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Article 50 of the EU AI Act has entered into force, setting transparency obligations for AI providers and deployers operating across the bloc. Enterprises running generative AI tools now have to comply with Article 50, which requires providers and deployers of certain AI systems to tell people when they’re interacting with a machine, and to mark AI-generated content so it can be flagged as such. Advancements in generative systems have made it harder to tell AI interaction from human conversation. Distinguishing AI-generated images from authentic ones is getting harder too. People are now also being exposed to emotion recognition and biometric categorisation tools without knowing it. The Commission links all of this to manipulation at scale and fraud, with impersonation and consumer deception following close behind on its list of concerns. Article 50 is the EU’s attempt to tackle the issue and ensure the responsible and safe rollout of AI across the bloc. What providers have to build Article 50 requires providers to design systems so that anyone interacting directly with an AI system knows it. The exception: cases obvious to a reasonably well-informed, observant, and circumspect person given the context. Law enforcement systems used to detect, prevent, investigate, or prosecute criminal offences sit outside the rule too, provided safeguards protect third-party rights, unless the public can use the system to report a crime. Providers of systems generating synthetic audio, image, video, or text face a separate duty under Article 50. Marking is the mechanism. The output needs a machine-readable mark, detectable as artificially-generated or manipulated. The Act asks for the aforementioned marking to be effective and interoperable “as far as this is technically feasible,” weighing implementation cost against the state of the art. Assistive editing that leaves deployer-supplied input essentially untouched falls outside the requirement; a routine photo touch-up doesn’t trigger it, but a wholesale AI-generated replacement does. What deployers must tell people Anyone running an emotion recognition or biometric categorisation system must inform the people exposed to it. Personal data gathered through that system still falls under existing data protection law: the GDPR governs the general case, the EU institutions data protection regulation applies where an EU body is running the system, and the Law Enforcement Directive covers policing contexts. Deepfakes get their own disclosure duty. Image, audio, or video content that’s been artificially-generated or manipulated has to carry a disclosure saying so. Artistic, satirical, or fictional work gets a lighter touch: the disclosure only needs to flag the content’s existence, worded so it doesn’t get in the way of enjoying the work. Text published to inform the public on matters of public interest carries its own rule. Deployers must disclose AI generation or manipulation of that text unless a human has reviewed it and someone holds editorial responsibility for the publication. Standard newsroom review clears the bar. Unedited AI output published straight to a public interest story does not. All disclosures need to land no later than the first interaction or exposure, in a manner that’s plain, distinguishable, and accessible under existing accessibility rules. No grace ******* covers informing someone after the fact. The compliance path Brussels favours Three bodies split enforcement. National market surveillance authorities handle most cases. The AI Office takes systems that fall under its own supervision. The European Data Protection Supervisor steps in when an EU institution itself acts as provider or deployer. The guidelines set out how providers and deployers can show they’ve met the marking obligation in Article 50. Signing on to the Code of Practice on Transparency of AI-generated Content is one path. Organisations that skip the Code have to demonstrate compliance through alternative means the Commission considers adequate. What those alternatives look like in practice isn’t spelled out in detail; that judgement falls to the market surveillance authorities doing the enforcing. The other transparency duties don’t have an equivalent code. No code, no shortcut. Telling people they’re talking to an AI is one duty. Disclosing deepfakes and flagging AI-generated public interest text round out the rest, and providers and deployers work out their own adequate measures, with the guidelines serving as a reference point rather than a checklist. Much of the document is definitional. It sets out what counts as a directly interactive AI system, what qualifies as synthetic content, and where the line sits between a deepfake and ordinary edited media. Standard editing sits outside scope by name, alongside assistive functions that leave deployer-supplied input intact. The guidance also works through the value chain question of who counts as a provider and who counts as a deployer, and what happens when both roles sit with the same organisation. Which of the four Article 50 obligations apply, and to whom, comes down to that provider-deployer distinction. Organisations weighing up the Code of Practice on Transparency of AI-generated Content against building their own labelling approach now have somewhere to start. The guidelines give them a Commission-endorsed reference point that goes beyond the bare text of the regulation. See also: OpenAI aligns safety practices with EU AI Act’s GPAI Code Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post EU AI Act Article 50 transparency rules enter force appeared first on AI News. View the full article

GSK has entered into a research collaboration with British biotechnology company Relation Therapeutics worth up to $110 million, expanding the companies’ existing work in AI-assisted drug discovery. Under the agreement, Relation will generate large-scale datasets measuring how human cells respond to genetic changes and drug interventions. The data will be used to train AI models designed to identify potential drug targets, including models within Relation’s MORGAN platform. The agreement places biological data generation alongside AI model development. Relation’s research approach links computational analysis with experiments that generate new information on human cells. The collaboration builds on earlier agreements between GSK and Relation focused on fibrotic diseases and osteoarthritis. Those projects involved observational studies designed to create two functional disease datasets for analysis using Relation’s Lab-in-the-Loop platform. The earlier work combined human genetics, single-cell multi-omics generated from human tissue, functional assays, and machine learning to identify and validate potential disease targets. How Relation generates biological data Relation describes its Lab-in-the-Loop approach as a combination of laboratory experimentation and computational analysis. Its work includes tissue profiling, single-cell and spatial transcriptomics, sequencing, and target validation, while machine learning is used for target identification, prioritisation, validation, and experimental design. The company also conducts perturbation experiments that measure how genetic changes affect cellular characteristics associated with disease. Those results can then be analysed alongside genetic and patient-derived biological data. Public repositories remain an important source of training material for biological foundation models, although combining information produced across different studies can introduce technical challenges. A 2025 review in Experimental & Molecular Medicine noted that repositories including CZ CELLxGENE, the Human Cell Atlas, and NCBI Gene Expression Omnibus give researchers access to large volumes of single-cell data. CZ CELLxGENE alone provides access to more than 100 million standardised cells, according to the review. Sampling methods, sequencing protocols, experimental procedures, and processing pipelines can differ between studies. Single-cell data can also contain technical noise and other artefacts, requiring careful dataset selection, filtering, composition balancing, and quality control during foundation-model training. Dataset overlap presents another issue. The review noted that the same or similar cells can appear across multiple public resources, potentially giving them disproportionate influence during training and creating data-leakage risks when training and test datasets overlap. The review found that assembling a high-quality, non-redundant dataset is as important as model architecture when building robust single-cell foundation models. ******* biological datasets do not guarantee better models Research published in Nature Methods in June this year examined how the size and diversity of pretraining data affected single-cell foundation models using a corpus of 22.2 million cells. Researchers trained 400 models and evaluated them across 6,400 experiments. The study found that current single-cell foundation models tended to reach performance plateaus after training on only a fraction of the available corpus. Unlike large language models, the systems assessed did not display clear data-scaling laws in which continually increasing training data consistently produced better results. The researchers found that model capacity, dataset size, and computational resources need to be balanced rather than simply increased together. The study did not establish that smaller or proprietary datasets are inherently better, but it found that adding more biological training data did not consistently lead to further performance gains. A separate study published in Genome Biology in 2025 assessed two single-cell foundation models, Geneformer and scGPT, across several zero-shot evaluation tasks. The models did not consistently outperform simpler approaches, while the researchers also identified challenges involving batch effects and cautioned against assuming that larger pretrained models automatically produce better biological representations. Pharma companies pursue specialised datasets Relation has already applied its data-generation approach to Osteomics, which it describes as a proprietary functional single-cell bone atlas. The project uses patient-derived samples and combines single-cell and spatial omics with imaging, genomics, proteomics, and clinical phenotype data. According to the company, Osteomics is being used to investigate disease biology, therapeutic targets, biomarkers, and patient subgroups in osteoporosis. Hospitals and research partners in the *** and Australia are involved in the observational study. Research published in Nature Genetics last month also examined the cellular and genetic determinants of skeletal disease using single-cell analysis, genetic data, and functional validation. Several Relation researchers were among the study’s authors. A 2025 Nature Biotechnology analysis of AI-focused biopharma deals identified specialised dataset providers as one of several trends emerging from recent partnerships. Other trends included larger upfront payments, new therapeutic modalities, and greater participation from larger biotechnology companies. The analysis said high-quality, disease-specific datasets are becoming an important input for causal and generative machine-learning models. It cited GSK’s separate agreement with Ochre Bio, worth $37.5 million for data licensing involving human liver single-cell and perfused-organ data. Another example involved AstraZeneca and Pathos AI entering a $200 million agreement with Tempus in 2025. Under the arrangement, Pathos was to develop oncology foundation models using de-identified clinical, genomic, and imaging data covering more than 150,000 patients. Access to sufficient high-quality data remains a constraint in AI drug discovery. A Nature research highlight on federated learning in pharmaceutical research identified limited access to suitable training data as a major bottleneck for AI applications, while noting that companies can also face restrictions on sharing proprietary information. AI-biopharma agreements therefore vary in how companies obtain data and computational capabilities. Some centre on access to AI platforms, while others cover joint development, data licensing, or the creation of new biological datasets. The GSK–Relation agreement includes both data generation and model development. Relation will produce human cellular datasets as part of the collaboration and use them to train AI models for identifying potential drug targets. (Photo by CDC) See also: How AI is shortening drug discovery timelines in China Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Why biological data matters more in AI drug discovery appeared first on AI News. View the full article

OpenAI has outlined how it aligns safety, security, and transparency work with the EU AI Act’s GPAI Code as enforcement approaches. The company has contributed to and endorsed the EU’s General-Purpose AI (GPAI) Code of Practice and the Code of Practice on Transparency of AI-Generated Content. Both emerged from multi-stakeholder processes. The GPAI Code sets a shared bar for transparency, safety, and security across general-purpose models sold or deployed in the EU. OpenAI points to a stack of existing practices as evidence it already operates near that bar: pre-release testing of models, published system cards accompanying major launches, and outside red-teaming through what it calls its Red Teaming Network. The company also maintains a public Model Spec document describing how it shapes model behaviour. Two internal frameworks sit underneath that work. The Preparedness Framework has been in place since 2023 and was updated in 2025; it sets out how OpenAI identifies, evaluates and manages serious risks from advanced systems. A separate Frontier Governance Framework builds on it, explaining how the company’s safety and security practices map onto legal requirements including the GPAI Code specifically. Together, OpenAI says, those two documents govern risk assessment, safeguards, model reporting, security posture, incident response, and how external experts get pulled into the process. OpenAI cites its participation in the Frontier Model Forum alongside collaborations with the US Center for AI Standards and Innovation and the *** AI Security Institute, plus contributions to third-party evaluation standards more broadly. The stated goal is shared safety research and clearer testing benchmarks across the industry, not just within one company’s walls. Provenance gets harder as modalities multiply The Transparency Code commitments centre on a different problem: helping people tell when content was made or altered by AI. OpenAI’s approach rests on two mechanisms that are meant to reinforce each other. Content Credentials, built on the C2PA standard, attach context directly to a file. SynthID watermarking provides a fallback signal for cases where that metadata gets stripped out somewhere along the way. Coverage is expanding from images into audio outputs, and OpenAI says it’s working toward extending provenance measures across further modalities, including text, as the underlying standards and tooling mature. The company is also building signals and guidance aimed at developers who need to meet their own transparency obligations when building on top of its models. None of this solves provenance outright. Metadata gets lost and labels don’t always survive a transfer between platforms. No single signal, whether cryptographic or watermark-based, catches everything on its own. OpenAI’s response is a layered approach paired with continued work across the wider standards community rather than a claim that any one mechanism closes the gap. Cybersecurity as the test case for adaptive governance Capabilities that help defenders spot and patch vulnerabilities are the same capabilities that could help an attacker find them first. OpenAI believes the answer is its Trusted Access for Cyber programme, designed to give vetted defenders access to more advanced cyber capabilities while limiting exposure for misuse. That programme now has a European deployment arm. OpenAI states it launched its EU Cyber Action Plan in early May 2026, working with EU and national cyber agencies, private sector partners, and infrastructure operators to give them access to its more advanced cyber models. The stated aim of the plan is to strengthen cyber resilience across the continent. Whether “most advanced” translates into measurable defensive gains inside these agencies is a claim from OpenAI itself; the source material offers no independent verification of outcomes from the programme. The company positions this work as consistent with the European Commission’s Action Plan on Cybersecurity and Artificial Intelligence, which calls for coordinated handling of AI’s risks alongside its use in strengthening defensive capability, including secure access arrangements for cybersecurity purposes specifically. OpenAI says it will keep adjusting its compliance approach as EU AI Act implementation continues, and that it expects to keep learning from regulators and the wider community involved in shaping the rules. The company argues that rules need enough flexibility to adapt as the technology moves, so that businesses and organisations can keep benefiting from it. The GPAI Code and the Transparency Code are still relatively new instruments, and OpenAI’s compliance documentation is a moving target rather than a finished product. Teams building on OpenAI’s models in regulated European markets should treat the current system cards and Frontier Governance Framework as a starting point for their own due diligence, not a substitute for it. See also: Zuckerberg details Meta’s personal AI superintelligence strategy Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI aligns safety practices with EU AI Act’s GPAI Code appeared first on AI News. View the full article

Mark Zuckerberg has published a WSJ op-ed that argues superintelligence must reach individuals, not just a handful of institutions. The Meta chief published the piece as a statement of company philosophy rather than a product announcement. It contains no release dates, no benchmark figures, no named models. What it does contain is an argument that the central question facing the industry is not whether superintelligence arrives, but who gets to use it once it does. Zuckerberg believes the choice is binary: systems concentrated inside a small number of institutions, or systems distributed as tools that individuals control directly. He calls his preferred version “personal superintelligence” and commits Meta to building toward it on three stated principles: individual empowerment as the source of prosperity, invention as the primary purpose of superintelligence, and balance of power as the foundation of safety. Meta takes aim at rival AI superintelligence labs The op-ed spends more time criticising the tone of the industry than describing Meta’s own technical work. Zuckerberg writes that it is “surprising that the discourse from many of those who are developing artificial intelligence is so filled with doom,” and questions why anyone convinced that AI will “eliminate most jobs and much of humanity’s relevance” would then rush to build it. He goes further, calling the idea that AI danger justifies concentrating power in a few hands “dangerous” in itself, and adds that “hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened hasn’t led to safe or positive outcomes.” None of the labs he’s describing are named in the piece, but I’m sure that readers can fill in that detail for themselves. What the op-ed does establish is a positioning claim: Meta wants to be read as the company arguing for broad distribution against a field it characterises as leaning toward centralisation. To illustrate his point Zuckerberg asks readers to imagine a single person with access to a superintelligent lawyer, gaining “an unfair advantage in court—even if his position was wrong on the merits,” which he says “would lead to a worse society.” However, Zuckerberg then flips the scenario: everyone has a superintelligent lawyer, and “justice would be carried out much more fairly and efficiently than it is today.” It’s a thought experiment, not a deployment. There’s no pilot programme, court system, or legal-services partner referenced anywhere in the source material, and the article should be read as an analogy supporting Zuckerberg’s broader claim that widespread access checks concentrated power rather than a description of anything Meta has built or tested. The same pattern holds for his historical references. He cites “the brothers in a bicycle shop who believed people could fly,” “the bookbinder’s apprentice with no schooling who figured out how to generate electricity,” and “the kid in a garage who thought personal computers could be for everyone” as evidence that progress tends to come from individuals rather than institutions. The op-ed does make a claim with direct relevance to enterprise planning: the balance between AI as an automation tool versus an empowerment tool. Zuckerberg writes that “if the balance leans toward automation, the impact on jobs and the economy may be negative.” Zuckerberg’s counter-position is that wide distribution of superintelligence produces more jobs, not fewer, largely because starting a business becomes possible “without raising large amounts of capital.” He expects the economy to tilt toward “a greater number of people working at small businesses rather than larger companies.” AI risk categories get uneven treatment Zuckerberg does distinguish between types of risk, and the distinction is useful for anyone trying to work out what Meta considers manageable versus what it considers to require outside coordination. On cybersecurity, he argues that “the history of open-source software has shown that giving everyone full access to powerful systems will be the best way to protect safety and security over time.” On biological risks, his position is different: he calls for “more coordination between governments and other institutions on responsibly deploying capable models.” That’s a meaningful split. One category gets an open-access argument grounded in software history; the other gets an admission that government coordination is necessary. The op-ed doesn’t reconcile how a company committed to broad distribution handles the second category in practice, and it doesn’t need to for the purposes of an opinion column. Nothing in the op-ed specifies how “personal superintelligence” would be packaged, priced, or governed inside an organisation. There’s no mention of enterprise controls, data handling, deployment architecture, or model access tiers. Zuckerberg closes by stating that “Meta is committed to building with the principles of individual empowerment, invention and balance of power,” which is a statement of intent rather than a roadmap. The op-ed sets out where Zuckerberg wants the argument to sit, but it doesn’t say what Meta will ship, when, or under what governance terms. See also: Meta, Microsoft, Nvidia, IBM, and others back open-weight AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Zuckerberg details Meta’s personal AI superintelligence strategy appeared first on AI News. View the full article

Cybersecurity wasn’t really something small and mid-sized businesses worried about too much a few years back. That’s changed fast. Once your customer data, your apps, your internal tools all end up online, protecting the server behind them stops being optional. And attacks aren’t getting any simpler either, which is part of the problem with sticking to old-school security methods on their own. If your business runs on a secure Linux VPS, whether that’s hosting a website, a business app, or just a dev environment, pairing that server with some AI-based tooling actually makes a real difference. It’s not about replacing the basics. It’s more that AI catches suspicious behaviour faster, and honestly, faster than any human sitting there watching logs ever could. Why AI Even Fits Here in the First Place Old security setups run on fixed rules mostly. Block this IP after enough failed logins. Flag a file if it matches something already known. That works fine against threats people have already seen before. It falls apart against anything new, which, unfortunately, is most of what’s actually out there now. AI comes at this differently. Instead of checking one login attempt or one file at a time, it’s looking at a pile of signals together, logins, traffic, resource usage, general behavior, trying to figure out if any of it looks off compared to what’s normal for that particular server. That’s really the whole trick behind it, and it’s why AI tends to catch stuff a lot earlier than someone manually scrolling through logs would. The Risks a Linux VPS Actually Faces Linux has a solid reputation for security, no argument there. But nothing’s bulletproof. A business running a VPS typically deals with a handful of recurring problems: Brute-force login attempts that show up more often than most people expect Malware sneaking in through software nobody got around to patching DDoS attacks aimed at simply knocking things offline Permissions that got misconfigured months ago and nobody noticed since Unauthorised access that started small and just sat there quietly until someone finally found it That last one is honestly the scary part. A lot of these problems don’t start as big dramatic breaches. They start as one tiny weakness that sits there for weeks, sometimes months, before anyone even realises it’s a problem. Where AI Actually Helps With Detection Say a server usually only sees admin logins during business hours, from one country. Then one day a login shows up from somewhere else entirely, at 3am, and whoever it is starts pulling sensitive files right away. A normal system checks the password, sees it’s correct, and moves on. That’s it. An AI-based system looks at the whole picture instead, notices this doesn’t match how that account usually behaves, and flags it before anything worse happens. That gap, between “the password was right” and “but this doesn’t look like something this account would normally do,” is basically the whole value of AI in this context. In its context a rule-based system was never built to notice in the first place. Keeping Software Patched Without Losing Your Mind Updates are still one of the simplest ways to keep a server secure. The problem is a real server usually has dozens, sometimes way more, of installed packages running, and going through every single update by hand just isn’t realistic for most people. AI tools help here by pointing out which software is outdated, which patches actually matter most, which vulnerabilities are being actively exploited somewhere else right now, and generally cutting down how much manual digging an admin has to do just to figure out what to fix first. Watching Network Activity That Would Otherwise Get Buried Attackers don’t always go straight for the kill either. A lot of the time they poke around quietly first, exploring a system before doing anything obvious. That’s exactly the kind of activity that’s easy to miss in a giant log file, but AI tools watching traffic, bandwidth, running processes, and file changes continuously tend to notice when something shifts, even subtly, and that gives a business a real shot at catching trouble before it turns into actual damage. Access Control Still Matters More Than Anything Weak logins are still one of the biggest reasons servers get compromised, full stop. None of the basics have changed here. SSH keys instead of passwords, root login turned off, multi-factor authentication wherever it’s supported, giving accounts only the access they actually need, checking who has access every so often instead of never. AI doesn’t replace any of that. What it adds is noticing when a login looks wrong even though the credentials technically check out, which a password check alone was never going to catch. Responding Fast Matters Just as Much as Catching the Problem How quickly a business reacts is often the actual difference between a small annoying incident and a genuinely bad one. AI-based systems can flag something suspicious, ping an administrator right away, block the offending IP, cut off a compromised service, and put together a report on what happened, all pretty much instantly. That doesn’t mean the human part goes away. It just means someone gets to spend their time figuring out why it happened instead of scrambling to contain it first. None of This Replaces Actual Admin Work AI helps a lot, but it’s not a substitute for someone who actually knows what they’re doing. Updates still need installing. Firewalls still need proper configuration. Old software still needs removing. Backups still need to exist, and actually get tested once in a while, not just sit there untouched. Security audits still need to happen periodically. AI works best as backup for an experienced admin, not as a stand-in for one. Where This Is Probably Going AI’s role in security is only going to grow as attacks keep getting more sophisticated. The models behind this keep getting better at spotting unusual behavior, predicting where the next vulnerability is likely to show up, and handling routine security tasks that used to eat up hours of someone’s week. For a business running anything on a Linux VPS, pairing decent AI-based monitoring with the fundamentals still gets you the most resilient setup. Nothing eliminates every risk completely. But staying on top of updates, watching things proactively, and running your server responsibly, through something like BlueVPS or whichever provider fits your setup, is still what all of this is actually built on. Treat AI as a genuinely useful assistant here, not a replacement for someone paying attention. That’s really the whole point. Businesses end up better protected, and they still keep the flexibility and performance that made a Linux VPS worth using in the first place. The post How AI is Changing Linux VPS Security for Businesses appeared first on AI News. View the full article

OpenAI has released a new field report tracking eight scientific computing projects where coding agents cut runtimes. The report documents projects that used Codex on its own in five cases and a combination of Codex and Anthropic’s Claude Code in three others. Worth flagging upfront: this is a vendor publishing a survey of its own product’s application in research settings, built from case studies written by the contributors involved. That doesn’t make the underlying pattern less worth examining. Research software has a documented maintenance problem. Tools built to accompany a single paper, coded by small academic teams without dedicated engineering support, tend to accumulate technical debt that nobody has the budget or mandate to pay down. OpenAI’s report argues agents can address that debt, and the eight projects it cites span genomics, immunology, statistics, and RNA sequencing. What tasks the agents undertook The tasks split roughly into three categories: packaging and build-system cleanup, performance optimisation on existing code, and full language or backend ports. cyvcf2, a Python library for reading genomic variant files, had its legacy build and packaging system replaced with a newer, unified process, according to contributor Brent Pedersen, who noted that going fast with agents is one thing, but going far in science still needs “expert guidance, understanding, taste, and care.” HI.SIM, a DNA-sequencing read simulator, saw two largely autonomous optimisation passes from GPT-5.2 and GPT-5.6 that contributor Andrew Ho says cut runtime by 31 percent across a representative test set without altering output. Ho, who describes himself as neither a genomics specialist nor a C programmer, called the outcome “nothing short of magical” from an end-user perspective, having previously lost time to performance bugs and packaging problems he could recognise but not personally fix. Hifiasm, used for genome assembly from PacBio HiFi reads, got a 25 percent runtime cut on its optimisation target and roughly 15 percent on separate human sequencing data, per contributor Suyash Shringarpure. Shringarpure described the agent setting up its own benchmark scaffolding and proposing candidates independently, though he stressed that supplying profiling results and steering the model away from repeated failure modes remained work only a human could do. MHCflurry, which predicts protein fragments presented to T cells, had its TensorFlow/Keras backend migrated to PyTorch while keeping compatibility with previously released model weights, a change contributors Alex Rubinsteyn, Sergey Feldman, and Timothy O’Donnell frame as the kind of “unglamorous, labour-intensive upkeep” that keeps open-source scientific projects alive rather than left to decay. bayesm-rs, a Rust port of statistical models from R’s bayesm package, matched the original software’s estimates within a pre-set tolerance and ran 2.3–2.7 times faster on a single processor thread, climbing to 4.4–9.5 times faster across eight threads. According to contributors Andrew Bai and Andrew Ho, the agents handled anything with a direct reference to check against quickly and correctly; extensions requiring statistical judgement the original code never pinned down needed direct human validation instead. Rust ports and a GPU redesign push the pattern further Three further projects – rustar-aligner, svb, and kuva – involved Rust builds carried out with coding agents, including a full recreation of STAR, a widely used RNA-sequence alignment tool that had lost active maintenance. Contributor James M. Ferguson says agents change what’s worth attempting: rewriting a 20,000-line aligner by hand isn’t a sensible use of time, but with an agent it becomes weeks of steered work. Verification, he added, is a separate matter entirely. A model can claim a plot looks fine, but checking over 900 of them by eye before release still fell to a person. RustQC consolidated 15 separate RNA-sequencing quality-control tools into a single program that contributor Phil Ewels says cut runtime by 60 times and disk input/output by 25 times, with companion rebuilds FastQC-Rust and Trim Galore running seven and three times faster respectively while preserving the original tools’ behaviour. Ewels also flagged the downside: cheap rebuilds bring their own risk, because tools that diverge in behaviour fragment the community and make results from different labs incomparable over time. “The technology is the easy part,” he said. “Stewardship is the open question.” HelixForge, a GPU-native rebuild of the mutation-simulation tool BAMSurgeon, reportedly cut runtime by around 60 times on a benchmark involving real human data, according to contributors Mamad Ahangari, Varun Goyal, and Hassan Masoudi, who also say it produced mutation frequencies closer to requested targets and resolved several bugs that generated artefacts in the original tool. Verification, not code generation, is the constraint now What comes through across all write-ups is that agents handled well-scoped implementation requests capably but couldn’t judge whether their own output was scientifically sound. Contributors describe agents expressing confidence in work that contained clear errors, which pushed the actual burden onto humans to build acceptance tests: exact output matching, parity checks against an existing tool, or answers established beforehand using simulated data. Projects tended to proceed in stages, with agents producing fast first drafts and the remaining time going into edge cases and small numerical discrepancies that a benchmark alone wouldn’t catch. Lower engineering costs cut both ways. They let a two-person team take on a rebuild that would once have needed a grant-funded engineering hire, and they make it easier for three different labs to produce three incompatible versions of the same tool. Changes to MHCflurry and cyvcf2 went back into their original upstream projects. rustar-aligner moved to new community stewardship because the tool it replaced had already been abandoned. The OpenAI report points toward a specific choice rather than a general endorsement: decide who owns a rebuilt tool, and secure that commitment, before the first line of agent-generated code ships. See also: Guardoc Health processes clinical documentation using Amazon Nova models Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI report links coding agents to faster science software builds appeared first on AI News. View the full article

Google’s AI-generated summaries appeared in 43% of US searches measured by Similarweb, up from 15% a year earlier, according to the market intelligence firm’s 2026 Generative AI Landscape report. AI Overviews display generated summaries within conventional Google results, while AI Mode provides a conversational interface for longer questions and follow-up prompts. Users can move from an AI Overview into AI Mode while retaining the context of their original search. Google said AI Mode uses a technique called query fan-out, which divides a question into related subtopics and runs multiple searches. The system uses information from those searches to produce a response with supporting links. Google expands conversational search Similarweb estimated that visits to Google’s AI Mode web experience increased from 126 million in June 2025 to 279 million in May 2026. Google separately reported in May that AI Mode had surpassed one billion monthly users, although its figure covers users rather than web visits and is not directly comparable with Similarweb’s estimate. The figures also measure different parts of Google Search. The 43% figure tracks the share of US searches displaying AI Overviews, while the AI Mode figures relate to activity within Google’s conversational search service. Similarweb recorded a 5.4% increase in the average length of Google searches following the introduction of AI Mode. The report said users were entering longer, more natural-language searches similar to prompts used with AI assistants. “People are now adopting a new, more natural way to search and discover,” Ethan Smith, chief executive of digital marketing firm Graphite, said in the Similarweb report. Users can provide more detail in a single search instead of relying only on short keyword combinations. Google’s AI interfaces can respond to these longer searches and support additional questions without requiring users to open an external website immediately. Similarweb said its estimates draw on first-party analytics, anonymised device information, external data partnerships, and publicly available web data. The company models those inputs to estimate traffic and user behaviour, meaning its figures are not direct counts supplied by Google or OpenAI. According to the report, generative AI websites received an average of 9.5 billion monthly visits between June 2025 and May 2026, up 70% from the previous year. Monthly unique visitors increased 57% to 655 million, while mobile application downloads rose 58% to 4.4 billion. Those figures cover the broader generative AI market, including standalone services such as ChatGPT, Gemini, Claude, and Perplexity. They provide context for Google’s expansion of AI within a search product that already reaches a wider internet audience. Similarweb also found that most ChatGPT users continued to use Google. Audience overlap among ChatGPT, Gemini, and Claude indicated that people were using several search and AI services rather than moving exclusively to one platform. Publishers have raised concerns about referral traffic when their content is incorporated into an AI-generated answer without producing a visit. Similarweb has previously identified news publishers among the sectors affected by changes in traffic from AI-based search tools. Google has responded by changing how external sources appear within AI Overviews and AI Mode. In May, the company announced direct links within responses, article suggestions, website previews, and more prominent references to original material. Google said the changes were intended to provide more information about linked pages and make relevant websites easier to identify. It did not release traffic data showing how the updates affected click-through rates to publishers or other external sites. Citations do not always deliver traffic Similarweb found that the share of ChatGPT responses containing web citations increased more than fivefold over the past year. The proportion rose from about 1.3% in June 2025 to 6.8% in May 2026, based on US desktop activity. The measurement covers responses displaying source references, rather than the proportion of user prompts that caused ChatGPT to search the web. Citations also do not confirm that a user opened the linked source. Citation rates varied by sector. Similarweb recorded higher rates for travel, retail, and sports responses, where information such as prices, availability, comparisons, and results can change frequently. The firm also found a difference between the pages cited in AI responses and those receiving referral traffic. About 65% of URLs cited by ChatGPT were located two or three folders below the main domain, including articles, product pages, and other detailed content. Folder-depth two alone accounted for 41.7% of cited URLs. By comparison, 58.8% of AI referral traffic landed on homepages. The results indicate that the content used to support an AI answer and the page visited after a click often serve different functions. Detailed pages can supply information for a response, while displayed links can direct users to a company or publisher’s main website. ChatGPT’s May 7 search update gave brand links greater prominence within generated responses. Similarweb recorded a 157.7% week-over-week increase in referral traffic following the update, while homepage referrals increased 354.7%. The firm observed the increases after the interface change but did not establish that the update was solely responsible. The data covers ChatGPT referral activity and does not measure clicks from Google AI Overviews or AI Mode. Before the update, about 26% to 32% of ChatGPT referral visits landed on homepages. The proportion increased to around 60% after May 7, based on desktop activity measured between April 30 and May 20, 2026. The difference between cited sources and referral destinations gives publishers and website operators separate figures to track. Citation data measures which pages appear as sources, while referral data records where users arrive after clicking a link. Cloudflare is also testing a Pay per Crawl service that allows participating website operators to permit, charge, or block individual AI crawlers. The private-beta programme allows publishers to set a fee that an authenticated crawler must pay before accessing content. The system can return an HTTP 402 “Payment Required” response when paid access applies. Cloudflare records completed paid requests, charges the crawler operator, and distributes the proceeds to the website owner. Cloudflare has also made configurable HTTP 402 responses available to paying customers through its AI Crawl Control service. Website operators can use the response to provide licensing terms or contact details to crawlers, while automated payments through Pay per Crawl remain in beta. The measures add another option for publishers deciding how AI services can access their material as generated answers become more common across search and standalone AI platforms. (Photo by Christian Wiediger) See also: Examining Google DeepMind’s AI bioresilience push Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Google AI Overviews become more common in search appeared first on AI News. View the full article

Guardoc Health says it processes over one million clinical documents daily using Amazon Nova models through Bedrock. Bringing AI into clinical documentation comes down to a specific kind of risk calculation. Get it wrong and the errors compound into denied Medicare claims under the Patient-Driven Payment Model, audit fines, litigation exposure, and in the worst cases, a missed condition that changes how a patient gets treated. However, get it right and the payoff shows up in fewer corrections, fewer hospital transfers, and lower compliance costs. Guardoc Health, which builds documentation software for long-term care providers, has published deployment figures it says support that outcome. The scale of the underlying problem Guardoc Health’s pipeline has to handle documents that arrive in nearly every format a clinical setting can produce: multi-page PDFs with handwritten physician annotations layered over printed text, prior authorisation forms where a checkbox state alone determines a coverage decision, medication lists that show up as clean tables in one chart and free text in the next, and patient intake forms mixing typed fields with rubber stamps and handwriting on the same page. Research published in BMJ Quality and Safety puts the number of US outpatients affected by diagnostic error at around 12 million a year, with information-handling failures cited as a contributing factor. At the volume Guardoc processes, a one percent error rate in condition detection alone would generate thousands of incorrect records daily. Each one carries its own patient safety or compliance consequence. Guardoc reports a 46 percent reduction in documentation errors, a 70 percent drop in audit fines, and more than $400,000 in annual ROI for a single facility, without publishing the baseline ******* or methodology behind those calculations. In a quarterly deployment spanning two facilities and 200 patients, the company says its system drove 847 documentation corrections, flagged 86 issues tied to PDPM reimbursement accuracy, and was associated with a 74 percent reduction in hospital transfers per 100 admissions. A separate case study covering seven facilities and 1,618 residents identified 10,612 issues, according to Guardoc. A retrieval pipeline built around cost as much as accuracy Guardoc’s architecture runs condition classification through retrieval augmented generation, pulling evidence from a patient’s own documentation before reasoning across it to produce a final answer. Amazon Textract extracts text and structural metadata from each incoming page first, at what the company treats as the lowest per-page cost point in the pipeline. That output gets chunked along clinical boundaries, so a medication list or a diagnosis section stays intact rather than getting split by arbitrary character count. Each chunk is embedded using Amazon Titan Text Embeddings V2 and stored in Amazon DynamoDB, partitioned by patient so retrieval never crosses patient boundaries. A custom pre-filter narrows the candidate set by document type and recency before a k-nearest neighbour search retrieves the chunks most relevant to a given classification query, returning page references only at this stage to keep data transfer light. Amazon Nova 2 Lite then runs a text-based pass to remove obvious non-matches. Only the pages that survive every prior filter reach Amazon Nova Pro, which receives the raw PDF bytes and reasons over layout, handwriting, signatures, and stamps to produce the classification that downstream systems act on. The design follows a cost-tiering logic throughout: cheap components handle high-volume work like embedding and coarse filtering, and the more computationally intensive multimodal reasoning gets reserved for the final stage where it’s actually required. The hard clinical documentation cases Two document types account for most of what earlier pipeline versions missed, according to Guardoc. The first is physician attestation fields on prior authorisation forms, where a handwritten note can override a printed checkbox. The second is patient-reported symptom sections, where handwriting often carries information that doesn’t appear anywhere else in the record. Medication extraction presents a related problem. Drug names, dosages, routes, and frequencies show up in structured tables, in prose buried inside physician notes, in handwritten additions to printed lists, and in scans that have been faxed through multiple hands. Guardoc’s hybrid pipeline runs Amazon Textract first for clean printed tables, then passes both the original PDF and the Textract output to Amazon Nova Pro to resolve wrapped table columns, handwritten additions, and non-standard formats that OCR alone can’t parse correctly. “With the Nova family, we’re making it easier for healthcare organisations to detect high-risk cases earlier and act before issues become costly,” said Assaf Amiaz, Director of Product at Guardoc Health. “By automating workflows that once required manual oversight, the Nova family helps teams reduce compliance gaps, prevent errors, and focus more of their time on improving patient outcomes.” See also: How AI is shortening drug discovery timelines in China Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Guardoc Health processes clinical documentation using Amazon Nova models appeared first on AI News. View the full article

Armenia is not a big country. It’s not a wealthy country. It’s not a famous country. And yet, it has become not only a consumer of different AI products akin to Clideo subtitles generator for video editing, or giants like OpenAI, but also entered the headlines of global news for several reasons, not the least of which is politics. Another reason is tech advances, precisely, the alleged ‘manufacturing of chips for NVIDIA’. The latter is a big name, which you surely recognize, so let’s discuss the situation in detail. First and foremost, news started circulating that NVIDIA was somehow “making chips in Armenia.” But clarifications are in order. Armenia is not becoming a chip fabrication country in the Taiwan sense. NVIDIA Blackwell chips are not going to be manufactured in Hrazdan or Yerevan; NVIDIA says Blackwell GPUs are manufactured using a custom TSMC 4NP process, which places the core chip-production story firmly inside the existing global semiconductor supply chain, not inside Armenia. Armenia is trying to do something else: become a regional AI compute hub, built around imported high-end NVIDIA infrastructure, U.S.-approved chip transfers, local telecom and energy capacity, and a growing argument that smaller countries need sovereign access to AI computing power. The centerpiece is Firebird, a U.S.-based AI cloud and infrastructure company operating between San Francisco and Yerevan. Armenia’s Ministry of High-Tech Industry says the first phase of the Firebird AI center near Hrazdan is scheduled around 2026 and involves a $500 million investment, more than 6,000 NVIDIA Blackwell GPUs, 18 MW of capacity and up to 110.6 exaflops of FP4 Tensor compute. The government presents the second phase as far larger: approximately $4 billion in total investment and more than 41,000 additional GPUs, a scale that, if delivered as described, would place Armenia unusually high on the global AI-compute map for a country of its size. Real Story Behind Infrastructure The confusion is understandable because the word “chips” is everywhere in this story. U.S. export approval was required. NVIDIA Blackwell GPUs are central to the project. Dell Technologies is involved on the server side. The U.S. and Armenia also signed an AI and Semiconductor Innovation Partnership MOU on August 8, 2025, which the U.S. International Trade Administration describes as covering secure semiconductor supply chains, integrated-circuit and electronics development, AI commercialization and Armenia’s position under the U.S. export-control framework. That sounds close to “chip production,” but it is not the same thing as building fabrication plants. In practical terms, Armenia’s emerging role is closer to AI infrastructure, cloud services, electronics/IC development, engineering talent and semiconductor-adjacent R&D than mass semiconductor manufacturing. Moreover, NVIDIA now frames major AI infrastructure as AI factories, i.e., systems that manufacture intelligence rather than physical goods. Its GB300 NVL72 platform, for example, is a rack-scale system combining 72 Blackwell Ultra GPUs and 36 Arm-based Grace CPUs in a liquid-cooled architecture designed for training and inference at very high density. Dell, one of the infrastructure partners in this broader market, describes its PowerEdge XE9712 with NVIDIA GB300 NVL72 as a rack-scale enterprise AI platform, built for high-output reasoning inference and improved throughput. In other words, Armenia’s project is not about making the chip; it is about hosting the machines that make large-scale AI development possible. Independent regional reporting supports this reading. Eurasianet reported in November 2025 that U.S. regulators approved the transfer of advanced NVIDIA chips for Armenia’s AI hub, while also noting Dell’s PowerEdge server role and the political importance of the approval process. OC Media, citing Bloomberg, reported that the project involves a 100 MW data center using NVIDIA Blackwell chips and Dell AI servers, with the first phase expected in 2026. OC Media also reported that part of the significance lies in export controls: Armenia had previously been affected by U.S. restrictions on exports of high-performance AI chips to certain countries, making approval a strategic as well as commercial milestone. Why Armenia Wants AI Compute So Badly For Armenia, the strategic logic is clear. The country already has a serious technology base relative to its size, especially in software engineering, mathematics, electronic design automation and diaspora-connected entrepreneurship. Synopsys Armenia, for example, describes its local operation as covering R&D and product support for electronic design automation, design for manufacturing and semiconductor IP, with more than 1,000 employees across Yerevan and Gyumri. That does not make Armenia a chip-manufacturing giant, but it does mean the country has long been more connected to the semiconductor value chain than casual observers may assume. The Firebird project attempts to add a missing layer: massive compute. Talent without computers becomes dependent on foreign cloud providers. Research without GPUs remains theoretical. Startups without local access to advanced infrastructure may build around someone else’s platform, pricing and policy limits. That is why the phrase “compute sovereignty” fits the Armenian case better than “chip production.” The argument is not that Armenia can replace existing semiconductor powers. The argument is that access to AI infrastructure is becoming a national development issue, the way broadband, energy grids and transport corridors once were. There is also a public-sector AI layer. Armenia has signed a cooperation agreement with Mistral AI, and Mistral’s own customer page says the partnership with Armenia’s Ministry of High-Tech Industry focuses on AI assistants, public services and government functions, with attention to open and adaptable models that fit Armenia’s language, infrastructure and use cases. That makes the country’s AI push less like a single data-center announcement and more like a broader attempt to connect infrastructure, public services, startups, universities and international partnerships. The Hard Part Comes After the Announcement However, there are obstacles on the way. The factories of AI require a massive amount of energy, capital and are politically charged. A 100 MW data centre would consume as much electricity as a city of around 120,000 people, and could consume significant cooling capacity, but Armenia’s Ministry of High-Tech Industry claims that the Firebird centre is fitted out with a closed loop water-cooling system that only requires a water change every few years. This is the case of Armenia’s own energy situation: U.S. International Trade Administration reports that Armenia has an adequate current electricity generating capacity with rising consumption and is still relying on a combination of nuclear, hydro and thermal generation. There is also a risk of execution. Firebird is young, the capital figures are big, the gap between an AI factory that has been announced and is fully leveraging and an AI compute hub that is fully utilized and commercially viable is huge. The project will require customers, a reliable power supply, cooling, network resiliency, export-control stability, and operational expertise and a local ecosystem that can absorb a part of the compute capacity. The Ministry reports that Team Telecom Armenia is installing next-generation fiber-optic infrastructure with up to 1 Tbps bandwidth, while according to OC Media, the project could include some portion of compute which would be allocated to domestic companies and the remaining compute that would be sold to U.S.-based companies operating in the region. What’s important about those details is that usage of the GPUs will impact local benefit – who will be getting them, at what price, and for what type of work. The most accurate way to describe Armenia’s AI moment, then, is not “NVIDIA is producing chips in Armenia.” It is this: Armenia is trying to turn advanced imported chips into domestic strategic capacity. If Firebird, Dell, NVIDIA-linked infrastructure, U.S. export approvals, local telecom investment and public-sector AI partnerships come together as planned, Armenia could become one of the more unusual AI infrastructure stories of the next few years: a small, landlocked country using computers as a development strategy. The gamble is bold. Armenia is not trying to become the next chip fab. It is trying to become a place where the next generation of AI systems can be trained, hosted and commercialized. The post Armenia’s AI Bet Is Not Chip Manufacturing. It Is Compute Sovereignty appeared first on AI News. View the full article

Insilico Medicine has reduced the time needed to produce some drug development candidates to about one year by combining artificial intelligence with laboratory research in China, according to CEO Alex Zhavoronkov. The Hong Kong-listed company’s fastest programme reached candidate nomination in nine months, while its typical timeline is about 13 months, Zhavoronkov said. He said conventional approaches usually take about four-and-a-half years to reach the same stage. The timeline covers early discovery and candidate selection, rather than the full process of bringing a drug to market. Clinical trials, manufacturing, and regulatory review remain separate stages. AI shortens candidate selection Insilico uses generative AI to identify biological targets, design potential drug molecules, and assess which compounds should advance to laboratory testing. The company said its programmes typically reach preclinical-candidate nomination within 12 to 18 months after researchers synthesise and test between 60 and 200 molecules. Its workflow combines AI-generated designs with researcher review and experimental validation. Laboratory experiments remain necessary to confirm the biological activity and drug properties of compounds selected by the models. Insilico said its AI-supported process allows teams to reach candidate nomination after testing a smaller set of synthesised molecules, although it has not provided a direct comparison with equivalent programmes developed without AI. Insilico said it has generated 31 preclinical candidates since 2021. Thirteen programmes have received investigational new drug clearances, allowing them to advance towards human studies, according to the company’s pipeline disclosures. The company conducts AI research in Montreal and Abu Dhabi, while much of its experimental validation and laboratory scale-up work takes place in China. Its Shanghai facility has automated parts of biological sampling and compound screening. Teams outside China develop and evaluate the company’s AI models, while researchers in Shanghai handle biological testing, screening, and scale-up. Zhavoronkov attributed part of the shorter development cycle to China’s research infrastructure, operating costs, and regulatory environment. He said pharmaceutical companies with research laboratories in China can remove about two years from traditional candidate-development timelines. China has expanded beyond manufacturing generic drug ingredients and now plays a larger role in developing new medicines. International drugmakers also work with ******** laboratories, contract research organisations, clinical-trial centres, and biotechnology companies. A Pfizer executive said clinical development in China could be conducted three times faster and at about half the cost of equivalent work in Europe. Drug candidates typically take five to seven years to reach the ******** market, compared with at least eight to 10 years in Western markets, according to Reuters. China introduced a 30-working-day review pathway in 2025 for eligible Class I innovative-drug clinical-trial applications. Applications requiring expert consultation or involving complex technical issues can be moved to a 60-working-day review *******. “We now compete with ******** pharmaceutical companies on timelines, and with traditional biotechnology companies in the West on novelty,” Zhavoronkov said. Insilico has entered research and development agreements with pharmaceutical companies including Eli Lilly and Japan’s Takeda. The company and Taiwan-based Bora Pharmaceuticals also announced a proposed strategic alliance that could exceed $2.5 billion if definitive agreements are signed and the collaboration is fully implemented. Although Insilico operates research facilities in China, Zhavoronkov said more than 90% of its revenue comes from Western pharmaceutical companies. He did not disclose how much revenue the company generates in China. Western licensing agreements are more lucrative for Insilico because China’s national insurance system offers lower reimbursement rates for highly novel drugs, Zhavoronkov said. The company also limits sales of most of its software within China because of geopolitical concerns, Zhavoronkov said. It plans to expand its research operations in Shanghai. Rentosertib moves towards Phase III trials Insilico announced and registered a Phase III trial of Rentosertib in July 2026. The oral drug is being studied for idiopathic pulmonary fibrosis, a disease that causes progressive scarring of the lungs. The company used AI to identify the drug’s biological target and generate and optimise its molecular structure. The Phase III study is designed to enrol 320 participants across 47 centres in China. It will compare Rentosertib with a placebo over 52 weeks, with the primary endpoint measuring the annual rate of decline in forced vital capacity, a standard measure of lung function. The trial was listed as not yet recruiting when its ClinicalTrials.gov record was updated on July 7. Enrolment was expected to begin in August 2026, with primary completion estimated for October 2029. Rentosertib previously completed a smaller Phase IIa study. The Phase III trial will test the treatment in a larger patient group over a longer *******. Candidate nomination remains an early development milestone. Drugs must still complete preclinical testing, human trials, manufacturing validation, and regulatory review before they can be approved for *****. Industry data have not established whether AI-designed drugs are more likely to succeed in later-stage trials. A 2024 analysis of AI-native biotechnology pipelines reported Phase I success rates of between 80% and 90%. The same study found a Phase II success rate of about 40%, broadly in line with the historical industry comparison used by the researchers. The researchers said the number of Phase II programmes was too small to determine whether AI improves later-stage clinical success. The analysis was based on publicly reported pipelines and did not compare otherwise identical AI-supported and conventional drug programmes. Insilico said it has produced 31 preclinical candidates and secured 13 investigational new drug clearances. Rentosertib is its first programme to reach the Phase III stage, while none of the company’s experimental medicines has received commercial approval. Automation changes biotech roles AI and laboratory robotics are also changing staffing requirements within Insilico. Zhavoronkov estimated that the company could automate or displace about 40% of its software-side workforce. He did not describe the figure as an announced staff reduction or apply it to the biotechnology industry as a whole. Insilico employs about 400 people. Laboratory scientists and software engineers are being retrained to manage AI evaluation systems, automated equipment, and robotics, Zhavoronkov said. The retraining is focused on AI benchmarks and robotic systems as the company automates more research and software functions, he said. (Photo by Julia Koblitz) See also: Bristol Myers Squibb buys Nvidia AI system for drug discovery Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How AI is shortening drug discovery timelines in China appeared first on AI News. View the full article

America’s AI Investment ***** Is Reshaping the Economy Artificial intelligence has become one of the defining investment stories in the United States, and the numbers continue to grow. Microsoft, Meta, Amazon and Alphabet are collectively committing hundreds of billions of dollars to AI infrastructure, while demand for advanced chips has turned NVIDIA into one of the world’s most valuable companies. It’s apparent that what started as a race to build smarter AI models is now a driving force behind investment in construction, manufacturing, energy and digital infrastructure, with effects that are spreading throughout the wider economy. AI Investment Fueling America’s Biggest Infrastructure Push in Years The headlines often focus on new AI models, but rarely focus on the resources required to make those models workable. Every chatbot, image generator and AI assistant relies on vast data centres packed with specialised processors, connected by high-speed fibre networks and powered by enormous amounts of electricity. Building that capacity requires billions of dollars in construction, engineering and equipment, and it creates a level of demand that reaches far beyond the technology sector. These investments are also reshaping expectations for the US economy, with productivity gains and capital spending influencing currency markets before they appear in official data. For investors, forex trading can provide an early view of how global markets are responding to America’s expanding AI economy. Productivity Is Influencing the Next Phase of Growth An important factor to consider is that the long-term value of AI isn’t going to be measured by how many new tools reach the market, but rather by whether businesses become more productive with those tools. That’s a process that’s already underway. For example, manufacturers are using AI to identify faults before products leave the factory, healthcare providers are reducing administrative workloads, and financial institutions are analysing market data in seconds instead of hours. In fact, Goldman Sachs estimates that generative AI could increase global GDP by around 7% over the next decade if adoption continues to accelerate, highlighting why businesses view AI as an investment in future growth rather than simply another software upgrade. Realising those gains, however, will require careful implementation. Businesses must invest in training, establish AI governance, and address data security and regulatory compliance to ensure new tools deliver lasting value rather than short-term efficiency gains. AI adoption is also changing how employees spend their time. Rather than replacing entire roles, many organisations are using AI as supplementary tools that automate repetitive tasks such as drafting reports, analysing large datasets, or handling routine customer enquiries. With repetitive tasks taken off their plates, employees can focus on higher-value work, while businesses can improve efficiency without fundamentally changing how they operate overnight. The Ripple Effect Extends Well Beyond Silicon Valley The companies building AI models are only one part of a much larger ecosystem. This can be seen in how utilities are expanding electricity generation to support new data centres, in how semiconductor manufacturers are increasing domestic production, and in how construction firms are winning contracts to build facilities capable of housing next-generation computing infrastructure. States like Texas, Arizona, and Virginia have become major beneficiaries for companies that want locations with reliable power, skilled workers, and room to expand. Together, these investments support local economies and strengthen AI-powered industries. Financial Markets Are Responding Long Before the Economy Fully Adjusts Markets rarely wait for quarterly GDP figures before reassessing growth prospects, inflation expectations, and interest-rate outlooks. Instead, investors respond to new information as it becomes available, particularly when major AI investment announcements point to stronger productivity or sustained business spending. This also helps explain why movements in the US dollar can sometimes reflect optimism about economic prospects long before those trends are apparent in official statistics (currency markets often react to expectations before they’re confirmed). Investors also watch whether AI investment translates into stronger earnings and sustained capital spending, both of which can influence interest-rate expectations and global capital flows. Understanding the broader impact of AI means looking beyond technology headlines and instead at the elements that influence the market: corporate earnings, employment reports, inflation data, and central bank decisions. Platforms like OANDA support this approach by combining access to the foreign exchange market with real-time market analysis, economic calendars, and research tools that help traders interpret macroeconomic developments. America’s AI Economy Is Still in Its Early Stages Unlike a couple of years ago, AI investment is no longer confined to technology companies or venture capital funding rounds; it’s reshaping supply chains, accelerating infrastructure projects, creating demand for skilled workers, and influencing how investors evaluate the outlook for the U.S. economy. Even though those changes will take years to play out, many of them are already visible today. The next chapter of the AI story will likely be measured not by faster models, but by how effectively businesses convert record levels of investment into increased workplace productivity, and how financial markets respond. The post America’s AI Investment ***** Is Reshaping the Economy appeared first on AI News. View the full article

Two dozen companies and organisations signed an open letter urging US policymakers to protect open-weight AI models. The letter, published today (PDF), carries signatures from a list that spans direct commercial rivals and organisations with little obvious overlap in business model: Meta, Microsoft, Nvidia, IBM, Dell Technologies, CrowdStrike, Palantir, ServiceNow, Hugging Face, Perplexity, Mistral, Andreessen Horowitz, Y Combinator, the Linux Foundation, Mozilla and others. The letter’s argument centres on a comparison between the open-source software movement of the 1980s and the current fight over whether AI model weights should circulate freely or stay locked behind commercial APIs. Open-weight models are AI systems where the trained parameters get published for anyone to download, inspect, modify and run on their own hardware. That’s distinct from closed models like the frontier products offered by OpenAI or Anthropic through API access only, where the underlying weights never leave the vendor’s infrastructure. The signatories frame open weights as the mechanism by which AI capability spreads beyond a handful of well-capitalised labs into what the letter calls the workflows of “factories, hospitals, farms, classrooms, and main street businesses.” Their argument runs on three tracks: Open weights lower the cost of entry for startups and public institutions that can’t afford to train frontier models from scratch or pay per-token fees at frontier prices for routine tasks. They increase competition across the stack, from chips to cloud infrastructure to applications, which the letter says keeps costs down and prevents value capture by a small number of providers. Open weights also give enterprise customers a way to avoid vendor lock-in, since organisations running open-weight models control their own data and can adapt the model to internal requirements without depending on a single vendor’s roadmap or pricing decisions. The security argument runs against instinct The letter’s most pointed section addresses the risk case directly, and it’s worth reading closely because it inverts the usual framing around open models and security. Once weights are released, the letter concedes, they’re beyond the original developer’s control. Modified versions become difficult to trace or reverse. A fine-tuned or stripped-down version of an open model can circulate with safety guardrails removed, and there’s no recall mechanism. The signatories argue the answer isn’t prohibition. Their case rests on a comparison to cybersecurity: defenders facing AI-equipped attackers need access to models with comparable capability to detect and simulate threats, which closed, permission-gated systems don’t easily provide. They extend this into a broader security claim, arguing that closed models aren’t inherently safer because they can be breached, misused, or fail in ways external researchers can’t observe or verify. Concentrating advanced capability behind a small number of closed providers, in this reading, creates single points of failure rather than removing them. Open models, by contrast, let outside researchers examine behaviour, run red-team exercises, and identify vulnerabilities across many teams rather than relying on one vendor’s internal testing. The letter draws a direct parallel to the “open-source is more secure than obscurity” argument that shaped decades of software security debate, though it doesn’t cite specific vulnerability-discovery data or incident figures to support the claim as applied to AI systems specifically. Distillation gets a specific defence The letter carves out space for one technique that’s become contentious in AI circles: distillation, where one model’s outputs get used to train or improve a second model. This is standard practice in machine learning research and product development, used for evaluation, validation, and capability transfer between models of different sizes. The signatories draw a line between distillation as a legitimate technique and what they call “unlawful efforts to extract value from closed models,” arguing the former shouldn’t get swept up in restrictions aimed at the latter. This reads as a direct response to disputes that flared after the rise of ******** models like DeepSeek and Kimi, when several US labs suggested rival models had been trained by distilling outputs from their own closed systems without authorisation. The letter’s position: address misappropriation through targeted legal and commercial mechanisms, not blanket restrictions on a technique the entire field depends on. What this signals for the policy fight ahead The letter arrives without a specific legislative or regulatory proposal attached. It’s a positioning document ahead of anticipated action on AI policy in Washington, calling on lawmakers to expand compute access for startups and researchers, fund shared training datasets and evaluation frameworks, and avoid what it calls “premature restrictions” on open models. This should be treated less as a settled policy outcome and more as an indicator of where major infrastructure and chip providers want the regulatory conversation to land. Players like Nvidia, IBM, and Dell have direct commercial reasons to want open-weight ecosystems to flourish since a wider range of deployable models sells more compute and services regardless of which lab produced the weights. Procurement teams weighing open-weight versus closed-model deployments should factor in that the policy environment favouring one approach over the other remains unresolved, and any restrictions on distillation or open releases could shift the economics of self-hosted AI within a single legislative cycle. See also: OpenAI pushes ChatGPT into patient health records Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. 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OpenAI is deploying a Health feature inside ChatGPT, giving users the option to connect Apple Health data and medical records to the chatbot. Logged-in users aged 18 and older can access it now on web and iOS, across the Free, Go, Plus, and Pro tiers. Users link Apple Health and, where supported, records from US hospital systems, One Medical, or Function Health. Once synced, ChatGPT can pull medications, lab results, recent visits, sleep data, and activity logs into any conversation in the app, rather than confining that context to a separate section. OpenAI ran an earlier version of this idea with a smaller test group, one that required users to open a dedicated health area to get responses grounded in their own data. The company found that more than 70 percent of health-related conversations among that group happened somewhere else entirely, in the middle of meal planning or an unrelated symptom query, not inside the dedicated space built for that purpose. That data led to this redesign. Rather than forcing users into a specific mode to get contextual answers, ChatGPT now draws on connected Health information across any conversation, provided the user has granted permission. A person planning a dinner out might get a restaurant suggestion that accounts for a logged dietary restriction. Someone asking about weekend plans might get activity suggestions adjusted for a recent injury noted in their synced records. The Health tab in the sidebar still exists, but its role has shifted to being a management hub: connecting accounts, reviewing synced data and trends, browsing suggested prompts, and returning to past health-related chats. What early testers of the Health feature in ChatGPT report OpenAI published numerous accounts from its early access group, and the details are worth weighing against the company’s own framing of the tool as support rather than diagnosis. Blake, a technical program manager, said: “The most useful part has been turning scattered medical history into something I can actually understand and explain. I have multiple overlapping issues and ChatGPT helped connect those pieces into a clear timeline, explain the medical terms in plain English, and create summaries I could share with a physical therapist or trainer.” On the shift from disconnected records to a usable pattern, Blake added: “Instead of just seeing disconnected diagnoses, imaging results, and surgery notes, I could understand the ******* pattern. It made the information more usable and gave me better language to advocate for myself with providers and trainers.” Reweti, a portfolio manager, pointed to longitudinal analysis as the differentiator over a standard search or a one-off doctor visit: “What I want is to infer patterns that aren’t obvious and make connections I wouldn’t have made on my own. “ChatGPT can access my existing labs because I’ve connected everything, and it can look over time—that’s the big advantage. It’s like having a research analyst. It allows me to be more proactive and own more of my health journey.” However, not every account was frictionless, and one is worth flagging given the stakes involved in surfacing clinical data through a chatbot interface. Shannon, a nurse, described finding an unexpected entry in her own chart through the tool. “Using Health has actually reinforced something I’ve believed for a while: one of AI’s greatest strengths isn’t replacing healthcare professionals, but helping patients better understand and navigate their own health information,” explained Shannon. “Discovering an unexpected chart entry through Health really highlighted that for me. It wasn’t AI creating a problem. It helped me identify something I can now appropriately follow up on with my healthcare providers.” That distinction, between a tool surfacing something for human follow-up versus a tool making a clinical call, is the line OpenAI needs the product to hold as usage scales. Other testers focused less on clinical nuance and more on the practical grind of manual data wrangling the feature is meant to replace. Daniel, a consultant, connected multiple sources and found the combined view more useful than isolated chat sessions. “It’s been great for coordinating labs and translating them into language I understand. Connecting Apple Health and MyChart makes the insights more grounded in what is happening across my life outside of just chat interactions,” said Daniel. Kathleen, a small business owner who’d lost a decade-long fitness habit to work pressure, described a lower-stakes but still concrete use case, with the model adjusting suggestions based on activity gaps it could see directly. “I’ll say, ‘I need something to help me move today,’ and Health can see I haven’t worked out in the last seven days and suggest starting slowly—with a walk or some stretching. It’s helping me make things manageable and get back into it in a reasonable way,” explained Kathleen. Carlton, an operations manager, had previously resorted to exporting spreadsheets from Apple Health and uploading them manually before every chat, a workaround the new integration is designed to eliminate. “Prior to Health, I was exporting massive spreadsheets from Apple Health and importing them into ChatGPT. Now, it feels a lot more streamlined. Being able to see years and years of my fitness journey in Health helped me see things in a different light—a ******* picture,” said Carlton. OpenAI puts weekly health-related ChatGPT queries at north of 300 million people, covering everything from decoding a lab result to prepping for a doctor’s visit. That figure, if accurate, puts ChatGPT in a position most digital health platforms would need years and considerable marketing spend to reach. The company is explicitly positioning Health as a support tool rather than a diagnostic one, and telling users to confirm anything important with their actual healthcare provider. OpenAI’s AI model performance claims and how they were tested OpenAI attributes the feature’s viability partly to newer models: GPT‑5.5 Instant, available to Free users, and GPT‑5.6 Sol, reserved for paid tiers. The company says GPT‑5.5 Instant made gains in recognising when urgent care might be needed and in explaining uncertainty, and that on its toughest health evaluations it performed comparably to OpenAI’s frontier Thinking models at the time. GPT‑5.6 Sol is described as the company’s strongest health model so far, built for reasoning across multiple data points such as lab trends over time. To validate these claims, OpenAI says it worked with hundreds of physicians to build health scenarios and rubrics scoring responses on accuracy, safety, communication, context awareness, completeness, and appropriate escalation to professional care. The company reports that every GPT‑5.6 model outperformed GPT‑5.5 on HealthBench Professional, an internal evaluation built for this purpose. A chart included in OpenAI’s announcement shows GPT‑5.6 Sol scoring higher than GPT‑5.5 Instant and GPT‑4o across categories including accuracy, communication, completeness, and following instructions, with physician-written responses used as a comparison baseline. OpenAI does say physicians tested the live Health product before release specifically to assess real-world performance and safety with connected data, which is a step beyond benchmark scoring alone, though the company hasn’t published the methodology or results of that testing in detail. Health data handling and the permission architecture Connected medical records and Apple Health data – along with any conversations that draw on them – are excluded from foundation model training and ad targeting, according to the company, regardless of a user’s broader ChatGPT training settings. Conversations that don’t touch Health data still follow whatever training preference a user has set separately. Access is permission-gated by default. ChatGPT asks before using connected health data to personalise a response, though users can switch to “always allow” and turn off the prompts entirely. That setting lives in Settings > Plugins > Health and can be reversed at any time. Disconnecting a data source triggers deletion from OpenAI’s systems within 30 days, though anything already surfaced in existing chat history sticks around until the user deletes those conversations manually. Memory creation is scoped narrowly, too. Memories can be created from health conversations but not directly from the raw connected records or Apple Health data itself. Users wanting to avoid memory creation altogether can use Temporary Chat or disable memory in settings. OpenAI also flags a specific edge case: actions that could expose Health data through other connected plugins, such as sending a training plan built from Apple Health metrics to a running partner. The company says additional checks apply before such actions execute, and that for sensitive cases ChatGPT may ask for explicit confirmation. OpenAI states it runs red teaming exercises targeting these scenarios, though no findings or failure rates from that testing have been made public. What happens to accuracy at the edges The practical friction point sits with data quality. A medication can stay listed in a patient’s synced record long after they’ve stopped taking it, and OpenAI uses that exact scenario to illustrate why synced data isn’t automatically current. Its guidance to users is blunt: flag changes to ChatGPT directly and check anything important against the original source rather than trusting the sync to stay accurate on its own. Wearable and fitness app data carries its own gaps, since availability depends on what each third-party app chooses to share through Apple Health, and OpenAI notes some proprietary scores from fitness apps may not transfer at all. The relevant question isn’t whether ChatGPT can summarise a lab result correctly in a demonstration, it’s whether the permission model, data deletion timelines, and escalation logic hold up when a user’s synced records are three months stale and the model is asked to reason across contradictory inputs. OpenAI’s physician testing addresses part of that concern; it doesn’t close the distance between a controlled evaluation and a user managing multiple chronic conditions with incomplete data syncing from four different apps. For those wishing to give the feature a try, it’s live now for eligible US users through the sidebar Health menu. See also: OpenAI Presence sells enterprise AI agents with engineers attached Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI pushes ChatGPT into patient health records appeared first on AI News. View the full article

The newest way to buy enterprise AI agents from OpenAI does not involve buying anything online. OpenAI Presence, announced on July 22, is a managed product delivered through a limited general availability programme, and the company states plainly that it is not yet available as a self-serve product. Deployments are led by OpenAI’s own Forward Deployed Engineers and a set of selected global systems integrators. That is a departure for a business that has largely run on API keys and seat licences. Presence is sold as a project rather than a product. Each engagement starts with a single job, such as resolving a billing dispute, handling an insurance claim, or clearing an employee IT service request. The agent is given only the knowledge and system access that the job requires, and the customer writes the rules governing what it can do, when it needs sign-off, and when a person takes over. After launch, Codex reads production sessions and escalations, then proposes changes the customer’s team tests and approves before rollout. OpenAI’s documentation is unusually candid about the labour involved. Its help centre sets out a six-stage process running from scoping business outcomes, through security, privacy and legal review, simulation and acceptance testing, staged rollout, and post-launch iteration. A Presence agent, it says, does not become production-ready simply by ingesting documents. The problem this is built to solve is real The managed model is easy to read cynically, and harder to dismiss on the evidence. Gartner has warned that more than 40% of agentic AI projects will be cancelled by the end of 2027, attributing the failures to governance, undefined business value and weak operational discipline rather than to model capability. Almost everything Presence bundles is aimed squarely at that diagnosis. Simulations and graders test whether an agent reached the right outcome, followed policy, used its tools correctly and escalated when it should, before anyone outside the company speaks to it. Guardrails intervene when an interaction moves past defined boundaries. Session records and action histories give reviewers something to audit. Escalation paths hand a person structured context rather than a cold transcript, and new versions go out through controlled rollout with rollback. Enterprises have spent two years discovering that the hard part of a production agent sits in integration, permissions and change management. A vendor that sends engineers to do that work is responding to what buyers have actually been failing at, rather than shipping another dashboard and calling the gap a customer problem. Where the constraint sits The trade-off shows up in the eligibility criteria. Access, OpenAI says, depends on workflow fit, implementation readiness and available delivery capacity. Delivery capacity is a consulting constraint. Software scales; engineers cleared into a bank’s core systems do not. Forward Deployed Engineer is a title borrowed from Palantir, where it describes staff embedded in customer operations for months at a time, and the economics attached to it look nothing like the economics of metered inference. By putting its own FDEs and named partners at the front of every deployment, OpenAI has stepped into the layer of the market occupied by the integrators it will also rely on to scale, which is a workable arrangement while volumes are small and a more complicated one later. It also puts a question on the table for anyone scoping a contract. When the model vendor is also the implementation partner, the lines of accountability for a policy misapplied in production need to be written down rather than assumed. The enterprise AI agents on display are still early OpenAI describes Presence as battle-tested, and its case for that language is that the product was assembled from years of deploying agents with enterprise customers before it was packaged and named. The claim is about accumulated practice rather than about the product’s time in market, and it is a reasonable one to make. The strongest single proof point is OpenAI’s own English-language phone support line, 1-888-GPT-0090. The company says the agent met or exceeded its internal benchmarks for frontline human support within weeks, now resolves 75% of inbound issues without human assistance, and cut human handoffs by 15 percentage points in ten days through the Codex improvement loop. Those are OpenAI’s figures, measured against OpenAI’s own grading criteria, on OpenAI’s own channel. The transparency is welcome, but the numbers are not independently verified. The three named customers sit earlier in the cycle than the launch framing implies. BBVA is exploring voice support for everyday banking in Mexico. SoftBank is testing Japanese-language conversations. IAG is exploring support during high-demand events such as severe weather. Daniel Ordaz, head of AI transformation at BBVA Mexico, describes the bank as a design partner helping shape and refine voice experiences for financial customer service. Design partners are normal and useful at limited GA. None of the three, though, is presented as running Presence at scale, which is worth holding alongside the word proven. What has not been disclosed Pricing is not published. Implementation scope and cost are set per customer and per deployment, which is ordinary for enterprise services and still leaves buyers without a public reference point for cost per resolved contact against an incumbent contact-centre vendor. The model is not named. Presence uses OpenAI models, the documentation says, with configuration selected for the workflow and subject to change as that workflow evolves. That flexibility is defensible engineering, because pinning a production agent to a frozen model version ages badly. Teams that have spent the past year building evaluation suites against specific versions will nonetheless want the contract to say what they are being held to when the configuration moves. Channel support during limited GA covers voice or chat, with contact-centre integration, routing, authentication and handoff design confirmed deployment by deployment. Data handling follows the same pattern, with the signed architecture and contract treated as the governing record rather than any published policy. Presence sits apart from ChatGPT Workspace Agents, which remain the self-serve path for teams building inside ChatGPT and Slack, while voice customers keep API access to OpenAI’s frontier models. The company now offers broadly the same capability three ways, separated less by what the technology can do than by who does the work. That leaves buyers choosing on delivery capacity as much as on model capability, and on OpenAI’s own account, delivery capacity is the part being rationed. See also: HP accelerates enterprise workflows with OpenAI Frontier Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI Presence sells enterprise AI agents with engineers attached appeared first on AI News. View the full article

Nvidia’s new Medical Physics Simulation framework treats healthcare robots as physical AI systems that need embodied experience to learn, not just code. Physical AI is the term Nvidia and much of the robotics industry now use to describe machines that have to learn how the world behaves through contact, force, and consequence, rather than through text or images alone. A language model learns from text. A physical AI system learns from what happens when a catheter meets a vessel wall, or when a robotic arm applies too much pressure to soft tissue. That kind of learning normally requires either a physical body operating in the physical world, or a simulation detailed enough to stand in for one. For healthcare robotics, physical bodies operating in real procedures are scarce, tightly regulated, and slow to generate the range of scenarios a robot actually needs to see. Medical Physics Simulation is Nvidia’s attempt to manufacture that embodied experience computationally. Announced as an open-source addition to the company’s Isaac for Healthcare platform, the framework generates the physical interactions a surgical or diagnostic robot would otherwise need years of clinical exposure to encounter: a guidewire catching on a calcified vessel wall, a kidney stone lodged at an unusual angle, or the soft-tissue response that only shows up in a small fraction of procedures. None of these edge cases arrive on schedule in an operating theatre. Simulation lets developers generate them on demand. Building physical intuition before a scalpel gets involved The framework combines two ways of modelling how devices behave inside a body. Classical physics simulation handles the mechanical rules that are already well understood, how a catheter bends, how much resistance a vessel wall applies, how contact forces shift as an instrument moves through tissue. Generative AI handles the part that’s harder to hand-code: visual scene dynamics learned from procedural data, delivered through a component Nvidia calls Cosmos-H Dreams. That combination is the physical AI proposition in miniature. Classical simulation gives a robot policy the physics it needs to obey. Generative simulation gives it the range of visual and anatomical variation it needs to generalise. Put together, and run at scale on GPUs using Nvidia’s Warp and Newton libraries, the framework can execute large numbers of parallel training environments instead of one scene at a time. Nvidia states that a benchmark running 8,192 parallel environments cut training time from over five hours to under two minutes. However, that demonstrates throughput – not clinical reliability – and it says nothing about how a policy trained this way performs against incomplete imaging, delayed sensor readings, and even anatomy that falls outside anything the simulation modelled. A language model that underperforms on an edge case produces a bad answer, but a physical AI system that underperforms on an edge case is operating inside a patient. The parallel-simulation approach is a real advance in how fast developers can explore failure modes. Whether those simulated failure modes match what actually goes wrong in a surgical suite is a separate question. Where the embodiment approach is being tested The organisations Nvidia names as early adopters are applying the physical AI approach at different depths, and the list is worth reading with that in mind rather than treating it as a uniform roster of deployments. CMR Surgical and Cambridge Consultants, the Capgemini-owned engineering firm, have gone furthest on the data side. CMR has contributed close to 500 hours of anonymised clinical data from its Versius Surgical Robotic System to the Open-H Embodiment dataset, spanning cholecystectomy, prostatectomy, hernia repair and hysterectomy procedures, and the pair are using Cosmos-H Dreams to model soft-tissue interaction physics and produce patient-specific simulations. “Open-source models allow us to build on shared knowledge, accelerating responsible innovation and, ultimately, gives us the potential to deliver more consistent care and better outcomes for patients worldwide,” said Chris Fryer, CTO at CMR Surgical. Johnson & Johnson MedTech is using the framework alongside a Cosmos-based foundation model to build a digital twin of its endoluminal MONARCH platform, focused on kidney-stone scenarios in urology. XCath is applying it to endovascular autonomy policy training, teaching a system the physical behaviour of navigating blood vessels without a human hand on the controls. Inner Logic is generating synthetic data to validate device mechanics, and says it intends to produce in silico evidence to support regulatory submissions, though no submission built on that evidence has been confirmed publicly. Medtronic Structural Heart sits earliest in the group, exploring simulated X-ray sensing for catheter navigation research. Each of these is a training exercise or dataset contribution. None is a deployed system operating on a patient with policies learned this way, and Nvidia doesn’t claim otherwise. The open-source case for physical AI systems Healthcare robotics carries a governance requirement most physical AI applications, including industrial and warehouse robots, don’t face to the same degree: regulators and clinical review boards need to see how a system arrived at its behaviour, not just confirm that the behaviour looked acceptable in testing. An open-source framework lets developers inspect the physics assumptions inside the simulation, reproduce results across different anatomies, and build an evidence trail suited to a submission before the FDA or an equivalent body. That’s a stronger argument for openness in physical AI than it is in most software categories, where a closed vendor pipeline hides the assumptions a team would otherwise need to defend to a regulator. It doesn’t settle the validation question on its own. Open code lets outside reviewers check the model’s logic, but it doesn’t confirm the model’s physical behaviour matches what happens in a body, and that confirmation still has to come from testing that none of these companies has published yet. Nvidia has built infrastructure that could shorten the pre-hardware phase of physical AI development for surgical and diagnostic robots, and running training at this scale in parallel is a departure from rebuilding a custom simulation scene for every workflow. You can hear more about this topic at the Physical AI Expo. See also: Bristol Myers Squibb buys Nvidia AI system for drug discovery Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Nvidia bets physical AI can solve healthcare robotics’ data problem appeared first on AI News. View the full article