AI In Autonomous Vehicle Technology

The future of AI may not be in the data centre. It may be on four wheels. Would you drive this one? Audi’s AI:TRAIL quattro concept gave us a glimpse of what the AI-native vehicle could look like. Flying drones instead of traditional headlights. Autonomous sensing beyond the vehicle. Electric power across all four wheels. Real-time intelligence at the edge. The most interesting part isn't the design. It’s the compute architecture. Think about what an AI vehicle needs to do: → See and understand its environment → Process multiple sensor streams in real time → Run increasingly sophisticated AI models → Make decisions with ultra-low latency → Operate reliably without always depending on the cloud → Continuously improve through software That means AI compute moves from the data centre to the edge. And this creates a fascinating convergence between automotive, AI, silicon and high-performance computing. The winning automotive platforms won't just be defined by horsepower or battery capacity. They'll increasingly be defined by: AI performance + energy efficiency + memory + connectivity + software ecosystem. We have already seen this transformation happen in PCs and data centres. Now it's happening in the car. The AI:TRAIL may have been a concept from 2019. But the question it raised is becoming very real in 2026: When every vehicle becomes an AI computer on wheels, who will build the silicon that powers it? That's where the next major semiconductor opportunity could be. #AI #Automotive via @supercarblondie #EdgeAI #Semiconductors #HPC #ArtificialIntelligence #FutureOfMobility #AMD #Innovation

Cloud Native technologies have long been at the heart of scalable applications. But now, with AI and Agentic Systems, the game is changing!   Unlike traditional AI automation, Agentic AI can make decisions, execute workflows, and adapt dynamically to system changes—without constant human oversight. This means self-healing, self-optimizing, and autonomous cloud-native infrastructure!  Here’s how Agentic AI can transform each layer of Cloud Native skills:  1. Linux & AI-Optimized OS   - AI-powered package managers automatically resolve compatibility issues.   - Agentic AI monitors system logs, predicts failures, and patches vulnerabilities autonomously.  2. Networking & AI-Driven Observability   - AI-driven network forensics using self-learning algorithms to detect anomalies.   - Agent-based routing optimizations, ensuring seamless traffic flow even in congestion.  3. Cloud Services & AI-Augmented Workflows   - Agentic AI predicts cloud workload demand and pre-allocates resources in AWS, Azure, and GCP.   - Autonomous cost optimization adjusts instance types, storage, and compute in real time.  4. Security & AI Cyberdefense Agents   - Self-learning AI security agents actively detect and mitigate cyber threats before they happen.   - Generative AI-powered penetration testing agents simulate evolving attack patterns.  5. Containers & Agentic AI Orchestration   - Autonomous Kubernetes controllers scale clusters before demand spikes.   - Agentic AI continuously optimizes pod scheduling, reducing cold starts and resource waste.  6. Infrastructure as Code + AI Copilots   - AI-driven infrastructure agents automatically refactor Terraform, Ansible, and Puppet scripts.   - Self-adaptive IaC, where AI updates configurations based on usage patterns and compliance policies.  7. Observability & AI-Driven Incident Response   - AI-powered anomaly detection in Grafana & Prometheus—flagging issues before failures.   - Agentic AI handles incident response, running diagnostics and executing pre-approved fixes.  8. CI/CD & Autonomous Pipelines   - Agentic AI writes, tests, and deploys code autonomously, reducing developer toil.   - Self-optimizing pipelines that rerun failed tests, debug, and retry deployment automatically.  The Future: Fully Autonomous Cloud Native Systems!  𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 → 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗔𝗴𝗲��𝘁𝗶𝗰 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The result? Zero-touch, self-managing environments where AI agents handle failures, optimize costs, and secure systems in real time.  𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆?

No words. No narration. Just one of the most powerful demonstrations of automotive safety technology I’ve seen. This video showcases Volkswagen’s Emergency Assist system, and it’s a masterclass in effective communication. Watch what happens when the system detects an unresponsive driver: 1. Alerts: It first tries to get the driver's attention. 2. Physical Intervention: It then does something fascinating...it jabs the brakes in an attempt to physically jolt the driver awake. 3. Autonomous Action: When there's no response, it takes control. It assesses the surroundings, activates the turn signal, and safely navigates the car across multiple lanes to the shoulder. 4. Secure & Call: Finally, it brings the vehicle to a complete stop, turns on the hazard lights, and automatically makes an emergency call. Think of the real-world implications. It's a lifeline for anyone experiencing a sudden medical event—a seizure, a heart attack, a narcoleptic episode. It also provides a critical safety layer for our aging population, helping older drivers maintain their mobility and independence with more confidence and peace of mind for their families. This technology will save lives. Period. Incredible work by the engineering and design teams at Volkswagen. This is human-centered technology at its best. What are your thoughts? #AutomotiveTechnology #RoadSafety #Innovation #Volkswagen #DriverAssist #Engineering #FutureOfMobility

A free ~64 page guide to engineering reliable AI agents! If you are a software engineer, you have likely already hit the "prototype plateau." You can get an LLM to answer a question or build an MVP, but getting it to reliably execute a 5-step agent workflow without hallucinating or looping is a harder engineering problem. We built the "Startup Technical Guide: AI Agents" to solve this. This is a manual for the cognitive architecture required to move from an agent demo to a deployed agent workforce. Why this matters for your stack: We move beyond simple "chatbot" patterns and dive into how to build agents that actually do work - like an agent for support or research, that monitors cloud logs and autonomously restarts a Kubernetes pod, or a support agent that executes refunds with ACID-compliant audit trails. Inside the guide: - The Toolkit: When to use the Agent Development Kit (ADK) for code-first control versus managed runtimes. - Architecture: How to implement ReAct loops (Reason + Action) so your agent can plan, act, and observe results before proceeding. - Grounding: Moving beyond basic RAG to Agentic RAG, where the model actively formulates search strategies rather than just passively receiving context. - AgentOps: How to implement trajectory evaluation. Don't just "vibe check" the output; trace the reasoning steps and unit test your tools. 🚀 Just launched: Enhanced tool governance To support this shift to production, we also just announced a massive update to "Vertex AI Agent Builder: the integration of the Cloud API Registry" For engineers, this solves the "duplicate work" problem. Instead of every developer rewriting the same tool definitions for BigQuery or Google Maps, we now provide a private registry. Admins can curate approved tools (including custom MCP servers via Apigee), and you can simply instantiate an ApiRegistry object in the ADK to pull them into your agent. It secures the supply chain and speeds up dev time. We also rolled out native state recovery in the ADK (no more lost context if a conversation crashes) and support for Gemini 3 Pro. The ecosystem is maturing fast. Grab the guide and start building. #ai #programming #softwareengineering #agents

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

Scientists across Amazon are revolutionizing how we verify AI systems with automated reasoning. Our teams are making AI more trustworthy, applying the same mathematical logic we’ve used for years to verify security at Amazon Web Services (AWS). It’s called “neurosymbolic AI”, and you can see this in action with robots like the one below navigating Amazon warehouses more efficiently, Rufus providing shoppers with more accurate answers, and AWS teams using this approach for everything from verifying AI-generated code correctness before shipping new Kiro features publicly, to Graviton chip optimization. Last week’s launch of Automated Reasoning checks in Amazon Bedrock Guardrails—catching AI errors with up to 99% accuracy—is another proof point in how we’re making AI safer for everyone. Learn more in the The Wall Street Journal about this game-changing verification technology. https://lnkd.in/g9H-hXnS

Deep stuff! We uncovered a startling link between #entropy, a bedrock concept in #physics, and how #AI can discover new ideas without stagnating. In an era where reasoning models can reflect on problems for days at a time (rather than generating quick, single-step solutions), our study shows how semantic entropy (the spread of meanings) and structural entropy (how evenly its links between concepts generated by the AI are distributed) together hold the secret to ongoing exploration as the model thinks through a problem. Specifically, we measured structural entropy using Von Neumann graph entropy (applied to the adjacency Laplacian), while semantic entropy came from a similarity-based embedding deep language embedding matrix. The key insight? Although semantic entropy consistently outpaces structural entropy, they remain in a near-critical balance—fueling "surprising edges" that introduce relationships between distant concepts. This mirrors physical systems on the brink of a phase transition, where a little bit of "disorder" keeps the process dynamic yet avoids chaos. The result is an AI that doesn’t just keep pace with known solutions but actively creates new pathways of thought over extended “thinking” sessions. As reasoning models become ever more capable—undertaking extended, multi-day "thought processes"—understanding fundamental principles is crucial. By weaving these insights into reinforcement learning strategies, we can reward models not just for correctness, but for venturing into novel conceptual ground. This opens the door to AI systems that actively cultivate new insights, rather than settling into narrow patterns or endlessly rehashing the same knowledge. Going Deeper When physicists describe entropy, they refer to the measure of "disorder" in a system: the number of ways particles can rearrange without altering the system’s energy. Yet entropy transcends molecules and heat. In this research, it emerges as the engine that drives AI reasoning models to keep generating fresh ideas over extended periods. The observed dynamics as the AI thinks about a problem reflects self-organized criticality—a state where systems hover between rigid order and random chaos. Much like a sand pile teetering on the edge of collapse, the AI preserves enough organizational structure to remain coherent, yet stays flexible enough to generate unexpected leaps in meaning. The fraction of "surprising edges" remains stable, offering evidence that the model naturally integrates new, distant ideas without toppling into confusion.

What do you know about Physical AI—besides robots? (Part 2) Physical AI isn’t just about giving AI a body—it’s far more challenging than we might expect. Unlike software AI, which can be broadly applied across industries with relatively minor adaptations, Physical AI is deeply industry-specific. Since it’s tightly linked to the physical world, every industry has unique constraints and requirements that shape how AI is built and deployed. Let’s take a look at how different it can be: (the examples I chose here are all have commercial products on the market) 1️⃣ Different Industries = Different Physical Constraints Each field has its own technical, safety, and material challenges, meaning no single AI model can handle them all: - AI in prosthetics must adapt to individual users’ biomechanics and interpret neural signals in real-time. - AI in self-driving cars must process road conditions, predict human behavior, and make split-second safety decisions. 2️⃣ Hardware & Materials Are Not Universal AI-powered prosthetics & exoskeletons → Need low-latency AI chips that process movement instantly. AI-powered glasses → Require miniaturized, ultra-low-power AI that fits in lightweight frames. AI-powered infrastructure (smart traffic, self-healing roads) → Needs durable AI-embedded materials and edge computing. 💡In software AI (like LLMs), scaling mostly requires more data and better algorithms, but Physical AI needs custom-built hardware for each industry. 3️⃣ Safety & Regulation Depend on the Industry AI prosthetics & medical devices → Require FDA or CE approval to ensure patient safety. AI-powered industrial robots → Must comply with OSHA and workplace safety standards. 💡Each industry has its own regulations and risks to manage - no single "AI safety rulebook" is available. 4️⃣ Learning & Adaptation is Industry-Specific Physical AI can’t rely on generic pre-training (unlike software AI such as LLMs)—it needs industry-specific real-world adaptation. Such as: AI-powered prosthetics → Need personalized learning to adapt to each user’s unique gait. AI-powered urban infrastructure → Requires AI that adjusts to local traffic patterns, weather, and real-time events. ... This is also why Physical AI progress happens in specific industries first, rather than scaling across all industries at once like software AI. 📍If you are a leader and looking for more in-depth Executive-level AI insights, check here (10 weeks program, expertise required) ➡️https://bit.ly/3C3GSgF So, if you were to develop a Physical AI product today, which industry would you target first? Image: CB Insights _______________ For more on AI, please check my previous posts. I share my learning journey here. Join me and let's grow together. Alex Wang #artificialintelligence #machinelearning #innovation #technology

If you’re an AI engineer building multi-agent systems, this one’s for you. As AI applications evolve beyond single-task agents, we’re entering an era where multiple intelligent agents collaborate to solve complex, real-world problems. But success in multi-agent systems isn’t just about spinning up more agents, it’s about designing the right coordination architecture, deciding how agents talk to each other, split responsibilities, and come to shared decisions. Just like software engineers rely on design patterns, AI engineers can benefit from agent design patterns to build systems that are scalable, fault-tolerant, and easier to maintain. Here are 7 foundational patterns I believe every AI practitioner should understand: → 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Run agents independently on different subtasks. This increases speed and reduces bottlenecks, ideal for parallelized search, ensemble predictions, or document classification at scale. → 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Chain agents so the output of one becomes the input of the next. Works well for multi-step reasoning, document workflows, or approval pipelines. → 𝗟𝗼𝗼𝗽 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Enable feedback between agents for iterative refinement. Think of use cases like model evaluation, coding agents testing each other, or closed-loop optimization. → 𝗥𝗼𝘂𝘁𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Use a central controller to direct tasks to the right agent(s) based on input. Helpful when agents have specialized roles (e.g., image vs. text processors) and dynamic routing is needed. → 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Merge outputs from multiple agents into a single result. Useful for ranking, voting, consensus-building, or when synthesizing diverse perspectives. → 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Allow all agents to communicate freely in a many-to-many fashion. Enables collaborative systems like swarm robotics or autonomous fleets. ✔️ Pros: Resilient and decentralized ⚠️ Cons: Can introduce redundancy and increase communication overhead → 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Structure agents in a supervisory tree. Higher-level agents delegate tasks and oversee execution. Useful for managing complexity in large agent teams. ✔️ Pros: Clear roles and top-down coordination ⚠️ Cons: Risk of bottlenecks or failure at the top node These patterns aren’t mutually exclusive. In fact, most robust systems combine multiple strategies. You might use a router to assign tasks, parallel execution to speed up processing, and a loop for refinement, all in the same system. Visual inspiration: Weaviate ------------ If you found this insightful, share this with your network Follow me (Aishwarya Srinivasan) for more AI insights, educational content, and data & career path.

IBM Research 𝗮𝗻𝗱 Yale University 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗳𝘂𝗹𝗹 360° 𝗿𝗲𝘃𝗶𝗲𝘄 𝘀𝗰𝗮𝗻 𝗼𝗻𝗲 𝗼𝗳 𝗵𝗼𝘄 𝘄𝗲 𝘁𝗲𝘀𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. ⬇️ They looked at 120+ evaluation methods — and mapped out what’s working and what’s missing. Currently everyone’s building AI agents. Almost no one agrees on how to properly evaluate them. This is critical, because without rigorous evaluation, we can’t trust these systems to be reliable, safe, or ready for real-world use. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁: ⬇️ 1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 ≠ 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 ➜ Agents aren’t static LLMs. They act, adapt, and evolve. Old-school metrics can’t keep up with real-world autonomy. 2. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗻𝗼𝘄 ➜ Benchmarks like LLF-Bench evaluate how agents process feedback and course-correct (which is crucial for evaulation quality). Without this, agents just repeat their mistakes. 3. 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝗶𝗴𝗻𝗼𝗿𝗲𝗱 — 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀𝗹𝘆 ➜ Top agents burn insane tokens and API calls. We need benchmarks that track performance and price. Otherwise no one can afford to deploy them. 4. 𝗙𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗼𝗽-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 ➜ It's critical to evaluate each individual component — otherwise, key weaknesses can go unnoticed and compromise the overall performance: * Breaking down complex tasks (planning) * Using tools and APIs (tool use) * Learning from feedback (reflection) * Remembering previous steps (memory) 5. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 ➜ New benchmarks simulate actual jobs: * Online shopping (WebArena) * Debugging code (SWE-Bench) * Helping customers (τ-bench) * Research tasks (PaperBench) * Multi-step workflows (OSWorld, CRMWorld) More in the comments and below! 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E