FDA Clears AI-Guided Robotic Surgery System for US Use

The FDA has approved a next-generation AI-powered robotic surgical system, which represents an important achievement in medical technology. The system enables surgeons to perform complicated operations through its combination of artificial intelligence, advanced imaging, and robotic technology. The platform provides real-time, high-resolution visualisation, along with predictive guidance, to improve surgical outcomes, reduce operating time, and protect patient safety. The approval allows hospitals and surgical centers throughout the United States to implement AI-powered robotic systems in their operating rooms.  

Transforming Surgical Practice  

The AI-augmented robotic technology provides surgeons with expanded capabilities by combining robotics and machine learning algorithms to analyse imaging data in real time during surgery or in the operating room. This development has enabled surgeons to use predictive assistance, anticipating the anatomical structures they will encounter as they create the least-traumatic path or route to their surgical goal. This development has revolutionised precision surgery, dramatically reducing the risk of surgical errors while improving the quality of care delivered to patients.  

Combining human wisdom with artificial intelligence has improved the accuracy of the path or method used to navigate complex anatomical structures required for cardiac and orthopaedic procedures. It collects data and learns from surgeries performed with it, improving its guidance and ultimately enabling safer, more efficient surgeries.  

Real-Time Visualization and Guidance  

The foundation of the platform’s capabilities is its advanced imaging process. Surgeons can see details of tissue structures, vascular systems, and organ placement as if through thousands of eyes (3-D digital imaging). This level of visibility will allow them to make better decisions during critical situations, such as during high-risk or delicate surgeries.  

Furthermore, the platform can incorporate predictive algorithms that allow it to identify potential blockages, suggest changes to improve surgical outcomes, and guide surgical instruments to the most efficient path to the target area. The ability to provide this type of information in a timely manner helps surgeons reduce tissue and blood vessel injury and improve the efficiency of surgical procedures.  

Robotic Precision and Control  

Robotic actuation – this system will convert a surgeon’s input into very precise instrument movements, eliminating hand tremor and enabling surgical procedures that would not be possible or would be very difficult with traditional means. These features are especially useful for microsurgery, minimally invasive surgery, and surgery in small/anatomically delicate areas.  

By combining robotic stability with artificial intelligence (AI), the surgeon can maintain control of their instrument(s) while executing complex manoeuvres, improving safety and outcomes. Hospital systems can use the technology to broaden their surgical offerings and provide higher-quality patient care.  

Implications for Minimally Invasive Surgery  

AI-based robotic surgery has a huge impact on minimally invasive surgical techniques. With a small incision, the length of hospital stay, the risk of infection, and the time to recovery are reduced. In addition, an AI-driven system increases surgical precision and provides predictive analytics to help reduce complications and improve efficiency.  

During a complex procedure, patients may experience fewer complications, less postoperative pain, and a faster recovery. Aside from giving patients access to more advanced treatments than they would have with open surgery, AI-based robotic surgical systems allow surgeons to use smaller incisions rather than larger/longer ones.  

Learning and Adaptation  

One of the main features of the system is machine learning. Each operation generates information that contributes to a growing central database of body structures, operation methods, and outcomes. The Artificial Intelligence algorithms provide ongoing improvement to the recommendations made based on data accuracy, direction, and overall efficiency of the technology.  

The adaptive learning approach enables hospitals that use the technology to benefit from the combined knowledge of all hospitals, enhancing the safety and success rates of future uses. In addition, individual guidance regarding the patient’s structure and circumstances is provided.  

Integration with Hospital Systems  

This system allows complete integration with any pre-existing hospital infrastructure. The system can connect to multiple imaging modality types, such as MRI, CT, and fluoroscopy, to provide the necessary intra-operative information and adjust the procedure if needed, as well as improve communication efficiency during collaborative surgical work.    

By connecting with electronic health records and surgical procedure planning tools, this system will provide additional workflow efficiencies to hospitals that use it, thereby allowing them to streamline their surgical procedures, maximise the use of their operating rooms, and coordinate with surgical staff and their supporting associates more efficiently.  

Enhancing Surgeon Training and Skill  

Robots guided by AI have implications for surgical training, as trainees can now practise procedures with real-time feedback, allowing them to learn complex movements in a fail-safe environment. Experienced surgeons can also use laser robotics as an aid, providing assistance with the precision aspect of the procedure but not taking away from clinical judgement.  

Through this method of training and education, we are developing a model for collaboration between humans, providing their skills, and AI, providing insight that allows for the transfer of education and skill development from traditional surgical techniques to next-generation technology.  

Patient Safety and Risk Reduction  

The FDA-approved system maintains a high level of safety focus. Predictive guidance, robotic precision, and continuous monitoring work together to reduce mistakes and complications. Real-time alerts and decision support also add an extra level of protection during high-risk or emergency surgical procedures.  

When using AI-assisted robotic surgery, hospitals can expect to improve surgical outcomes, reduce complications, and increase patients’ confidence in more complex procedures. These align with larger healthcare goals of improving quality, efficiency, and safety.  

Potential for Widespread Adoption  

AI-assisted robotics will improve the availability of advanced surgical procedures as more hospitals across the United States implement AI-based robotic systems to assist with surgeries. New technologies will allow small surgical centers to perform advanced procedures and enable hospitals in urban areas to improve overall performance and patient outcomes by using artificial intelligence and robotic systems in high-volume facilities.  

Furthermore, the potential for AI-based robotics to be used in telesurgery can provide remote or tele-surgical capabilities, enabling access to and support for areas with a high need for advanced surgical services that lack access to a high-quality surgeon. This development could revolutionise the delivery of advanced surgical services nationwide.  

Market Implications and Industry Impact  

FDA clearance will likely lead to increased investment and innovation in AI-driven medical devices. Hospital networks and device companies achieve better outcomes in precision surgical procedures.  

Approval may also enable Technology firms, healthcare organisations, and academic institutions to collaborate, thereby increasing research opportunities and advancing AI-driven systems.  

Future Directions  

In the future, it is anticipated that increasingly intelligent algorithms will be developed using artificial intelligence to predict risk factors for patients, automate some surgical activities, and provide real-time augmented reality applications that enhance visualisation. The ongoing development of more sophisticated haptics, smaller surgical instruments, and improved communication capabilities will likely further enhance robotic surgery systems.  

The ultimate goal of robotic surgery systems is to combine the strength of artificial intelligence, the precision of surgical systems, and the experience of human surgeons, thereby raising the standard of surgical performance, improving patient outcomes, and revolutionising the delivery of healthcare.  

Conclusion: A New Era in Surgical Innovation  

An innovative advancement in healthcare technology is AI-supported robotic-assisted surgery. The AI technology enables the surgical team to visualise surgical anatomy in real time, leverage predictive analytics for guidance throughout the operation, and achieve superior surgical precision by using robotic arms to perform manoeuvres with greater accuracy.  

The future of surgery holds vast potential, as AI can augment surgical proficiency and expeditiously restore patients to their pre-operative functional level while using less invasive surgical techniques and reducing recovery time. Hospitals that are incorporating this technology into their facilities across the U.S. are at the forefront of the surgical practice revolution, where robotics will play a dominant role. 

Source: https://www.stereotaxis.com/ 

Breakthrough Chip Makes AI 100x More Efficient Without Cloud 

The new chip design will completely change artificial intelligence by delivering 100 times better performance through its architecture, which operates independently of cloud services. The research combines memristor-based in-memory computing with secure processing methods to enable AI models to execute directly on devices while consuming minimal power. The advancement enables smartphones, Internet of Things devices, and edge systems to execute advanced artificial intelligence tasks locally, improving processing speed, user data protection, and environmentally friendly operation.  

Redefining AI Hardware  

In a typical AI environment, vast amounts of cloud computing power are required to handle the immense volume of data generated by AI applications. This introduces latency issues due to long-distance data movement, as well as high energy consumption and privacy concerns when moving such sensitive information across networks. The memristor-based chips provide a different architecture that combines computation and memory into a single integrated unit.  

Because this new architecture eliminates the need to move large amounts of data back and forth between RAM and the CPU and GPU for AI computation, it addresses a key bottleneck in AI implementations. Additionally, the memristor-enabled chip enables AI algorithms to be executed directly on the device, creating opportunities for new real-time AI applications across robotics, autonomous vehicles, wearables, smart sensors, and more.  

Energy Efficiency and Sustainability  

A major benefit of the new chip is its energy efficiency. In general, AI processing consumes a lot of electrical power, mostly from massive data centres. moves much less, and memristor switching consumes very little energy.  

The lower electrical cost of AI processing, enabled by energy-efficient chips, also means a lower carbon footprint. With rapid growth in AI use across sectors, energy-efficient hardware solutions will be key to ensuring large-scale AI implementations are environmentally sustainable.  

On-Device AI and Privacy  

By enabling local IoT device operation, the chip also helps address growing concerns about data privacy. All types of sensitive information (e.g., personal health data, financial transaction information, and proprietary business information) can be processed on the device without being transmitted off the device.  

In addition, on-device processing reduces response latency across all the examples above; therefore, these AI models can provide real-time responses. This level of capability is crucial for scenarios like autonomous navigation, real-time translation, and augmented/virtual reality (VR/AR), where speed and immediacy are critical to the user experience and operational dependability.  

Memristor-Based In-Memory Computing  

At the core of this discovery exists memristor technology. Memristors are memory devices that store data and enable simultaneous information processing. The system performs computations at the location of data storage because it can process information without using a standard CPU-GPU architecture that separates memory and processing tasks.  

The chip uses multiple memristors, which the system organises into arrays that can perform AI computations simultaneously. The system uses parallel processing to manage extensive neural networks, thereby improving performance without increasing energy consumption or physical dimensions.  

Security and Trust  

The microprocessor’s performance is enhanced by multiple security features. It executes calculations on a physically secure medium, reducing exposure to external threats and data loss. This form of securing a design for AI applications will be crucial due to their ability to provide value in sensitive sectors such as healthcare, banking, the military, and autonomous systems.  

Mixing AI high-performance processing capabilities with superior security on a single chip represents a significant advancement and will ultimately provide users (businesses or end users) with the most complete AI solution.  

Implications for Edge Computing  

The new technology is likely to accelerate the adoption of edge computing by bringing AI functionality closer to where data is generated and collected, thereby moving away from using cloud servers for computation and instead having edge applications perform computations locally. Therefore, when computing locally via edge computing, edge applications can provide quicker response times than cloud computing, with increased reliability and substantially lower operating costs.  

Manufacturers, logistics companies, smart cities, and autonomous systems will all benefit from this breakthrough technology. Edge computing will enable real-time analytics, predictive maintenance, and adaptive control systems to operate more efficiently than continuously relying on the cloud for computing.  

Transforming AI Applications  

With this new chip, more complicated AI models can be run on smaller and more portable devices and allow developers to implement complex neural networks into various apps directly on board devices, providing for a much larger pool of potential applications, such as for computer vision, natural language processing, and reinforcement learning by way of hosted or offline processing capabilities.  

By providing access and capabilities to small and mid-sized companies, start-ups, and researchers to innovate with on-device integrations, AI will be democratised, offering smaller organisations greater accessibility without the perceived need to deploy large amounts of infrastructure.  

Competitive Advantage in AI Hardware  

The increasing demand for AI worldwide has made hardware efficiency and speed key competitive advantages. AI acceleration continues to be an area of ongoing investment from major companies, including NVIDIA, Intel, and others; however, the introduction of a memristor-based chip offers a fundamentally different approach to AI processing, combining memory and computation to create a new level of security. This combination of memory and computation provides value to those seeking high-performance, low-power AI applications.  

Many market analysts believe that innovations such as this will change the requirements for AI infrastructure, reduce reliance on traditional cloud-based solutions, and alter how companies economically deploy AI.  

Future Directions and Development  

The researchers are investigating how to continue scaling this technology by increasing memristor density, improving fabrication processes, and integrating the chip into a wide range of devices and platforms. Additional refinements will enable even larger neural networks to support enhanced AI capabilities and broader use in consumer and enterprise devices.  

Moreover, the technology provides an avenue for hybrid AI systems that allow some processing to occur locally, while more complex or aggregated processing can leverage cloud resources, creating a flexible and efficient AI ecosystem.  

Potential Challenges  

Although there is great promise in using memristor-based AI chips on a large scale, there are still many challenges that must be overcome before they can be fully adopted in an everyday consumer setting: manufacturing them at scale, g software, and optimising the way AI models will utilise what is called “in-memory” processing power. All these items will need to be solved by researchers and engineers so that we can make memristor-based AI chips commercially viable and ready for widespread use.  

However, reports from research laboratories indicate significant potential for memristor-based AI chips, and partnerships between chip manufacturers and AI developers may help accelerate the transition from laboratory prototypes to commercially available products.  

Broader Implications  

The breakthrough will impact beyond just the performance of artificial intelligence: it could usher in new standards for energy-efficient computers, secure processing at the device level, and the rapid deployment of intelligent systems. Improving how technology reduces reliance on cloud infrastructure could enable resilient systems, reduce costs, and increase global access to artificial intelligence.  

Smart devices will soon allow individuals and businesses to work with devices that are both efficient, respect privacy and provide quicker insights into their operations than ever before, changing the way Artificial Intelligence becomes a part of everyday life.  

Conclusion: A New Era of AI Efficiency  

The latest memristor-based chip signifies a major step forward for artificial intelligence hardware. The integration of in-memory processing, security, and energy efficiency enables devices to run high-performance AIs without relying on cloud-based services.  

The advantages offered by this innovative memristor chip will enable AI applications to operate more quickly and efficiently, while prioritising user privacy, than ever before. Additionally, they will create a host of new opportunities across a variety of industry sectors, leading to entirely new methods of deploying AI. In continued development, this chip could change our view of the AI landscape, providing powerful, efficient, and secure AI solutions for many more people and devices.

Source: https://phys.org/ 

NASA Artemis Reaches Farthest Human Distance from Earth Since 1972

NASA’s Artemis 2 mission has achieved a historic milestone by sending astronauts to their farthest point from Earth since the Apollo missions of the early 1970s. The team accomplished a lunar flyby, showcasing the operational capabilities of the Artemis spacecraft, the Space Launch System (SLS), and its deep-space exploration life-support systems. NASA has established its new sustainable lunar exploration mission through this accomplishment, which will enable upcoming missions to Mars and other deep-space destinations. 

Breaking Distance Records  

The Artemis 2 Mission was a significant milestone in human spaceflight, as it sent the first astronauts farther from Earth than at any time in almost 50 years. The mission will take astronauts on a ‘loop’ around the moon before returning them to Earth and will test navigation, communications, and spacecraft performance under deep-space conditions (i.e., the distance from Earth creates new challenges for signal delay, radiation exposure, life support, etc.).  

The data gathered from this record-breaking journey will be extremely valuable for future long-duration missions and will provide insight into spacecraft operations, astronaut health, and system reliability. By going beyond what Apollo missions did, Artemis 2 shows that NASA is capable of expanding humanity’s footprint into our solar system.  

Testing Spacecraft and Systems  

This mission is a major evaluation of NASA’s unified Artemis systems. The Orion spacecraft, which features new propulsion, navigation, and environmental control systems, has proven ready for the complex operations required to enter orbit around the Moon and beyond. The Space Launch System (SLS), the world’s most powerful rocket, provided the thrust to enable astronauts to safely follow a high-energy path.  

During this mission, tests were conducted on life-support systems, radiation shielding, and onboard communications to the extent permitted by the time and distance of the journey. These tests will ensure that all future Artemis missions, including human-crewed Artemis missions to orbit the Moon and eventually Mars, can operate safely and efficiently for long periods weeks or months.  

Crew Experience and Human Factors  

The Artemis 2 crew is being tracked to assess how they respond physiologically and psychologically during deep space travel, including exposure to microgravity, long periods of time away from Earth (isolation), and reduced visibility of Earth. Information collected during this experiment will be used to develop astronaut training programmes, design future spacecraft, and plan long-duration missions (greater than 30 days).  

The astronauts participating in this study have also been involved in experiments on the efficiency of life support systems, health monitoring during tasks under remote operational conditions, and decision-making under limited information during operational tasks. The emphasis on a human-centric approach ensures that advances in technical capability are aligned with crew safety, comfort, and productivity.  

Lunar Flyby and Science Opportunities  

While Artemis 2 serves mainly as a test flight, its lunar flyby also gives scientists many opportunities to gather data and make observations. Instruments aboard have gathered data on radiation levels in deep space, how space weather behaves, and how spacecraft behave there. The trajectory of Artemis 2 through the lunar flybys will provide unique vantage points to study the Moon’s topography and how the Earth and Moon interact.  

The information gathered during the lunar flybys will help establish plans for the Artemis programme, improve scientific understanding of lunar topography, enhance mission design, assist in identifying resources, and aid in risk mitigation strategies for crewed missions to the Moon and Mars.  

Implications for Artemis Programme Goals  

The goal of the Artemis programme is to facilitate permanent human exploration on and around the Moon by enabling a sustainable lunar exploration programme. Artemis 2 is an important milestone in the success of Artemis 3, which will land humans on the surface of the Moon. The objectives of Artemis 2 include verifying deep-space travel capabilities and verifying the integrity of spacecraft systems, both of which are necessary to provide the infrastructure to support prolonged missions of weeks or months.  

The Artemis missions include significant contributions from international partners in the form of technology, research, and operational support and knowledge transfer among programme participants and maximise scientific return.  

Advancing Space Technology  

The Artemis Program is the next step for NASA to showcase advanced technologies that will help to develop new discoveries on the Moon, as well as technologies that can be incorporated into future missions beyond the Earth’s orbit, such as food and water systems, power systems, communications systems, and environmental control/modular, adaptable technology that will support longer-duration, greater-distance flights than Artemis 2 can achieve.  

The Artemis 2 mission highlights the criticality of leveraging data in decision-making, employing automation, and building redundancy into spacecraft systems to protect crews against unexpected events in deep space.  

Inspiring Public Engagement  

Artemis 2 is more than a major technological/scientific achievement for NASA; it represents an inspirational milestone for people everywhere as humanity journeys to new frontiers through exploration. Artemis 2 exemplifies what humanity can achieve when it invests in science and technology, encouraging young people and their communities to pursue these goals through educational programmes, live broadcasts, and public outreach.  

The success of Artemis 2 is revitalising interest in travelling to the Moon, and there are many possibilities for humanity to travel to Mars and beyond.  

Supporting Long-Term Human Exploration  

The Artemis 2 mission will be instrumental in providing data to support the development of safe, long-term human activities in space. Data gathered will help understand how astronauts can work in deep space for long periods by monitoring radiation levels, assessing how well life support systems perform, and evaluating how crews will perform under conditions of isolation.  

The information gained from this mission will inform the design of vehicles, habitats, and operational protocols for future human-rated deep-space exploration missions. Specifically, the planning will focus on sustaining human life for months or years, away from Earth.  

International Collaboration and Partnerships  

NASA’s Artemis program is collaborating with countries and businesses to develop technologies, operational processes, information-sharing, standards, and best practices for the successful joint exploration of the Moon and Mars by providing a platform for Artemis II to validate the technologies and processes necessary for these missions.  

These partnerships will result in technology exchanges, research collaborations, and economic growth.  

Future Directions in Deep-Space Exploration  

Artemis 2 represents the path towards more ambitious missions, such as exploring the Moon’s surface, establishing lunar orbiting stations, and sending humans to Mars. NASA is also looking to use information gained from Artemis 2 to improve spacecraft design, mission planning, and crew support systems.  

Artemis 2 will also contribute to NASA’s long-term goal of sustainable operations in space, enabling scientific discovery and supporting commercial and research activities in orbit around the Moon and beyond. 

Conclusion: Humanity Pushes Farther Into Space  

Artemis 2 marks a landmark event in the history of human space travel; it has broken distance records since the Apollo programme and has tested all the capabilities needed for long-duration human missions in outer space. Conducting successful tests of spacecraft systems, evaluating crew performance, and demonstrating operational protocols are key accomplishments that advance NASA’s efforts to develop a long-term plan for exploring the Moon and sending astronauts to Mars.  

This milestone demonstrates our ability to extend our reach into the universe through human ingenuity, innovative technologies, and international cooperation. This new era of discovery will allow us to better understand both space and our place within it.

Source: https://www.nasa.gov/news/ 

NVIDIA Unveils System for Ultra-Precise Robotic Movement

  • NVIDIA will soon release Open Isaac Gr00T humanoid models for download on Hugging Face.  
  • NVIDIA RTX Pro 6000 Blackwell workstations and RTX PRO servers help accelerate robot simulation and training by providing robust computing power for faster model development, data processing, and overall improved productivity in robot engineering tasks.  
  • Agility Robotics, Boston Dynamics, Foxconn, Lightwheel, Neura Robotics, and XPENG Robotics are among many robot makers adopting NVIDIA Issac.  

At Computex, Nvidia announced the release of Nvidia ISAAC GR00T N1.5, the first update to its open and customizable foundation model for humanoid reasoning and skills, enabling users to create more adaptable robots tailored to specific applications and unique needs. The company also introduced NVIDIA ISAC GR00TD Dreams, a blueprint for generating synthetic motion data that helps accelerate robot learning and adaptability, as well as new NVIDIA Blackwell systems designed to reduce time-to-market for Vah humanoid robot development.  

Companies like Agility Robotics, Boston Dynamics, Fourier, Foxlink, Galbot, Mentee Robotics, Neura Robotics, General Robotics, Skild AI, and XPENG Robotics are now adopting NVIDIA platform technologies. These technologies are helping to move humanoid robot development and deployment forward.  

“Physical AI and robotics will bring the next industrial revolution,” said Jensen Huang, founder and CEO of Nvidia. From AI brains for robots to simulated worlds to practice in, or AI supercomputers for training core models, WE Media provides building blocks for every style of the robotics development journey.  

New Isac GR00T Data Generation Blueprint closes the Data Gap 

Presented during Huang’s Computex keynote, NVIDIA Isaac GR00T Dreams is a blueprint that generates large amounts of synthetic motion data, or neural trajectories, allowing physical AI developers to efficiently teach robots new behaviors and better adapt to changing environments, reducing the need for costly real-world data collection.  

Developers can begin by post-training Cosmos-predicted world-based models (WFMs) for their robot using just a single image. gr00t dreams weekly creates videos showing the robot performing new tasks in different settings. The blueprint then extracts user-friendly action tokens from these videos, enabling developers to efficiently train robots to perform new tasks without extensive manual annotation.  

The GR00T blueprint complements the Isaac GR00T Mimic blueprint, which was released at the NVIDIA GTC conference in March. While GR00T Mimic uses the NVIDIA Omniverse and the NVIDIA Cosmos platforms to augment existing data, GR00T Dreams uses Cosmos to generate entirely new data.  

New Isac GR00T Models: Advanced Humanoid Robot Development 

NVIDIA Research used the GR00T Dreams Blueprint to create synthetic training data, develop GR00T N1.5, and update GR00T N1 in only 36 hours. This process would have taken nearly three months if done manually.  

GR00T N 1.5 is better at acclimating to new environments and workspace setups. It can also recognize objects based on user instructions. This capability greatly improves the model’s success rate on common material-handling and manufacturing tasks such as sorting or putting away objects. Early users of GR00T and models include AeiRobot, FoxLink, Lightwheel, and Neura Robotics. AEI Robot leverages these models to help Alice understand natural language instructions and perform complex pick-and-place tasks in factory environments. Foxlink Group utilizes them to enhance the flexibility and efficiency of industrial robot manipulators. Lightwheel applies the models to review synthetic data to accelerate the deployment of humanoid robots in factories. Neural Robotics is evaluating the models to advance its household automation work.  

New Robot Simulation and Data Generation Frameworks Accelerate Training Pipelines 

Developing advanced humanoid robots requires substantial and varied data, which can be costly to collect and process. Testing robots in real-world settings also entails additional costs and risks.  

To help address the difficulties of data collection and testing, NVIDIA introduced these simulation technologies:  

  • NVIDIA Cosmos Reason, a new WFM that uses chain-of-thought reasoning to help curate higher-quality, more accurate synthetic data for physics. Physical AI model training is now available on Hugging Face.  
  • Cosmos Predict 2, used in GR00T Dreams, is coming soon to Hugging Face, featuring performance enhancements for high-quality world generation and reduced hallucination.  
  • NVIDIA Isaac GR00T: A blueprint for generating exponentially large quantities of synthetic motion trajectories for robot manipulation using just a few human examples.  
  • Open-source physical AI dataset now includes 24,000 high-quality human-humanoid robot motion trajectories, enabling faster, more accurate development and evaluation of GR00TN models and providing developers with a valuable free resource to accelerate project timelines.  
  • NVIDIA ISAC Sim 5.0: A simulation and synthetic data generation framework will soon be openly available on GitHub.  
  • NVIDIA ISIC Lab 2.2, an open source robot learning framework that will support new evaluation environments to help developers test GR00T N models.  

Foxconn and Foxlink are using the GR00T Mimic Blueprint to accelerate their robotics training pipelines by generating synthetic motion manipulation. Agility Robotics, Boston Dynamics, Fourier, Mentee Robotics, Neura Robotics, and XPENG Robotics are simulating and training their humanoid robots using NVIDIA Isaac Sim and Isaac Lab. Skilled AI is using the simulation frameworks to develop general robot intelligence, and General Robotics is integrating them into its robot intelligence platform.  

Universal Blackwell Systems For Robot Developers 

Global systems manufacturers are building NVIDIA RTX Pro 6000 workstations and servers, supplying a single architecture that easily runs every robot development workload, from training and synthetic data generation to robot learning and simulation.  

Cisco, Dari Technologies, Hewlett Packard Enterprise, Lenovo, and Supermicro announced NVIDIA RTX Pro–powered servers and Dari Technologies HBI. Lenovo announced NVIDIA RTX Pro 6000 Blackwell–powered workstations.  

When developers need more computing power for large-scale training or data generation, they can use Nvidia Blackwell systems such as these. These are available on Nvidia DGX, in the cloud with top cloud providers, and through Nvidia Cloud Partners, and can deliver up to 18 times better data processing performance.  

Developers will soon be able to deploy their robot-based models to the NVIDIA Jetson Thor platform. This will allow for faster One Robot inference and better runtime performance.  

You can watch Huang’s Computex keynote and find out more at NVIDIA GTC Taipei.  

Source: NVIDIA Powers Humanoid Robot Industry With Cloud-to-Robot Computing Platforms for Physical AI 

SEC Filing Flags Instability in AI Startup Amid Market Pressure 

In February 2026, an SEC filing revealed severe instability at an AI startup. The company immediately announced a major restructuring, slashing 26% of its global workforce to counter market pressure and drive efficiency.  

Important Facts About the Instability 

  • Restructuring plan: as a first step. The board approved the plan on February 4, 2026, aiming to make the company run more efficiently.  
  • Workforce reduction: the company cut 26% of its global workforce, with most redundancies occurring soon after the announcement  
  • Financial impact: As a result of these actions, the company expects to incur pre-tax restructuring costs estimated at about $10 to $12 million in the fourth quarter of 2026. These costs mainly reflect severance and one-time termination payments for laid-off employees. The company also expects a temporary dip in productivity and potential disruption as teams adjust, but anticipates these upfront expenses will support long-term financial stability.  
  • Cost savings: The company aims to reduce non-employee costs by about 30% by the second half of 2027. Additional cuts are expected. These changes should help the company achieve profitability.  
  • Context: the decision occurred among a broader AI sentiment reset in early 2026. During this period, tech stocks dropped sharply because investors worried that big investments in AI might not pay off, causing stocks to fall about 70% over six trading days after new AI tools were launched  

Wider Market Context 

  • AI disruption fears: Investors are alarmed that new AI automation tools could upend established software business models.  
  • Massive layoffs: more than 51,000 tech jobs were cut in the first quarter of 2026 as companies focused more on AI-based efficiency.  
  • AI washing scrutiny: The SEC is cracking down, demanding funds substantiate AI claims or face enforcement for deceiving investors.  

Investors have been drawn to the promises of AI, but recent events show there are serious legal risks since early 2024. US regulators have increased their scrutiny of tech marketing claims. As a result, many companies are now being investigated for overstating their AI capabilities. Regulatory supervision by securities authorities, especially around public statements, is at the center of this effort. These actions go beyond just making headlines day-to-day effect, company evaluation, fundraising, and corporate image. So far, penalties have already topped $700,000, and the alleged fraud exceeds $60 million. Industry professionals need to understand the enforcement process, key rules, and new risk signals. This article brings together recent cases, official statements, and practical advice in one place. Keeping informed can help leaders avoid expensive mistakes. Let’s look at how the situation has changed and where to focus attention next.  

AI Claims Under Scrutiny 

Companies often present ordinary software as if it were advanced machine learning. Now regulators want proof that the algorithms being promoted actually make decisions. Investigators have found that some startups used manual processes behind flashy dashboards. The SSE pointed out these issues when it settled with advisors Delphi and Global Predictions in March 2024. Their marketing claimed they used proprietary AI for portfolio construction, but internal records showed little automation.  

The commission called this practice “AI washing,” which it considered misleading advertising under securities law. Similar problems were also found in later cases involving Joonko, Rimar Capital, Presto Automation, and Nate. These cases included exaggerated claims of autonomy, hidden use of third-party technology, or undisclosed human involvement; as a result, what investors believed was often very different from reality. These early cases have made the market more cautious. Now, regulatory supervision treats hype as possible securities fraud. Understanding the timeline of these investigations gives stakeholders a better context.  

Regulatory Supervision Enforcement Timeline 

To understand how we arrived at the current policy, consider the following timeline of key events that have shaped the regulatory landscape.  

  • March 18, 2024: The SEC fined Delphi and Global Predictions $400,000 for false AI claims.  
  • June 11, 2024: The SEC accused Joonko founder Ilit Raz of $21,000,000 investor fraud.  
  • October 10, 2024: Rimar Capital settled and paid about $310 for exaggerated AI trading capabilities.  
  • June 14, 2025: Presto Automation admitted to inaccurate disclosures, yet  avoided a financial penalty.  
  • April 9, 2025: The SEC alleged that Nate founder Albert Saniger raised $42,000,000 on fabricated AI operations.  

Collectively, these events highlight the rapid rise in regulatory accountability and help explain what is driving this broader coverage. Let’s agree. I mean the legal foundation behind these enforcement actions.  

Core Legal Tools Applied 

Recent actions are based on traditional securities laws, such as Section 17(a) and Rule 10b-5, which ban material misstatements or omissions. Advisors must also follow the marketing rule, which prohibits misleading advertising without solid proof. There is no rule specific to AI, but regulatory supervision uses these existing laws very effectively. Regulators often review records, data systems, and vendor contracts to verify the accuracy of claims. Companies must update their reports when they switch from experiments to real systems.  

If there is a gap between what is promised and what is delivered, it may constitute fraud. Legal experts recommend keeping records to back up every claim about algorithms. A compliance program should include checks on models and review disclosures before any press release. These steps support internal approval and ensure evidence is ready for any investigation. Regulators already have strong tools to address hype, and financial penalties make the risk even clearer.  

Recent Financial Penalties Snapshot 

These enforcement actions have real financial consequences for companies. The following summary provides a quick overview of recent fines and fraud allegations.  

  • $400,000 in civil penalties were levied against Delphi and Global Predictions in March 2024  
  • $310,000 in combined penalties paid by REMAR Capital Entities in October 2024.  
  • Over $63 million in alleged investor losses in Joonko and Nate complaints.  

As these examples show, penalties are just one risk reputation damage, and investor losses can be even more costly. Presto’s stock price, for instance, fell sharply after it corrected its disclosures, as both direct and indirect impacts are now expected. Companies should expect and should account for these compliance costs early. Now let’s explore investor risk factors beyond financial penalties.  

Critical Investor Risk Factors   

Given these risks, investors pay closer attention to details like human involvement, undisclosed code use, and transparent, honest metrics. Build lasting trust and can facilitate fundraising. Regulatory supervision has led more boards to require AI audits before approving new campaigns. These audits check both technology and legal compliance. Additionally, rating agencies monitor enforcement news as part of ESG and governance scores. The focus has shifted to proven results. With this context, we now present practical compliance steps to help companies adapt.  

Practical Corporate Compliance Book   

Preparing for scrutiny starts with strong governance, maintaining an up-to-date list of models, and conducting cross-team reviews of documentation. The following checklist summarizes proven compliance safeguards.  

  • Document algorithm objectives, inputs, and restrictions in plain language.  
  • Maintain statistically valid testing logs that demonstrate the claimed performance.  
  • Disclose personal oversight, third-party services, and fallback procedures.  
  • Secure board approval for advertising materials featuring AI assertions  

Regularly reviewing and updating practices every quarter is key to staying compliant with evolving risks. Regulators often request supporting evidence. Professionals can benefit from certifying their skills in risk management and disclosures. Following this playbook minimizes penalties and surprises. Ongoing change requires readiness. Next, let’s consider how oversight will evolve.  

Evolving Future Oversight Outlook 

Experts expect more international cooperation to fight false AI claims. The SEC may also release extra guidance or risk alerts. At the same time, Congress is discussing new laws on algorithmic accountability. These laws could make current enforcement practices official. Regulatory supervision may also expand to cover supply chain tracking and marking model outputs. Whistleblower programs already offer rewards for reporting misleading disclosures. Companies that plan for these changes can gain a competitive edge. The upcoming changes will benefit transparent, well-managed businesses. Leaders should take action now.  

Regulatory supervision is now a constant factor in every discussion about AI. Early settlements with advisors and orders against public companies have increased both penalties and brand risk. Still, companies can succeed by backing up their claims, checking their code, and ensuring their advertising matches their actual capabilities. Investors should look for honesty and involvement before investing. Compliance teams need to keep records, monitor vendor changes, and update disclosures promptly. By building an active culture, organizations can turn risks into opportunities. For more guidance and skill building, see the linked certification program. Take action now to stay ahead of enforcement. 

Source: Regulatory Oversight Tightens on AI Claims: Inside SEC Crackdown 

$5B Healthcare Merger Pivots to AI in Major US Shift

Two of the country’s top regional care providers finalized a $5B merger on April 6, 2026, denoting a major milestone in the industry. The merger brings together Atlantic Health Alliance and Pacific Medical Group, creating Meridian Health Systems. This move is not only about expanding facilities; it also signals a shift toward automated, data-driven healthcare. By combining their financial resources and patient data, Meridian Health Systems plans to tackle the rising costs of traditional clinical operations. The merger is designed to support a nationwide digital system focused on predictive diagnostics and automated administrative tasks. This change is part of a larger trend in the US where managing information is becoming more important for healthcare sustainability than simply increasing physical capacity.  

Building The Unified Clinical Data Lake 

A key part of the Meridian merger is building one of North America’s largest unified clinical data lakes. In the past, patient information was scattered across separate systems, making it hard to track health outcomes over time and across areas. Meridian is now investing in semantic interoperability, enabling different electronic health records to share information in a common language. This enables spotting patterns in chronic diseases that smaller providers might miss by analyzing anonymous data. Using data from more than 15,000,000 patients, the system can create risk-stratification profiles to help doctors intervene before patients need emergency care.  

This move toward predictive care is made possible by a centralized command center. Instead of each hospital managing ICU beds and other resources independently, Meridian uses a real-time network that connects all 45 of its main medical centers. This setup allows resources to be shared, so specialists can support remote clinics via high-quality video links. As a result, patients in remote areas get the same level of expert care as those in big cities, making the standard of care more equal through fast, automated coordination.  

Automating The Administrative Burden 

One of the main reasons for the $5,000,000,000 valuation is the capacity to streamline administrative tasks. Right now, up to 30% of US healthcare spending goes toward billing, coding, and insurance verification. Meridian plans to handle this by rolling out a zero-touch revenue cycle management system. This system uses advanced technology to review clinical notes as they are written and automatically generate accurate billing codes that meet the requirements of many insurance plans. As a result, there are fewer denied claims, and nurses can spend less time on paperwork and more time with patients.  

Automation also improves Meridian’s supply chain management. Thanks to the merger, Meridian can use its large buying power with an automated procurement system. This system tracks expiration dates and inventory for millions of medical supplies, from basic items like gloves to specific drugs. It can even predict when and where supplies will be needed most by looking at local health trends, such as slow seasons or heat waves. By sending supplies to the right places ahead of time, Meridian reduces waste and ensures important equipment is always available, cutting down on the usual delays and inefficiencies in large medical systems.  

Advancing Diagnostic Fineness in Specialized Care 

Meridian allocates resources to AI-powered diagnostic platforms within oncology and radiology departments. These systems extend beyond imaging recognition, applying automated digital pathology and multiparametric MRI analysis to detect nuanced pathological features. Initial post-merger validation demonstrated that these platforms identified neoplastic lesions that would otherwise have been missed during manual interpretation. Serving as a secondary, algorithmic reviewer, the technology augments radiologists’ capacity to comprehensively evaluate complex imaging datasets.  

The platform incorporates pharmacogenomic profiling to enhance therapeutic accuracy. Patients may elect to undergo genotyping to identify allelic variants that influence drug metabolism. Meridian’s system cross-references anonymized genetic data with current clinical guidelines to recommend individualized dosages for therapies such as antihypertensive agents or chemotherapeutic regimens. This approach, rooted in precision medicine, minimizes the need for empirical treatment adjustments. Consequently, patients experience expedited recovery and fewer adverse drug reactions, as each treatment plan is customized to their unique genetic composition.  

Governance and Moral Data Sovereignty 

To uphold data stewardship, Meridian has instituted an independent ethics and data sovereignty oversight board. This entity monitors the transparency and algorithmic fairness of automated processes. The merger framework enforces a privacy-by-design mandate, requiring that any information used for algorithm training be irreversibly de-identified and stored in encrypted, jurisdictionally compliant data zones. These protections ensure patient data cannot be repurposed for risk adjustment, actuarial modeling, or marketing, strictly confining system use to direct clinical purposes.  

These safeguards are vital for maintaining public trust as healthcare digitizes. Building on these protections, Meridian has also opened a patient access portal where people can see how their data is used and which automated tools affected their care. This transparency builds trust between patients and providers, with technology serving as an invisible assistant to help doctors make better decisions.  

The Crystalline Pulse of a New Era 

As these two major healthcare companies combine their digital systems, an important transformation in American medicine is underway. Kuron patient care is becoming more proactive and technology-driven. Clinics are more responsive and able to meet patient needs, while hospitals use smart technology to prevent problems before they arise. With these advancements, concerns about errors may diminish, replaced by trust that every procedure is handled with precision. Soon, much of our healthcare will be managed seamlessly in the background, providing confidence and peace of mind that technology reliably supports our recovery.  

Source: Sec Gov Archives

Anthropic Launches Claude for Teams With Enterprise AI Controls

Anthropic is launching Claude Enterprise, a new AI chatbot plan for companies needing advanced admin controls and security. This offering directly competes with ChatGPT Enterprise, introduced by OpenAI last year.  

Claude lets enterprise firms upload company data for analysis, Q&A, graphics, web pages, or as a custom AI assistant.  

Anthropic is adding features to Claude that mirror ChatGPT’s business offerings.  

The reality is that Claude has been usable for companies for a year. Candidly, we’ve had a product in the market for a lot less time. Anthropic product lead Scott White told TechCrunch that we’re responding to the needs of our customers in a high-velocity city with a smaller team.  

In May, Anthropic launched Claude Team for small businesses. Since then, it has released mobile apps for iOS and Android. Now it is directly competing with ChatGPT Enterprise, which is widely used by Fortune 500 companies.  

Claude Enterprise stands out with a 500,000 token context window, more than double that of ChatGPT Enterprise or the Claude Team Plan.  

Claude Enterprise also provides collaborative workspaces, called projects and artifacts, where multiple users can upload and edit content. These features help businesses manage complex projects with various data sources and participants. Anthropic considers these workspaces a key competitive advantage.  

Another competitive advantage is GitHub integration, which enables direct synchronization between Claude and the customer’s codebases. This feature, leveraged by engineering teams, streamlines onboarding, bug fixes, and feature development, distinguishing Claude from some enterprise AI tools.  

Similar to ChatGPT’s Enterprise plan, Claude Enterprise allows businesses to assign a primary owner for their workspace. This owner can set different access levels for projects and data, and monitor system activity to ensure security and compliance.  

Anthropic also says, as OpenAI does, that it does not train its models on Claude enterprise customer data. This is important for businesses that want to keep their trade secrets out of Claude or ChatGPT’s knowledge base in the future.  

Anthropic has not shared the pricing for Claude Enterprise. White said it costs more than the $30 Team plan, but offers greater value. OpenAI also keeps its enterprise pricing private.  

White says Anthropic has been working in a private beta for months with early adopters such as GitLab, Mid Journey, IG Group, and Menlo Ventures (an investor in Anthropic).  

However, gaining expanded adoption will be key. AI model developers like Anthropic have come under pressure to sell API access at ever-lower prices. Products like Claude enterprise offerings can drive revenue to a similar extent; however, broad adoption is needed to offset the high insurance costs they entail. It’s not clear that any AI model developers are profiting from these business-specific plans just yet. 

Source: Anthropic launches Claude Enterprise plan to compete with OpenAI

Amazon Patent Uses Gaze to Isolate Audio in AR Glasses

Amazon has filed a patent for AR glasses that use foveated beamforming. This technology relies on eye‑tracking data to identify what the user is looking at, then isolates and improves audio from that spot while reducing background noise by matching audio focus to the user’s gaze. The system aims to make it easier to hear in noisy places, such as picking out a single speaker in a crowd or focusing on sound from a specific device.  

How the Gaze Activated Audio System Works 

  • The AR glasses integrate an array of microphones and utilize infrared or visible-spectrum cameras for high-precision eye tracking. The eye-tracking sensors continuously monitor pupil movement and direction to extract real-time gaze coordinates, while the microphones capture spatial audio signals from the environment. Together, these components enable the system to distinguish between relevant sounds and background noise based on the user’s focus.  
  • The system calculates a spatial target area corresponding to the user’s gaze point using the extracted eye movement data. It then applies real-time digital beamforming to steer the microphones’ sensitivity toward the focus area, pinpointing sounds originating from the user’s line of sight.  
  • Acoustic signals from the user’s gaze-aligned focus area are digitally amplified and filtered for clarity, while adaptive noise cancellation algorithms minimize interference from other directions. This process enhances intelligibility and allows the wearer to perceive target sounds more distinctly.  
  • The patent specifies that the system modulates AR application behaviors by correlating gaze information with audio focus, allowing app functions to dynamically adapt to user attention and contextual audio cues.  

Key Features And Applications 

  • Users can utilize this system to concentrate on a specific speaker or sound source, similar to a hearing aid enhanced with AR visual support.  
  • Amazon’s broader AI plan also entails recognizing environmental sounds, such as sirens and household noises, and building custom sound models that understand their context.  
  • The technology integrates gaze direction with detected objects to enable actions such as activating a device by looking at it or focusing on its corresponding sound.  
  • The system can also work with AI assistants, making it easier to take voice commands from specific people even in noisy environments.  

This technology likely relates to Amazon’s ongoing development of Echo Frames and other wearable devices designed to enhance users’ visual and auditory experiences.  

A gauge-tracking technique uses a head-mounted device that sends data to a server. The device captures images of what the user sees and information about where the user is looking. The server runs an image recognition algorithm to identify the viewed items and creates a log of them.  

Technical Field  

This disclosure is about client‑server computer processing techniques. It focuses primarily on a gaze-tracking system.  

Background Information 

Eye tracking systems use cameras to measure where a person is looking by tracking eye movement and position. These systems have been used in human-computer interaction, psychology, and other research fields. Several methods exist for measuring eye movement. One such method is analyzing video images to find eye position. So far, most eye tracking systems have been used for research. They are often intrusive, expensive, or unreliable. A reliable, affordable, and easy-to-use system could have many practical, everyday users.  

Summary 

This disclosure describes different ways to implement a gaze-tracking system. In one example, the method includes receiving images of what the user sees from a head-mounted device and sending them to a server over a network. The server also gets information about where the user is looking. It uses image recognition to find items in images and logs what the user viewed.  

Another example involves capturing real-time images of what the user sees with a forward-facing camera built into the eyeglasses. A separate gaze-tracking camera on the glasses records images of the user’s eye. The system uses these eye images to determine where the user is looking, then identifies which item in the scene the user is focusing on.  

Another version of the system uses eyeglasses frames with sidearms for the users’ ears and lenses that are partly transparent and partly reflective. A forward-facing camera on the frame records images of what the user sees, while another camera records the user’s eye by reflecting it off the lens. A processing system connects to both cameras to match the eye image with the scene image, helping track what or whom the user is looking at.  

Further details and other examples are provided in the drawings, description, and claims. 

Source: Gaze tracking system

Apple Patent Enables Non-Invasive Glucose Tracking on Watch

Apple is working on a non-invasive blood glucose monitoring system for the Apple Watch that could let users check their blood sugar without piercing their skin. The project began over ten years ago and was at the proof-of-concept stage as of 2024.  

Key aspects of Apple’s glucose monitoring technology are detailed below, outlining how the system works and where the project currently stands.  

  • Technology approach: Apple uses silicon photonics and optical absorption spectroscopy. The system sends certain wavelengths of light, possibly in the terahertz range, into the fluid under the skin. Glucose absorbs the light, and the sensor measures the reflected signal.  
  • Secretive Development Cologne. The project is part of Apple’s Exploratory Design Group (XDG), a very secretive team similar to Google X. It was previously known by the codename E5. The work began after Apple bought the startup Rare Light in 2010.  
  • Current progress: the technology is functional, but too large for a smartwatch. At this stage, engineers are working to shrink the prototype from its current iPhone-sized form, which can be strapped to a person’s bicep, to a size suitable for an Apple Watch. Development is ongoing as of 2024.  
  • Target audience: The aim is to give people an early warning if they are pre-diabetic so they can make lifestyle changes and avoid developing type 2 diabetes.  
  • Human trials over the past decade. Apple has tested the system on hundreds of people starting soon after the project began and continued through 2025. These include participants with pre-diabetes and type 2 diabetes.  
  • Potential launch: Although significant progress has been made, the technology is unlikely to be available to consumers before 2027. Some estimates indicate a possible launch that year, depending on final development and regulatory approvals.  

Challenges And Competition 

  • Accuracy: Non-invasive monitoring is affected by factors such as skin tone, hydration, and temperature, which can affect readings.  
  • Competition: Other companies, such as Know Labs, Hagar, and Rockley Photonics, are also developing non-invasive methods.  
  • FDA advises: The FDA has previously warned customers not to use unapproved smartwatches or rings to measure blood glucose levels, noting that current approved systems still require skin penetration.  

Note that the FDA has not yet approved or cleared any smartwatch that provides non-invasive direct blood glucose measurements.  

The Apple Watch Series 13, expected in 2027, is rumored to be the first model with blood sugar monitoring. Apple has reportedly been working on this feature for years; in 2021, after the Series 7 reports surfaced, Apple was developing a blood sugar system set to launch. Nothing came of that, and in 2024, Apple was said to be trialing such an Apple Watch app for health data-collection studies rather than for public release.  

Now, analyst Jeff Pu says that blood sugar monitoring will be the main feature of the Apple Watch Series 13 in 2027. It might be called the Apple Watch with blood monitoring, but he gives no further details.  

According to social media reports, Pu has only provided dates for this feature. It is unclear if his information comes from supply chain sources or if he is making predictions based on his earlier reports about Apple.  

Some of Pu’s earlier reports have been accurate, but he also has a history of making release predictions that turn out to be wrong.  

Apple has steadily added health features to the Apple Watch, including blood oxygen level monitoring. However, this feature is currently disabled on Apple Watch models sold in the US because of a patent dispute. Rarely is Apple the only company attempting to develop a non-invasive blood sugar monitoring system. In January 2025, PreEvnt previewed a clip-on device that works via breath analysis.  

Source: Apple Watch 13 May Gain Blood Sugar Monitoring 

OpenAI Launches Safety Program to Test Next-Gen AI Models

OpenAI is launching a public safety bug bounty program to help address AI abuse and safety risks as technology evolves. We want to keep our systems safe and prevent real harm.  

This new program works alongside OpenAI’s Security Bug Bounty by accepting reports about abuse and safety risks, even if they are not traditional security vulnerabilities. We want to keep working with safety and security researchers to find and fix these issues. OpenAI’s safety and security bug bounty teams will examine all submissions and may remove them from one program and add them to another based on their details.  

Program Overview 

The new safety bug bounty program focuses on the following AI-specific safety scenarios:  

Agentic risks, including MCP.  

  • Third-party prompt injection and data exfiltration: when an attacker’s input reliably controls a victim’s browser or ChatGPT agent to perform harmful actions or leak sensitive data. The attack must be reproducible in over 50% of tests.  
  • An OpenAI agent product carries out a forbidden action on OpenAI’s website on a large scale.  
  • An OpenAI agent product performs an unlisted potentially harmful action. Reports must demonstrate that the risk of significant harm is probable.  
  • All testing for NCP risk must follow the terms of service of any third parties involved.  

Open AI Proprietary Information 

  • Model outputs that reveal proprietary innovation without reasoning.  
  • Vulnerabilities that reveal other OpenAI proprietary information.  

Account And Platform Integrity 

  • Vulnerabilities affecting account or platform integrity, including ways to bypass automation defenses, modify trust signals, or evade account restrictions, suspensions, and bans.  
  • If users can access features, data, or functions they are not authorized to, please report these issues to the Security Bug Bounty program. In contrast, the safety bug bounty program addresses risks of abuse and safety issues that do not always involve unauthorized access. This distinction ensures each program targets its relevant risk area.  

Jailbreaks are not included in this program, but we sometimes run private bug bounty campaigns for specific harm types, such as bio-risk content issues in ChatGPT Agent and GPT-5. Researchers interested in these programs are welcome to apply when they are available.  

Flaws not listed that directly harm users and have clear fixes may be eligible for rewards on a case-by-case basis. Bypasses that only cause rude language or reveal easily found information are not in scope.  

How to Participate  

Join our safety bug bounty program today and help us make AI safer for everyone. Your expertise can directly prevent real-world harm. We invite you researchers, ethical hackers, and the safety and security community to partner with us in building a trustworthy AI ecosystem. Apply now and be part of the solution.  

Source: Introducing the OpenAI Safety Bug Bounty program