Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Teledyne Marine Announces 2026 Global Photo & Data Contest with Cash Prizes
Teledyne Marine has officially launched its 2026 Photo & Data Contest, inviting global participants to submit images and datasets captured using its technologies. The contest runs from July 1 to October 15, 2026, with winners determined through public voting and expert judging. Categories include Voters' Choice, Best Data, Adversity, Moment of Zen, and Underwater, with prizes such as a HERO13 Black camera and KODAK PIXPRO cameras for jury-selected winners. This annual competition aims to celebrate the innovative projects and expertise within the marine community, encouraging the use of Teledyne Marine products in various environments. The contest not only highlights the technical capabilities of Teledyne's equipment but also fosters community engagement through social media sharing. Participants must secure at least five public votes for their entries to qualify for expert evaluation, which will weigh technical quality and storytelling equally. Winners will be announced in October 2026 via a press release and social media channels. Teledyne Marine's Senior Vice President, William Egan, expressed excitement about this year's submissions, reflecting on the previous year's diverse entries that showcased the challenges and achievements in marine research. No further timeline was disclosed at the time of publication.
teledyne marine global photo & data contest 2026
PHOTOS: The sights of Berlin Air Show 2026 on Day 2
On the second day of Germany's largest aerospace expo, a diverse array of photographs captured the dynamic atmosphere and significant developments within the industry. The event, held in Friedrichshafen, showcases cutting-edge technology and innovations from leading aerospace companies and startups. Attendees, including industry professionals, government officials, and aviation enthusiasts, gathered to explore advancements in aviation, space exploration, and defense systems. The expo serves as a vital platform for networking, knowledge exchange, and collaboration, reflecting the growing importance of aerospace in addressing global challenges. Through various presentations and exhibitions, participants engaged with the latest trends and future directions of the sector, emphasizing the industry's commitment to sustainability and technological progress.
Air Warfare Global Air Force Europe Germany ILA Berlin Multimedia 2026
Computing Power Paradox: General-Purpose Chips in Shortage as Shanghai Zhangjiang Builds China Silicon Photonics Hub
In the midst of ongoing discussions regarding China's computing power dynamics, Shanghai Zhangjiang has established itself as a significant national center for silicon photonics. This development comes as over 20 companies have set up operations in the area, covering the entire value chain of the technology. The rise of Zhangjiang as a hub reflects the country's strategic focus on advancing its capabilities in this critical sector, which is essential for enhancing computing power and addressing potential shortages. The concentration of firms in Shanghai is indicative of a broader push to innovate and strengthen China's position in the global technology landscape.
Technology
Teradyne Announces Production System for Double-Sided Wafer Probe Test for Silicon Photonics
Teradyne, a prominent provider of automated test equipment, has announced a strategic partnership with ficonTEC, a global leader in automated optical assembly solutions. This collaboration, revealed in North Reading, Massachusetts, aims to enhance the capabilities of both companies in the rapidly evolving field of semiconductor testing and assembly. By combining Teradyne's expertise in test systems with ficonTEC's advanced optical assembly technologies, the partnership seeks to address the growing demand for efficient and reliable testing solutions in the semiconductor industry. The initiative is expected to streamline processes and improve product quality, ultimately benefiting customers and driving innovation in the sector.
Japan's Shimizu bets on humanoid robots to tackle construction labor crunch
Artificial intelligenceJapan's Shimizu bets on humanoid robots to tackle construction labor crunchCompany eyes fiscal 2030 for robots that can walk around, paint and coat wallsShimizu is testing out the ability of this robot from China's Unitree to patrol construction sites on foot. (Photo by Kohei Okuyama)KOHEI OKUYAMAJuly 8, 2026 05:02 JSTTOKYO -- Japanese general contractor Shimizu plans to introduce AI-powered humanoid robots at its construction sites by around fiscal 2030, aiming to have them handle such work as painting and plastering in a bid to alleviate the industry's severe labor shortages, Nikkei has learned.Read NextArtificial intelligenceJapan eyes AI-powered comeback in factory robot race with China, EuropeConstructionJapan builders turn down big projects because of labor crunch: pollArtificial intelligenceJapan backs SoftBank-led AI models with up to $6.2bn in chasing US, ChinaBusiness dealsJapan's Shimizu to buy Okinawa-based builder focused on US military basesTechnologyVideo game engines find new homes in construction and retailBusiness dealsJapan builder Obayashi buys peer Multiplex Global for $540mLatest on Artificial intelligenceArtificial intelligenceCan China and US find common ground on AI governance in Geneva?Artificial intelligenceJapan weighs AI-powered disaster relief distributionArtificial intelligenceChinese AI usage by US firms soared after Mythos restrictionsSponsored ContentAbout Sponsored ContentThis content was commissioned by Nikkei's Global Business Bureau.
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 Llms
$4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute
$4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute $4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute Stjepan Kalinic Sun, July 5, 2026 at 8:31 AM PDT 6 min read RACE.MI TSLA Benzinga and Yahoo Finance LLC may earn commission or revenue on some items through the links below. Substitution is one of the fundamental economic forces. If a product goes up in price, consumers have a direct incentive to switch to a cheaper substitute. While branding power dictates some price flexibility, such calculations are more straightforward for fungible commodities. When copper costs about $15,000 a metric ton, manufacturers have every right to ask – does every wire really need to be copper? With data centers, grid upgrades and green-energy projects tightening supply, the answer from automakers is increasingly no. Aluminum, trading at $3,100 per ton, is being promoted wherever physics allows. Don't Miss: A single bad hire can set a startup back years. Here are the 5 hires founders most often misjudge — and why Still Learning the Market? These 50 Must-Know Terms Can Help You Catch Up Fast Driving Investment and Corporate Consolidation Aside from being much cheaper, the metal is lighter and good enough for many vehicle applications. The appeal to save on weight is just a bonus for range-anxious electric vehicles. Ferrari has used aluminum in bodies, engines, and chassis for years and has recently begun using aluminum power cables in the 296 hybrid and other models. The payoff can be meaningful: wiring weight savings of up to 20%. "We are not choosing aluminum because it's cheaper; we choose the material that has better performance," the firm's communications executive Dario Esposito said per Reuters. Market interest is driving asset transactions, as Alcoa Corp. has just signed a binding agreement to acquire most of South32 Ltd.'s aluminum value chain for $4.1 billion. These include assets in Australia, South Africa and Brazil, but not the Mozal operation in Mozambique. The largest domestic aluminum producer expects the transaction will generate about $900 million in synergies. JPMorgan estimates the aluminum substitution could affect about 2% of global copper demand this year, and potentially as much as 6% by 2030. Trending: Avoid the #1 Investing Mistake: How Your 'Safe' Holdings Could Be Costing You Big Time A Partial Substitute Still, aluminum is not copper with a discount sticker. It is less electrically conductive, meaning cables often must be thicker to carry the same current. Those properties create problems in tight spaces – shared by both data centers and automobiles. For high-performance systems and specialized applications, copper's efficiency still remains ahead. Story Continues Then, there are environmental and geopolitical complications. The final phase of aluminum production is energy-intensive, often generating a much larger carbon footprint than copper. Energy prices have squeezed domestic producers and closed smelters, while trade frictions, including U.S. tariffs, further complicate sourcing. Cable makers provide some guidance on the issue. Xavier Mathieu, VP of Nexans, the second-largest global cable manufacturer, said buyers typically start switching when copper costs about 3.5 times as much as aluminum. The current ratio exceeds 4.2. The math means aluminum will keep swallowing market share where weight and space permit, but copper's performance edge still means it is the hedge, not the heir. Photo by laowaika via Shutterstock Read Next: Skip the Regrets: The Essential Retirement Tips Experts Wish Everyone Knew Earlier. Think you're saving enough for your kids? You might be dangerously off — see why Building Wealth Across More Than Just the Market Building a resilient portfolio means thinking beyond a single asset or market trend. Economic cycles shift, sectors rise and fall, and no one investment performs well in every environment. That's why many investors look to diversify with platforms that provide access to real estate, fixed-income opportunities, precious metals, and even self-directed retirement accounts. By spreading exposure across multiple asset classes, it becomes easier to manage risk, capture steady returns, and create long-term wealth that isn't tied to the fortunes of just one company or industry. Arrived Backed by Jeff Bezos, Arrived Homes makes real estate investing accessible with a low barrier to entry. Investors can buy fractional shares of single-family rentals and vacation homes starting with as little as $100. This allows everyday investors to diversify into real estate, collect rental income, and build long-term wealth without needing to manage properties directly. FarmTogether Farmland has historically held its value through market volatility and delivered returns uncorrelated to stocks and bonds. For accredited investors, FarmTogether offers direct access to high-quality U.S. farmland starting at $15,000 — fully ma
China claims new quantum computer solves task in microseconds what Frontier can’t in years
China has unveiled its latest photonic quantum computer, Jiuzhang 4.0, which aims to achieve quantum supremacy. This advanced technology was introduced during a press conference held in Beijing on October 15, 2023. Researchers from the University of Science and Technology of China, who developed Jiuzhang 4.0, assert that it can perform complex calculations at speeds unattainable by traditional supercomputers. The motivation behind this development is to enhance China's position in the global quantum computing race, a field that holds significant implications for various industries, including cryptography and materials science. Jiuzhang 4.0 utilizes photonic technology to process information, which allows it to manipulate quantum bits more efficiently than its predecessors. This breakthrough is expected to pave the way for further advancements in quantum technology, potentially revolutionizing computing capabilities and fostering innovation in numerous sectors.
Chip Complexity Drives Innovation in Automated Test Equipment
Recent advancements in semiconductor technology are reshaping the landscape of various industries, particularly in artificial intelligence and automotive sectors. Key innovations include the development of high-performance computing (HPC) capabilities, Angstrom-scale silicon process nodes, and silicon photonics, which are set to enhance processing power and efficiency. These breakthroughs are taking place as companies strive to meet the growing demand for faster and more efficient computing solutions. The ongoing evolution in semiconductor technology is crucial for enabling sophisticated AI applications and improving the performance of electric vehicles (xEVs). As the automotive industry increasingly integrates advanced computing systems, the need for cutting-edge semiconductor solutions has never been more pressing. This wave of innovation is driven by the need for greater computational power and energy efficiency, as industries seek to leverage AI for enhanced decision-making and automation. The integration of these technologies is expected to accelerate the development of smarter, more efficient systems across various sectors. As these advancements continue to unfold, they promise to not only transform the semiconductor industry but also to have a profound impact on global technology trends, shaping the future of computing and transportation.
Robotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
