AI Applications

The University of Houston has been selected by the US Department of Energy (DOE) to lead a nearly $750,000 collaborative research project that will harness artificial intelligence (AI) to accelerate the design of next-generation nuclear and fusion energy systems. Funded through the DOE’s Genesis Mission, this project brings together researchers from UH, Lawrence Livermore National Laboratory, and the University of Pennsylvania to develop AI-powered engineering tools capable of dramatically reducing the time needed to model complex heat-transfer systems critical to advanced nuclear reactors and future fusion technologies.

The award builds on UH’s growing leadership in AI and energy innovation. By combining AI with fundamental laws of physics, researchers aim to improve the accuracy and speed of computer models used to accelerate the development of reliable, carbon-free energy technologies. This project is a prime example of how data analysis tools can be leveraged to drive breakthroughs in energy research.

The interdisciplinary project focuses on molten salts, whose unique characteristics make it uniquely suited for applications in energy production and storage technologies like nuclear reactors and hybrid energy systems. The goal for the project, titled “Physics-Informed AI Surrogates for Turbulent Forced Convection in Energy Systems,” is to develop a faster, more reliable AI tool that predicts how heat moves through turbulent liquid coolants used in advanced nuclear-fission reactors and future fusion systems.

According to Myoungkyu Lee, a professor at UH’s Cullen College of Engineering, molten salts are attractive for several advanced reactor and fusion blanket concepts but are also among the hardest ones to model. Today’s standard engineering tools were developed for fluids like water and air, and can significantly misjudge heat flow in molten salts. The most accurate simulations are too computationally expensive for everyday design work, so engineers currently bridge the gap with conservative safety margins.

The project aims to cut down this guesswork by developing an AI-assisted model that reproduces results much faster while still following actual laws of fluid flow and heat transfer. Unlike many AI systems, this model would flag areas where its predictions may be unreliable. The goal is to develop a tool that runs much faster than today’s most detailed simulations while keeping errors small.

The award adds to UH’s growing portfolio of energy research, and the broader commitment to bringing together world-class researchers, cutting-edge technologies, and collaborative partnerships with industry, government agencies, and laboratories to advance AI applications across energy, engineering, manufacturing, and other critical industries. This latest award further strengthens UH’s leadership in energy innovation.

The project will receive $180,000 from the DOE, while Lawrence Livermore National Laboratory and University of Pennsylvania will each receive $380,000 and $190,000 respectively. By combining some of its greatest assets – leadership in energy and engineering, world-class research, and national partnerships – UH is solidifying its role as a leader in AI applications for businesses.

According to Ramanan Krishnamoorti, Vice President for Energy and Innovation at UH, this project combines the university’s strengths. ‘Being part of this effort will continue to solidify UH’s role as The Energy University,’ he said.

The development of reliable, carbon-free energy technologies is a pressing challenge that requires innovative solutions. By harnessing AI to accelerate design processes, researchers can make significant strides in advancing next-generation nuclear and fusion energy systems.

Belgian and Dutch Tesla owners can now enjoy the company’s advanced voice assistant, Grok, after months of waiting. The feature was first introduced to other European markets earlier this year, but it has finally been rolled out to these two countries as part of the latest summer update.

The new voice control system replaces the previous rigid list-based interface with a more conversational approach. Users can now interact with their cars in a more natural way, setting destinations by describing them rather than dictating addresses. This marks a significant improvement over the old system, which required precise phrasing and repetition to work correctly.

Grok is available on eligible versions of the Model S, Model 3, Model X, and Model Y, provided they have an AMD infotainment chip installed since around 2021. Unfortunately, owners with older Intel-based hardware will not be able to access this feature, as no software update can change their car’s underlying architecture.

The Summer Release brings a range of new features beyond just navigation. Grok can now consult the owner’s manual and provide advice on vehicle maintenance and usage, answer general questions, look up music and podcasts, and adjust climate control settings. The manual function is particularly useful, as many owners rarely read their car’s manual but often have unanswered questions.

Grok ships with five different voices and a range of personalities to suit various tastes. While some options are designed for children, others cater specifically to adults, including modes described by Tesla as ‘unhinged’ or ‘sexy.’ However, it remains unclear whether these more provocative settings belong on the dashboard, especially considering European regulators may take an interest in this aspect.

To use Grok effectively, owners need either a Premium Connectivity subscription or a stable Wi-Fi connection. In practice, most users will likely opt for connectivity while driving, as accessing Wi-Fi from a moving vehicle is not very reliable.

It’s worth noting that Grok does not replace any part of Autopilot or Full Self-Driving systems and only provides information through voice commands. The assistant focuses on answering questions, searching for music, and setting destinations without taking control of the car’s driving functions.

The introduction of Grok in Belgian and Dutch Teslas highlights an interesting aspect of AI assistants in cars: their most useful features often lie in their mundane capabilities rather than flashy personalities or voices. By providing straightforward answers to common queries and streamlining navigation tasks, these systems can genuinely improve the driving experience.

Michaels has introduced Ask Mike, an artificial intelligence shopping assistant that helps customers find what they need to create their unique vision. The tool uses Google Cloud’s Gemini Enterprise for Customer Experience.

The AI-powered agent at the heart of Ask Mike lets users describe their creative ideas and get instant recommendations. This is a big change from traditional keyword filtering, where shoppers were stuck with generic results. Instead, Ask Mike guides them through a conversation to find what they’re looking for.

Since its launch in May, customers have been using Ask Mike to discover products and plan events like birthday parties. About 60% of interactions so far are focused on finding specific items that meet their needs. It’s clear people like having a personalized creative partner at their fingertips.

The majority of users rely on Ask Mike as a tool for brainstorming ideas or getting advice on materials and projects. For instance, they might ask about choosing the right fabric for curtains or get guidance on creating a punch-needle pillow.

Heather Bennett, Michaels’ president and chief customer officer, says Ask Mike is more than just a search function – it’s designed to be a creative partner that helps customers bring their ideas to life. The launch of this AI-powered assistant comes as businesses start embracing the power of artificial intelligence in commerce.

Research by PYMNTS Intelligence shows most online shoppers rely on AI when researching purchases. They expect human support, data protection, and clear ways to cancel or reverse a purchase before making a payment. This is why Ask Mike’s ability to provide personalized guidance and manage loyalty programs is so appealing – it meets those expectations head-on.

The benefits of using AI assistants like Ask Mike are many: they can help personalize offers, improve customer loyalty, boost repeat purchases, and simplify shopping experiences for merchants. However, there are also concerns around pricing, fraud prevention, liability rules, and protecting customer data when working with these agents.

Global energy company Shell has selected AI-driven solutions from RELEX Solutions to streamline its convenience retail planning. The implementation will cover store forecasting, replenishment, and fresh ordering across the UK Mobility & Convenience network.

The partnership marks a significant step towards automating convenience retail planning for one of the world’s largest energy companies. With over 550 stores operating under various banners in the UK, Shell aims to improve its operational efficiency and customer experience through centralized, automated planning models.

RELEX Solutions will provide capabilities that enable store teams to generate order proposals at the store level, taking into account factors such as fresh product lifecycle, promotional demand, and weather-driven sales patterns. These proposals can then be accessed and reviewed directly through the RELEX mobile replenishment solution, allowing for adjustments before orders are confirmed.

The connected approach offered by RELEX provides central teams with visibility and control while preserving store-level flexibility. This is expected to reduce waste, improve availability, and free up staff time for higher-value work.

Janine Albrecht Webb, UK General Manager of Shell Mobility & Convenience, expressed enthusiasm about the partnership: ‘RELEX gives us the tools to plan smarter across our UK convenience network.’

The implementation’s success hinges on RELEX’s ability to integrate AI-driven forecasting and replenishment capabilities seamlessly into Shell’s existing operations. Mikko Kärkkäinen, Co-founder and Group CEO at RELEX Solutions, noted that this integration is crucial for delivering value quickly: ‘Speed matters in a network like Shell’s… We’re able to get them live and deliver value far faster than a transformation of this scale would normally take.’

The partnership between Shell and RELEX marks an important milestone in the adoption of AI tools for businesses. By leveraging these technologies, companies can streamline their operations, improve customer experience, and drive growth.

A new trend is emerging in the world of automation, one that’s quietly reconfiguring how businesses approach robotics. At its core lies a shift towards ‘Robotics as a Service’ (RaaS), where companies can access and deploy robotic solutions without having to invest heavily in infrastructure or maintenance.

The driving force behind this change is the increasing demand for flexible and efficient operations. With RaaS, businesses can streamline their automation processes by leveraging pre-integrated robotics systems that are designed to adapt quickly to changing production requirements.

A key player in this space is IDS Imaging Development Systems, which has recently released a new 3D camera called Nion. This cutting-edge device boasts exceptional resolution and speed, making it ideal for applications such as logistics, warehousing, industrial automation, and robotics.

The heart of the Nion lies in its advanced Time-of-Flight (ToF) sensor technology, specifically the AF0130 from onsemi’s Hyperlux ID family. With a 1.2-megapixel resolution that offers up to four times more detail than conventional VGA-based ToF cameras, this device can provide high-resolution depth data with real-time performance.

Active laser illumination ensures accurate measurements in low-light conditions and even direct sunlight, while the IP67-rated housing makes it suitable for use in dusty, wet, or thermally challenging environments. Integration is also simplified through a user-friendly API that supports IDS peak and complies with GigE Vision standards.

The Nion 3D camera marks a significant step forward in automation technology, enabling faster, safer, and more flexible operations across various industries. As businesses continue to seek ways to automate their processes efficiently, RaaS solutions like this one are likely to become increasingly popular.

African businesses operating on the ALAT by Wema Bank platform will soon have access to streamlined financial management thanks to a new partnership between Wema Bank and financial operating system provider, Duplo. The collaboration aims to bring automation capabilities to companies’ financial operations, freeing up time for more strategic decision-making.

The integration of Duplo’s technology into ALAT addresses a common pain point for expanding businesses: the fragmented nature of their financial management systems. Finance departments often juggle multiple tools and platforms, including spreadsheets, emails, banking portals, and accounting software, which can lead to errors, delays, and limited visibility over cash flow.

This scattered approach not only hampers efficient growth but also exposes companies to operational risks. By automating payments, overseeing expenses and approvals, issuing and collecting invoices, and governing financial workflows from a single connected environment, Duplo’s technology aims to simplify financial management for ALAT business users.

The partnership reflects Wema Bank’s focus on building a digital banking platform that supports the actual needs of businesses. According to Tunde Mabawonku, executive director – finance & digital optimization at Wema Bank, ‘This integration strengthens our product offering and enables us to deliver a more comprehensive digital banking experience for our customers while supporting business growth across Nigeria.’

Duplo CEO Yele Oyekola emphasizes the importance of giving businesses greater clarity and control over their financial operations. Many companies still manage payments, expenses, and reconciliation across multiple systems, which slows down decision-making and creates unnecessary operational risks. By partnering with Wema Bank, Duplo aims to deliver its automation experience within a platform that business users already trust.

Existing ALAT business customers will enjoy three months of complimentary access to the Duplo platform, allowing them to trial its features without extra charge. This integration marks a significant step towards providing businesses in Nigeria with more comprehensive digital banking services and supporting their growth.

ServiceUp, a company specializing in fleet repair management, has introduced an innovative platform that leverages artificial intelligence (AI) to automate various aspects of fleet maintenance workflows. The new system is designed to streamline the process for collision repair, mechanical service, and preventive maintenance across Class 1 through Class 8 fleets.

The platform’s AI-powered repair agents can manage tasks such as repair intake, shop selection, estimate review, approval routing, invoice validation, and repair status monitoring. This integration aims to reduce manual coordination by automating routine activities that often lead to vehicle downtime.

Fleet operators who have adopted the ServiceUp platform report significant improvements in their maintenance workflows. According to data from these customers, vehicle downtime has been reduced by an estimated 50%, total repair costs decreased by 30%, and manual coordination was cut by 62%. These results demonstrate the potential of AI-driven automation in fleet management.

Brett Carlson, CEO of ServiceUp, emphasizes that the company’s goal is not to replace human decision-making but rather to alleviate the administrative burden on fleet managers. ‘Fleet repair has run the same way for decades,’ he notes. ‘We think it can work better.’ The platform aims to turn information about vehicle health and maintenance needs into actionable repair activity.

The system integrates with existing fleet management systems, telematics platforms, and predictive maintenance tools. This integration enables AI agents to evaluate factors such as shop capabilities, capacity, cost, and previous performance when selecting a repair facility for each vehicle.

Smart shop routing is one of the key features of ServiceUp’s platform. It evaluates various criteria to determine the most suitable repair shop for each job, taking into account factors like vehicle location, shop capabilities, capacity, cost, and past performance. The system can also monitor whether a selected shop has accepted the repair order, identify potential delays, and escalate service-level risks.

Fleet managers using ServiceUp’s platform have control over the level of automation they want to implement. They can choose from manual, co-pilot, or fully autonomous settings for each task, allowing them to adjust the system according to their specific needs.

The company emphasizes that its goal is not to remove fleet managers from the repair process but rather to reduce routine coordination and minimize vehicle downtime. By automating administrative tasks, ServiceUp aims to free up time for more strategic decision-making and improve overall efficiency in fleet maintenance management.

ServiceUp’s platform supports collision, mechanical, and preventive maintenance work across Class 1 through Class 8 vehicles. Repairs are handled through a unified workflow rather than separate processes for each type of service. This streamlined approach enables faster processing times and improved accuracy in managing vehicle repairs.

In addition to automating routine tasks, ServiceUp’s AI agents can review proposed repairs against fleet-defined pricing limits, warranty requirements, and approval policies. Routine estimates can be approved automatically, while exceptions are sent to a person for review. Invoices are also compared with approved estimates before payment to identify any discrepancies or additional work.

The company has reported significant benefits from its customers who have adopted the ServiceUp platform. These include reduced vehicle downtime, lower total repair costs, and decreased manual coordination. By leveraging AI-driven automation, ServiceUp aims to improve efficiency in fleet maintenance management and provide better outcomes for both fleets and their operators.

The Port Authority of New York and New Jersey (PANYNJ) has completed a major overhaul of its airport websites, introducing intuitive designs that simplify travel planning for visitors. The revamped sites provide real-time information on security wait times, parking availability, airport amenities, and customizable lists of concessions at John F. Kennedy International, Newark Liberty International, LaGuardia, and New York Stewart International airports.

The new website design is based on usage analytics from the previous websites, with customized layouts tailored to each airport’s unique needs. This comprehensive approach ensures that travelers can quickly find the information they need without navigating through multiple pages or scrolling endlessly.

Port Authority Chairman Kevin O’Toole emphasized the importance of digital savvy in modern travel experiences: ‘Having a virtual assistant embedded into our airport websites gives travelers an easy one-stop shop for their questions about a particular airport.’ He noted that users expect seamless online search experiences, which is why PANYNJ prioritized user experience in its website redesign.

The AI assistants integrated into the new websites are purpose-built to provide tailored assistance specific to each airport. Trained by Port Authority staff knowledgeable about each airport’s operations, these virtual helpers can answer a wide range of questions, from flight information and security wait times to food options and accessibility accommodations.

Each AI assistant is unique to its respective airport, avoiding confusion and limiting information overload while offering a customized experience for travelers unfamiliar with the region. The assistants continuously pull operational data and draw other relevant information from sources such as federal agency websites, live flight data, and concessions information.

The Port Authority Executive Director Kathryn Garcia highlighted the benefits of these new features: ‘Our new airport websites make it easier to find information when you need it, without an online scavenger hunt that may or may not be updated.’ She noted that first-time visitors will appreciate the streamlined experience, particularly during peak events like World Cup games and America250 celebrations.

Sarah McKeon, Director of Aviation at PANYNJ, emphasized the importance of providing customers with website tools that simplify travel planning: ‘The airport experience begins before customers even arrive at our airports. Researching the airport beforehand can help alleviate anxiety in unfamiliar environments.’ The upgraded websites aim to make this process easier and more efficient for all travelers.

While live video and messaging apps offer real-time assistance, the AI-powered assistants embedded within the website allow users to ask specific questions without leaving the site or opening another browser tab. This feature is particularly useful for those seeking information on flight details, security wait times, or accessibility accommodations.

The knowledge base used by these virtual assistants was curated by airport subject matter experts and maintained through regular updates with operational data from various sources. The AI does not process payments or store personally identifiable information during interactions, ensuring user trust and confidence in the system.

Future updates to the websites will include additional features such as live departure times for AirTrain JFK at John F. Kennedy International Airport. Users can also expect customized experiences through an airport experience planner that provides tailored guidance based on their specific needs, including accessible journeys or family-friendly spaces within each airport.

The Port Authority has been investing in enhancing its airports’ infrastructure and services. In January 2023, Newark Liberty opened a new Terminal A, which received a rare perfect rating from Skytrax the following year. The agency is currently planning the redevelopment of the rest of Newark Liberty International Airport.

A team of researchers, led by Nobel laureate Giorgio Parisi and collaborator Francesco Zamponi, has made a significant breakthrough in solving a long-standing mathematical problem. The solution was found using the generative artificial intelligence (AI) tool Claude, developed by Anthropic. This achievement marks a major milestone in understanding the physics of ‘jamming,’ a phenomenon that had been puzzling researchers for over a decade.

The concept of jamming refers to the sudden transition from a fluid system to a rigid-but-disordered one. To illustrate this idea, imagine a pool table covered with billiard balls. As more and more balls are added, the table eventually becomes congested, and each ball is securely held in place by its neighbors. This disordered, completely frozen situation is known as a jammed state.

The study authors had previously mathematically described jamming and offered numerical solutions in a 2014 paper. However, they noticed that two parameters, $a$ and $b$, would mysteriously always add up to 1. The researchers were puzzled by this relationship and struggled to find an explanation for it.

In separate work, physicist Matthieu Wyart took a different approach but arrived at the same relation. This suggested that entirely new physical concepts were needed to link their work with Wyart’s and explain why $a+b=1$. Despite efforts over the past decade, no progress had been made in finding these new concepts or understanding the reason behind this relationship.

Parisi, who won the 2021 Nobel Prize in physics, decided to revisit the problem. He turned to Anthropic’s Claude AI tool, which he believed could offer a fresh perspective on the issue. After successfully reproducing the 2014 numerical result using Claude, Parisi prompted the AI to prove why $a+b=1$. The initial output contained some errors that required revision, but the fundamental idea was correct.

The researchers were surprised when they found that the solution was hidden directly within the equations themselves. They didn’t need any external physical assumptions or deep connections between functions. This achievement highlights the potential of AI in providing instant access to a vast repository of mathematical training and formal skills that may lie outside one’s usual domain.

Zamponi, who collaborated with Parisi on this project, noted that it is possible for a pure mathematician working full-time on such equations might have spotted the solution. However, he emphasized how Claude gave them instant access to these skills, which they couldn’t see themselves. Zamponi also acknowledged that interacting with AI forces him to reconsider his definitions of reasoning, intuition, and creativity.

The team’s experience with Claude has sparked a new approach to problem-solving in physics. They are now applying this collaborative method to another challenging problem involving the ‘random sequential addition of hard hyperspheres.’ While the AI significantly accelerates writing and optimizing code, Zamponi emphasized that human guidance remains indispensable at least in this case.

The solution to the jamming problem has been published in the Journal of Statistical Mechanics: Theory and Experiment. The study’s authors hope that their work will contribute to a deeper understanding of complex systems and inspire further research in the field.

Honda and Acura are integrating Google Gemini, an artificial intelligence assistant, into certain models that already feature Google software. This upgrade will enable drivers to interact with the system in a more conversational manner than its predecessor, allowing for more natural communication on the road.

The integration of Gemini is designed to enhance connectivity and access to information while driving. Drivers can use voice commands to ask for directions, recommendations, or other types of assistance. The new AI assistant will also be able to answer layered questions and retain context to provide more informed suggestions in the future.

Drivers who are signed into their Google account on compatible models will have access to Gemini’s features. This includes the ability to engage in a ‘genuine conversation’ with the system, rather than relying on strict voice commands as was required for the old Google Assistant. The AI assistant can also assist drivers with tasks unrelated to driving or navigation.

According to Honda, this integration marks an essential step towards improving connectivity and access to information while driving. Jessika Laudermilk, assistant vice president of Honda National Auto Sales, notes that ‘connected technology is no longer a convenience for Honda customers—it’s an essential part of the driving experience.’

The upgraded software will be available on nine Honda models and three Acura models. However, not all trims and model years qualify for the update. The included models are listed below: 2024 Acura ZDX in all trims; 2025-current Acura MDX in all trims; 2026-current Acura ADX A-Spec Advance;

Additionally, the following Honda models will receive the upgrade: 2023-current Honda Accord Touring Hybrid; 2025-current Honda Civic Sedan Si and Sport Touring Hybrid; 2025-current Honda Civic Hatchback Sport Touring Hybrid; 2024-current Honda Prologue in all trims; 2026-current Honda Pilot in all trims;

Lastly, the upgrade will also be available on 2026-current Honda Passport in all trims, as well as 2026-current Honda CR-V Sport Touring Hybrid and 2026-current Honda Prelude in all trims. It’s worth noting that not all trim levels or model years are eligible for this update.

The integration of Google Gemini is part of a broader effort by both Honda and Acura to provide drivers with more comprehensive access to information on the road. By leveraging AI assistants, these manufacturers aim to enhance the driving experience and make it easier for drivers to stay connected and productive while behind the wheel.

A recent survey by Wolters Kluwer has revealed a significant shift in the adoption of artificial intelligence (AI) tools within law firms. The 2026 Future Ready Lawyer Survey polled 810 lawyers and found that an astonishing 92% now use at least one AI tool as part of their daily workflow. This is not just a trend; it’s table stakes, meaning that every firm has access to the same large language models (LLMs), such as ChatGPT, Claude, or Gemini. The question now shifts from which AI tools are being used to what sets each firm apart in terms of its AI implementation.

The problem lies not with the LLM itself but rather with the quality and relevance of the data it’s fed. Think of an LLM like a high-performance engine; no matter how sophisticated, if you’re putting low-grade fuel into it, you’ll get unreliable output. This can lead to review cycles, rework, risk, and wasted time. The key is not just having access to AI tools but also ensuring that the data they process is accurate, trusted, and client-ready.

The only thing a competitor cannot replicate, regardless of which LLM model they’ve licensed, is your firm’s unique content – its matter history, work product, institutional judgment, and precedent. This is not a new concept; it’s simply an urgent reminder that firms need to prioritize structuring, surfacing, and governing their own knowledge if they want to stay ahead in the AI-driven landscape.

The three layers of evaluation for any firm’s information estate are crucial: surfaced, connected, and current. Surfaced refers to whether documents, precedents, clauses, and expertise can be easily found; connected means that matters, clients, people, work product, and communications should be linked so an AI tool sees relationships rather than isolated files; and current implies that the data is kept up-to-date in real-time.

Most firms are failing at least one of these layers. When they do, poor data quality upstream of the model becomes the bottleneck. This highlights a significant gap between the tools deployed by most firms and the outcomes they expect from AI adoption. The question now shifts to what sets each firm apart – not which LLM it uses but how its unique content is structured, governed, and made accessible.

The legal context graph, as proposed by NetDocuments, offers a comprehensive framework for addressing these challenges. It’s a live, connected representation of your firm’s knowledge across three dimensions: document intelligence, matter and project context, and institutional knowledge. Document intelligence captures the meaning and context within every file; matter and project context links work together to provide a coherent view of each case or project; and institutional knowledge makes expertise and judgment accessible at any moment.

When these layers are working in harmony, AI tools no longer produce generic output but rather client-ready results grounded in your firm’s unique documents, history, and expertise. This is the real value that firms need to focus on – not just having access to AI tools but leveraging their own content as a competitive moat.

The strategic implications of this shift are significant for law firms. In a world where every firm can access the same LLMs, the only durable differentiation lies in institutional knowledge deliberately structured, governed, and made accessible. Firms that invest in this infrastructure now will build a compounding advantage with each matter organized, document profiled, piece of data extracted, and workflow codified becoming part of their legal context graph.

The governance questions surrounding AI adoption are also critical: who owns the legal context layer? Who controls the data those tools see? How is sensitive content protected by ethical walls? What happens to output generated today – does it become reusable institutional knowledge or vanish with a closed chat window?

These operational questions determine whether AI becomes a liability or a lasting advantage. The model itself is a commodity that anyone can leverage; your firm’s unique content and its context are where the real value resides. By recognizing this distinction, firms will be able to differentiate themselves in an increasingly crowded landscape.

Automate 2026, held in Chicago, showcased the latest advancements in automation and robotics. Among the exhibits were humanoid robots that drew significant attention with their human-like appearance and capabilities. However, a closer look reveals a gap between marketing hype and industrial feasibility.

The humanoid robot market has made tremendous progress in recent years, with improvements in hardware and software. The ‘physical AI’ story is driving innovation in robotics, making it more strategic for industries. Nevertheless, the industry needs to discuss humanoids differently, focusing on their actual capabilities rather than just their appearance.

A robot that performs a routine task in a booth may look impressive but does little to demonstrate its industrial feasibility. A humanoid robot’s ability to perform a choreographed task once is not equivalent to delivering value safely and reliably across shifts. The industry must separate real transformation from theater, evaluating humanoids based on what they can actually do.

The humanoid conversation has a significant gap between vision and reality. On one side lies the promise of general-purpose robots that can move through human-designed environments, handle tools, adapt to changing workflows, and take on tasks traditional automation struggles with. This vision is compelling because industrial environments are filled with difficult-to-automate tasks like material handling, inspection, loading/unloading, machine tending, basic tool use, and irregular manipulation.

On the other side lies reality – industrial operations prioritize uptime, safety, throughput, maintainability, integration, and cost. They require systems that work consistently under various conditions, not just in controlled environments. Many humanoid systems can demonstrate useful capabilities but struggle to prove their reliability under industrial conditions.

The question we need to ask is: ‘Is a humanoid the best automation architecture for this outcome?’ The answer may be yes, but often it will be no. If a wheeled mobile manipulator or other alternative can perform the task faster, with lower risk, at a lower cost, and easier certification, then the humanoid form factor becomes a liability rather than an advantage.

The biggest bottleneck in humanoids is scaling. Many systems can demonstrate a single task but struggle to survive the prototype-to-production valley. If users and integrators must custom-engineer these robotic solutions for each segment of each task, reaching full-scale deployment will be slow.

ARC survey data highlights robot scaling challenges as one of the significant hurdles. Industrial deployment often requires deterministic, certifiable safety systems. However, many required standards for humanoids are still evolving, and certification frameworks are not fully in place.

The feasibility gap will close through reliability engineering, safety validation, application-specific tooling, closer integration with existing industrial systems and workflows, and proof that humanoids can deliver measurable operational value.

Humanoid robots need to prove they can do real work in real environments with real economics. Until then, industrial leaders should stay curious, run focused pilots, demand operational evidence, and resist the temptation to confuse human-like motion with industrial value.

The current wave of humanoids may not be about the future of manufacturing but rather a legacy integration layer. Industrial environments have been engineered around humans for longer than they’ve been designed for robots. When we talk about humanoids fitting naturally into these environments, it’s because the environment was built for them – or more accurately, built for us.

There’s real value in bringing a machine that can adapt to existing infrastructure rather than redesigning the factory for automation. Brownfield integration is one of the hardest problems in industrial automation. Ripping and replacing infrastructure is expensive, disruptive, and often unrealistic.

If the only reason a robot needs to look human is because the environment was designed for humans, then we have to ask: are we optimizing for the right system? In some cases, the better answer may still be to redesign the process, simplify the workflow, or deploy a non-humanoid system that performs the task more efficiently.

A humanoid might be the most flexible way to deal with legacy constraints but doesn’t necessarily make it the optimal long-term solution. Humanoids might find their first big success not because they represent the future of automation but because they are compatible with the past.

Human-like bodies are only one possible embodiment of advanced robotic capability. Any robot can, in principle, gain many of these capabilities. In real industrial use cases, other form factors will often make more sense. Over time, capabilities may converge, but for now, it’s smarter to focus on what a system can do rather than its shape.

The idea that large language models alone are the answer is also being challenged. Physics-driven models matter because industrial systems need to understand the likely consequences of actions in the real world – a much more serious industrial requirement than generating fluent text.

Humanoids will continue attracting attention, but the message should be clear: focus on capability over form factor. The companies that win won’t bet solely on a form factor; they’ll build the full Physical AI stack and deploy whatever robotic systems best execute the work.

Chipmaker Intel Corp. is expanding its partnership with Google Cloud into the realm of agentic artificial intelligence, aiming to automate and accelerate silicon development through the use of Google’s Gemini Enterprise platform.

In a move that marks a significant shift in their collaboration, Intel plans to deploy Gemini across its global workforce, enabling it to streamline workflows across various operations, including corporate, engineering, supply chain, and marketing. This will allow the company to leverage autonomous agents to accelerate chip design lifecycles and boost innovation companywide.

According to Cindy Stoddard, Vice President and Chief Information Officer at Intel, the partnership is part of an ambitious ‘AI-powered transformation’ aimed at enhancing employee productivity and efficiency. Gemini Enterprise will serve as a central hub for employees to build and deploy agents, scaling silicon development with elastic cloud infrastructure.

The collaboration between Intel and Google Cloud has been ongoing for some time, but this marks a new phase in their partnership. The chipmaker is now moving beyond isolated AI pilot projects, making dedicated agentic coding assistance and engineering automation capabilities available to its entire organization.

Intel plans to use Gemini’s advanced reasoning skills to streamline development pipelines and automate complex workflows currently performed manually with customized line-of-business agents trained on Intel’s unique business processes. This will enable the company to optimize its development simulations and developer workloads, automating much of this work in the process.

The partnership also extends to marketing and communications teams, who will be able to generate hyper-targeted content for specific audiences using Gemini. Early pilots have already shown promising results, with agents capable of recommending relevant subject matter experts and generating executive-ready messaging.

Intel’s ambitions go beyond just automating workflows; it wants to rely on Gemini to optimize its development simulations and developer workloads. By doing so, the company should be able to dramatically speed up its chip design processes. To support this effort, Intel will augment its existing on-premises compute capabilities with Google Cloud’s C4 and N4 instances.

The announcement builds upon a longstanding partnership between Google and Intel, which has seen them collaborate on various projects, including AI chip interconnects and 5G networks. In April, Google announced that it would adopt multiple future iterations of Intel Corp.’s Xeon central processing units for its public cloud platform to support AI and general-purpose computing workloads.

Holger Mueller, an analyst with Constellation Research, believes this move is a smart one for Intel, allowing the company to leverage Google Cloud’s infrastructure. However, he notes that some might view it as a ‘barney deal’ lacking substance, given Intel’s Xeon processors power Google Cloud’s C4 and N4 compute instances.

Google Cloud Chief Product and Business Officer Karthik Narain is more optimistic about the partnership’s potential to transform enterprise AI capabilities. He emphasizes that pairing Intel’s engineering expertise with Google Cloud’s agentic AI tools creates an autonomous foundation that will fundamentally accelerate how companies design, operate, and scale for the AI wave.

A new state-run artificial intelligence (AI) platform has been launched in California to support government workers. The platform, called Poppy, is a collection of 10 different models tailored to fit the diverse and challenging work of government. This AI assistant was made possible by Executive Order N-12-23 signed by Governor Gavin Newsom late last year.

The development of Poppy involved overcoming several challenges, including privacy, security, governance, and appropriate use considerations. Despite these hurdles, the California Department of Technology (CDT) successfully implemented a consolidated suite of models in just under a year. This achievement is significant given that getting any model up and running within state government can be a daunting task.

According to Shera Mui, deputy director of CDT’s Platform Services, what started as an effort to create a statewide chatbot quickly evolved into something more useful – a platform that could be managed and adapted to new technologies in real-time. ‘We discovered early on that we didn’t want to create large language models; that was not going to be the smart thing to do,’ Mui explained.

The team at CDT took a different approach, focusing on creating a platform that would allow users to securely choose a model best suited for their task. This flexibility is particularly useful in government work, where tasks can range from deep analysis of legal documents to creating code for new websites. The platform’s ability to keep all user data and information secure has been a major driving factor behind its development.

The early access phase involved giving 70 departments with around 100 users each the opportunity to test the first iteration of Poppy. This pilot program helped drive discussion around potential use cases, which in turn informed the development of subsequent models. The platform’s flexibility is one of its key strengths – it allows users to choose from multiple models without having to duplicate data or compromise on security.

Dr. Lucy Andrews, a scientist with the California Department of Water Resources (DWR), has seen firsthand the impact that Poppy can have in labor- and time-intensive work. She noted that the tools have played a critical part in determining the intellectual and financial value of her department’s contributions across various sources. This is particularly important for DWR, which invests tens of millions of dollars annually in scientific research.

Previously, tracking these contributions was a manual process that involved weeks of data entry using an Excel spreadsheet. With access to AI tools like Poppy, this time-consuming task has been significantly reduced – users can now complete it within 1-2 hours with a slow internet connection. The ability to track and analyze scientific relationships more effectively is also a major benefit for DWR.

Andrews noted that the department’s focus on accountability means they are taking a conscientious approach to model selection and use cases before implementing Poppy. This includes considering the environmental impact of running data centers, which support AI operations. While there may be some concerns about energy consumption, staff is working to minimize this by carefully selecting models and optimizing their usage.

Early metrics from the statewide launch show that users are interested in document summaries, policy analysis, and complex code creation – all key areas where Poppy’s capabilities can make a significant difference for government workers. Some departments have also expressed interest in exploring image generation features as part of future additions to the platform. However, more work needs to be done on outlining a long-term roadmap for these developments.

As for what’s next for Poppy, Mui sees opportunities for further expansion and improvement. This includes adding new features, tailoring models to specific departmental needs, and streamlining data sets to enhance cooperation among departments. CDT has requested $1 million in funding during the budget process toward this end – a significant investment that reflects the potential of AI tools like Poppy.

It’s worth noting that while Poppy is an advanced toolset for government workers, no decision is left solely to an AI system. Humans maintain control over all outcomes and are responsible for ensuring that AI-generated information aligns with their goals and objectives.

Financial technology giant Revolut has teamed up with leading AI platforms to bring conversational trading to its users. The integration of its crypto exchange, Revolut X, with these AI assistants lets people trade and analyze their portfolios using natural language commands.

The list of integrated tools includes Anthropic’s Claude, Google’s Gemini, OpenClaw, and Cursor. These partnerships allow users to execute trades, backtest strategies, and set up custom alerts through simple conversational prompts.

Revolut X now supports a wide range of features that make it easier for traders to analyze the market and manage their portfolios. Users can place both market and limit orders, backtest strategies across over 300 tokens available on the platform, and even receive Telegram notifications when specific price targets are hit.

The technical underpinnings of this feature rely on Anthropic’s Model Context Protocol (MCP), which was first released in November 2024. This protocol is layered on top of Revolut X’s existing trading API to enable seamless integration with the AI assistants.

Revolut has also made available a dedicated GitHub repository that provides installation support for more than 50 AI applications. This move shows the company’s commitment to making its platform accessible and user-friendly for developers as well as users.

As before, all orders require user review and approval before execution, ensuring that no trade goes through without human confirmation. Revolut explicitly disclaims liability for any errors caused by AI-driven actions – they’re not taking on extra risk just because it’s a machine making the call.

The integration of these AI assistants with Revolut X comes hot on the heels of AIR, its in-app AI assistant for general financial tasks, which launched back in April 2026. This new feature is specifically designed for traders and operates at a deeper technical level than its predecessor, enabling users to tackle more complex trading strategies.

A new accelerator program aimed at supporting startups, research teams, and non-profits working on environmental challenges has been launched by Google DeepMind. The three-month AI for the Planet program will provide selected organizations with access to cutting-edge machine learning tools and expertise to tackle pressing issues such as climate change, sustainable agriculture, and nature protection in Asia Pacific.

The program is open to APAC-headquartered organizations that are working on projects involving artificial intelligence and environmental innovation. Applications are now being accepted until July 26th, 2026, with the selected teams set to join a three-month accelerator program starting from September this year. The program includes a week-long in-person bootcamp, virtual support through December, and an in-person Demo Day at the end of the program.

Google DeepMind will select between 10 to 15 organizations for the inaugural cohort, which will receive access to the Google AI stack, including specialized frontier models, as well as tailored support from Google experts. The program is designed to sit across multiple areas, including AI skills development, startup support, research commercialization, and environmental innovation.

The application criteria cover a range of projects, including those focused on climate change, sustainability, energy, nature protection, sustainable agriculture, and forest protection. Applicants must demonstrate a plan for integrating Google AI into their solutions, either through general models or specialized ones such as AlphaEarth, Forestry, or Perch. Additionally, selected teams will need to have established in-house technical capabilities in AI and machine learning.

Sanjay Jain, Head of Google for Education, India, highlighted the launch on LinkedIn, writing: ‘Got a big vision for the planet and the AI chops to back it up?’ The program is aimed at organizations with a functional prototype or minimum viable product, early validation or proven traction, and a roadmap where AI is central to their current solution or future technical development.

The accelerator will begin with an in-person bootcamp from September 7th to 11th, during which participants will meet Google mentors and define project goals through keynotes, workshops, and diagnostic sessions. After the bootcamp, selected organizations will receive three months of virtual support from September to December, guided by a dedicated relationship manager.

Participants will also have access to one-to-one mentorship and technical guidance from Google and industry experts, as well as tailored technical and business training. Selected startups or projects may be eligible for cloud credits through the Google for Startups Cloud program or Google Cloud for research, subject to eligibility review and approval. The accelerator is equity-free for the duration of the program.

The program will culminate in an in-person Demo Day in December 2026, where participating teams can present their AI-driven environmental work to mentors, investors, partners, and Google teams. Google DeepMind describes the program as a route for early-stage projects to move from experimentation into applied problem-solving: ‘Our mission is to use frontier AI to tackle the world’s most critical environmental challenges.’ The launch adds another AI accelerator route for APAC organizations working at the edge of research, climate technology, and applied AI development.

Applications for the first AI for the Planet cohort close on July 26th, 2026. Google DeepMind is also hosting virtual open forums during the application phase to provide more information about the program and answer questions from interested applicants.

In late June, UpDoc made a significant announcement. The small digital health company revealed that its diabetes app had received clearance from the US Food and Drug Administration (FDA) in December 2025. This marks a milestone for patient-facing large language models (LLMs), but it’s also a reminder of the vast gap between AI therapy and FDA oversight.

The FDA cleared UpDoc’s app as part of the same product category as drug dose calculators, which is narrow compared to the broader landscape of AI-enabled medical devices. The agency has authorized over 1,200 such devices, yet none have been approved for mental health applications. This discrepancy raises questions about why LLMs are being used in therapy and companionship without regulatory scrutiny.

The use of generative AI in therapy/companionship was the number one application in Harvard Business Review’s annual ranking for 2025. OpenAI disclosed that more than a million people per week engage with ChatGPT, discussing sensitive topics like suicide. However, these conversations often occur on products that have never been reviewed by the FDA.

Courts and regulatory bodies are stepping into the void left by the FDA’s inaction. A dozen product-liability cases related to ChatGPT’s role in suicides and mental health harms are now consolidated in a single proceeding in San Francisco Superior Court. States like Illinois, Rhode Island, and Pennsylvania have also taken action, with some banning AI-provided therapy altogether.

The first patient-facing LLM got through the FDA by keeping the model away from critical decisions. UpDoc’s app uses a locked-down algorithm for dosing logic, while the language model handles conversations with patients. This approach may not be sustainable as more companies push to integrate LLMs into their products, potentially giving models greater decision-making authority.

The consequences of this trend are unclear, but one thing is certain: AI therapy is evolving rapidly, and regulatory frameworks must adapt to keep pace. If you or someone you know is struggling with mental health issues, there are resources available, including the 988 Suicide and Crisis Lifeline in the United States.

Waze, the popular navigation app owned by Google, is getting a significant upgrade with the expansion of its Gemini AI technology. This move marks a deeper integration of artificial intelligence into Waze’s reporting tools and destination search capabilities. The update aims to make navigation more conversational, allowing drivers to interact with the app using natural voice commands.

Waze has already been utilizing Gemini AI in its Conversational Reporting feature, which enables users to report traffic incidents through spoken language. However, this new development takes it a step further by incorporating Gemini’s capabilities into destination search and map updates. The goal is to provide a more personalized experience for drivers while reducing the need to navigate menus or manually enter information.

The first new feature powered by Gemini AI focuses on destination search. Before starting navigation, users can tap the voice icon and ask questions like ‘Find me a coffee shop that’s open right now’ or ‘Find me parking close to Grand Mall.’ Waze will then respond with a list of options, which drivers can begin navigating using voice commands.

The feature is currently being rolled out to the Waze beta community globally on Android and iOS. This expansion demonstrates Google’s commitment to leveraging AI tools for businesses – in this case, enhancing its own navigation app with cutting-edge technology.

Waze is also expanding its Conversational Reporting capabilities, which use Gemini AI to help drivers report traffic incidents through natural speech. The new update extends this capability to map corrections, allowing users to suggest changes like road closures or outdated addresses. This approach aims to make updating maps easier by replacing traditional reporting forms with a conversation-style interface.

Gai Berkovich, VP & General Manager at Waze and Site Lead at Google IL, notes that drivers can simply say ‘The road is closed here’ for the app to send details to local map editors who verify and update the map. This process streamlines data analysis tools by automating tasks and reducing manual input.

Alongside these Gemini-powered updates, Waze is introducing personalized navigation that takes into account a driver’s previous trips alongside the app’s understanding of local traffic patterns. For example, users who typically prefer highways over local roads with frequent stops will see those routes suggested first. Drivers can choose alternative routes or disable this feature through the app’s settings.

Waze also addresses one of the long-standing frustrations among drivers: too many voice alerts. A new ‘less chatty’ mode reduces the number and length of spoken instructions while continuing to provide critical warnings about hazards, turns, and lane changes. This update is designed for drivers who prefer fewer interruptions while listening to music or podcasts.

Another significant addition is a dedicated motorcycle mode, tailored around the different needs of two-wheeled vehicles. Unlike cars, motorcycles can travel on narrower roads and are more sensitive to road conditions. The new mode uses Waze’s real-time traffic data, motorcycle-specific routing information, and input from a dedicated group of motorcycle map editors to suggest suitable routes and highlight hazards relevant to riders.

The motorcycle mode is rolling out in Argentina, Brazil, Colombia, Malaysia, Mexico, Peru, and the Philippines on Android and iOS. Additional countries are planned for rollout.

At the recent Automate 2026 exhibition, Festo showcased its solutions to address the growing demands on original equipment manufacturers and system integrators. The company presented initiatives and technologies aimed at solving deployment and integration challenges in automation projects.

The complexity of machines is putting pressure on OEMs and system integrators. Machines are becoming increasingly complex with more devices, networks, and flexible production requirements. This increases the burden on engineering teams to work within limited technical resources and keep projects moving forward.

Festo’s exhibits at Automate 2026 highlighted its focus on addressing these challenges through various technologies and initiatives. The showcase included motion control, handling systems, connectivity, engineering tools, and workforce development solutions designed to improve startup visibility and reduce integration effort.

One of the key products showcased by Festo was its next-generation VTUX valve terminal platform. This IP67-rated platform integrates pneumatic control, vacuum generation, and I/O into a lightweight modular design that can be mounted directly near the application.

The benefits of this integration include reduced tubing runs, cabinet space, installation effort, and the number of separate automation components required on the machine. Festo’s VTUX terminal also features integrated vacuum monitoring, which improves troubleshooting visibility while helping to reduce compressed air consumption.

This compact design makes it ideal for end-of-arm tooling applications. Festo has further enhanced its VTUX platform with the introduction of a new CTED multi-protocol interface allowing direct connection to Industrial Ethernet networks.

The CTED module supports multiple control environments, including EtherNet/IP, EtherCAT, Modbus TCP, PROFIBUS, and CC-Link. This flexibility enables manufacturers to adapt their systems more easily to changing production requirements by eliminating the need for designing automation architectures around a single standard.

Another critical aspect of robotic performance in high-mix production environments is end-of-arm gripping systems. Festo showcased adaptive gripping and vacuum technologies that improve handling reliability while simplifying end-of-arm integration.

The company’s HPSX adaptive gripper uses food-safe silicone fingers to handle delicate and irregularly shaped products without requiring tool changes. This makes the HPSX well-suited for applications in the food, pharmaceutical, and cosmetic industries.

Festo also demonstrated its Simplified Motion Series of electric actuators which combines motor and drive into a single integrated motion package commissioned directly on the device without specialized software. This design simplifies commissioning, reduces complexity, and is more cost-effective compared to traditional electric automation systems.

The SMS series includes various configurations such as toothed belt axes, spindle axes, mini slides, electric cylinders, and rotary drives. These actuators support digital I/O and IO-Link connectivity enabling remote parameterization, backup functions, and process monitoring while reducing commissioning effort and engineering overhead.

Festo also showcased its engineering tools designed to automate workflows for designers working on component sizing, compatibility verification, accessory selection, documentation, and ordering across multiple systems. These tools compress these workflows from hours into minutes allowing engineers to configure multi-axis handling systems, size electric and pneumatic motion components, access CAD models, validate compatible accessories, generate quotations, and simplify ordering within a connected engineering environment.

These engineering tools also help reduce deployment risk by ensuring selected components and accessories are interoperable before systems are built or commissioned. Additionally, they preserve project configurations for future modification, expansion, or reorder, helping manufacturers to adapt their systems more easily to changing production requirements.

Festo’s Didactic learning system supports workforce development through hands-on mechatronics and industrial automation training tailored to the needs of manufacturers, OEMs, and technical education programs. The MPS 400 learning system is a modular Industry 4.0 learning factory that provides experiential training in automation technology, intelligent machine networking, IO-Link connectivity, and modern mechatronics workflows used throughout advanced manufacturing environments.

Festo has been continuously elevating the state of manufacturing with innovations and optimized motion control solutions for over 100 years. With more than 50 years of presence in the U.S., Festo prepares workers for current and future manufacturing technologies through its Didactic Learning Systems and partners.

The decommissioning of Tesla’s original Model S and Model X assembly line at its Fremont factory in Northern California has been completed in just 46 days. The teardown involved heavy machinery dismantling concrete pits, removing robotic arms and conveyors, and clearing the space for new production. This rapid process marks a significant step towards repurposing the valuable factory floor space for high-volume production of Tesla’s Optimus humanoid robot.

The decision to retire the Model S and Model X originated during Tesla’s Q4 2025 Earnings Call in late January 2026, where CEO Elon Musk announced that production of these vehicles would wind down by the end of Q2 2026. He described it as bringing the programs to an ‘honorable discharge.’ Custom orders ceased around early April 2026, with the final vehicles rolling off the line in early May.

A special signature delivery ceremony on May 20 marked the emotional close for these vehicles, which had defined Tesla’s early success and luxury EV segment since the Model S launch in 2012. The primary reason for tearing down the lines was to repurpose the valuable factory floor space for high-volume production of Optimus, a crucial step towards Tesla’s broader strategic shift from traditional vehicle manufacturing toward robotics and artificial intelligence.

Tesla is targeting rapid scaling with internal ambitions pointing toward tens or even hundreds of thousands of units annually by the end of 2026. Longer-term, the company is constructing a much larger second-generation Optimus facility at Giga Texas, with potential capacity reaching millions of units per year. The goal is to make Optimus a transformative product that could eventually surpass Tesla’s automotive business in scale and value.

Optimus, designed by Tesla, is a general-purpose humanoid robot aimed at performing repetitive or dangerous tasks in factories, warehouses, and eventually homes. Powered by Tesla’s AI and Neural Networks, it aims to be a versatile, affordable platform for various industries. Production of Optimus Gen 3 is already underway in limited form at Fremont, with full-scale output on the converted line expected to begin in late July or August.

Tesla’s aggressive pivot towards its next major initiative, Optimus, marks a significant shift in focus from traditional vehicle manufacturing. The company views this move as crucial for leveraging its expertise in autonomy, AI training, and high-volume production. As one era closes at Fremont, another is rapidly taking shape with the potential to revolutionize industries beyond automotive.

Tesla’s CEO Elon Musk has been vocal about his vision for a future where robots like Optimus play a significant role in various sectors. He believes that by making these technologies accessible and affordable, Tesla can drive widespread adoption and create new opportunities for businesses and individuals alike.

The decommissioning of the Model S and X lines is just one aspect of Tesla’s broader strategy to transform its operations and focus on emerging technologies like robotics and AI. As the company continues to push boundaries in innovation, it remains to be seen how this shift will impact the automotive industry as a whole.