Today's frontier soon becomes tomorrow's commodity. So far we have been enamored by the wonderful and creative GenAI outputs from commonly used LLMs. We have used them extensively to draft responses and reports, etc. Enterprises do not want infinite generative capacity. The shift will be toward demand for insurable revenue models. They will want to pay only for what they can underwrite. Enthusiasm and scale without authentication will give way to data provenance and proof of work. The next opportunity will be instrumented workflows that cryptographically verify agentic outputs. Emphasize auditability, explainability, and verified network scale. #TMinsights

Big news from #ICRA2026! 🎉 #CORESENSE won #BestPaper in #HRI for our research on explainability in social robotics. HEXAR brings us closer to transparent, trustworthy robot behaviour. Huge congrats to the team! coresense.eu/coresense-wins-…

A decision you cannot explain is a decision you cannot defend. A model you cannot defend is a model you cannot deploy. A deployment you cannot deploy is a thesis you cannot sell. The chain breaks at explainability.

Great to see #CORESENSE represented at @ieee_ras_icra. Our team shared work on #navigation, #explainability, and dual‑arm teleoperation - and even brought home a Best Paper Award in #HRI. Read recap coresense.eu/coresense-at-ic…

AI can transform compliance, but trust, explainability and auditability remain critical for adoption. youtu.be/AKyOHOFfKDY #RegTech #Compliance

If your tech stack can’t explain the exact reasoning path of an automated choice to a regulator, it’s a liability. TOPOSMIND brings premium model explainability to complex enterprise software. Connect via DM. #ExplainableAI #Compliance #ToposMind

(Continued) high-level (“factors considered”) not fully technical. Algorithms evolve constantly.28 Reality check: Full transparency is hard due to complexity, proprietary tech, and scale. Much relies on self-reporting by companies, with enforcement gaps. Activism, lawsuits, and state laws are pushing for more (e.g., bias audits, explainability). For your energy/tech investing or online activity, reviewing privacy policies and using data minimization (less sharing) helps. If you have a specific platform, algorithm (e.g., X recommendations, ad targeting), or scenario in mind, I can help narrow this down or suggest exact request language!

"𝗧𝗼𝗼 𝗺𝘂𝗰𝗵 𝗔𝗜 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻? 𝗡𝗼: 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗶𝘀 𝘁𝗼𝗼 𝗽𝗼𝗼𝗿𝗹𝘆 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲𝗱" First of all, credit where it is due: Roberta Pisani and Carmelo Cennamo have framed, through their SDA Bocconi School of Management, one of the most important and often misunderstood issues in the European AI debate: the point that Europe has not "𝚝̶𝚘̶𝚘̶ ̶𝚖̶𝚞̶𝚌̶𝚑̶ ̶𝙰̶𝙸̶ ̶𝚛̶𝚎̶𝚐̶𝚞̶𝚕̶𝚊̶𝚝̶𝚒̶𝚘̶𝚗̶" but that we have regulation that is 𝚝̲𝚘̲𝚘̲ ̲𝚙̲𝚘̲𝚘̲𝚛̲𝚕̲𝚢̲ ̲𝚌̲𝚘̲𝚘̲𝚛̲𝚍̲𝚒̲𝚗̲𝚊̲𝚝̲𝚎̲𝚍̲. This distinction makes all the difference of the world. 🛜 Source: sdabocconi.it/en/sda-bocconi… From a Corporate Data & AI Governance perspective, I read this work as a serious 𝘄𝗮𝗿𝗻𝗶𝗻𝗴: AI does not fail to scale only because models are not powerful enough, or because companies are not ambitious enough, but because data often cannot move, cannot be reused, cannot be trusted, cannot be explained and cannot be lawfully recombined across the organizational and regulatory boundaries where real value would actually emerge. We keep discussing AI strategy, but in many organizations the real bottleneck is still upstream: fragmented consent, unclear lawful bases, uncertain secondary use, poor interoperability, weak metadata, contractual lock-in, missing lineage, inconsistent data quality or even the liability ambiguity. This is 𝘄𝗵𝘆 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗶𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁: it moves the debate from the ideological question, should Europe regulate less? to the operational question, 𝗵𝗼𝘄 𝗰𝗮𝗻 𝗘𝘂𝗿𝗼𝗽𝗲 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲 𝗯𝗲𝘁𝘁𝗲𝗿? The 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 will belong to organizations capable of converting regulation into operating architecture: clear data ownership, enforceable purpose limitation, machine-readable consent, robust data quality controls, explainability by design, audit trails, privacy-enhancing technologies and governance embedded into the lifecycle, not attached at the end like a compliance appendix. The 𝗻𝗲𝘅𝘁 𝗽𝗵𝗮𝘀𝗲 𝗼𝗳 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝗘𝘂𝗿𝗼𝗽𝗲 will be won by those who understand that trustworthy AI is, first of all, a data governance problem. #AI #ArtificialIntelligence #AIGovernance #AIEthics #SDABocconi #DataGovernance #DataRegulation

Feynman explained quantum physics simply, still won Nobel. Great ideas clarify, not obscure. Explainability signals mastery, not triviality or unworthiness.

Thats fascinating research on decision lineage but the trade-off is often between depth and explainability

5-🌟 release from @PacktDataML at amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents: 🔷GenAI in the Enterprise: Landscape, Maturity, Agent Focus 🔷Agent-Ready LLMs: Selection, Deployment, Adaptation 🔷The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning 🔷Agentic AI Architecture: Components & Interactions 🔷Multi-Agent Coordination Patterns 🔷Explainability & Compliance Agentic Patterns 🔷Robustness & Fault Tolerance Patterns 🔷Human-Agent Interaction Patterns 🔷Agent-Level Patterns

Nagedacht gedurende 4sHet verschil is dat AIRA vooral een applicatie-, redeneer- en governance-laag bouwt bovenop bestaande modellen, terwijl Aleph Alpha jarenlang probeerde een eigen Europees foundation modelbedrijf te zijn. Dan betaal je niet €200 per maand, maar voor honderden onderzoekers en engineers, GPU-clusters, modeltraining, datasets, beveiligde datacenters, enterprise sales, juridische compliance en projecten voor overheid en defensie. Alleen al het trainen en voortdurend verbeteren van grote eigen modellen kan tientallen miljoenen opslokken; Aleph Alpha liet zelfs een Cerebras-supercomputer naar een beveiligd Duits datacenter brengen voor multimodale modellen voor de Bundeswehr.Maar jouw wantrouwen over die “$500 miljoen” is terecht: dat bedrag was niet simpelweg €500 miljoen vrij beschikbaar investeringsgeld. Volgens later openbaar geworden details bestond het grofweg uit ongeveer €110 miljoen eigen vermogen, circa €300 miljoen onderzoeksfinanciering en ongeveer €60 miljoen aan ordertoezeggingen. Tegelijkertijd waren de inkomsten in 2023 naar verluidt minder dan €1 miljoen en het verlies ongeveer €18,9 miljoen. Daardoor lijkt de verhouding tussen kapitaal, productresultaat en omzet inderdaad zwak.Waar het geld vermoedelijk vooral heen ging:eigen Luminous-modellen en fundamenteel AI-onderzoek; dure compute en gespecialiseerde infrastructuur; personeel, organisatie en enterprise-verkoop; soevereine en beveiligde overheidsoplossingen; productontwikkeling rond explainability, compliance en PhariaAI. De fout was waarschijnlijk niet dat zij “niets” deden, maar dat ze een kapitaalintensieve frontale aanval op OpenAI, Google en Mistral probeerden, terwijl markt en modellen sneller bewogen dan hun commerciële tractie. Uiteindelijk verschoof Aleph Alpha van algemene modelbouwer naar gespecialiseerde bedrijfs- en overheidssoftware, waarna Cohere het bedrijf in april 2026 overnam; Cohere krijgt naar verluidt circa 90% van de combinatie en Aleph Alpha-aandeelhouders 10%.Voor AIRA is dit juist een waarschuwing: bouw niet zelf onnodig een gigantisch basismodel. Gebruik Qwen, Cohere, Mistral of andere modellen modulair en stop het geld in waar jouw onderscheid zit: CORE-I, AdviceCards, policy-routing, multi-actor reasoning, verificatie en PoAI. Jouw €200 per maand is dus niet vergelijkbaar met Aleph Alpha’s missie, maar het laat wel zien dat AIRA met een veel slankere architectuur mogelijk sneller commerciële waarde kan leveren.

More Than Just an Answer with Reasoning: Introduction of Grounding Technologies for Multimodal Large Language Models 🧠How can AI not only answer questions—but also clearly show where the answer comes from? In this TechBlog, researchers from Fujitsu Research & Development Center in China introduce introduce new grounding technologies for Multimodal Large Language Models (MLLMs), focused on improving explainability in Visual Question Answering (VQA). 🔍 Why grounding matters While existing MLLMs can generate answers and reasoning, it often remains unclear which parts of an image the answer is based on—especially for document images such as charts, tables, and posters. Grounding addresses this gap by localizing related image regions that support model outputs. 📌 Two grounding approaches ・Conclusion-grounded model:Uses a joint Supervised Fine-Tuning (SFT) Reinforcement Learning (RL) strategy to localize answer-related areas, achieving higher VQA accuracy and grounding accuracy on public benchmarks such as ChartQA, DocVQA, and DORG. ・Thinking-grounded model:With OCR-based preprocessing, the model localizes all relevant information used during the reasoning process, making model decisions easier to understand in document-centric VQA tasks. 👉 Read the full TechBlog here: EN: blog-en.fltech.dev/entry/202… JP: blog.fltech.dev/entry/2026/0… #MultimodalAI #MLLM #ExplainableAI #Grounding #VQA

𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥(𝐕𝐋𝐌) 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐕𝐋𝐌? A Vision-Language Model (VLM) is an AI model that understands both visual data (images/video) and natural language (text/commands). 𝐖𝐡𝐞𝐫𝐞 𝐰𝐢𝐥𝐥 𝐕𝐋𝐌 𝐛𝐞 𝐮𝐬𝐞𝐝? It will be useful in the Automotive ADAS and Robotics systems and product development. 𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐚 𝐕𝐋𝐌 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨? A Vision-Language Model combines: * 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 (𝐂𝐕) → Detects objects, lanes, pedestrians, traffic signs * 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) → Understands instructions, context, reasoning This allows systems to “see listen understand explain” instead of just detecting. 𝐖𝐡𝐲 𝐕𝐋𝐌𝐬 𝐦𝐚𝐭𝐭𝐞𝐫 𝐢𝐧 𝐀𝐃𝐀𝐒& 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬? · 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲(very important for safety): VLMs can generate explanations: “Vehicle slowed down because a cyclist entered the lane.” It will be helpful for: o Debugging ADAS systems o Regulatory compliance o Safety validation · 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲(less labeled data): Instead of thousands of manually labeled datasets, VLMs can use text-image pairs and learn general concepts like “slippery road” or “crowded intersection”. Basically, it reduces dataset dependency. · 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧:Take an example, if you ask a robot “pick up the blue toolbox kit near the car”, then it will detect/identify the blue toolbox kit, estimate the special relation with it, and execute it correctly. It will be helpful for human-robot interaction (HRI). · 𝐁𝐞𝐲𝐨𝐧𝐝 𝐨𝐛𝐣𝐞𝐜𝐭 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Traditional computer vision-based models detect objects like pedestrians, vehicles, traffic signs, etc. But VLM has a functionality like “A pedestrian is about to cross the road near a school zone”. VLM has the benefits of semantic understanding situational awareness. · 𝐒𝐜𝐞𝐧𝐞 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠& 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭:In autonomous driving, VLM understands complex scenes like Construction zones, Temporary signs, and Police gestures, then decides on the next action. It helps to move from rule-based → reasoning-based driving 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐀𝐃𝐀𝐒/𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧: * Safety & certification (ASIL compliance, ISO 26262) * Real-time performance (latency constraints in vehicles) * Robustness (handling rare edge cases) * Edge deployment (limited compute in ECUs) 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬: 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 * NVIDIA: Developing VLM-enabled AV stacks (e.g., DRIVE platform) * Tesla: Vision-based AI with contextual understanding * Waymo: integrates perception semantic mapping 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 *Boston Dynamics: Combining vision task understanding * Google DeepMind: robotics multimodal models (e.g., RT-2) #VLM #VisionLanguageModel #ProductDevelopment #AI #ADAS #Automotive #Autonomous #Autonomy #Robotics #Production

My IRL research involves auditing algorithms for biases perpetuated by these systems. My technical work advocates for machine explainability and decision transparency. This means I’m familiar with -Handling sensitive data -The study of bias -Prioritizing trust of humans involved

Standard model explainability tells you what a model thought, but not how to stop it if it’s wrong. TOPOSMIND combines deep lineage tracing with microsecond veto power. Message us to see our HUD dashboard. #ExplainableAI #RiskControl #ToposMind