BSI Group (British Standards Institution), as the UK's National Standards Body, develops and publishes standards, provides certification (e.g., ISO 9001, 14001, 27001, 45001), training, auditing, testing, and consulting services for management systems, compliance, sustainability, and risk management across industries.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
Patreon, the membership platform for creators, is intensifying its efforts to prevent AI bots from scraping its content for training purposes. The company announced its collaboration with Cloudflare to block access to AI bots that attempt to use creators' work without permission. This move comes as AI scraping has evolved, prompting Patreon to strengthen its defenses since implementing initial measures in 2023. The significance of this action lies in the growing concern among online publishers and content creators regarding the unauthorized use of their work by AI models. With the introduction of new features like the redesigned Home Feed and Quips, more content could potentially be exposed to crawlers. Cloudflare's tools, including the Pay Per Crawl marketplace, enable website publishers to restrict AI bots, reflecting a broader industry trend towards protecting creator rights. Looking ahead, Patreon is committed to refining its AI policies and enforcement tools using Cloudflare's AI Crawl Control technology. The company aims to ensure that creators have a say in how their work is utilized by AI companies, contrasting with the prevailing norm where creators often have little control over AI training on their content. No further timeline was disclosed at the time of publication.
AI cloudflare PatreonOn July 16, NVIDIA CEO Jensen Huang announced an expansion of collaboration with Japanese companies in the field of 'physical AI' during an event in Tokyo. This initiative marks a strategic move to integrate NVIDIA's technology into Japan's manufacturing sector, particularly through a partnership with Toyota to develop AI models for the Woven City traffic control system. The collaboration with Toyota is central to NVIDIA's strategy, as the company will provide GPUs and development tools to Toyota's subsidiary, Woven by Toyota. This partnership aims to embed NVIDIA's technology into the city's digital twin platform, Omniverse, enhancing factory production and driving manufacturing robots with the Isaac platform. Additionally, Huang revealed plans to deepen cooperation with major Japanese industrial automation firms, including Fujitsu, Fanuc, Yaskawa Electric, and Kawasaki Heavy Industries, as part of the Cosmos Coalition. This initiative aims to strengthen Japan's position in the global AI robotics market, with a government goal of achieving a 30% market share by 2040. No further timeline was disclosed at the time of publication.
Physical AI Robotics Industrial Automation AI TechnologyHyundai Motor Group announced its acquisition of SoftBank Group's approximately 10% stake in Boston Dynamics, making the U.S. robotics company a wholly owned subsidiary. The transaction, valued at about 500 billion won ($335 million), is aimed at enhancing Hyundai's ability to integrate advanced robotics into its operations. This acquisition is significant as it provides Hyundai Motor Group with increased strategic flexibility in managing Boston Dynamics, enabling long-term investment decisions and potential public offerings. The automaker plans to deploy Boston Dynamics' Atlas humanoid robot at a Georgia manufacturing plant starting in 2028, initially for parts-sequencing tasks, with an expansion into broader manufacturing processes by 2030. The move comes amid intensified industrial action by Hyundai's labor union, which has raised concerns about job security and the impact of robotics on employment. With an average of 2,000 unionized workers expected to retire annually by 2032, the union warns that without new hires, membership could decline significantly, highlighting the ongoing tension between automation and workforce stability.
Recently, the embodied intelligence company DexRobot announced the completion of hundreds of millions in Series A funding, with strategic investment from Shanghai Electric. This round of financing reflects ongoing support from both industrial and financial capital. By 2026, the company has successfully completed both angel and Series A funding rounds. The raised capital will primarily focus on the development of full-stack dexterous manipulation technology, core product iterations, and the implementation of solutions in education, power, and industrial sectors, as well as building an industrial ecosystem. Additionally, Shanghai Electric's subsidiary, Shanghai Mechanical and Electrical, has established two joint ventures with DexRobot—Lingji Yidong and Lingji Zhiliang. With a total registered capital exceeding 100 million, these ventures will concentrate on mass production of core components and industrial scene solutions, marking a transition from technology development to large-scale industrial delivery.
Teradyne, Inc. (NASDAQ: TER) is set to announce its financial results for the second quarter of 2026 on Tuesday, July 28, 2026, at 4:30 p.m. Eastern Time. Following the release, a conference call will take place on Wednesday, July 29, 2026, at 8:30 a.m. ET to discuss the results and management's business outlook. This announcement is significant as it reflects Teradyne's ongoing commitment to transparency and investor engagement. The company's performance in the second quarter will provide insights into its automated test equipment and advanced robotics systems, which are crucial for maintaining quality standards in semiconductor and electronics manufacturing. Investors and analysts should monitor the upcoming conference call for insights into Teradyne's business outlook and strategic direction. Presentation materials will be available starting at 7:30 a.m. ET on the day of the call, and a replay will be accessible on Teradyne's website afterward. No further timeline was disclosed at the time of publication.
Ant Group's robotics subsidiary, Robbyant, has adopted a unique approach by prioritizing the development of a robot brain over hardware design. This strategy leverages the AI infrastructure behind Alipay, which processes billions of payment transactions daily, to enhance robots' ability to perceive their environment and make autonomous decisions. The introduction of the LingBot-VA 2.0 marks a significant advancement as it is the world's first embodiment-native pre-trained VA base model, specifically tailored for robots. In real-world tests, robots equipped with this model achieved a remarkable success rate of 93.6% in dual-arm tasks, showcasing the potential of this innovative technology. As hardware becomes increasingly standardized, the focus on software intelligence and data advantages is emerging as a new competitive edge. Ant Group's initiative to create a robot brain from its extensive payment data aims to bridge the gap between the digital and physical worlds, positioning Robbyant as a leader in the robotics field.
Robotics AI Machine Learning AutomationMidea Group's subsidiary KUKA has signed a strategic cooperation agreement with YINGKE Medical and YINGKE Recycling, valued at over 300 million RMB. This deal includes 1,500 industrial robots, 500 mobile and embodied intelligent robots, and 50 automated storage systems, showcasing Midea's commitment to expanding its industrial robotics capabilities. This partnership signifies a pivotal shift for Midea, transitioning from project-level procurement to a comprehensive group-level strategic collaboration. The collaboration aims to enhance production efficiency through advanced automation solutions, integrating AI visual recognition and adaptive grasping technologies. Looking ahead, Midea's focus on its ToB business model is expected to accelerate, with KUKA playing a crucial role in this transformation. The industry will be watching closely to see how this strategic partnership evolves and contributes to Midea's growth in the industrial automation sector. No further timeline was disclosed at the time of publication.
Industrial Robotics Automation Solutions Smart Manufacturing B2B TechnologyLingbo, a robotics subsidiary of Ant Group, has announced the development of a novel robot intelligence platform that utilizes data from Ant's extensive payment ecosystem. This initiative aims to enhance embodied AI capabilities and was unveiled recently, marking a significant step in the integration of financial data into robotics technology. The significance of this development lies in its unconventional approach to AI, which leverages real-time transaction data to inform and improve robot decision-making processes. By tapping into Ant Group's vast data resources, Lingbo aims to create more adaptive and intelligent robotic systems that can better understand and respond to human interactions in various environments. Looking ahead, Lingbo's next steps in this project remain unclear, as no further timeline was disclosed at the time of publication. Industry observers will be keen to see how this integration of financial data into robotics will influence the market and the potential applications that may arise from this innovative approach.
TechnologyOceaneering International, Inc. (“Oceaneering”) announced that its Brazilian subsidiary, Marine Production Systems do Brasil LTDA (“MPS”), won a contract from Petróleo Brasileiro S.A. (“Petrobras”) to deliver ROV services offshore Brazil. The award follows a competitive tender process. The contract term is four years and operations are expected to begin in 2027.
oceaneering petrobras contract rov services offshoreOne 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 LlmsChina's HEIS 2026 framework — the world's first comprehensive national standard for humanoid robots and embodied AI — sets technical specs across six pillars covering AI models, components, safety, and ethics. Here's what every EU and North American robotics manufacturer, integrator, and policy maker needs to know, and why the international standards race is already underway.
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