Announcing Just Security’s Artificial Intelligence and Emerging Technologies Initiative  Just Security

How machines make sense of sentence structure: Combinatory Categorial Grammar The post Grammar as an Injectable: A Trojan Horse to NLP appeared first on Towards Data Science.

Blockchain and cyber security  Allianz Commercial

Federal employee alleges DOGE activity resulted in data breach at labor board  NBC News

Anthropic has unveiled a custom collection of Claude AI models designed for US national security customers. The announcement represents a potential milestone in the application of AI within classified government environments. The ‘Claude Gov’ models have already been deployed by agencies operating at the highest levels of US national security, with access strictly limited to those working within such classified environments. Anthropic says these Claude Gov models emerged from extensive collaboration with government customers to address real-world operational requirements. Despite being tailored for national security applications, Anthropic maintains that these models underwent the same rigorous safety testing as other Claude models in their portfolio. Specialised AI capabilities for national security The specialised models deliver improved performance across several critical areas for government operations. They feature enhanced handling of classified materials, with fewer instances where the AI refuses to engage with sensitive information—a common frustration in secure environments. Additional improvements include better comprehension of documents within intelligence and defence contexts, enhanced proficiency in languages crucial to national security operations, and superior interpretation of complex cybersecurity data for intelligence analysis. However, this announcement arrives amid ongoing debates about AI regulation in the US. Anthropic CEO Dario Amodei recently expressed concerns about proposed legislation that would grant a decade-long freeze on state regulation of AI. Balancing innovation with regulation In a guest essay published in The New York Times this week, Amodei advocated for transparency rules rather than regulatory moratoriums. He detailed internal evaluations revealing concerning behaviours in advanced AI models, including an instance where Anthropic’s newest model threatened to expose a user’s private emails unless a shutdown plan was cancelled. Amodei compared AI safety testing to wind tunnel trials for aircraft designed to expose defects before public release, emphasising that safety teams must detect and block risks proactively. Anthropic has positioned itself as an advocate for responsible AI development. Under its Responsible Scaling Policy, the company already shares details about testing methods, risk-mitigation steps, and release criteria—practices Amodei believes should become standard across the industry. He suggests that formalising similar practices industry-wide would enable both the public and legislators to monitor capability improvements and determine whether additional regulatory action becomes necessary. Implications of AI in national security The deployment of advanced models within national security contexts raises important questions about the role of AI in intelligence gathering, strategic planning, and defence operations. Amodei has expressed support for export controls on advanced chips and the military adoption of trusted systems to counter rivals like China, indicating Anthropic’s awareness of the geopolitical implications of AI technology. The Claude Gov models could potentially serve numerous applications for national security, from strategic planning and operational support to intelligence analysis and threat assessment—all within the framework of Anthropic’s stated commitment to responsible AI development. Regulatory landscape As Anthropic rolls out these specialised models for government use, the broader regulatory environment for AI remains in flux. The Senate is currently considering language that would institute a moratorium on state-level AI regulation, with hearings planned before voting on the broader technology measure. Amodei has suggested that states could adopt narrow disclosure rules that defer to a future federal framework, with a supremacy clause eventually preempting state measures to preserve uniformity without halting near-term local action. This approach would allow for some immediate regulatory protection while working toward a comprehensive national standard. As these technologies become more deeply integrated into national security operations, questions of safety, oversight, and appropriate use will remain at the forefront of both policy discussions and public debate. For Anthropic, the challenge will be maintaining its commitment to responsible AI development while meeting the specialised needs of government customers for crtitical applications such as national security. (Image credit: Anthropic) See also: Reddit sues Anthropic over AI data scraping 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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Anthropic launches Claude AI models for US national security appeared first on AI News.

This audio is auto-generated. Please let us know if you have feedback. President Donald Trump signed an executive order (EO) Friday that scratched or revised several of his Democratic predecessors’ major cybersecurity initiatives. “Just days before President Trump took office, the Biden Administration attempted to sneak problematic and distracting issues into cybersecurity policy,” the White House said in a fact sheet about Trump’s new directive, referring to projects that Biden launched with his Jan. 15 executive order. Trump’s new EO eliminates those projects, which would have required software vendors to prove their compliance with new federal security standards, prioritized research and testing of artificial intelligence for cyber defense and accelerated the rollout of encryption that withstands the future code-cracking powers of quantum computers. “President Trump has made it clear that this Administration will do what it takes to make America cyber secure,” the White House said in its fact sheet, “including focusing relentlessly on technical and organizational professionalism to improve the security and resilience of the nation’s information systems and networks.” Major cyber regulation shift Trump’s elimination of Biden’s software security requirements for federal contractors represents a significant government reversal on cyber regulation. Following years of major cyberattacks linked to insecure software, the Biden administration sought to use federal procurement power to improve the software industry’s practices. That effort began with Biden’s 2021 cyber order and gained strength in 2024, and then Biden officials tried to add teeth to the initiative before leaving office in January. But as it eliminated that project on Friday, the Trump administration castigated Biden’s efforts as “imposing unproven and burdensome software accounting processes that prioritized compliance checklists over genuine security investments.” Trump’s order eliminates provisions from Biden’s directive that would have required federal contractors to submit “secure software development attestations,” along with technical data to back up those attestations. Also now eradicated are provisions that would have required the Cybersecurity and Infrastructure Security Agency to verify vendors’ attestations, required the Office of the National Cyber Director to publish the results of those reviews and encouraged ONCD to refer companies whose attestations fail a review to the Justice Department “for action as appropriate.” Trump’s order leaves in place a National Institute of Standards and Technology collaboration with industry to update NIST’s Software Software Development Framework, but it eliminates parts of Biden’s order that would have incorporated those SSDF updates into security requirements for federal vendors. In a related move, Trump eliminated provisions of his predecessor’s order that would have required NIST to “issue guidance identifying minimum cybersecurity practices” (based on a review of globally accepted standards) and required federal contractors to follow those practices. AI security cut Trump also took an axe to Biden requirements related to AI and its ability to help repel cyberattacks. He scrapped a Biden initiative to test AI’s power to “enhance cyber defense of critical infrastructure in the energy sector,” as well as one that would have directed federal research programs to prioritize topics like the security of AI-powered coding and “methods for designing secure AI systems.” The EO also killed a provision would have required the Pentagon to “use advanced AI models for cyber defense.” On quantum computing, Trump’s directive significantly pares back Biden’s attempts to accelerate the government’s adoption of post-quantum cryptography. Biden told agencies to start using quantum-resistant encryption “as soon as practicable” and to start requiring vendors to use it when technologically possible. Trump eliminated those requirements, leaving only a Biden requirement that CISA maintain “a list of product categories in which products that support post-quantum cryptography … are widely available.” Trump also eliminated instructions for the departments of State and Commerce to encourage key foreign allies and overseas industries to adopt NIST’s PQC algorithms. The EO dropped many other provisions of Biden’s January directive, including one requiring agencies to start testing phishing-resistant authentication technologies, one requiring NIST to advise other agencies on internet routing security and one requiring agencies to use strong email encryption. Trump also cut language directing the Office of Management and Budget to advise agencies on addressing risks related to IT vendor concentration. In his January order, Biden ordered agencies to explore and encourage the use of digital identity documents to prevent fraud, including in public benefits programs. Trump eliminated those initiatives, calling them “inappropriate.”  Trump also tweaked the language of Obama-era sanctions authorities targeting people involved in cyberattacks on the U.S., specifying that the Treasury Department can only sanction foreigners for these activities. The White House said Trump’s change would prevent the power’s “misuse against domestic political opponents.” Amid the whirlwind of changes, Trump left one major Biden-era cyber program intact: a Federal Communications Commission project, modeled on the Energy Star program, that will apply government seals of approval to technology products that undergo security testing by federally accredited labs. Trump preserved the language in Biden’s order that requires companies selling internet-of-things devices to the federal government to go through the FCC program by January 2027.

Few roles evolve as quickly as that of the modern CIO. A great way to prepare for a future that is largely unknown is to build your adaptability skills through diverse work experiences, says Bhavik Rao, CIO for the Americas at Vertiv. Learn from your wins and your losses and carry on. Stay free of comfort zones and run towards the chaos. Leaders are born of challenges and not from comfort.Bhavik shares what he’s facing now, how he’s navigating it, and the hard-won lessons that helped shape his approach to IT leadership.Here’s what he had to say:What has your career path looked like so far? I actually started my career as a techno-functional consultant working with the public sector. That early experience gave me a solid grounding in both the technical and process side of enterprise systems. From there, I moved into consulting, which really opened up my world. I had the opportunity to work across multiple industries, leading everything from mobile app development and eCommerce deployments to omnichannel initiatives, data platforms, ERP rollouts, and ultimately large-scale digital transformation and IT strategy programs. It was fast paced, challenging, and incredibly rewarding.  That diversity shaped the way I think today. I learned how to adapt quickly, connect dots across domains, and communicate with everyone from developers to CXOs. Eventually, that path led me to Vertiv, where I now serve as the CIO for the Americas, in addition to leading a couple of global towers, such as data/AI and engineering systems, for example. I’ve been fortunate to lead initiatives that drive operational efficiency, scale GenAI adoption, and turn technology into a true business enabler.   Related:What are the highlights along your career path? There have been several defining moments, both wins and challenges, that have shaped how I lead today. One of the most pivotal chapters has been my time at Vertiv. I joined when the company was still owned by private equity. It was an intense, roll-up-your-sleeves kind of environment. Then, in 2020, we went public -- a huge milestone. But just as we were ramping up our digital transformation, COVID hit, and with it came massive supply chain disruptions. In the middle of all that chaos, I was asked to take over a large-scale transformation program that was struggling. bhBhavik RaoIt wasn’t easy. There were legacy challenges, resistance to change, and real execution pressure. But we rallied, restructured the program, and launched it. That experience taught me a lot about leading under pressure, aligning teams around outcomes, and staying focused even when everything feels like it’s shifting. Related:Another major learning moment was earlier in my career when I lost a large national account I’d spent over seven years building. That was a tough one, but it taught me resilience. I learned not to attach my identity to any one outcome and to keep moving forward with purpose. Then, there are the moments of creation, like launching VeGA, our internal GenAI platform at Vertiv. Seeing it go from idea to impact, with thousands of users and 100+ applications, has been incredibly energizing. It reminded me how powerful it is when innovation meets execution. I’ve also learned the power of being a “player-coach.” I don’t believe in leading from a distance. I get involved, understand the challenges on the ground, and then help teams move forward together.  What’s your vision for the future of sovereign AI? For me, sovereign AI isn’t just a regulatory checkbox; it’s about strategic autonomy. At our company, we are trying to be very intentional about how we scale AI responsibly across our global footprint. So, when I think about sovereign AI, I define it as the ability to control how, where, and why AI is built and deployed with full alignment to your business needs, risk posture, and data boundaries. Related:I’ve seen firsthand how AI becomes a competitive advantage only when you have governance, infrastructure flexibility, and contextual intelligence built in. Our work with VeGA, for example, has shown that employees adopt AI much faster when it’s embedded into secure, business-aligned workflows and not just bolted on from the outside. For CIOs, the shift to sovereign AI means: Designing AI infrastructure that can flex whether it’s hosted internally, cloud-based, or hybrid Building internal AI fluency so your teams aren't fully reliant on black-box solutions Creating a framework for trust and explainability, especially as AI touches regulated and legal processes It’s not about doing everything in-house, but it is about knowing what’s mission-critical to control. In my view, sovereign AI is less about isolation and more about intentional ownership. What do you do for fun or to relax? Golf is my go-to. It keeps me grounded and humble! It’s one of those games that’s as much about mindset as it is about mechanics. I try to work out regularly when I am not traveling for work.  I also enjoy traveling with my family and listening to podcasts.   What advice would you give to young people considering a leadership path in IT? Be curious, stay hands-on, don’t rush the title, and focus on impact. Learn the business, not just the tech. Some of the best technologists I’ve worked with are the ones who understand how a supply chain works or how a sale actually closes. Also, don’t be afraid to take on messy, undefined problems. Run toward the chaos. That’s where leadership is born. And finally, surround yourself with people smarter than you. Build teams that challenge you. That’s where real growth happens. 

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing for high levels of control during the generation process. As researchers began to apply these techniques to visual data, they found that structured prediction not only preserved spatial integrity but also supported tasks like image manipulation and multimodal translation effectively. Despite these benefits, generating high-resolution images remains computationally expensive and slow. A primary issue is the number of tokens needed to represent complex visuals. Raster-scan methods that flatten 2D images into linear sequences require thousands of tokens for detailed images, resulting in long inference times and high memory consumption. Models like Infinity need over 10,000 tokens for a 1024×1024 image. This becomes unsustainable for real-time applications or when scaling to more extensive datasets. Reducing the token burden while preserving or improving output quality has become a pressing challenge. Efforts to mitigate token inflation have led to innovations like next-scale prediction seen in VAR and FlexVAR. These models create images by predicting progressively finer scales, which imitates the human tendency to sketch rough outlines before adding detail. However, they still rely on hundreds of tokens—680 in the case of VAR and FlexVAR for 256×256 images. Moreover, approaches like TiTok and FlexTok use 1D tokenization to compress spatial redundancy, but they often fail to scale efficiently. For example, FlexTok’s gFID increases from 1.9 at 32 tokens to 2.5 at 256 tokens, highlighting a degradation in output quality as the token count grows. Researchers from ByteDance introduced DetailFlow, a 1D autoregressive image generation framework. This method arranges token sequences from global to fine detail using a process called next-detail prediction. Unlike traditional 2D raster-scan or scale-based techniques, DetailFlow employs a 1D tokenizer trained on progressively degraded images. This design allows the model to prioritize foundational image structures before refining visual details. By mapping tokens directly to resolution levels, DetailFlow significantly reduces token requirements, enabling images to be generated in a semantically ordered, coarse-to-fine manner. The mechanism in DetailFlow centers on a 1D latent space where each token contributes incrementally more detail. Earlier tokens encode global features, while later tokens refine specific visual aspects. To train this, the researchers created a resolution mapping function that links token count to target resolution. During training, the model is exposed to images of varying quality levels and learns to predict progressively higher-resolution outputs as more tokens are introduced. It also implements parallel token prediction by grouping sequences and predicting entire sets at once. Since parallel prediction can introduce sampling errors, a self-correction mechanism was integrated. This system perturbs certain tokens during training and teaches subsequent tokens to compensate, ensuring that final images maintain structural and visual integrity. The results from the experiments on the ImageNet 256×256 benchmark were noteworthy. DetailFlow achieved a gFID score of 2.96 using only 128 tokens, outperforming VAR at 3.3 and FlexVAR at 3.05, both of which used 680 tokens. Even more impressive, DetailFlow-64 reached a gFID of 2.62 using 512 tokens. In terms of speed, it delivered nearly double the inference rate of VAR and FlexVAR. A further ablation study confirmed that the self-correction training and semantic ordering of tokens substantially improved output quality. For example, enabling self-correction dropped the gFID from 4.11 to 3.68 in one setting. These metrics demonstrate both higher quality and faster generation compared to established models. By focusing on semantic structure and reducing redundancy, DetailFlow presents a viable solution to long-standing issues in autoregressive image generation. The method’s coarse-to-fine approach, efficient parallel decoding, and ability to self-correct highlight how architectural innovations can address performance and scalability limitations. Through their structured use of 1D tokens, the researchers from ByteDance have demonstrated a model that maintains high image fidelity while significantly reducing computational load, making it a valuable addition to image synthesis research. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Teaching AI to Say ‘I Don’t Know’: A New Dataset Mitigates Hallucinations from Reinforcement FinetuningNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces LLaDA-V: A Purely Diffusion-Based Multimodal Large Language Model for Visual Instruction Tuning and Multimodal ReasoningNikhilhttps://www.marktechpost.com/author/nikhil0980/NVIDIA AI Introduces Fast-dLLM: A Training-Free Framework That Brings KV Caching and Parallel Decoding to Diffusion LLMsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" A still from a demo shot created using MotionMaker, the new generative AI toolset introduced in Maya 2026.1 for roughing out movement animations. Autodesk has released Maya 2026.1, the latest version of its 3D modeling and animation software for visual effects, games and motion graphics work.The release adds MotionMaker, a new AI-based system for generating movement animations for biped and quadruped characters, especially for previs and layout work. Other changes include a new modular character rigging framework inside Bifrost for Maya, plus updates to liquid simulation, OpenPBR support and USD workflows. Autodesk has also released Maya Creative 2026.1, the corresponding update to the cut-down edition of Maya for smaller studios. MotionMaker: new generative AI tool roughs out movement animations The headline feature in Maya 2026.1 is MotionMaker: a new generative animation system.It lets users “create natural character movements in minutes instead of hours”, using a workflow more “like giving stage directions to a digital actor” than traditional animation. Users set keys for a character’s start and end positions, or create a guide path in the viewport, and MotionMaker automatically generates the motion in between. At the minute, that mainly means locomotion cycles, for both bipeds and quadrupeds, plus a few other movements, like jumping or sitting. Although MotionMaker is designed for “anyone in the animation pipeline”, the main initial use cases seem to be layout and previs rather than hero animation. Its output is also intended to be refined manually – Autodesk’s promotional material describes it as getting users “80% of the way there” for “certain types of shots”. Accordingly, MotionMaker comes with its own Editor window, which provides access to standard Maya animation editing tools. Users can layer in animation from other sources, including motion capture or keyframe animation retargeted from other characters: to add upper body movements, for example. There are a few more MotionMaker-specific controls: the video above shows speed ramping, to control the time it takes the character to travel between two points. There is also a Character Scale setting, which determines how a character’s size and weight is expressed through the animation generated. You can read more about the design and aims of MotionMaker in a Q&A with Autodesk Senior Principal Research Scientist Evan Atherton on Autodesk’s blog. According to Atherton, the AI models were trained using motion capture data “specifically collected for this tool”. That includes source data from male and female human performers, plus wolf-style dogs, although the system is “designed to support additional [motion] styles” in future. Bifrost: new modular character rigging framework Character artists and animators also get a new modular rigging framework in Bifrost.Autodesk has been teasing new character rigging capabilities in the node-based framework for building effects since Maya 2025.1, but this seems to be its official launch. The release is compatibility-breaking, and does not work with earlier versions of the toolset. The new Rigging Module Framework is described as a “modular, compound-based system for building … production-ready rigs”, and is “fully integrated with Maya”. Animators can “interact with module inputs and outputs directly from the Maya scene”, and rigs created with Bifrost can be converted into native Maya controls, joints and attributes. Bifrost: improvements to liquid simulation and workflow Bifrost 2.14 for Maya also features improvements to Bifrost’s existing functionality, particularly liquid simulation. The properties of collider objects, like bounciness, stickiness and roughness, can now influence liquid behavior in the same way they do particle behavior and other collisions. In addition, a new parameter controls air drag on foam and spray thrown out by a liquid. Workflow improvements include the option to convert Bifrost curves to Maya scene curves, and batch execution, to write out cache files “without the risk of accidentally overwriting them”. LookdevX: support for OpenPBR in FBX files LookdevX, Maya’s plugin for creating USD shading graphs, has also been updated. Autodesk introduced support for OpenPBR, the open material standard intended as a unified successor to the Autodesk Standard Surface and Adobe Standard Material, in 2024. To that, the latest update adds support for OpenPBR materials in FBX files, making it possible to import or export them from other applications that support OpenPBR: at the minute, 3ds Max plus some third-party renderers. LookdevX 1.8 also features a number of workflow improvements, particularly on macOS. USD for Maya: workflow improvements USD for Maya, the software’s USD plugin, also gets workflow improvements, with USD for Maya 0.32 adding support for animation curves for camera attributes in exports.Other changes include support for MaterialX documents and better representation of USD lights in the viewport. Arnold for Maya: performance improvements Maya’s integration plugin for Autodesk’s Arnold renderer has also been updated, with MtoA 5.5.2 supporting the changes in Arnold 7.4.2.They’re primarily performance improvements, especially to scene initialization times when rendering on machines with high numbers of CPU cores. Maya Creative 2026.1 also released Autodesk has also released Maya Creative 2026.1, the corresponding update to the cut-down edition of Maya aimed at smaller studios, and available on a pay-as-you-go basis.It includes most of the new features from Maya 2026.1, including MotionMaker, but does not include Bifrost for Maya. Price and system requirements Maya 2026.1 is available for Windows 10+, RHEL and Rocky Linux 8.10/9.3/9.5, and macOS 13.0+.The software is rental-only. Subscriptions cost $255/month or $2,010/year, up a further $10/month or $65/year since the release of Maya 2026. In many countries, artists earning under $100,000/year and working on projects valued at under $100,000/year, qualify for Maya Indie subscriptions, now priced at $330/year. Maya Creative is available pay-as-you-go, with prices starting at $3/day, and a minimum spend of $300/year. Read a full list of new features in Maya 2026.1 in the online documentation Have your say on this story by following CG Channel on Facebook, Instagram and X (formerly Twitter). As well as being able to comment on stories, followers of our social media accounts can see videos we don’t post on the site itself, including making-ofs for the latest VFX movies, animations, games cinematics and motion graphics projects.