highlighted the challenges and triumphs experienced by women in traditionally male‐dominated fields, emphasizing the importance of building supportive networks, offering mentorship opportunities, and recognizing the immense value women bring to innovation. Several participants shared their overall enthusiasm for the event, with comments like “The event was splendid,” capturing the excitement around seeing women leaders shape conversations about the future of AI and sustainability. Their stories and perspectives demonstrated that empowering women is not merely a moral imperative—it’s also a catalyst for driving better outcomes, from product design to organizational culture. The session offered a clear roadmap for creating more inclusive practices and inspired attendees to champion diversity as a core element of sustainable progress.Watch the full session here: <a href="https://altair.com/resource/accelerating-action-panel-on-empowering-women-to-lead-the-change?lang=en">Accelerating Action: Empowering Women to Lead the Change</a><h2>Spotlight on Industry Perspectives</h2>In addition to the panel sessions, Future.Industry 2025 featured presentations and collaborations that provided unique insights from industry leaders and innovators:<ul><li>Landon Cooper, Design Lead at FRC 862 Lightning Robotics, shared how the event’s collaborative spirit fueled his team’s approach:</li></ul>“Getting to work with Erik on presenting my team’s work was really great. The feedback of his team truly helped me learn Altair better for use on my robot!”<ul><li>Ryan Smith, Global Marketing and Strategy Director at Dow, participated in the Enlighten Award sustainability panel and highlighted the role of innovation and collaboration in shaping sustainable solutions:</li></ul>“Altair’s Future.Industry 2025 was an exceptional experience. The event was well-organized and provided a fantastic platform for discussing innovative solutions in sustainability. I particularly valued the opportunity to participate in the Enlighten Award sustainability panel, where I had the opportunity to discuss Dow’s SPECFLEX CIR, a Sustainable Polyurethane Solution. The discussions on AI ethics, inclusive innovation, and the role of simulation in driving sustainable design were incredibly insightful and inspiring. Overall, it was a great opportunity to connect with industry leaders and explore the future of sustainable technology.”<ul><li>Dr. Mamta Arora, Manav Rachna University, emphasized the value of knowledge exchange and future collaborations:</li></ul>“Altair’s Future.Industry 2025 was an enriching experience, offering insightful discussions on cutting-edge industry trends. As a speaker, I truly valued the engaging audience, seamless event organization, and the opportunity to contribute to meaningful conversations on AI-driven innovations. The conference provided a great platform for knowledge exchange, and I look forward to future collaborations.”These firsthand accounts underscore the event’s broad appeal, spanning academia, youth innovation programs, and large multinational organizations.<h2>Explore the Resource Library: Your Next Steps</h2>For those who missed out or wish to revisit the insights shared at Future.Industry 2025, Altair has curated a comprehensive resource library. From the Sustainable Computing session that examines HPC and AI energy efficiency to the Engineering 2030 panel that outlines how AI will transform design processes, all the recordings are now available for on-demand viewing.Browse all the recorded sessions here: <a href="https://events.altair.com/future-industry-2025/?utm_campaign=GLB-2025-03-ALT-CVG-Future-Industry&amp;utm_source=wevolver.com&amp;utm_medium=sponsored-article&amp;utm_content=future-industry-2025-wevolver-sponsored-article-two">Future.Industry 2025 Resource Library</a>Moreover, Altair has also made individual sessions available for those looking for a more focused learning experience. Delve deeper into the topics that resonate most with you, whether it’s HPC optimization, next-generation engineering workflows, or the role of diversity in driving technology breakthroughs. Additionally, explore highlights from five can’t-miss presentations that cover just-in-time manufacturing with 3D printing, the evolving role of AI in engineering, sustainable design merging ecology with technology, digital twins, and the power of diversity in innovation. For more details, check out Altair’s summary of these presentations <a href="https://altair.com/blog/articles/five-cant-miss-presentations-from-future-industry-2025">here</a>.<h2>Conclusion</h2>Future. Industry 2025 successfully merged visionary ideas with practical insights, creating a platform to explore AI’s role in driving sustainability. Attendee feedback highlighted enthusiasm for advanced simulation tools and a strong demand for more real-world demonstrations, reflecting a community eager to learn, collaborate, and innovate.

Altair’s Future.Industry 2025, a premier global virtual event, united professionals, innovators, and technology enthusiasts to explore the convergence of advanced technologies and sustainability.

Pioneering a Sustainable Future with AI: Highlights from Altair's Future. Industry 2025

A researcher in the Department of Computer Science and Engineering is developing a security-focused large language model to defend against malware.

"ChatGPT" for Computer Security

The research is part of an attempt to grapple with the effort to make expensive, long-term investments to mitigate the impacts of climate change. The substantial uncertainty related to long-term climate change makes it difficult for political leaders to make investments now that are designed to protect citizens for decades or longer. The difficulty is enhanced by the vast number of variables that go into any such decision and by the fact that the variables are likely to shift in unforeseen ways.In a March 18 <a href="https://www.pnas.org/doi/10.1073/pnas.2402826122">article </a>in the Proceedings of the National Academy of Sciences, the researchers looked at flooding, which has caused increasing damage along the coastal United States and around the world. Governments are building coastal defenses against flooding, but they cannot rely on past conditions to guide defenses that will be needed in the future.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742909563584-16x9-for-web-Rutgers-Cape-Shore-Lab-Berm-1536x864.jpg" style="width:100%px" class="fr-fic fr-dib" />The Rutgers Cape Shore Laboratory is protected by a berm at its location on the Delaware Bay in Cape May Courthouse, New Jersey.“Defenses are being built to protect coastal regions for the next few decades or longer,” said co-author <a href="https://engineering.princeton.edu/faculty/ning-lin">Ning Lin</a>, a professor of <a href="https://cee.princeton.edu/">civil and environmental engineering</a> at Princeton. “Climate projects are largely uncertain over long time horizons.”Lin said that to deal with this uncertainty, planners must be flexible and ready to adapt their plans to future observation of climate conditions. Although this is extremely challenging because of the complexity of climate science, Lin said that harnessing advances in data science can provide an effective strategy.<a href="https://www.bobkopp.net/">Robert Kopp</a>, a co-author of the study and a distinguished professor of Earth and Planetary Sciences at Rutgers said uncertainty about the impact of melting ice sheets on sea levels has led to “controversy about how planners should consider the possibility of rapid ice-sheet loss.Kopp said that flexible approaches can help communities prepare for worst-case scenarios without paying too much for protection.  “Planning for high-end sea-level rise costs a lot, and there’s a good chance it won’t be necessary, but failing to plan for it can be devastating,” he said.In the PNAS article, the team describes how they simulated efforts to defend Manhattan against sea level rise through the end of this century. The goal was to determine whether any decision-making process that systematically incorporates observations and updating would prove superior to others over such a long period of time. To do this, the researchers simulated decisions by city planners in 10-year intervals up to the year 2100. The researchers compared their decision-making process with existing methods. For example, using the static method of building a seawall for a historic 100-year flood plus a sea-level-rise projection — as currently applied by New York and other coastal cities — is one method; designing a dynamic seawall that will be increased in height over time according to projected future climate change is another.The researchers graded each method by its efficiency — the cost of defenses plus the estimated damage caused by flooding. For example, spending $10 million on a seawall that allowed $50 million in property damage ($60 million cost) would be less efficient than spending $30 million on a seawall with $15 million in property damage ($45 million cost). The researchers found that calculations of a dynamic seawall based on reinforcement learning that systematically incorporates observations of sea level rise over time increased efficiency when compared with other methods (detailed in the paper). Compared with other systems, reinforcement learning lowered costs by 6-36% in a scenario modeling climate change under moderate carbon emissions. For a high emissions scenario, the decrease was 9-77%.Reinforcement learning is a type of machine learning in which a program makes decisions and receives positive reinforcement based on results. Designers train the program by running it through vast amounts of simulated decisions, and it learns by trial and error rather than through explicit instructions from programmers. This is essentially the way many AI systems operate. It is particularly effective for situations that are extremely complex and subject to rapid changes over time. Computer scientists have used reinforcement learning to train AI to perform tasks such as playing chess, driving cars, and controlling robots and drones. The method has also been used for large systems used to store power or control water supplies.For their case study, the researchers looked at defenses proposed for low-lying areas in Manhattan. After Hurricane Sandy caused tremendous damage in New York and New Jersey in 2012, the U.S. Army Corps of Engineers proposed constructing a series of seawalls to defend Manhattan called the Big U. Some sections of the Big U are being built and others are in the planning stage. But a final completion plan for the entire system has not yet been set.The study looked at proposals for the Big U and how defenses against coastal flooding should be changed to respond to future threats to New York from the sea. Flooding is driven both by sea level rise and storms. Both are affected by climate change, which in turn is impacted by global carbon emissions. The researchers wanted to evaluate decision methods every 10 years and allow for adjusting the Big U to reflect available threat data at each interval.Because the impacts of climate change over a long period remain uncertain, the researchers wanted to study methods of making the best decisions for future changes with the data available at the time. Traditionally, engineers have built protective systems like seawalls and levees to resist historic floods, building protection from floods that would occur only once in 50 or 100 years. But because the climate is changing, such systems no longer work. Building the Big U seawalls to match the highest storm surge over the past 100 years would leave Manhattan vulnerable as climate change drives higher storm surge levels.The researchers looked at a number of decision-making methods that take into account changing conditions. Most methods allowed for changes based on key variables and some allowed planners to make future projections that would also influence decisions. For example, as a temporary measure, residents could flood-proof their homes (“accommodate”), but eventually, higher sea levels would necessitate a high seawall (“protect”). In some cases, a seawall could prove too costly to protect everyone and residents would be encouraged or compelled to leave threatened communities (“retreat”). In a situation with only a few variables to account for, the benefits of a plan can be estimated relatively easily. But uncertain climate change presents an extremely complex scenario. The researchers showed that reinforcement learning can be used to design integrated strategies, including retreating from low-lying areas, protecting property further inland at higher elevation, and accommodating in between, with a 5-15% reduction in cost compared to the one-dimensional seawall strategy.Lin, one of the lead researchers, said defending Manhattan is not only complex, but it also requires making difficult decisions under uncertain conditions. For each time interval, planners must make decisions based on observed sea level rise and roughly 80,000 scenarios of future sea level rise and corresponding decision reactions. The difficulty of the decisions compounds as the number of intervals increases.While climate adaptation decisions are not simple, Lin said reinforcement learning is  a highly efficient system for incorporating observations and updating plans to derive optimal solutions for limiting impacts from extreme events. Reinforcement learning also outperforms previous methods by avoiding losses induced by uncertain changes in future global carbon emissions.“The analysis of New York City’s situation is by no means unique,” said <a href="https://spia.princeton.edu/faculty/omichael">Michael Oppenheimer</a>, a study co-author and professor of geosciences and international affairs at Princeton. “The method can be applied widely, although its benefit compared to other systems of analysis would vary from place to place.”<hr />The paper, “<a href="https://www.pnas.org/doi/10.1073/pnas.2402826122">Reinforcement learning-based adaptive strategies for climate change adaptation: An application for coastal flood risk management</a>,” was published March 18 in the Proceedings of the National Academy of Sciences. Besides Kopp, Lin and Oppenheimer, co-authors include Kairui Feng, formerly of Princeton and now at Tongji University, Shanghai, China; and Siyuan Xian of Princeton. The authors are part of the Megalopolitan Coastal Transformation Hub, a Rutgers-led, National Science Foundation-funded consortium of research institutions working to advance the science of how coastal climate hazards, landforms and human decisions interact to shape climate risk and advance climate adaptation in the New York City-New Jersey-Philadelphia region.

A house, being relocated to higher ground, is transported by barge through Little Egg Inlet along New Jersey’s southeastern coast. Photos by Matthew Drews, Rutgers University

Researchers from Princeton and Rutgers University have used reinforcement learning, a method frequently deployed to train artificial intelligence, to show how flexible responses can substantially increase the cost-effectiveness of steps to defend cities like New York against climate change.

AI technique boosts climate change defenses

Wevolver: David. So what are we looking at here?David: Thanks. Well, what we have here is an STM32N6. Now, this is a microcontroller-based product. So this is an MCU with an NPU. And what we're doing in this specific example here is object detection and anomaly detection all in one. So we can place a coffee pod or two onto the conveyor belt and we will see that coffee pod has been detected. Now we're also detecting, as I said, anomalies or defect detection. So if I place these coffee pods which have defects in them, we'll see over on the right hand side of the screen where the defects are in a heat map. I'll take this one here, which has some puncture wounds in it. And we'll do the object detection as well as the defects. This is super interesting because this is an MCU based device, extremely low power, and because it's got the MCU and the NPU, we can actually run two distinct models at the same time on the device. Pretty cool.David: All right, and now we're over at a second demo pod here. For this one though, I'm going to actually pass the mic over to my colleague Dmitry, who will walk you through it. Dmitry? Dmitry: Hey. Yeah, I'm Dmitry. And here we see the modal cascade. There are two models running on this particularly powerful device made by Qualcomm. It's 9075, recently announced chip. So we have the object detection model which detects vehicles and then once it's detected, it is being passed to a visual language model. You have a choice of lava, open source, lava and AMG. This is in in-house model by Qualcomm. So we have crafted a prompt here which is the vehicle class, vehicle manufacturer, sign identification. If press generate all, it'll just take a few moments. And keep in mind that both the detection and the visual language model, they both run on this device. It doesn't go anywhere to the clouds, you can see the answers being streamed in real time. Yeah, let's wait for the last one here. Yeah, it gets most of it very correctly. Let's see the last one actually. Yeah, that's all good. And the value proposition for us at Edge Impulse is in fact that you cannot blindly trust visual language models. You use ChatGPT, you know it can make mistakes, it says they are right at the bottom. So in that case, if you really are serious and you want to put this into production, you want to validate your prompt, you want to create it, validate it and check the accuracy against the data that you have, and this is exactly what we are providing at Edge Impulse. So you create your prompt here, you tweak it around, you test it, and then you pass it on to the production model running there. That's about it.David: All right, thanks, Dmitry. Appreciate it. And I think that's going to do it for us here from Embedded World 2025. If you have any questions, be sure to reach out edgeimpulse.com. Thanks.

Unveiling the Future of Embedded AI: Object Anomaly Detection and More at Embedded World 2025

Edge Impulse's Object Anomaly Detection Technology, And Other Innovations at Embedded World 2025

Few people realize that the carbon footprint of digital technology is on par with that of the aerospace industry, accounting for between 2% and 4% of global carbon emissions. And this digital carbon footprint is expanding at a rapid pace. When it comes to power use, the approximately 11,000 data centers in operation today consume just as much energy as the entire country of France did in 2022, or around 460 TWh per year. Will the widespread adoption of generative AI send those figures soaring?The new technology will clearly affect the amount of energy that’s consumed worldwide, but exactly how is hard to quantify. “We need to know the total cost of generative AI systems to be able to use them as efficiently as possible,” says Manuel Cubero-Castan, the project manager on <a href="https://www.epfl.ch/about/sustainability/sustainable-it/">Sustainable IT at EPFL</a>. He believes we should consider the entire life cycle of generative AI technology, from the extraction of minerals and the assembly of components – activities whose impact concerns not only energy – to the disposal of the tons of electronic waste that are generated, which often gets dumped illegally. From this perspective, the environmental ramifications of generative AI go well beyond the power and water consumption of data centers alone.<h2>The cost of training</h2>For now, most of the data available on digital technology power use relates only to data centers. According to the International Energy Agency (IEA), these centers (excluding data networks and cryptocurrency mining) consumed between 240 TWh and 340 TWh of power in 2022, or 1% to 1.3% of the global total. Yet even though the number of centers is growing by 4% per year, their overall power use didn’t change much between 2010 and 2020, thanks to energy- efficiency improvements.With generative AI set to be adopted on a massive scale, that will certainly change. Generative AI technology is based on large language models (LLMs) that use power in two ways. First, while they’re being trained – a step that involves running terabytes of data through algorithms so that they learn to predict words and sentences in a given context. Until recently, this was the most energy-intensive step.Second, while they’re processing data in response to a prompt. Now that LLMs are being implemented on a large scale, this is the step requiring the most energy. Recent data from Meta and Google suggest that this step now accounts for 60% to 70% of the power used by generative AI systems, against 30% to 40% for training.<h2>ChatGPT query vs conventional Google search</h2>A ChatGPT query consumes around 3 Wh of power, while a conventional Google search uses 0.3 Wh, according to the IEA. If all of the approximately nine billion Google searches performed daily were switched to ChatGPT, that would increase the total power requirement by 10 TWh per year. Goldman Sachs Research (GSR) estimates that the amount of electricity used by data centers will swell by 160% over the next five years, and that they will account for 3% to 4% of global electricity use. In addition, their carbon emissions will likely double between 2022 and 2030. According to IEA figures, total power demand in Europe decreased for three years in a row but picked up in 2024 and should return to 2021 levels – some 2,560 TWh per year – by 2026. Nearly a third of this increase will be due to data centers. GSR estimates that the AI-related power demand at data centers will grow by approximately 200 TWh per year between 2023 and 2030. By 2028, AI should account for nearly 19% of data centers’ energy consumption.If we begin using generative AI technology on a massive scale, with ever-bigger LLMs, the resulting energy gains will be far from enough to achieve a reduction in overall carbon emissions.- Manuel Cubero-Castan, project manager on Sustainable IT at EPFLHowever, the rapid expansion of generative AI could wrong-foot these forecasts. Chinese company DeepSeek is already shaking things up – it introduced a generative AI program in late January that uses less energy than its US counterparts for both training algorithms and responding to prompts. Another factor that could stem the growth in AI power demand is the limited amount of mining resources available for producing chips. Nvidia currently dominates the market for AI chips, with a 95% market share. The three million Nvidia H100 chips installed around the world used 13.8 TWh of power in 2024 – the same amount as Guatemala. By 2027, Nvidia chips could burn through 85 to 134 TWh of power. But will the company be able to produce them at that scale?<h2>Not always a sustainable choice</h2>Another factor to consider is whether our aging power grids will be able to support the additional load. Many of them, both nationally and locally, are already being pushed to the limit to meet current demand. And the fact that data centers are often concentrated geographically complicates things further. For example, data centers make up 20% of the power consumption in Ireland and over 25% in the US state of Virginia. “Building data centers in regions where water and power supplies are already strained may not be the most sustainable choice,” says Cubero-Castan.There’s also the cost issue. If Google wanted to be able to process generative AI queries, it would need to set up 400,000 additional servers – at a price tag of some 100 billion dollars, which would shrink its operating margin to zero. An unlikely scenario.<h2>Untapped benefits</h2>Some of the increase in power consumption caused by generative AI could be offset by the benefits of AI in general. Although training algorithms requires an investment, it could pay off in terms of energy savings or climate benefits. For instance, AI could speed the pace of innovation in the energy sector. That could help users to better predict and reduce their power use; enable utilities to manage their power grids more effectively; improve resource management; and allow engineers to run simulations and drive advances at the leading edge of modeling, climate economics, education and basic research. Whether we’re able to leverage the benefits of this kind of innovation will depend on its impacts, how extensively the new technology is adopted by consumers, and how well policymakers understand it and draft laws to govern it.The next-generation data centers being built today are more energy efficient and allow for greater flexibility in how their capacity is used. By the same token, Nvidia is working to improve the performance of its chips while lowering their power requirement. And we shouldn’t forget the potential of quantum computing. When it comes to data centers, the IEA calculates that 40% of the electricity they use goes to cooling, 40% to running servers and 20% to other system components including data storage and communication. At EPFL, Prof. Mario Paolone is heading up the <a href="https://heatingbits.epfl.ch/">Heating Bits</a> initiative to build a demonstrator for testing new cooling methods. Five research groups and the <a href="https://ecocloud.epfl.ch/">EcoCloud Center</a> have teamed up for the initiative, with the goal of developing new processes for heat recovery, cogeneration, incorporating renewable energy and optimizing server use.<h2>Keeping the bigger picture in mind</h2>Another (painless and free) way to cut data centers’ power use is to clear out the clutter. Every day, companies worldwide generate 1.3 trillion gigabytes of data, most of which ends up as dark data, or data that are collected and stored but never used. Researchers at Loughborough Business School estimate that 60% of the data kept today are dark data, and storing them emits just as much carbon as three million London–New York flights. This year’s Digital Cleanup Day will be held on 15 March, but you don’t have to wait until spring to do your cleaning!Cubero-Castan warns us, however, to keep the bigger picture in mind: “If we begin using generative AI technology on a massive scale, with ever-bigger LLMs, the resulting energy gains will be far from enough to achieve a reduction in overall carbon emissions. Lowering our usage and increasing the lifespan and efficiency of our infrastructure remain essential.”The energy impact of generative AI mustn’t be overlooked, but for now it’s only marginal at the global level – it’s simply adding to the already hefty power consumption of digital technology in general. Videos currently account for 70% to 80% of data traffic around the world, while other major contributors are multiplayer online games and cryptocurrency. The main drivers of power demand today are economic growth, electric vehicles, air-conditioning and manufacturing. And most of that power still comes from fossil fuels.<hr /><h3>References</h3>This article was published in the issue of Dimensions which was released on 14 March 2025. Dimensions, an EPFL magazine that showcases cutting-edge research through a series of in-depth articles, interviews, portraits and news highlights. Published four times a year in both English and French, it can be sent to <a href="https://www.epfl.ch/about/news-and-media/fr/dimensions/abonnements/">anyone who wants to subscribe</a> as well as <a href="https://epflalumni.ch/page/avantages" rel="noopener" target="_blank">contributing members of the EPFL Alumni Club</a>. It is also distributed free of charge on EPFL’s campuses.

Data centers are major electricity consumers. © iStock

It takes ten times more electricity for ChatGPT to respond to a prompt than for Google to carry out a standard search. Still, researchers are struggling to get a grasp on the energy implications of generative artificial intelligence both now and going forward.

Can energy-hungry AI help cut our energy use?

The Arm AI Readiness Index report is the culmination of a detailed research process crafted by a diverse team of leading experts, researchers, and thought leaders in the field of artificial intelligence. This report provides an authoritative analysis of the current state and future prospects of AI across various industries globally.<h3>Rich Data from Extensive Survey</h3>At the heart of the AI Readiness Index is a robust survey that reaches across continents and sectors. Conducted with 655 business leaders from countries including the U.S., U.K., Germany, Taiwan, and Japan, this survey offers a unique cross-sectional view of AI adoption and strategic implementation worldwide. These insights help map out the progression of AI technology from an aspirational tool to a fundamental driver of corporate strategy and operational efficiency.<h3>Expert Contributions and Analytical Depth</h3>The report is enriched by contributions from experts such as Rene Haas, Arm CEO, Dr. John Soldatos, a respected figure in AI academic circles, and Dr. Nora von Ingersleben-Seip, a leading researcher in AI policy. With extensive knowledge and specialized expertise within areas ranging from technical infrastructure needs, to organisational transformation, sustainability and the regulatory landscape, each contributor brings specific insights that illuminate various aspects of AI readiness.<h3>Structured Analysis and Case Studies</h3>Combining extensive survey data with expert analysis, the report offers a nuanced perspective on the complexities of AI adoption. It not only assesses where businesses currently stand but also charts a path forward with strategic recommendations. This is further enhanced by detailed case studies that provide real-world examples of AI implementation, showcasing both successes and challenges faced by organizations in integrating AI into their operations.<h3>A Guide for Tomorrow’s AI Strategies</h3>The Arm AI Readiness Index serves as both a mirror reflecting the current state of AI in industries worldwide and a compass guiding businesses at various stages of their AI journey. It is an invaluable resource for engineers, business leaders, and policymakers aiming to harness AI's potential responsibly and effectively.

We are pleased to have partnered with Arm to produce the Arm AI Readiness Index.&nbsp;This global report is the culmination of a detailed research process crafted by a diverse team of leading experts, researchers, and thought leaders in the field of artificial intelligence. The Arm Readiness Index aims to provide an authoritative analysis of the current state and future prospects of AI across various industries globally.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743071387388-ArmReadinessIndex.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe id="JotFormIFrame-250844647953468" title="ARM Report - Download Form (Chapter 1,2 &amp; 8)" allowtransparency="true" src="https://form.jotform.com/250844647953468" frameborder="0" style="min-width:100%;max-width:100%;height:740px;border:none;" scrolling="yes" 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;&nbsp;&nbsp; </iframe><h2 dir="ltr" id="isPasted">Understanding Global AI Readiness </h2><h3 dir="ltr">Rich Data from Extensive Survey</h3>At the heart of the AI Readiness Index &nbsp;is a robust survey that reached across continents and sectors. Conducted with 655 business leaders from countries including the U.S., U.K., Germany, Taiwan, and Japan, this survey offers a unique cross-sectional view of AI adoption and strategic implementation worldwide. These insights help map out the progression of AI technology from an aspirational tool to a fundamental driver of corporate strategy and operational efficiency.<h3 dir="ltr">Expert Contributions and Analytical Depth</h3>The report is enriched by contributions from experts such as Will Abbey, Executive Vice President and Chief Commercial Officer, Arm and Dr. John Soldatos, a respected figure in AI academic circles, and Dr. Nora von Ingersleben-Seip, a leading researcher in AI policy. With extensive knowledge and specialized expertise within areas ranging from technical infrastructure needs, to organisational transformation, sustainability and the regulatory landscape, each contributor brings specific insights that illuminate various aspects of AI readiness.<h3 dir="ltr">Structured Analysis and Case Studies</h3>Combining extensive survey data with expert analysis, the report offers a nuanced perspective on the complexities of AI adoption. It not only assesses where businesses currently stand but also charts a path forward with strategic recommendations. This is further enhanced by detailed case studies that provide real-world examples of AI implementation, showcasing both successes and challenges faced by organizations in integrating AI into their operations.<h3 dir="ltr">A Guide for Tomorrow’s AI Strategies</h3>The Arm AI Readiness Index serves as both a mirror reflecting the current state of AI in industries worldwide and a compass guiding businesses at various stages of their AI journey. It is an invaluable resource for engineers, business leaders, and policymakers aiming to harness AI's potential responsibly and effectively.<blockquote>"AI is no longer a technology of tomorrow—it is the driving force of now." -&nbsp;Will Abbey, Executive Vice President and Chief Commercial Officer, Arm</blockquote><h2 dir="ltr"> </h2><h2> </h2><h2> </h2><hr><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2> </h2><h2>Report Foreword</h2>Throughout history, certain technological advancements have fundamentally altered our world. The printing press democratized knowledge. The industrial revolution transformed manufacturing. The internet connected humanity in ways previously unimaginable. Today, we stand at another such pivotal moment with artificial intelligence.Like those historic inflection points, artificial intelligence (AI) is not merely an incremental improvement on what came before; it represents a paradigm shift that is redefining how we work, communicate, create, and solve problems. And like those earlier transformations, the full impact of AI will be determined not just by the technology itself, but by how we choose to develop, deploy, and govern it.What was once considered the realm of science fiction has become an integral part of our daily lives and business operations. The AI Readiness Index report represents a comprehensive effort to understand where we stand today in a rapidly transforming world and chart a course for the future.As we navigate this transformative era, one thing has become abundantly clear: AI is no longer a technology of tomorrow—it is the driving force of now. Our research reveals that 82% of global business leaders are already using AI applications, with overwhelming majorities expecting to increase their investments in the coming years. Yet despite this widespread adoption, only 39% report having a clearly defined, comprehensive strategy in place.This gap between adoption and strategic implementation underscores the complex challenges organizations face. From building robust technical foundations and addressing security concerns to navigating evolving regulatory landscapes and developing necessary talent, the path to AI success requires careful consideration of multiple interconnected factors.What makes this moment particularly significant is the shifting nature of the conversation itself. We have moved beyond asking whether AI will transform our industries to asking how we can responsibly harness its potential to drive innovation, efficiency, and growth. The focus has rightfully expanded from purely technical considerations to encompass broader implications for security, sustainability, governance, and workforce development.The AI Readiness Index aims to serve as both a mirror and a compass—reflecting the current state of AI readiness across industries and regions while providing direction for organizations at various stages of their AI journeys. By combining extensive survey data from 665 business leaders with expert analysis and case studies, we offer a nuanced view of the challenges and opportunities that lie ahead.As you explore the findings in this report, I encourage you to consider not just where your organization stands today, but where it needs to be tomorrow. The organizations that will thrive in this new era will be those that approach AI with both ambition and responsibility—embracing its transformative potential while thoughtfully addressing the technical, ethical, and human dimensions of implementation.The future of AI is being written today, by leaders willing to ask difficult questions, take bold risks and make strategic investments in the foundations of tomorrow's success. My hope is that this report will serve as a valuable resource as you write your organization's chapter in that future.<style type="text/css" id="isPasted">p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 18.0px Helvetica} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Helvetica}</style>Will AbbeyExecutive Vice President and Chief Commercial Officer, Arm<hr><h2>About Arm</h2>Arm is the industry’s highest-performing and most power-efficient compute platform with unmatched scale that touches 100 percent of the connected global population. To meet the insatiable demand for compute, Arm delivers advanced solutions that allow the world’s leading technology companies to unleash the unprecedented experiences and capabilities of AI. Together with the world’s largest computing ecosystem and 20 million software developers, we are building the future of AI on Arm.&nbsp;<a href="http://www.arm.com" target="_blank" rel="noopener noreferrer"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742914642481-Untitled+design+%2815%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a>

Introduction

<h2 dir="ltr" id="isPasted">Momentum, Without a Map</h2>In boardrooms and strategy sessions across the globe, artificial intelligence has moved from a distant aspiration to an immediate priority. Our extensive survey of 655 business leaders from countries including the U.S., U.K., Germany, Taiwan and Japan, reveals a notable reality: AI adoption is accelerating rapidly, with 82% of global businesses already deploying AI applications in their day-to-day operations. This broad adoption of artificial intelligence spans industries and regions, demonstrating that AI is no longer the exclusive domain of tech giants but has become an essential technology for enterprises of all types.Customer service stands at the forefront of this adoption wave, with 63% of organizations deploying AI to enhance customer interactions. Document processing (54%), IT operations (51%), and security applications (51%) follow closely behind, showing how AI has rapidly infiltrated core business functions. This isn't merely experimentation—it's transformation at scale.The enthusiasm for AI runs deep within organizational hierarchies. An overwhelming 90% of leaders report their organizations are receptive to AI-driven changes, with 83% considering AI adoption "urgent."&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894240443-arm_infographics-01.jpg" class="fr-fic fr-dii">Most tellingly, 82% of businesses have secured executive sponsorship at the highest levels, with CEOs and senior management personally championing AI initiatives. This top-down commitment signals that AI has transcended technical curiosity to become a strategic imperative.Financial commitments reinforce this strategic shift. Eight in ten organizations have established dedicated AI budgets, with regional variations highlighting different approaches to investment. The Asia-Pacific region leads in budget allocation, with 86% of APAC businesses dedicating resources to AI initiatives, compared to 76% in Europe and 75% in the United States. However, American organizations are investing more aggressively, with 57% committing more than 10% of their IT budgets to AI, surpassing European (46%) and APAC (45%) counterparts.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894258176-arm_infographics-02.jpg" class="fr-fic fr-dii">This financial commitment shows no signs of wavering, as 87% of business leaders anticipate increasing their AI budgets over the next three years.What's driving this rush toward AI? In a word, efficiency. A compelling 63% of global leaders identify operational efficiency as the primary benefit they expect from AI, with 80% making it the central focus of their 2025 AI strategy. As one business leader articulated, "AI will eliminate repetitive work, allowing employees to engage in more strategic and creative tasks." Another explained how "AI advancements will impact our organization by enhancing efficiency in decision-making and innovation across operations while requiring continuous adaptation to new technologies and regulatory changes."Yet beneath this momentum lies a concerning paradox.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894273344-arm_infographics-03.jpg" class="fr-fic fr-dii">Despite widespread adoption and executive support, only 39% of organizations have developed a clearly defined, comprehensive AI strategy. Even fewer—just 37%—have implemented a robust change management plan to guide AI implementation across their businesses. &nbsp;This gap underscores that while AI is deployed, many organizations may not fully understand how to integrate it effectively or ready their workforce for the changes ahead.&nbsp;One Vice President in the financial industry's IT department acknowledged: "We have our work cut out for us in formalizing these toolsets. Their use has grown organically within the business units, so we have duplication. I think we're getting there, and that's certainly part of my role—to figure that out."This lack of a cohesive strategy raises critical questions about the sustainability of current AI initiatives and their long-term impact on business performance.<iframe id="JotFormIFrame-250844647953468" title="ARM Report - Download Form (Chapter 1,2 &amp; 8)" allowtransparency="true" src="https://form.jotform.com/250844647953468" frameborder="0" style="min-width:100%;max-width:100%;height:740px;border:none;" scrolling="yes" 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><h2 dir="ltr">Proceed with Caution</h2>The enthusiasm for AI must be tempered with a sobering reality: many organizations remain ill-equipped to scale their AI initiatives effectively. Our research reveals significant shortcomings in three critical areas—infrastructure readiness,&nbsp;talent availability, and&nbsp;data quality—each representing a potential barrier to realizing AI’s full potential.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894315616-arm_infographics-04.jpg" class="fr-fic fr-dii">Infrastructure limitations are particularly concerning. Only 29% of organizations have systems or storage resources to meet growing AI demands. Even fewer—a mere 23%—have dedicated power infrastructure to handle the increased energy requirements of AI workloads. This means 77% of businesses have only begun upgrading their facilities—if they have any power management strategy. Additionally, 65% of leaders acknowledge that their organizations lack comprehensive energy efficiency optimization measures for AI systems, raising questions about operational costs and environmental impact as these workloads grow.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894343051-arm_infographics-05.jpg" class="fr-fic fr-dii">The talent gap poses an equally significant challenge. A third of business leaders (34%) report their organizations are "significantly under-resourced" or "under-resourced" when it comes to AI expertise, while nearly half (49%) identify a lack of skilled talent as a primary barrier to successful AI implementation. As one C-level executive in manufacturing lamented, "It's about manpower talent. It's so hard to find people who know AI and can talk about AI."Despite recognizing the talent gap, organizations are taking inconsistent approaches to addressing it. While two-thirds (66%) of leaders express intentions to upskill existing employees to adapt to increased AI integration, 39% still don't have dedicated programs to develop AI skills among their workforce. This disconnect between recognized need and decisive action threatens to widen the talent gap rather than close it.Perhaps most fundamentally, organizations struggle with data readiness—the essential foundation for effective AI applications. Nearly half of businesses leverage customer data (49%) and operational data (48%) for AI initiatives. However, data management practices remain largely rudimentary. Only about half (53%) of organizations have basic data automation processes for AI/ML models, while 18% rely on manual or ad hoc data cleaning procedures. Not surprisingly, 46% of leaders cite data quality and accessibility issues as a major barrier to successful AI implementation.Industry leaders recognize these challenges. One Vice President in the financial industry's IT department explained: "Our data quality and cataloging is so critical when it comes to AI...That is work in front of us." Another C-level IT executive in manufacturing emphasized the need for integrated data platforms: "We need to get the data into one platform so we can try to leverage the other functions to use AI."These infrastructure, talent, and data challenges cannot be overlooked. Without addressing these fundamental capabilities, organizations risk their AI investments delivering diminishing returns as they attempt to scale beyond initial proof-of-concept deployments.<h2 dir="ltr">Security Sensitivity</h2>As organizations increasingly integrate AI into their operations, a new dimension of risk has emerged that demands urgent attention: data security and privacy. Security concerns are growing proportionally, with personally identifiable information (PII) rapidly becoming central to AI initiatives.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894375730-arm_infographics-06.jpg" class="fr-fic fr-dii">Nearly half (49%) of businesses already use customer data to power their AI initiatives, and the trend is accelerating. Looking ahead, 56% of leaders indicate their organizations plan to utilize personally identifiable information for future AI applications. This growing reliance on sensitive data introduces significant security considerations.As one director in the life sciences and biotechnology industry explained: "I think when you get into some of these companies, ours in particular, we have to be a little bit more safeguarded because of the data sets we have. Not all of it can be shared in public knowledge, but AI makes looking at that data, interpreting that data, analyzing that data 1000 times easier."The expanded use of sensitive data in AI systems introduces security considerations and ethical concerns about bias and fairness. AI systems are only as objective as the data they're trained on, raising important questions about how organizations ensure their AI applications make fair and unbiased decisions.Our research reveals a significant gap in this area. Nearly half (47%) of business leaders acknowledge their organizations have limited bias detection and correction processes in their AI systems. Even more concerning, 17% reported having no formal bias correction process and relying instead on ad hoc bias checks. This inconsistent approach to bias mitigation poses significant risks, especially as AI becomes more deeply integrated into consequential business decisions.Forward-thinking leaders recognize these challenges. As one Vice President in the financial industry observed: "We have to make sure that data is bias-free, and to do that, we have to come up with policies and standards controls that need to be implemented within the organization by support of data scientists and data stewards." This recognition drives 44% of leaders to identify AI ethics and data engineering as the most critical skills their organizations will need in the next five years.On a more positive note, organizations are beginning to implement security measures for their AI systems. Only 5% of businesses report no specific AI security measures. The vast majority are taking at least some precautions, with 56% conducting regular security audits of AI models and infrastructure, 56% performing security testing of AI-powered applications, and 51% conducting vulnerability assessments for AI systems.Despite these measures, significant concerns persist. Nearly half (48%) of business leaders identify data privacy breaches through model extraction as a top AI security concern. One Vice President of Marketing and Sales in retail observed: "I think there's a big open question on IT security that large companies are going to worry about."As AI systems become more deeply integrated with sensitive business and customer data, organizations must evolve from basic security practices to comprehensive security strategies that address the unique vulnerabilities associated with AI technologies.<h2 dir="ltr">Preparing for an AI-Powered Future</h2>One prediction seems certain as we look toward the horizon: AI adoption will continue to accelerate across all industries. Organizations that prepare effectively today will position themselves to capitalize on this technological revolution, while those that fail to address fundamental readiness gaps risk falling behind.The business case for AI continues to strengthen among organizations using AI; 65% report that their AI applications are meeting or exceeding expectations for return on investment. This positive experience drives further adoption, with 92% of business leaders indicating plans to expand their AI usage in some capacity.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742894395006-arm_infographics-07.jpg" class="fr-fic fr-dii">However, successfully scaling AI initiatives requires addressing several critical preparation gaps. A third (34%) of business leaders identify integration with existing security infrastructure as a top AI security concern. Meanwhile, 43% acknowledge their employees demonstrate only a basic understanding of AI at best, with additional training urgently needed. These challenges have led two-thirds (67%) of business leaders to identify AI security among the most critical AI-related skills their organizations will need to develop over the next five years.Organizations must balance enthusiasm with strategic planning as they prepare for an AI-powered future. By systematically addressing infrastructure limitations, closing talent gaps, enhancing data quality, and strengthening security measures, businesses can move beyond initial experimentation to realize AI's transformative potential at scale.The path forward is clear, if challenging. Organizations must translate their AI enthusiasm into comprehensive strategies that address foundational readiness gaps. Those who successfully navigate this transition will be positioned to reap the efficiency gains and competitive advantages that AI promises. For the rest, the gap between AI ambition and AI readiness may prove increasingly difficult to bridge.

The Global State of AI Readiness: A Data-Driven Analysis

The Technical Foundation: Technology Requirements for AI Success

Policy and Governance: Shaping the AI Regulatory Landscape

AI Safety and Risk: Navigating the Path to Responsible Innovation

Trust and Security in the AI Era

Sustainability as a Core Metric for AI Readiness

Mark Hinkle, CEO and Founder of Peripety Labs, Founding Publisher The Artificially Intelligent Enterprise<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742899266206-1742899266206.png" width="624" height="329" class="fr-fic fr-dii">AI adoption in companies is accelerating, but employees on the ground are struggling to keep up. There’s often a palpable gap between the rapid pace of AI advances and what employees feel prepared to handle.The truth is that many workers feel overwhelmed by constantly evolving tools and lack the training to use them effectively. Nearly<a href="https://www.gallup.com/workplace/652727/strategy-fail-without-culture-supports.aspx#main#:~:text=Employees%20are%20broadly%20optimistic%20about,AI%20applications%20at%20least%20weekly">&nbsp;seven in ten</a> employees never use AI at all in their work, and only about one in ten use AI every week. One major reason for this usage gap is that employees haven’t been given enough guidance or training – only<a href="https://www.gallup.com/workplace/652727/strategy-fail-without-culture-supports.aspx#main#:~:text=One%20reason%20for%20this%20usage,17">&nbsp;15% of U.S. workers</a> say their organization has communicated a clear AI strategy, and a mere 11% feel “very prepared” to work with AI in their role.When people aren’t sure why or how to use new AI tools, it’s no surprise they stick to familiar routines. This lack of preparedness is widespread. Even though 75% of companies have now adopted some form of AI technology, just<a href="https://www.randstad.com/randstad-ai-equity/">&nbsp;one-third of employees</a> have received any AI-related training in the last year. In other words, companies are rolling out AI faster than their teams can absorb it. The result? Employees feel left behind and anxious. They might even quietly push back against AI projects – skipping the use of a new system or voicing doubts – not because they hate innovation but because they feel unready and unsupported.Corporate leaders risk underutilizing their AI investments when the workforce isn’t on board. Consider that only about<a href="https://www.gallup.com/workplace/652727/strategy-fail-without-culture-supports.aspx">&nbsp;one in five digital transformation initiatives fully achieves its goals</a>, often due to cultural and adoption issues. When employees aren’t empowered to use AI, those expensive platforms and algorithms can turn into costly shelfware, delivering only a fraction of the promised productivity gains. In short, technology alone can’t transform your business; your people have to transform along with it.As we saw in the AI Readiness Index survey results (Chapter 1), enterprises recognize they have significant work to do when it comes to building AI-ready organizations. The gap between AI adoption intentions and organizational readiness presents both a challenge and an opportunity for business leaders looking to harness AI's transformative potential.<ul><li dir="ltr">Only 39% of leaders&nbsp;report their organization has a&nbsp;clearly defined comprehensive AI strategy. The remaining organizations are either still developing their approach (13%) or have no formal strategy at all (4%).&nbsp;</li><li dir="ltr">Over a third (34%) of respondents&nbsp;indicated their organizations are either significantly under-resourced or under-resourced with AI talent. Even more concerning, 39% reported having no dedicated programs for developing AI skills among employees.</li><li dir="ltr">While&nbsp;80% of business leaders&nbsp;say there is a designated budget for AI initiatives, only 9% allocate more than 20% of their technology budget to AI, suggesting most organizations are still taking a cautious approach to AI investment.</li><li dir="ltr">Improving operational efficiency&nbsp;(80%) and&nbsp;enhancing customer experience&nbsp;(70%) top the list of AI initiatives.&nbsp;</li><li dir="ltr">48% of leaders worried about data privacy breaches&nbsp;through model extraction.</li><li dir="ltr">Other top challenges include lack of skilled talent (49%), data quality and accessibility issues (46%), and regulatory and compliance concerns (46%).&nbsp;</li></ul><h2 dir="ltr">7.1 Building an AI-Ready Culture: A Roadmap</h2>So, how can you turn things around and make your organization truly AI-ready? The good news is that the very obstacles we’ve described – the skills gap, employee anxiety, and underutilized tools – can be overcome with the right strategies. It starts with a shift in culture and mindset from the C-suite to the front lines.Leaders must actively bridge the gap through education, communication, and support. Leaders must bridge the AI skills gap to alleviate employee frustration and resistance. In practice, building an AI-ready culture means investing as much in your people as you do in technology. Here’s how to get started:<h2 dir="ltr">7.2 Invest in Upskilling and Continuous Learning</h2>Make AI literacy a core competency across your organization. Provide<a href="http://theaios.ai/">&nbsp;training programs</a>,<a href="https://allthingsopen.ai/sessions/desktop-productivity">&nbsp;workshops</a>, and easy-to-access online courses to help employees at all levels build confidence with AI tools. This isn’t a one-time effort – encourage continuous learning as AI evolves.Not only will this boost adoption, but it also pays off financially: developing your existing talent is far more cost-effective than hiring new experts (hiring a new worker can cost up to<a href="https://www.weforum.org/stories/2024/01/ai-training-workforce/#:~:text=options%20and%20make%20them%20more,of%20upskilling%20an%20existing%20employee">&nbsp;seven times</a> more than upskilling an existing employee). Fortunately, your employees want to learn –<a href="https://www.weforum.org/stories/2024/01/ai-training-workforce/#:~:text=Companies%20of%20all%20sizes%20should,of%20upskilling%20an%20existing%20employee">&nbsp;57% of workers</a> say they want their company to provide AI training to grow their skills. By offering meaningful upskilling opportunities, you signal that AI is a priority and that every team member has a stake in this transformation.While internal training programs are essential, organizations can also benefit from the broader educational ecosystem discussed in Khaled Benkrid's sidebar to this chapter, including academic partnerships and industry consortiums like the&nbsp;<a href="https://newsroom.arm.com/news/semiconductor-education-alliance">Semiconductor Education Alliance</a>.<h2 dir="ltr">7.3 Lead with Change Management and Clear Communication</h2>Adopting AI is as much about people and processes as it is about tech. Combat resistance by involving employees early and communicating openly about your AI vision. Set a clear AI strategy and share it widely – what are you implementing and why? How will it make jobs easier or more interesting? Helping people understand the purpose behind the technology, can calms AI uncertainty and reduce fear.It’s also crucial to address the elephant in the room: job security. Reassure your teams that AI is there to augment their work, not replace them, &nbsp;for example by highlighting how automating tedious tasks frees up time for more creative, high-value work. Back up these assurances with action: offer reassignment or additional training for roles that are likely to change.Also, empower leadership and managers to champion the change. When top executives and team leaders actively use AI tools and encourage their teams to try them, it sets the tone that AI is a trusted part of the workflow, not a fad. For instance, JPMorgan Chase recognized the importance of top-down support; one of its senior leaders<a href="https://www.okoone.com/spark/industry-insights/jpmorgan-accelerates-ai-adoption-with-focused-prompt-engineering-training/">&nbsp;announced</a> that every new employee would receive prompt engineering training to get them ready for an AI-driven future.By making AI knowledge an onboarding requirement, the company send a powerful message that embracing AI is a core value. Effective change management also means creating feedback loops – giving employees a voice to share their concerns and insights as you implement AI. This helps you identify stumbling blocks early and helps staff feel heard during the transition.<h2 dir="ltr">7.4 Foster a Hands-on, Inclusive AI Culture</h2>To truly embed AI into your business DNA, encourage a culture of experimentation and inclusion. Make AI tools available to employees in their day-to-day work and encourage them to “play” with these tools in a low-pressure setting.One idea is to designate team-based AI champions or mentors – tech-savvy staff who can help their peers discover useful AI use cases and troubleshoot issues. Celebrate quick wins and share success stories: for example, if someone on the sales team uses an AI tool to automate part of a proposal and saves hours, highlight that achievement in your internal newsletter or meetings.These stories make AI’s benefits concrete and relatable, turning skeptics into curious adopters. It’s also wise to integrate AI goals into performance metrics or incentives, gently nudging teams to incorporate AI where it makes sense.Remember to be inclusive in your approach: ensure training and tool access reach everyone, not just tech teams or a select few. Today, many organizations have a bias where only certain roles get AI training – but success with AI requires broad participation.)One thing I strongly suggest avoiding is an AI-only group or a<a href="https://en.wikipedia.org/wiki/Tiger_team">&nbsp;Tiger Team</a>. If only a small group gets skilled up, you risk creating an AI “have and have-not” divide. Instead, strive for AI fluency across departments. This inclusive approach was echoed in a recent global workforce study, which found that empowering workers at all levels is key to unlocking AI’s full potential. When everyone, from entry-level employees to senior managers, has at least a baseline understanding of AI, your company can truly capitalize on collective intelligence and creativity augmented by these new tools.By following this roadmap – upskilling your people, managing change thoughtfully, and nurturing an innovative culture – you create an environment where AI can thrive. It transforms AI from something intimidating into something empowering. Employees who once felt overwhelmed or threatened by AI start to feel curious and competent, especially as they see their skills grow and their workload shift to more rewarding tasks. And the impact on the business is tangible.Companies that invest in their workforce’s AI readiness stand to maximize the productivity gains of their AI investments. Instead of half-used software licenses or stalled pilot projects, you have teams actively leveraging AI to solve problems, make better decisions, and drive efficiency. In short, an AI-ready culture turns AI from a shiny new thing in the toolbelt into a natural extension of how work gets done.AI Adoption isn’t Just a Digital Transformation – it’s a People Transformation.Close the skills gap by empowering your employees, and watch resistance melt into enthusiasm. With the right culture in place, your organization can ride the AI wave with confidence, ensuring that both your people and your technology are working in sync to move the business forwardThe companies that get this right not only see higher ROI on their AI projects but also a more engaged, future-ready workforce poised to innovate. In the age of AI, your competitive advantage boils down to your team’s ability to adapt and excel alongside intelligent machines. By building an AI-ready culture today, you prepare your business – and your people – to thrive tomorrow.<h2 dir="ltr">Arm Sidebar: Addressing the Skills Challenges in an AI-Driven Workforce</h2>Khaled Benkrid, Senior Director, Education and Research, Arm<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742899266105-1742899266105.png" width="624" height="351" class="fr-fic fr-dii">In a world where humans and machines are working together more than ever, the ability to build and use AI tools effectively is becoming a fundamental skill. Yet, as AI continues to evolve at an unprecedented pace, workforce development is struggling to keep up. Many professionals lack the specialized knowledge needed to develop, deploy, and optimize AI-driven solutions, creating a widening skills gap that threatens to stall innovation.&nbsp;<h2 dir="ltr">Current Skills Challenges in the AI Workforce</h2>As AI adoption accelerates across industries, the demand for a workforce proficient in AI technologies has surged. Emerging technologies, AI tools and systems require a level of familiarity and expertise that many workers lack, making it difficult for businesses to find professionals with the expertise needed to develop and integrate AI effectively.&nbsp;This skills gap can affect every job role, making staff and professional development essential. Companies must invest in training programs that help employees understand AI’s capabilities and applications. Furthermore, the future workforce must &nbsp;combine human ingenuity with new and emerging AI technologies; going beyond just the technical skills.&nbsp;Addressing these challenges requires engineers, practitioners, policy makers and students to acquire foundational skills and concepts through education and training. To meet this demand, the current curricula must undergo substantial revision, equipping students with knowledge that aligns with new AI-driven programming and decision-making.<h2 dir="ltr">Context-specific Education for Regional and Global Variations in Skills Challenges</h2>As AI is being adopted worldwide, the skills gap has emerged as a widespread challenge. There are notable regional variations in readiness and focus. While some countries lead in AI adoption, regulation, and standard-setting, others lag behind. Even within countries, priorities differ based on regional needs. For instance, in the U.S., rural communities may emphasize AI applications in agriculture as opposed to more urban communities that will focus on AI for smart cities–highlighting the importance of context-specific education and training.<h2 dir="ltr">The Evolution of Educational Requirements</h2>The emergence of AI has disrupted traditional educational requirements and degree programs. While computing has been a relatively recent addition to school curricula, it is already being transformed by AI. This demands regular updates to ensure that education keeps pace with technology advancementss. As a result, learning models must become more agile to let the dynamic curriculum evolve alongside industry needs.Reskilling efforts must begin at the pre-university level, focusing on foundational abilities, knowledge and skills, and introducing students to AI concepts early. This requires educators and institutions to distill trends and skills into actionable knowledge that can be integrated into curricula. Due to the fast pace of AI developments and supporting technologies, frequent updates to education and training materials are essential. This ensures that students and professionals are prepared for the ongoing evolution.<h2 dir="ltr">The Role of Academic Institutions in Workforce Preparation</h2>Colleges, universities and academic institutions are responsible for shaping the future workforce. By conducting research and integrating AI into their curricula, they ensure that graduates possess the skills required by the wider industry. Collaboration with industry partners allows academic programs to align with the evolving demands of the job market, creating a seamless transition for students entering the workforce.<h2 dir="ltr">The Role of the Semiconductor Education Alliance</h2>Recognizing the scale of the challenge, initiatives like the Semiconductor Education Alliance (SEA) have emerged to bridge the gap between academia, industry, and government agencies.[1]&nbsp;SEA’s work focuses on developing knowledge, skills, and abilities (KSA) frameworks, which help by providing rigorous descriptions of the competencies required for software and hardware engineers.[2]&nbsp;These frameworks are crucial for designing high-quality curricula and training materials that align with industry needs. The global community of SEA members also emphasize the importance of the “3 Cs” – content, community, and careers – to help build a robust ecosystem that prepares individuals for AI-driven roles.<h2 dir="ltr">Arm Collaborations with Academia</h2>Arm has been actively working to catalyze industry-academia collaborations. By developing free educational tools and training kits, Arm has facilitated the widespread adoption of its content across thousands of universities. Meanwhile through SEA, Arm continues to work closely with academic institutions, contributing to educational standards and linking education with industry requirements.&nbsp;More recently, Arm and SoftBank, alongside other companies, have announced support for a US-Japan partnership to accelerate AI innovation. This includes funding for PhD students, as well as capstone projects, internships and other student experiences to feed the AI talent pipeline.&nbsp;<a href="https://www.cam.ac.uk/news/arm-donates-ps3-5-million-for-cambridge-phd-students-to-study-computer-architecture-and">Arm also announced a critical investment into the University of Cambridge</a> new CASCADE (Computer Architecture and Semiconductor Design) Centre, which plans to fund 15 PhD students over the next five years to undertake groundbreaking work in intent-based programming to realize the potential of AI through next-generation processor designs. Such academic collaborations help ensure a steady pipeline of talent and innovations equipped to meet the demands of an AI-enabled world.<h2 dir="ltr">A Comprehensive Approach to Equipping the AI Workforce</h2>This ecosystem approach complements the organizational culture strategies outlined in the main chapter. While Hinkle focuses on building AI readiness through internal training, change management, and inclusive approaches within organizations, these efforts are most effective when supported by the broader educational infrastructure discussed here. The integration of AI into the workforce requires a concerted effort to address the current skills gap. By revising curricula, investing in training, and fostering collaboration between academia, industry, and government, we can prepare the future workforce for success. Initiatives like SEA, and industry-leaders like Arm show how the collaborative spirit needed to adapt to this transformative era. This ensures that the global workforce is equipped to thrive in understanding, developing and using AI applications.

Building an AI-Ready Culture

Case studies

Charting progress and opportunities in a transforming world. 

Arm AI Readiness Index

In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.<iframe height="200px" width="100%" frameborder="no" scrolling="no" src="https://player.simplecast.com/8b738738-5a69-4f97-ac33-8d48a50ec427?dark=false"></iframe> <hr /><h3>Episode Notes</h3>In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.Become a founding reader of our newsletter: <a href="http://read.thenextbyte.com/">http://read.thenextbyte.com/</a><hr /><h2 id="isPasted">Transcript</h2>What's up folks, welcome back to the Next Byte podcast. And in this one, we're talking about DNA sequencing. Specifically, we're talking about a team over at Stanford that has created a ChatGPT-like AI model that can predict what comes next if you give it any DNA sequence, including yours.  that's got you a little bit spooked or excited or just curious. Well, then let's get into it.What's up friends, this is The Next Byte Podcast where one gentleman and one scholar explore the secret sauce behind cool tech and make it easy to understand.Farbod: This is another one of those biology ones. So, you already know I'm gonna struggle, but that's okay. We learn when we struggle. I was…Daniel: You just need to test your limits, man!Farbod: And that's what I'm doing. I'm trying here, but it was so interesting that I knew we needed to talk about it. So, for the audience, please bear with me. This is not my area of expertise, but God am I trying. So, for this episode, we're going to Stanford. You know the West Coast is the best coast, but these folks at Stanford, they're doing some crazy things with DNA sequencing.  And to be completely honest, they've kind of done this before, but now they're doing it better. So, what am I talking about? They've developed a chatGPT-like model, right? It's structured like a large language model, but instead of processing the meaning of the word that you're typing or the paragraph that you're writing or whatever and then giving you an answer or a follow-up or completing your train of thought, it takes as an input. A sequence of nucleotides, a sequence of DNA, and it completes it for you or it makes sense of it and it tells you what genes might be associated with each other. Now, before we get into that, I think we got to take it a couple steps back and by we, I mean me because I know I need to take it, a couple steps back. So, they did an amazing job in this article, by the way, explaining things like you're a five-year-old, which again is wonderful for me, and they have a three-minute video that I highly recommend you check out. It's going to be attached to the show notes. But they explained the issue at hand like this. They said, imagine you're reading the book, and this book has three billion letters in it, right? And every one of those letters is a nucleotide. These nucleotides make up the DNA that everything we know of in this world is pretty much composed of. And it's actually kind of straightforward. We represent them as four letters, A, C, G, and T. And standalone, they don't really mean much, but the sequence of them in different patterns together make up specific genes. Now, as we know, if you've been watching the news, this gene causes this, and poor, broken nucleotide sequences can, for most people, be harmless. For some people, it can cause cancer. So, there's a lot of value in understanding what those sequences mean, how they're related to each other, et cetera, et. Now, as I mentioned, this team had already worked on a chatGPT-like model before called the EVO-1 that was trained using 300 billion nucleotides, mostly from bacteria and stuff like that, to help them proof of concept this idea of, if we had a model that could predict what comes next. Could we better understand what the next evolutionary step would be for this plant or these bacteria or this human? Or could we better understand how the sequence that is at the one millionth element is related to the one at the 100,000th element? And if so, what does that tell us about the chances that you might have cancer? Or could we edit this out? The possibilities and the downsides of what already exists, basically.Daniel: Well, and when we talked about the technological development of having sequenced the human genome several decades ago, that didn't necessarily crack the code for us on what everything in human DNA means. That was just us looking at the DNA and writing a list, writing that three billion letter long book of just A’s, C’s, G’s, and T’s. It's awesome that like there's only four letters, but essentially, we didn't understand how those letters comprise genes and genes, they liken it in the article to like words. So, like basically, the major development in sequencing the human genome was like figuring out the alphabet and the order of the letters in the alphabet. But we did not yet understand the vocabulary. We didn't understand the words. And beyond that, we don't understand the grammar about how you put several words together to create phrases. And that's how physical characteristics are expressed or through a combination of several different genes. So, this is essentially helping us bridge the gap. And like in my mind, truly cracked the code on the meaning of DNA. If we can start to predict protein form and function by looking at the order of the A's, C's, G's and T's, predict harmful slash beneficial genes, potentially create new genetic sequences for medicines or treatments like this. This is bridging the gap between the letters in the physical realm, whereas before it was awesome that we understood what word of the letters were in.Farbod: You know, just like EVO2 is making sense of these nucleotide sequences, I'm happy you're here to make sense of my train of thought about biology.Daniel: EVO2 wouldn't exist without EVO1, buddy.Farbod: You're right. And like, I guess that frame of thinking is even better when comparing it to something like ChatGPT or really any other large language model, because the key breakthrough there, I think we mentioned it five episodes ago. The key breakthrough there was the transformer architecture, which said in relation to this wider body of text, what does this letter mean and this letter mean, instead of trying to make sense of a sequence of words back-to-back, which then lost meaning once you reach the 50th word. So again, they've taken the same underlying engine here, but now applying it to what does this sequence of genes mean, these nucleotides mean. And I think, I mentioned that EVO1 had 300 billion nucleotides, which already sounds quite impressive, but it was mostly bacteria and whatnot. Well, they upped it for EVO2, and now they include animals, plants. Overall, there's nine trillion nucleotides that are represented in this model. The only thing that they left out by design was viruses. The team said they didn't want this tool to be something that bad actors could use to develop and refine existing viruses, which I personally thank them. I think no one wants another round of the coronavirus. So, kudos for thinking ahead on that.Daniel: And this is like, it's pretty monumental here. So, in addition to like excluding viruses, right? Basically, except for viruses, the training data now is 27 times bigger than before. It includes all known living species that we have a DNA sequence for, plus some that are extinct. So, it's like, it's pretty much everything that we've ever sequenced ever, except for viruses so that we don't have people going, creating designer viruses to go try and get people sick. But every living being that we have the DNA sequence for has been included in the training data for this next generation. That's a big deal to increase your training data size, your training data set by a factor of 27, right? It's not like they've trained on 10 % more sequences than before. This is a huge deal.Farbod: Absolutely. And not only have they increased the number of data points that they're training on, but the input is also, what does ChatGPT call them, the number of tokens that you can input for a query? Yeah.Daniel: Yeah.Farbod: So, they've upped it to a one million letter sequence of nucleotides. So, you can have one million letters of combination of ACGT and it can then do whatever you want with it, analyze it, predict the next set of sequences, cetera, et. And I was wondering why one million? Apparently in the world of biology, the one million mark is, it's critical because it's long enough that it takes into account historically significantly far enough genes to make sense of rather than just taking into account the recent developments or deficiencies.Daniel: That makes sense. I appreciate the significance around there. And this makes me feel more so.  ChatGPT is an awesome analogy to give people, especially because I feel like a lot of people have had exposure to ChatGPT. But I'm hoping within our audience, maybe there's folks who've had a greater exposure to things like a co-pilot in coding or things like cursor composer. This actually feels a lot like cursor composer to me where in the cursor editor, you can go in the composer bar and you can have your code base living there, which is like a sequence of DNA. And you can tell it, hey, I want to generate a change to do this to the code  and composer will go and make those changes based off of its understanding of the code. This feels a lot like that, right? So, some of the technical features that it has, similar actually again to cursor composer is like autocomplete. They make a big deal about it in cursor. They call them tabs, but basically like you're writing and it understands what you're trying to code and you can just hit tab and it completes it, right? Autocomplete for code. They have autocomplete for DNA here based off of patterns in DNA sequences. They also have prediction models that can understand real world function. So instead of saying like change all the A's to C's or you know, control F my ACGTs and change them to TCGAs, you can say, I actually just want to change how this physical characteristic appears in the real world. And then it'll go change the DNA to reflect that. And then the interesting part, which is really awesome in my mind, it tends to be somewhat controversial. But we've seen a lot of work with CRISPR gene editing, which basically allows us to like sequence DNA, fabricate it, and then inject it into a DNA sequence. And then in a living being and basically edit a living being's DNA. They're able to essentially predict what the outcome of CRISPR gene editing will look like in the real world to propose edits essentially propose future experiments and tests.Farbod: Yes. That's what I gonna bring up I think, you know, the audience might have mixed feelings about that but what stood out to me? They were like look within this model with an EVO2 we can whenever you're predicting a new gene, we can tell you if it already exists if it doesn't if you implement it like what kind of behavior it could have, and then obviously you can do lab testing, put it into a living cell and see how it pans out. One thing that I thought was interesting in regards to CRISPR and gene editing, they were like, what if we could use this tool to modify bacteria to consume microplastics and to clean up oil spills, right? So again, I know it's a mixed feeling type of topic, especially as it relates to human beings, but the benefits of this tool are actually pretty significant. And even on the human-Daniel: This doesn't necessarily dictate how the findings are applied, right? This is an engine for understanding DNA, right? That's what we should be excited about, is it's an engine for us to understand what's going on. Even if you decide not to go edit your genes, to get rid of a disease, you can understand what diseases you might be predisposed to based off of your DNA.Farbod: Yeah, and you know, most of the stuff that they talked about are all the positives that we can all agree on, like modifying the bacteria. They said this can be used to significantly cut down on the discovery phase of new drug development because you can find which proteins work best for addressing whatever symptom. We talked about these two episodes ago, I think, on the snake venom episode, which has been a hit, by the way. They did mention that EVO2 could help with human evolution because we could predict what genes could be beneficial to us, but that's a topic for another day. I think what we should focus on here is that this is an incredible platform that they have open source. It's available to all researchers that they want others to use in their workflow and build on top of so that we can make better drugs and help our environment.Daniel: Well, and I think two really important things to mention. And you mentioned one of them and I kind of want to double down on it. One of them is direct quote from the researchers. They said it's really, really good at drug discovery, which is awesome. So obviously, helping identify treatments for potential diseases is awesome. But one of the other things that they can mention is like they, this model can distinguish between harmless DNA mutations to disease, disease causing ones. So, where we've got a lot of concern around free radicals and/or mutations in your DNA, potentially cancer causing, as you mentioned before, is like, we're trying to limit exposure to anything that could cause any type of mutation in your DNA. And that's where we get a lot of conflicting advice from people like, oh, like don't put on sunscreen, but also don't get sunburned and don't eat this and don't eat that. And don't do that. Like it feels like you're, you're screwed either way. I feel like this will potentially help us get more clarity around which types of causes for DNA mutations are most likely to cause disease and then help us hone our focus on like, what's the 80-20? What's the Pareto principle? Like what's the 20% of effort that I can do to protect myself from 80% of bad outcomes? I feel like that's one of the most important things that you and I will experience in our day-to-day life is a little bit more clarity around a lot of the conflicting guidance that we get. I think the most specific one that I think of is like around sun exposure. Like they're like, you need sun exposure for vitamin D, but don't get so much sun exposure that it causes mutations in your skin can cause melanoma. So, if you're outside, make sure you put on sunscreen, but also sunscreen can like cross through the blood brain barrier and cause disruptions in your hormones. Try and give us some clarity around a lot of the conflicting advice like that, where it feels like you can't win either way. Like, hopefully this will help us understand more what changes to make in a day-to-day life that actually have an impact on the presence of disease in our DNA.Farbod: Dude. Yeah, I totally agree that was a good hot take that I completely missed. And the sunscreen one, I'm totally with you. I think about it every morning when I put on my sunscreen. With that said, I'm going to wrap things up. So, folks, if you've heard about DNA sequencing and what a miracle it was that we cracked it a couple decades ago, you might be shocked to hear that it didn't crack the code all the way. Look, we understand what the nucleotides are that make up our DNA. You know, the four different ones, the A, C, G and T. But these folks over at Stanford are now developing a model or have already developed a model that can make sense of the different genes that are part of this DNA sequence and how they interact with each other. But more importantly, almost like you're using ChatGPT, it can predict what comes next. Now, why is this valuable? Well, by understanding the relation between the different genes, they can understand what is harmful, what can be used to develop new tools, how we can modify an existing sequence to give us beneficial properties. For example, one they called out was modifying bacteria to eat microplastics or clean up oil spills. And the next bit, it could even help us understand what good way we can go for evolving the human genome so that we get all the best properties possible.Daniel: That's Pretty fireman.Farbod: Sweet. That's the pod.Daniel: All right. See you.<hr />As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte"> shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" target="_blank" rel="nofollow"> Apple</a> podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" target="_blank" rel="nofollow"> Spotify</a>, and most of your favorite podcast platforms!--The Next Byte: We're two engineers on a mission to simplify complex science &amp; technology, making it easy to understand. In each episode of our show, we dive into world-changing tech (such as AI, robotics, 3D printing, IoT, &amp; much more), all while keeping it entertaining &amp; engaging along the way.

In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.

Podcast: Stanford Predicts The Next Human Evolution

AI, as you may have noticed, is everywhere. Whether it is a discussion about political elections, the implications for recruitment, education or for manufacturing, AI is seemingly always a key part of the conversation. Applications of AI models are nascent across all use cases, including within AM. In this area, the National Institute of Standards and Technology (NIST) is currently running the Advanced Informatics and Artificial Intelligence for Additive Manufacturing (AI2AM) project to develop the methods, models, standards, and best practices to leverage AI and related technologies to move AM towards “born qualified” and “first part correct” outcomes. <table><tbody><tr><td><h3 id="isPasted">Stay Informed on the Latest FormNext 2025 Updates! Sign up to the FormNext newsletter to get notified as soon as tickets are available.</h3><a href="https://info.mesago.de/-lp/5HKPB14784/p4jkP173" target="_blank" id="isPasted" class="button-main-action">Sign up here</a></td></tr></tbody></table>Despite the fledgling nature of much of the technology, in the world of AM, different types of AI do, will or could find use across the process chain.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQyMjg2ODAxMzY5LUNoZXNzLndlYnAuMTI4MC53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii" />In addition to design, AI offers support in further process steps in Additive Manufacturing. Image: James Woodock<h2>Ideation and design</h2>AI can play a significant role in the DfAM space, with generative AI able to create complex designs that make full use of the benefits of AM. As well as being embedded in major CAD software like Autodesk Fusion 360 and Siemens NX, standalone products like Meshy AI allows users to create 3D models — exportable as .stl files — from either text prompts or photographs. Deploying deep learning and generative design in this way drastically reduces the design time while allowing more solutions to be trialed in a reduced timeframe.Once part files have been generated, several AI processes including machine learning, predictive analytics and physics-informed AI software can then be used to analyze and fix files prior to printing. By analyzing the parts and mining known datasets about how different AM systems build, solutions like 1000 Kelvin’s AMAIZE, can predict issues with individual builds based on the part geometry, machine parameters and materials choice, reducing the number of failed builds.Here is a concrete example: The Citrine Platform was used in collaboration with the Alliance for the Development of Additive Processing Technologies (ADAPT) Consortium to optimize the design and production of hinges for Mine-Resistant Ambush Protected (MRAP) light tactical vehicles. AI-driven optimization led to a 50% increase in strength, 38% reduction in weight, and consolidated the original six parts down to one.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQyMjg2ODM3ODc4LVRyYWRlc2hvdy53ZWJwLjEyODAud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" class="fr-fic fr-dii" />Exhibition stand designed with AI. Image: James Woodcock<h2>Defining the build</h2>Machine learning (ML) models can analyze huge material datasets and combine that information with data from previous builds to predict how a given material will behave during the printing process. This allows engineers to make informed decisions about which materials to use for an application based on real-world data.Predictive analytics AI ensures that schedules are optimized for maximum efficiency, helping manufacturers reduce downtime by planning and scheduling optimal times to run certain print jobs, based on machine availability, material stocks, and job priorities.<h2>AI as QA </h2>AM processes, especially in metals, require the complex choreography of chemistry and physics. Things can and do go wrong. AI-based computer vision systems use deep learning to track each layer of a print as it is being produced, flagging defects such as layer shifts, warping, or other inconsistencies in real-time. Where fixes can be implemented, feedback loops can action corrections, at the very least part builds can be stopped at the point of failure instead of letting the process run.Once the parts are built, inspection is still required — a time-consuming process open to human error. AI-driven automated inspection systems solve this by using computer vision and deep learning to analyze parts post-production. Companies like EOS have integrated computer vision into their platforms to provide real-time process monitoring (EOSCONNECT), ensuring that defects are identified early in the process. Oqton’s Build Quality combines build simulation, monitoring, and part inspection to ensure parts are built to specification without human interference.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQyMjg2ODY1MTQ0LVJvYm90LndlYnAuMTI4MC53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii" />AI also helps to monitor and maintain machines. Image: James WoodcockPerennial bugbear, post-processing, can also be automated with AI-driven robotic systems, further optimizing the production workflow, and ensuring consistent results by handling support removal and surface finishing.<h2>Future failure alert</h2>Predictive maintenance — i.e., fixing a system before it breaks — is one of the most significant areas where AI adds value to users of AM systems. By using machine learning and IoT/IIoT-enabled sensors, AI can monitor the health of AM machines and predict when maintenance is required before a breakdown occurs. This proactive approach minimizes unplanned downtime, reduces repair costs, and extends machine lifespan. By integrating predictive maintenance solutions, manufacturers can achieve 20% efficiency gains and increase machine utilization, significantly enhancing profitability. <h3>Explanation of terms</h3>Machine Learning           A subset of AI focused on building systems that learn from data to make predictions or decisions without being explicitly programmed for each task.Deep LearningA subset of machine learning that uses neural networks to model complex patterns in data. Deep learning is particularly effective for tasks like image and speech recognition.Generative Models       AI models designed to generate new data samples that resemble the training data. These models can create new text, images, music, or other content.Neural Networks A set of algorithms modelled loosely after the human brain, designed to recognize patterns.<table><tbody><tr><td><h3>Stay Informed on the Latest FormNext 2025 Updates! Sign up to the FormNext newsletter to get notified as soon as tickets are available.</h3><a href="https://info.mesago.de/-lp/5HKPB14784/p4jkP173" target="_blank" rel="nofollow" class="button-main-action">Sign up here</a></td></tr></tbody></table>

An overview of the current state of artificial intelligence in Additive Manufacturing with a special focus on the areas of idea generation and design, the construction process, quality assurance and future error prevention.

The next hype cycle?

<html><head></head><body>Event-based computing is transforming AI processing, delivering smarter, faster, and hyper-efficient solutions across industries like IoT, healthcare, and automotive. It enables smart assistants to respond to user commands intelligently without excessive battery drain and extends battery life in essential wearable devices that continuously monitors your voice, pulse, stride, breathing, activity and surroundings. As technology evolves, event-based computing paves the way for more sustainable and innovative applications in everyday life.Spearheading this transformation, <a href="https://brainchip.com/">BrainChip</a>, a pioneer in event-based AI, has recently introduced their Akida™ Pico solution. Unveiling a revolutionary ultra-low power co-processor designed to fully leverage the capabilities of event-based architectures.&nbsp;BrainChip’s <a href="https://brainchip.com/akida-pico-announcement/">Akida Pico</a> stands out by operating on less than a single milliwatt of power, enabling AI functionality in energy-and size-constrained devices, like wearables, sensor hubs, and IoT devices. By directly processing data streaming from the sensor, Akida Pico reduces reliance on the cloud, delivering substantial advantages in latency, privacy, and energy efficiency.<h2 dir="ltr">Why Event-Based Computing Matters in the Real World</h2>Standard approaches to AI processing often involve processing large amounts of data, much of which may be irrelevant to the task at hand. This inefficiency consumes significant energy and introduces latency, which can be detrimental in scenarios requiring real-time responsiveness. Event-based computing flips AI processing on its head, analyzing only essential inputs as they occur. Inspired by neural processing in the brain, event-based architectures focus on temporal patterns, enabling intelligent systems to respond faster and with far greater energy efficiency.&nbsp;The practical implications of event-based computing and Akida Pico’s capabilities are far-reaching. By focusing on temporal patterns and relevant events, this approach is especially effective in:&nbsp;<ul><li dir="ltr">Voice and Audio Processing: Event-based computing excels at tasks such as voice wake detection, keyword spotting, speech noise reduction, and audio enhancement, where responsiveness and low power consumption are critical.</li><li dir="ltr">Continuous Monitoring: For applications like presence detection and motion tracking, Akida Pico ensures efficient, real-time analysis without the need for constant power-hungry processing.</li><li dir="ltr">Edge AI for Wearables: By delivering personalized insights on minimal energy budgets, Akida Pico supports next-generation wearable devices in healthcare, fitness, and consumer electronics.</li></ul><h2 dir="ltr">Redefining AI Performance for Today’s Applications</h2><h3 dir="ltr">Core Features of Akida Pico:</h3><ul><li dir="ltr">Event-Based Data Processing:&nbsp;The architecture focuses on processing relevant events, reducing unnecessary computations and conserving energy.</li><li dir="ltr">Ultra-Low Power Consumption: Consuming less than 1mW, Akida Pico is ideal for battery-powered devices requiring extended operation times.</li><li dir="ltr">Compact Design: A minimal die area and configurable data buffer allow integration into space-constrained designs without compromising functionality.</li><li dir="ltr">False Alarm Filtering:&nbsp;The co-processor’s neural networks filter out non-critical events, preserving power and reducing system wake-ups.</li><li dir="ltr">Developer-Friendly Integration:&nbsp;BrainChip’s MetaTF™ development environment supports TensorFlow, Keras, and PyTorch workflows, simplifying the development and deployment of Temporal-Enabled Neural Networks (TENNs).</li></ul><h2 dir="ltr">Accelerating Development with MetaTF</h2>A key enabler of Akida Pico’s success is the MetaTF software framework, which allows developers to easily compile and optimize AI models for event-based processing. By supporting popular tools like TensorFlow and PyTorch, MetaTF lowers the learning curve for engineers, enabling faster prototyping and deployment of edge AI solutions.MetaTF supplies tools to convert a model with floating point weights and activations to a model with low bit-width weights and activations while maintaining model performance. These tools also convert quantized models trained using traditional deep learning methods to event-domain models for execution with low-latency and low-power on the Akida Pico Processor.&nbsp;<h2 dir="ltr">Expanding Possibilities of Edge AI Across Industries</h2>Event-based computing is driving innovation across multiple sectors, and the Akida Pico is positioned to lead the charge. Its ability to deliver high performance with ultra-low power consumption makes it a valuable asset in:<ul><li dir="ltr">Industrial IoT:&nbsp;Continuous monitoring of equipment and infrastructure is more efficient and cost-effective with Akida Pico’s energy-saving capabilities.</li><li dir="ltr">Automotive Systems: From early detection systems to low-latency decision-making, Akida Pico enhances the functionality of AI-driven automotive solutions.</li><li dir="ltr">Defense and Security:&nbsp;The co-processor’s real-time event detection and low power requirements are critical for mission-critical systems that operate in resource-constrained environments.</li></ul><h3 dir="ltr">Explore the Future of Edge AI</h3>BrainChip provides a range of software, hardware and IP products that can be integrated into existing and future designs, with a roadmap for customers to deploy multi-modal AI models at the edge.<hr><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">Discover&nbsp;<a href="https://brainchip.com/akida-foundations/"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space-collapse: preserve;">more about their Akida series</a><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">&nbsp;and how the&nbsp;<a href="https://brainchip.com/akida-pico-announcement/"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space-collapse: preserve;">Akida Pico Ultra Low Power</a><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">&nbsp;can elevate your projects in speech, audio, and medical monitoring.</body></html>

Event-based computing is transforming AI processing, delivering smarter, faster, and hyper-efficient solutions across industries like IoT, healthcare, and automotive.

Smarter, Faster, Hyper-Efficient: Why Event-Based Computing is Key to AI at the Edge

<html><head></head><body>Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark.Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions. The method would allow you to point to the bowl or trace a trajectory to it on a screen, or simply give the robot’s arm a nudge in the right direction.Unlike other methods for correcting robot behavior, this technique does not require users to collect new data and retrain the machine-learning model that powers the robot’s brain. It enables a robot to use intuitive, real-time human feedback to choose a feasible action sequence that gets as close as possible to satisfying the user’s intent.When the researchers tested their framework, its success rate was 21 percent higher than an alternative method that did not leverage human interventions.In the long run, this framework could enable a user to more easily guide a factory-trained robot to perform a wide variety of household tasks even though the robot has never seen their home or the objects in it.“We can’t expect laypeople to perform data collection and fine-tune a neural network model. The consumer will expect the robot to work right out of the box, and if it doesn’t, they would want an intuitive mechanism to customize it. That is the challenge we tackled in this work,” says Felix Yanwei Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a <a href="https://arxiv.org/pdf/2411.16627" target="_blank">paper on this method</a>.His co-authors include Lirui Wang PhD ’24 and Yilun Du PhD ’24; senior author Julie Shah, an MIT professor of aeronautics and astronautics and the director of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); as well as Balakumar Sundaralingam, Xuning Yang, Yu-Wei Chao, Claudia Perez-D’Arpino PhD ’19, and Dieter Fox of NVIDIA. The research will be presented at the International Conference on Robots and Automation.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741875298807-MIT-Policy-Steering-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">The interactive system developed by MIT researchers allows laypeople to easily correct a robot’s behavior using simple interactions. For instance, a user can guide a robot’s actions by pointing to an object on a screen, as demonstrated here by graduate student Felix Yanwei Wang.&nbsp; Credit: Melanie Gonick, MIT<h2>Mitigating misalignment</h2>Recently, researchers have begun using pre-trained generative AI models to learn a “policy,” or a set of rules, that a robot follows to complete an action. Generative models can solve multiple complex tasks.During training, the model only sees feasible robot motions, so it learns to generate valid trajectories for the robot to follow.While these trajectories are valid, that doesn’t mean they always align with a user’s intent in the real world. The robot might have been trained to grab boxes off a shelf without knocking them over, but it could fail to reach the box on top of someone’s bookshelf if the shelf is oriented differently than those it saw in training.To overcome these failures, engineers typically collect data demonstrating the new task and re-train the generative model, a costly and time-consuming process that requires machine-learning expertise.Instead, the MIT researchers wanted to allow users to steer the robot’s behavior during deployment when it makes a mistake.But if a human interacts with the robot to correct its behavior, that could inadvertently cause the generative model to choose an invalid action. It might reach the box the user wants, but knock books off the shelf in the process.“We want to allow the user to interact with the robot without introducing those kinds of mistakes, so we get a behavior that is much more aligned with user intent during deployment, but that is also valid and feasible,” Wang says.Their framework accomplishes this by providing the user with three intuitive ways to correct the robot’s behavior, each of which offers certain advantages.First, the user can point to the object they want the robot to manipulate in an interface that shows its camera view. Second, they can trace a trajectory in that interface, allowing them to specify how they want the robot to reach the object. Third, they can physically move the robot’s arm in the direction they want it to follow.“When you are mapping a 2D image of the environment to actions in a 3D space, some information is lost. Physically nudging the robot is the most direct way to specifying user intent without losing any of the information,” says Wang.<h2>Sampling for success</h2>To ensure these interactions don’t cause the robot to choose an invalid action, such as colliding with other objects, the researchers use a specific sampling procedure. This technique lets the model choose an action from the set of valid actions that most closely aligns with the user’s goal.“Rather than just imposing the user’s will, we give the robot an idea of what the user intends but let the sampling procedure oscillate around its own set of learned behaviors,” Wang explains.This sampling method enabled the researchers’ framework to outperform the other methods they compared it to during simulations and experiments with a real robot arm in a toy kitchen.While their method might not always complete the task right away, it offers users the advantage of being able to immediately correct the robot if they see it doing something wrong, rather than waiting for it to finish and then giving it new instructions.Moreover, after a user nudges the robot a few times until it picks up the correct bowl, it could log that corrective action and incorporate it into its behavior through future training. Then, the next day, the robot could pick up the correct bowl without needing a nudge.“But the key to that continuous improvement is having a way for the user to interact with the robot, which is what we have shown here,” Wang says.In the future, the researchers want to boost the speed of the sampling procedure while maintaining or improving its performance. They also want to experiment with robot policy generation in novel environments.</body></html>

Graduate student Felix Yanwei Wang nudges a robotic arm that is manipulating a bowl in a toy kitchen set up in the group’s lab. Using the framework Wang and his collaborators developed, slightly nudging a robot is one way to correct its behavior. Credit: Melanie Gonick, MIT

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

Robotic helper making mistakes? Just nudge it in the right direction

<html><head></head><body>Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot. From this, it calculates the fastest route. Similar algorithms could also be used for humanoid robots or autonomous driving to enable safe interaction between robots and humans.The little robot weaves its way through crowds of people on its wheels like a human. To make this possible, researchers from TUM Professor Angela Schoellig's Learning Systems and Robotics Lab have combined computing power, sensors and mathematical skills. ‘Our robot models the way people will react to its movements to plan its paths. This is the big difference to other approaches that typically ignore this interaction,’ explains Prof. Schoellig.<h2>New route calculated ten times per second</h2>A lidar system constantly sends laser beams into the surroundings, measures their reflections, and uses them to build a precise 360-degree map of what the robot sees. A special focus is placed on people walking around nearby. At the same time, sensors in the wheels measure the robot's own speed and the distances traveled. A computer processes this information, calculates the estimated distances people will cover in the next two seconds, and simultaneously plans the optimum route to the destination. ‘Our robot adjusts its route ten times a second while simultaneously recognizing people's paths,’ explains TUM researcher Sepehr Samavi.<h2>System learns from data based on people in crowds</h2>The robot, which Sepehr Samavi has named ‘Jack’, learns behavioral patterns from humans so that it does not constantly stop due to the risk of collisions. ‘Our mathematical model, on which the planning algorithm is based, was derived from human movements and translated into equations,’ explains Professor Schoellig. For Jack's decision, this means that he does not stop immediately as soon as a person approaches him. He takes into account that people will adapt to the situation, react and change their route slightly so that they do not collide with him. If someone remains on a collision course contrary to expectations, the robot changes its plans quickly and takes a different route - but does not stop.&nbsp;The researchers also incorporate data sets that show people's behavior in crowds. The robot, which has already been used outside of the laboratory, is constantly learning and becoming more human-like: ‘Jack knows his destination, observes people and sees where they are going to constantly optimize his own paths,’ says Prof. Schoellig, ‘almost like a human being.’&nbsp;The TUM researchers have already reached the third evolutionary stage with the new algorithm. Instead of ‘only’ reacting to a situation (stage 1) or ‘merely’ predicting the movements of oncoming people (stage 2), the TUM robot is interactive (stage 3). ‘On the one hand, it predicts other people's movements, but it also manages to influence these people through its behavior and simultaneously avoid collisions,’ explains researcher Sepehr Samavi. &nbsp;&nbsp;<h2>Development for use in autonomous driving</h2>It is precisely such interactive scenarios that form the bottleneck in autonomous driving, says Prof Schoellig. For example, if a vehicle starts accelerating on a highway on-ramp, many drivers will change lanes or tap the brakes. The new approach makes it possible to consider the reaction of others in such a scenario. However, the researchers are initially looking at applications in delivery robots or with wheelchair users. The advantage: these vehicles can reach their destination independently and reliably. Even humanoid robots could benefit from the new algorithms. However, the intelligent vehicle has one decisive disadvantage: ‘A moving robot can simply stop if necessary - humanoids are still quite wobbly and quickly lose their balance,’ says Prof. Schoellig.<hr>PublicationsSepehr Samavi, James R. Han, Florian Shkurti, and Angela P. Schoellig; SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization; IEEE Transaction on Robotics; 2025; <a href="https://ieeexplore.ieee.org/document/10726864" target="_blank" rel="noreferrer">https://ieeexplore.ieee.org/document/10726864</a><hr>Further information and links- Prof. Angela Schoellig heads the Chair of Safety, Performance, and Reliability for Learning Systems at the TUM School of Computation, Information and Technology. She is responsible for international relations on the board of the Munich Institute of Robotics and Machine Intelligence (MIRMI) and also coordinates the Robotics Institute Germany, which is funded by the Federal Ministry of Education and Research (BMBF) – <a href="https://www.robotics-institute-germany.de/" target="_blank" rel="noreferrer">https://www.robotics-institute-germany.de/</a>. She is also a member of the Munich Data Science Institute (MDSI) at TUM. &nbsp;&nbsp;- TUM researcher Sepehr Samavi introduces to Jack: <a href="https://youtu.be/7pxZhRulXVY" target="_blank" rel="noreferrer">https://youtu.be/7pxZhRulXVY</a>- Scientific video: <a href="https://www.youtube.com/watch?v=BY23UXwM1sM" target="_blank" rel="noreferrer">https://www.youtube.com/watch?v=BY23UXwM1sM</a></body></html>

Sepehr Samavi and Prof Angela Schoellig next to robot Jack. Credit: Astrid Eckert / TUM

Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot.

Robot Jack moves like a human

<html><head></head><body>Millions of Americans undergo surgery each year. After surgery, preventing complications like pneumonia, blood clots and infections can be the difference between a successful recovery and a prolonged, painful hospital stay – or worse. More than 10% of surgical patients experience such complications, which can lead to longer stays in the intensive care unit (ICU), higher mortality rates and increased health care costs. Early identification of at-risk patients is crucial, but predicting these risks accurately remains a challenge.New advancements in artificial intelligence (AI), particularly large language models (LLMs), now offer a promising solution. A recent study led by&nbsp;<a href="https://engineering.washu.edu/faculty/Chenyang-Lu.html">Chenyang Lu</a>, the Fullgraf Professor in computer science &amp; engineering in the McKelvey School of Engineering and director of the&nbsp;<a href="https://aihealth.wustl.edu/">AI for Health Institute</a> (AIHealth) at Washington University in St. Louis, explores the potential of LLMs to predict postoperative complications by analyzing preoperative assessments and clinical notes. The work, published online Feb. 11 in&nbsp;<a href="https://www.nature.com/articles/s41746-025-01489-2">npj Digital Medicine</a>, shows that specialized LLMs can significantly outperform traditional machine learning methods in forecasting postoperative risks.“Surgery carries significant risks and costs, yet clinical notes hold a wealth of valuable insights from the surgical team," Lu said. "Our large language model, tailored specifically for surgical notes, enables early and accurate prediction of postoperative complications. By identifying risks proactively, clinicians can intervene sooner, improving patient safety and outcomes.”Traditional risk prediction models have primarily relied on structured data, such as lab test results, patient demographics, and surgical details like procedure duration or the surgeon’s experience. While this information is undoubtedly valuable, it often lacks the nuance of a patient's unique clinical narrative, which is captured in the detailed text of clinical notes. These notes contain personalized accounts of the patient's medical history, current condition, and other factors that influence the likelihood of complications.Lu and co-first authors Charles Alba and Bing Xue, both graduate students working with Lu at the time the study was conducted, employed specialized LLMs trained on publicly available medical literature and electronic health records. They then fine-tuned the pretrained model on surgical notes to make better predictions about surgical outcomes. The resulting method – the first of its kind to process surgical notes and use them to make predictions about postoperative outcomes – can go beyond structured data to recognize patterns in the patient's condition that might otherwise be overlooked.&nbsp;Based on nearly 85,000 surgical notes and associated patient outcomes from an academic medical center in the Midwest collected between 2018 and 2021, the team reported that their model performed far better than traditional methods in predicting complications. For every 100 patients who experienced a postoperative complication, the team’s new model correctly predicted 39 more patients who had complications than traditional natural language processing models.Beyond the number of patients who could potentially have surgical complications caught early and mitigated, the study also showcases the power of foundation AI models, which are designed to multitask and can be applied to a wide range of problems.“Foundation models can be diversified, so they’re generally more useful than specialized models. In this case, where lots of complications are possible, the model needs to be versatile enough to predict many different outcomes,” said Alba, who is also a graduate student in WashU’s&nbsp;<a href="https://datasciences.wustl.edu/">Division of Computational &amp; Data Sciences</a>. “We fine-tuned our model for multiple tasks at same time and found that it predicts complications more accurately than models trained specifically to detect individual complications. This makes sense because complications are often correlated, so a unified foundational model benefits from shared knowledge about different outcomes and doesn’t have to be painstakingly tuned for each one.”“This versatile model has the potential to be deployed across various clinical settings to predict a wide range of complications,” said&nbsp;<a href="https://anesthesiology.wustl.edu/people/joanna-abraham-phd/">Joanna Abraham</a>, associate professor of anesthesiology at WashU Medicine and a member of the Institute for Informatics (I2) at WashU Medicine. “By identifying risks early, it could become an invaluable tool for clinicians, enabling them to take proactive measures and tailor interventions to improve patient outcomes.”&nbsp;<hr>Alba C, Xue B, Abraham J, Kannampallil T, Lu C. The foundational capabilities of large language models in predicting postoperative risks using clinical notes.&nbsp;njp Digital Medicine, published online Feb. 11, 2025. DOI:&nbsp;<a href="https://www.nature.com/articles/s41746-025-01489-2">https://www.nature.com/articles/s41746-025-01489-2</a>&nbsp;</body></html>

Study led by Chenyang Lu explores the potential of large language models to predict postoperative complications by analyzing preoperative assessments and clinical notes. (Photo: iStock)

AI for Health Institute team unveils a versatile large language model to enhance perioperative care

Foundation AI model predicts postoperative risks from clinical notes

<html><head></head><body>Felix Heide’s goal was big: help computers see.&nbsp;He did not know he was starting on a path to develop artificial intelligence that uses light instead of electricity, requires vastly less energy than a conventional computer, and produces results hundreds of times faster.“The question for me was always how can we use algorithms to sense and understand the world?” said&nbsp;<a href="https://engineering.princeton.edu/faculty/felix-heide">Heide</a>, an assistant professor of&nbsp;<a href="https://www.cs.princeton.edu/" data-type="link" data-id="https://www.cs.princeton.edu">computer science</a> at Princeton University.At first, Heide, who joined the faculty in 2020, focused on using machine learning to more effectively draw information from light captured by cameras. He succeeded in creating cameras that use visible light or radar signals to detect objects around&nbsp;<a href="https://engineering.princeton.edu/news/2020/06/24/new-radar-lets-cars-spot-hazards-around-corners">blind corners</a> and see&nbsp;<a href="https://engineering.princeton.edu/news/2021/06/29/metropolis-project-backs-solutions-safer-more-resilient-cities">through fog</a> — important goals for aiding drivers or ensuring the safety of autonomous vehicles.&nbsp;Soon, however, Heide realized that making more progress would require rethinking what a camera is, what a lens is.&nbsp;With his first graduate student,&nbsp;<a href="https://ethan-tseng.github.io/">Ethan Tseng</a>, Heide began to explore metasurfaces — materials whose geometry gives them special properties. Instead of bending light inside a piece of glass or plastic, like a conventional lens, a metasurface diffracts light around tiny objects, like light spreading out as it passes through a slit. To build metasurfaces, Heide and his team struck up a collaboration with the lab of&nbsp;<a href="https://people.ece.uw.edu/majumdar_arka/">Arka Majumdar</a> at the University of Washington, who are experts in ultra-small devices that control the interaction of light and matter.“This is a completely new way of thinking about optics, which is very different from traditional optics,” Majumdar said in a&nbsp;<a href="https://www.ece.uw.edu/spotlight/majumdar-computer-vision/">news story</a> published by the University of Washington.In their first breakthrough, the combined research team built a high-resolution camera smaller than a&nbsp;<a href="https://engineering.princeton.edu/news/2021/11/29/researchers-shrink-camera-size-salt-grain">large grain of salt</a>. First reported in 2021 and now widely cited in scientific journals and by news media, their tiny camera depended on the exact positioning of millions of tiny pillars on a metasurface. The team used machine learning to arrange the pillars — each about a 1000th of a millimeter tall and much narrower in width — to make the maximum use of the light hitting the device.&nbsp;Like a conventional lens, this surface focused the light onto a sensor, but it extracted a lot of information from a very small amount of light. The team wrote software that reconstructed this compressed information into surprisingly crisp color pictures.<h2>Surpassing an AI benchmark</h2>Then the researchers realized something profound. The light coming out of these complex arrays of pillars did not need to look to a human eye at all like the object that produced the image. The pillars could function as highly specialized filters that organized the optical information into categories, such as edges, light areas, dark areas, or even qualities that a human viewer might not be able to perceive or make sense of, but that could be useful to a computer that receives this pre-processed information.Suddenly their lens was not just benefiting from artificial intelligence, it was&nbsp;performing functions a lot like what happens in the most sophisticated AI systems that recognize images. Could the lens itself tell the difference between a dog and a horse?“We realized we don’t need to record a perfect image,” Heide said. “We can record only certain features that we can then aggregate to perform tasks such as classification.”The team pressed ahead with this idea and by the end of 2024, they succeeded in creating a system that could identify an object in an image using less than 1% of the computing needed by conventional techniques. Their metasurface lens — actually an array of metasurface lenses — had done 99.4% of the work.In doing so, the team surpassed a key benchmark. Computer vision burst into practical use around 2012 when an artificial intelligence system called AlexNet achieved an unprecedented level of accuracy in identifying images. Heide’s system has now beaten the performance of AlexNet when tested against industry-standard databases of images, such as the ImageNet library developed at Princeton.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741614629348-metasurface-D1-v4_1600x900.jpg" style="width: 100%px;" class="fr-fic fr-dib">AlexNet “started this whole deep neural network revolution that we have seen play out over the last decade,” said Heide. Now, he said, it appears within grasp for optical systems to start a new revolution in ultra-fast, low-energy-use artificial intelligence.&nbsp;“This could potentially extend beyond image processing, and this is where we are just touching the tip of the iceberg,” Heide said.Francesco Monticone, an associate professor of electrical and computer engineering at Cornell University, said the work by Heide and Majumdar “elegantly and convincingly demonstrates the promise of optical metasurfaces” for low-power, low-latency computing systems. Monticone said that the ability of metasurfaces to perform many operations in parallel and to be finely manipulated across many variables could offer advantages over electronic processors and that Heide and Majumdar’s findings “represent a major step in the quest for ‘optical advantage’ in computing.”<h2>Building on a Princeton legacy</h2>The artificial intelligence that Heide’s lens achieved is rooted in the work of Princeton physicist and neuroscientist John Hopfield, who won a Nobel Prize last year for introducing the idea of an artificial neural network. Hopfield was interested in how the brain learns and remembers. In 1982, he proposed computer algorithms that represent neurons mathematically and represent learning and memory by adjusting the strength of connections between the virtual neurons.&nbsp;Soon after that researchers around the world began trying to construct Hopfield networks using light signals. The prospect of using light to compute is tantalizing. Fast as electrons are, photons are faster. Nothing moves faster than the speed of light.As a practical matter, however, it was the electrical, computer-chip-based systems that eventually took off. The systems that drive today’s artificial intelligence require trillions upon trillions of calculations to adjust the virtual connections in Hopfield’s network, eating up enormous amounts of computing power and energy.Heide’s system offers a different paradigm. His optical system can perform the equivalent of hundreds of millions of computer calculations (called floating point operations or FLOPS) in an instant. Like Heide’s diffractive “filters,” computer-based neural networks apply mathematical filters (known as kernels) to their input data to extract specific pieces of information. Looking at even a few pixels of a picture requires a considerable number of calculations, so modern AI systems look for very specific features in a small number of pixels at a time. In Heide’s system, large and complex filtering operations happen naturally as light passes through, which allows for a few large complex filters, or kernels, to analyze the whole image at once. In each metasurface lens within the array, the tiny pillars reorganize and abstract the light without any electrical input or active control.Heide and Tseng said this concept of a few large optical “kernels” as opposed to many small ones is key to their success. Another key is the integration of hardware and software.In a sense, Tseng said, what the team is doing is like the evolution of animal vision.“Animal vision is joint between optical hardware and neural back-end processing,” Tseng said, citing examples of the mantis shrimp, dragonfly, or cuttlefish that can sense a property of light waves called polarization, which conventional optics don’t pick up. “There are animals that have more exotic vision than what we have, and we suspect that the hardware of their eyes is working together with their brain to perform various tasks.”For Heide, the freedom to follow his curiosity and stray from pure computer science into the different field of optics is unique to universities. As was his move from the specific quest to improve cameras to the threshold of a broad new computing paradigm.&nbsp;“Often I get the question: Working in AI, how do you compete with industry?” said Heide, who also credits the National Science Foundation for supporting much of the work. “This is a perfect example of something that would have never happened in industry because it’s at the intersection of disciplines. It’s so disparate that we wouldn’t have found it without that freedom.”<iframe width="640" height="360" src="https://player.vimeo.com/video/1061275878" 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> </body></html>

This ultracompact camera has optical elements that function as a neural network and can classify images at the speed of light. Image courtesy of the researchers

Researchers began to explore metasurfaces — materials whose geometry gives them special properties. Instead of bending light inside a piece of glass or plastic, like a conventional lens, a metasurface diffracts light around tiny objects, like light spreading out as it passes through a slit.

A new way of seeing, a new way of computing

High-Bandwidth Memory (HBM) is a 3D-stacked DRAM designed for ultra-high bandwidth and efficiency. Used in GPUs, AI, and HPC, it tackles memory bottlenecks by stacking dies vertically near processors. This article explores its evolution, architecture, and impact on modern computing.

High Bandwidth Memory: Concepts, Architecture, and Applications

A team of scientists has developed a method to illuminate the dynamic behavior of nanoparticles, which are foundational components in the creation of pharmaceuticals, electronics, and industrial and energy-conversion materials. 

artificial intelligence

Profiles