Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Top 7 AI Agent Platforms for Industrial Manufacturing in 2026
The manufacturing sector is undergoing a significant digital transformation, marked by substantial investments in Internet of Things (IoT) sensors, Manufacturing Execution Systems (MES), industrial analytics, and predictive maintenance solutions over the past decade. This shift has provided manufacturers with unparalleled operational visibility, enabling real-time monitoring of equipment, production lines, quality metrics, and material flows. Despite these advancements, production managers continue to face challenges in optimizing processes and improving efficiency. The integration of these technologies aims to enhance productivity and streamline operations, ultimately driving the industry towards a more data-driven future.
AI agents Manufacturing ai agents autonomous manufacturing digital manufacturing ERP integration
Innocean, SBVA launch startup growth platform
Innocean, the advertising and marketing division of Hyundai Motor Group, announced on Monday its collaboration with SBVA, a venture capital firm previously known as SoftBank Ventures Asia. This partnership aims to establish UP 2026, a growth platform focused on fostering collaboration with high-growth startups and exploring new business opportunities. SBVA, affiliated with the SoftBank Group, has a diverse investment portfolio that includes over 100 startups across various sectors such as artificial intelligence, the Internet of Things, and robotics. The initiative reflects Innocean's commitment to innovation and strategic growth in an evolving market landscape.
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China’s Xiaomi unveils robotic arm that enables remote, hands-free home EV charging
Xiaomi, the prominent Chinese technology company, recently unveiled its strategic vision for the future of smart home technology during a press event held in Beijing. This announcement, made on October 15, 2023, highlights the company's commitment to enhancing user experience through innovative products and seamless integration of smart devices. The initiative aims to address the growing demand for interconnected home environments, driven by an increase in remote work and digital lifestyles. By leveraging advancements in artificial intelligence and the Internet of Things, Xiaomi plans to create a more intuitive and responsive ecosystem that simplifies everyday tasks for consumers. During the event, executives showcased a range of new products designed to work harmoniously within this ecosystem, emphasizing the importance of user-friendly interfaces and energy efficiency. The company also outlined its plans for expanding partnerships with other tech firms to enhance compatibility and functionality across various platforms. Xiaomi's vision reflects a broader trend in the tech industry, where companies are increasingly focused on creating smart solutions that cater to the evolving needs of consumers. As the market for smart home devices continues to grow, Xiaomi's proactive approach positions it as a key player in shaping the future of home automation.
Energy
Industry 4.0: putting a spotlight on the connector technology shaping the future
The rapid advancement of Industry 4.0, along with the Internet of Things (IoT) and intelligent automation, is significantly transforming the industrial landscape, impacting sectors ranging from defense to aerospace. As of October 2023, these technologies are driving the evolution of the industrial internet, enhancing connectivity and operational efficiency in various applications, including drone technology and space connectivity systems. This shift is motivated by the need for increased productivity, improved data management, and the ability to respond swiftly to market demands. Companies are leveraging these innovations to streamline processes, reduce costs, and enhance decision-making capabilities. The integration of IoT devices and automated systems is facilitating real-time data analysis, enabling industries to optimize their operations and maintain a competitive edge in an increasingly digital economy.
Partner Content
Industry 4.0: putting a spotlight on the connector technology shaping the future
The rapid advancement of Industry 4.0, the Internet of Things (IoT), and intelligent automation is significantly transforming the industrial landscape, impacting various sectors including defense, drone technology, and space connectivity. This evolution is driven by the increasing demand for efficiency and connectivity in industrial operations. As companies seek to leverage these technologies, they are integrating smart systems that enhance productivity and streamline processes. The shift is evident as industries adopt automated solutions to optimize their operations, reduce costs, and improve overall performance. The ongoing developments in these areas are expected to reshape the future of industrial practices, fostering innovation and competitiveness on a global scale.
Partner Content
Over 1900 Quality Companies to Gather at July Electronics Expo in Shanghai
The electronica Shanghai 2026 is set to occur from July 1 to 3 at the Shanghai New International Expo Centre. This significant event will host over 1,900 exhibitors and is expected to draw more than 70,000 professionals from the electronics industry. The focus will be on emerging trends such as smart electric vehicles (EVs), artificial intelligence (AI), and the Internet of Things (IoT). By providing a platform for industry leaders and innovators, the event aims to foster discussions on the future of electronics and technological advancements.
Electronics Expo AI Smart Vehicles IoT Semiconductors
Quiz: Industrial Connectivity Trends
Recent developments in industrial networks are significantly transforming the landscape of manufacturing connectivity. As companies increasingly adopt advanced technologies, the integration of Internet of Things (IoT) devices and cloud computing is reshaping how manufacturers communicate and operate. This shift is particularly evident in the ongoing evolution of smart factories, which leverage real-time data to enhance efficiency and productivity. The changes are occurring across various sectors, with a notable emphasis on automation and data analytics. By October 2023, many manufacturers have begun to implement these technologies to streamline operations and reduce costs. The push for greater connectivity is driven by the need for improved supply chain management and the ability to respond swiftly to market demands. Experts highlight that the transition to more interconnected systems is not merely a trend but a necessary adaptation to remain competitive in a rapidly changing global market. Manufacturers are increasingly recognizing the importance of collaboration and data sharing among partners to optimize processes and innovate products. As this transformation continues, the implications for workforce dynamics and skill requirements are becoming apparent. Companies are investing in training programs to equip employees with the necessary skills to thrive in this new environment. The ongoing evolution of industrial networks is poised to redefine traditional manufacturing paradigms, fostering a more agile and responsive industry capable of meeting the challenges of the future.
Process / Communication
How to Achieve Remote Monitoring and Diagnostics for Controllable Robot Systems
In the evolving landscape of smart manufacturing, the significance of collaborative robots is shifting from mere physical performance to the ability to be managed remotely. As production environments become increasingly decentralized, companies are prioritizing remote monitoring and diagnostics to oversee robot health, predict maintenance needs, and troubleshoot issues without on-site presence. To achieve effective remote management, a combination of advanced hardware sensors and cloud-based software is essential. Utilizing the Industrial Internet of Things (IIoT), data from robots—including motor temperature and power consumption—is streamed to centralized dashboards. Secure data transmission protocols like OPC UA and MQTT facilitate communication with Manufacturing Execution Systems, enabling the use of "Digital Twin" technology. This allows real-time mirroring of a robot's movements, triggering automated alerts for predictive maintenance to prevent costly downtimes. JAKA is at the forefront of this innovation, moving beyond traditional operations to create a connected ecosystem. Their "Smart, Simple, Small" philosophy ensures that managing JAKA systems is as user-friendly as mobile applications. With advanced wireless teaching and cloud management tools, users can monitor their fleet of robots globally from a single interface. JAKA's software suite enables remote diagnostics, providing real-time feedback on robot status, which is crucial for maintaining continuous production across various locations. The integration of AI-driven vision and sensing further enhances remote monitoring capabilities. By investing in JAKA, companies are securing a future-proof solution that ensures control and productivity, regardless of geographical constraints.
Beyond Cobots: Integrating Robotic Automation with AGVs and IIoT Systems
In recent years, manufacturing has experienced a significant transformation as companies shift from standalone automation to interconnected and flexible systems. JAKA, a leader in collaborative robot technology, has observed this evolution, where production environments are increasingly designed around coordinated robots, autonomous guided vehicles (AGVs), and Industrial Internet of Things (IIoT) platforms. This transition enables automation to adapt dynamically to real production conditions while ensuring safety and flexibility in workplaces that prioritize human interaction. Initially, collaborative robots were embraced for their ability to work safely alongside human operators, facilitating smoother automation processes. As their applications have matured, integrating these robots with AGVs and IIoT systems has become a logical progression. This integration allows for synchronized material handling and processing tasks, enhancing efficiency. IIoT connectivity further supports real-time data exchange, enabling predictive maintenance and improved process visibility, which is crucial for maintaining flexibility in production lines. AGVs play a pivotal role in extending automation beyond fixed workstations. When connected through IIoT infrastructure, these vehicles and robots can share crucial information, reducing idle time and manual interventions while enhancing workflow traceability. This coordination not only boosts operational efficiency but also increases transparency, allowing for continuous optimization and informed decision-making. To facilitate this integrated approach, JAKA has developed the Ai12, a collaborative robot designed for easy deployment through wireless teaching and graphical programming. This technology enhances safety and adaptability, allowing for seamless human-robot interaction. JAKA envisions a future where industrial robotic automation is not merely a collection of isolated machines but a cohesive system that evolves with production demands, fostering smarter and more responsive industrial environments.
Startup helps retailers track their products in real-time
Cartesian, a technology company, has developed an innovative system for locating objects, leveraging technology originally invented at the Massachusetts Institute of Technology (MIT). This cutting-edge system has the potential to revolutionize various industries, including manufacturing, logistics, and robotics. By enhancing the accuracy and efficiency of object tracking, Cartesian aims to streamline operations and improve productivity across these sectors. The technology, which was first conceptualized at MIT, is now being adapted for practical applications in real-world settings. As industries increasingly rely on automation and precise tracking systems, Cartesian's advancements could play a crucial role in shaping the future of operational efficiency.
Research Startups Innovation and Entrepreneurship (I&E) Electrical engineering and computer science (EECS) Machine learning Wireless
What Makes AI Art Worth Collecting?
In May, an anonymous artist who goes by SHL0MS on X posted that he had used AI to generate an image inspired by Claude Monet and asked people to weigh in on how it missed the mark. More than 600 responses called out issues, saying the colors were off, the depth was all wrong, and that AI didn’t understand how light worked.SHL0MS then revealed that the image was of a real Monet, one of around 250 variations of water lilies the artist had painted in his lifetime. He had simply downloaded a high-resolution image from Wikimedia and cropped out the signature. He minted the exchange as an NFT (a unique digital collectible recording ownership of the work), titled it “Inferior Image,” and sold it for just over US $40,000 after 28 bids.The stunt exposed how charged the conversation around AI art has become, and how quick people are to dismiss anything AI-generated as slop—even when it’s not. Yet even as those arguments continue, a market for AI-generated art has begun to form anyway. It’s fragmented and contested, but bigger than most people realize.Jediwolf, an anonymous collector who says he has spent more than 20 years acquiring digital and AI art, was watching the experiment unfold in real time on X. He had never interacted with SHL0MS before, but when the NFT went up for auction he made a bid and won. “I was buying a unique moment in time,” he says, “captured by an artist and preserved as a token.”The Monet was not AI art, but most of what Jediwolf buys is. One of Jediwolf’s digital collections, which he calls UnderTheGAN—a play on GANs, or generative adversarial networks, the AI technology that preceded today’s diffusion models—comprises roughly 100 works valued at around $72,000, focused on early AI art from 2015 to 2020, before the medium went mainstream. He describes his role as part collector, part researcher, part curator, trying to document a fast-moving field.“A decade ago, digital art was often treated as peripheral to the ‘serious’ art world,” he says. “Today, it is increasingly difficult to separate contemporary culture from the internet.”AI Art Moves Into MuseumsThe market for AI art extends beyond NFTs: AI-generated pieces are also finding their way into physical installations. Last month saw the opening of Dataland, the world’s first generative AI museum, in downtown Los Angeles. It was spearheaded by Refik Anadol, a digital artist who has built a career out of transforming data into large-scale immersive experiences. The opening exhibition has pieces that use data that Anadol collected from rainforests around the world, with real-time weather information from 16 rainforests feeding into all five galleries. In three of the rooms, the imagery also shifts in response to visitors’ own biometric data, tracked by bracelets they wear. Like any museum it sells tickets, ranging from $49 to $79, and has a gift shop. This shop, however, uses visitors’ biometric data collected during their visit to generate a unique design printed on a T-shirt. For $15,000, a robotic painting system called Qualia creates a one-of-a-kind canvas from that same data, painted once a day, with a waiting list already forming. A founding collection of 1,000 AI data sculptures that evolve based on environmental data from global rainforests sold out in 34 minutes at $5,000 each.The system running it all, which Anadol calls the Large Nature Model, was trained on more than 500 million nature images representing 2.2 million species, gathered through field expeditions to 16 rainforests and partnerships with institutions including the Smithsonian and the Cornell Lab of Ornithology.For Anadol, AI art requires a different kind of transparency than any medium that came before it. Because commercial AI tools have shaped how most people understand the technology, artists working with it seriously have to be more open about their process than painters or photographers ever did.“For AI art, we have to know where the data comes from, we have to know which model is trained and how it’s trained,” he says. “We can’t just think about authenticity and uniqueness if a service and product is the fundamental layer of the artwork.”The reviews for Dataland have mostly been positive, with one critic calling it the Citizen Kane of immersive experiences. But Anadol is used to a more divided reception. His 2022 installation at MoMA—a 7-by-7-meter screen of AI-generated fluid forms with shifting colors and sounds—drew 3 million visitors and entered the permanent collection, even as New York Magazine called it “a massive techno lava lamp.” Anadol sees the skepticism as nothing new, just the latest version of a resistance that has greeted all new media. “Every art form has gone through similar cycles of denial,” he says. “We are living in a renaissance that started 10 years ago, and I just don’t think everyone is aware of it yet.”Who Is Buying AI Art?The broader market data points in multiple directions at once. According to the Art Basel and UBS Art Market Report 2026, digital art’s share of sales nearly tripled between 2024 and 2025, and just over half of all fine art collectors surveyed had purchased a digital artwork in 2025, making it the third most popular category after painting and sculpture (the report does not break out AI art specifically).Meanwhile, Christie’s shuttered its pioneering digital art department in September, folding digital works back into its broader contemporary sales after none of its dedicated auctions broke $400,000.The most data-rich window into buyer behavior comes from a less glamorous corner of the market. After one major stock image platform allowed AI-generated images, monthly sales jumped 80 percent, according to Samuel Goldberg, an economist at Stanford Graduate School of Business who published a research paper about the shift. Traditional contributors began leaving the platform as generative images flooded in, and creators using AI tools rushed to fill the gap. “It looks like consumers like generative AI,” Goldberg says, “and it seems like nongenerative artists could be getting crowded out of the market.” Stock images are essentially a commodity version of art, according to Goldberg, and because image-generating models are already very good at producing them, what’s happening there may be a preview of what’s coming for other creative goods markets—including fine arts—as the technology improves.Artists are typically among the first to test the limits of a new technology; early adopters have created AI art since the 1970s. What’s new now is the ability for anyone to generate an image in seconds with a text prompt. That, according to Christiane Paul, curator of digital art at the Whitney Museum of American Art, is not the same thing at all. What fills those stock-image platforms, and what most people encounter when they think of AI art, does not qualify as art.True AI art, Paul says, is a subcategory of digital art that uses artificial intelligence as both a tool and a medium, engaging with it practically and conceptually, doing things like training custom models, building extensions, and layering control systems. “A visual created by a prompt is not art,” she says. What serious AI artists are actually doing is much more than typing a few words into DALL-E.Far from the shortcut most people assume, working seriously with AI as an artistic medium is, by her account, brutally hard. Every artist she talks to says the same thing. “It is much, much harder than a paintbrush to handle,” she says. “You are literally communicating with a system with a completely different logic.”Thanks to bubblemaps.io for its research assistance on the NFT market.
Ai-art Generative-ai Digital-art Blockchain
Small-AI Models Gain Traction Around the World
One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo.More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of “small AI.”Small AI for Global Health Care AccessSmall AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)“Most people are discussing AI from the LLM/generative side. But that needs a lot of computing power, electricity, massive data, and skilled people to manage it,” Ajay Banga, president of the World Bank, said last January at the World Economic Forum, in Davos. “Outside the developed world, other than maybe India and China, very few countries have that combination.”By contrast, small AI can deliver useful, even life-saving services to people in areas that have none of those things, Banga said. In India, where the government’s AI plans call for more development of small AI, many such systems are working for farmers.For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.“This is the most important area in AI nowadays,” says Marcelo José Rovai, a professor at the Institute of Engineering and Information Systems at the Federal University of Itajubá, in Brazil, who was involved in all three projects. “It’s growing very fast.”Low-Power, Small-AI Models on Devices Small AI models can run on a variety of low-power devices, including [from left to right] an Arduino Nano 33 BLE Sense, a Seeed Wio Terminal, and an Arduino Portenta.Moez AltayebFor Alonge, Rovai, and other advocates, small AI is not just “a promising trend,” as that November World Bank report calls it. It may be, in the long term, the form of AI that will touch the most lives and remain sustainable after some of the giant models become too costly for most users.“I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.“If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.There is no strict definition of “small AI,” but people often use the term for language models with at most a few billion parameters. (Compare that to cutting-edge models, which can include more than a trillion.) That’s small enough to run directly on a phone or a Raspberry Pi. That’s what allows these applications to run on devices without a connection to a data center and use only a few watts of power, often supplied by a battery or a solar panel.Despite their small footprint, these models aren’t fundamentally different technology from that of gigantic AI models, Rovai says. Many instances of small language models were created the same way the phone-based version of Alonge’s pharmaceuticals scanner was—by “pruning” large models, or removing the parameters that weren’t involved in the task. The result is a system that’s less capable generally but still very good at the specific job it was pruned for, Rovai says. A lighter version of RxAll’s RxScanner spectrometer sends its results to an AI model run locally on a phone to check that a drug’s molecular signature is genuine.RxAllOther small models are created by “distillation.” They are trained to mimic a large model, until their performance approaches that of their “teacher,” Rovai says. In other cases, a larger model’s precision is reduced, for example, so that a model run on 32-bit architecture can run on 8-bit designs. In situations where the machine learning application is being used to classify data or predict patterns (like an ant infestation), it’s trained from the beginning on a small device, not derived from a larger model at all. Running all these small, specialized systems is becoming easier, Rovai says, for two reasons.The first reason is that hardware is getting better and more capable while using less power, he says. This means more and more phones can run small AI—especially those equipped with neural processing units, which are specialized chips that handle AI tasks like facial recognition and changing the brightness, shadows, or contrast in a photo.In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”Support for Small-AI DevelopmentConvinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.” And for all that small AI can benefit people without access to big AI, the technology can’t solve the larger problems of development and digital inequality, Alonge says. Implementing small AI won’t allow nations to escape the challenge of creating an ecosystem to support AI: reliable power, a supply chain that works, and an educational system that develops the talents needed to create AI tools.Though his drug-scanning system can run for days on a phone with no connection, “you still want to be able to enable periodic syncing for updates with new signatures for the medications and analytics,” Alonge says. “And even when you are using batteries, reliable power is important. That phone battery is not going to last forever.”In many parts of the world, the future of small AI isn’t assured, he says. “It works, and many places will eventually need to use it. The question is whether or not the political actors are wise enough to invest in infrastructure to support it long term.”
Small-language-models Artificial-intelligence LlmsRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
