Why AI Vision Fails: Lighting, Clarity, Capture Quality

As an AI Automation Builder, the world of tech is fundamentally changing. The era of purely text-based automation is giving way to Spatial Intelligence, which is the next definitive frontier in AI, according to Dr. Fei-Fei Li, the "Godmother of AI" and creator of ImageNet. The core shift is the move from Large Language Models (LLMs), which process 1D data (language), to Large World Models (LWMs). Language, though powerful, is a "lossy way" to capture the world. True intelligence—the ability to act, reason, and interact—requires understanding the 3D structure and physics of an environment. Evolutionarily, 3D perception took over 500 million years to develop; AI must now replicate this. For my work, this means automation is becoming embodied. The "digital Cambrian explosion" fueled by LWMs will enable AIs to move beyond software and into the physical realm. The challenge is no longer just optimizing information, but building systems that can accurately generate and reconstruct the 3D world. The new automation landscape includes: • Robotics: Intuitive, language-guided robots for complex tasks (e.g., in healthcare or logistics). • Creation: Tools that truly understand design, architecture, and physics for the creation of new materials and immersive virtual worlds (the "multiverse"). We are building for a future where AI does, rather than just describes, making this the hardest—and most essential—problem in AI today.

Think of AI as your engineering copilot - not a substitute. From generative design that explores millions of possibilities, to digital twins and predictive analytics that optimize performance before a prototype is even built. AI is transforming how engineers create, test, and deliver solutions.              Discover how AI is reshaping the engineering landscape, the new skills engineers need, and why companies are racing to build AI-ready teams.   Read the full blog: https://bit.ly/3JmQpmx #Zobility #ArtificialIntelligence #EngineeringInnovation #AIDrivenEngineering #GenerativeDesign #DigitalTwins #PredictiveAnalytics #FutureOfEngineering #InnovationInAction #TechTransformation #AIRevolution

Challenges involved in designing AI Systems: In chip design, it is essential to constantly adapt, and get used to new design processes, methods, and ways to design, because the chips industry is constantly evolving, and changing. Physical AI is able to make a lot of things, pretty much anything, from robots to chips. However, the can lead to significant changes in the design process. The reason for this, is because AI uses significantly less power, than other types of AI. When Physical AI is connected with Agentic AI, it has to be able to interact with humans. This type of AI acts differently than other AI models. Physical AI has 2 main parts, the first being management, using resources to the best way possible, and the system not crashing. The second method is that Physical AI is very different from Edge AI, since Edge AI uses mostly software, and audio only, while Physical AI is more complex, and can be used for multiple scenarios, such as sensors helping it adapt to the environment. An example of Physical AI would be Robot-taxis, because they need to adapt to the environment (the road, or air if driving or flying), and be aware of what's going on.

🤖 𝗔𝗜 𝗶𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 — 𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿’𝘀 𝗡𝗲𝘄 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 Some people fear AI. But what if we saw it differently — not as competition, but collaboration? AI can now generate concepts, simulate stress, and optimize designs. But it still needs human judgment — our experience, logic, and creativity. The best engineers of the future won’t compete with AI. They’ll lead it — by combining mechanical wisdom with digital intelligence. 💭 Have you tried using AI in your design or analysis work yet? How did it help? #ArtificialIntelligence #DesignEngineering #FutureOfWork #Innovation #MechanicalEngineering

AI no longer lives just in servers 🧠 It’s stepping into the real world. 🤖 Embodied systems and agentic AI are the next frontier. We’ve talked about large models, reasoning capabilities, and modular architectures. But here’s what’s coming: AI with a body, senses, and agency. Agents that “see” a problem, “move” to solve it, “adapt” to the physical world. Take a few signals: - Gemini Robotics 1.5 lets robots perceive, plan, and act in new environments without domain-specific training. - Embodied AI isn’t just novelty any more; it’s research-ready, with surveys showing how world models, multi-modal sensors, simulation ↔ real-world transfer, and embodied agents are maturing. Why this matters for you (AI engineers, researchers, creators): - If you’re only building for text or vision, you’re cutting off half the world. Embodied systems add motion, touch, context, and real-world feedback. - Your learning path: once you’ve mastered model size + reasoning + architecture, the next layer is interaction + environment + physical grounding. - For your brand message (progress · play · purpose · peace), this is the play piece: building AI that moves and does, not just talks. - For product/design: Deployment of embodied AI introduces new challenges (hardware, sensors, ethics, safety, adaptation); mastering that gives you an edge. Question for you: What domain do you think embodied AI will hit first in scale and impact: manufacturing, healthcare, home automation… or something totally unexpected? 👇 #AI #EmbodiedIntelligence #AgenticAI

The next steps for Generative AI (GenAI) are primarily defined by a shift toward autonomous, action-oriented systems known as Agentic AI, and enhanced capabilities in multimodal processing. Key developments include: Technological Advancements Agentic AI: The most significant next step is the rise of AI agents that can operate autonomously to achieve complex, multi-step goals with minimal human intervention. Unlike current GenAI that mostly responds to prompts, agents can plan, execute, and learn from real-world interactions by interfacing with other software, databases, and APIs. Multimodal AI: Future models will seamlessly process and generate content across different modalities—text, images, audio, and video—simultaneously. This will lead to more dynamic and intuitive interactions, such as creating a full multimedia marketing campaign from a simple text prompt. Smaller, Specialized Models: Alongside massive frontier models, there will be a surge in smaller, domain-specific language models (SLMs) that are more cost-effective, faster, and tailored for specific industry applications (e.g., healthcare, finance, law). Real-time Processing and Context: AI systems will integrate real-time data retrieval (via techniques like self-improving Retrieval-Augmented Generation, or RAG) and long-term memory to provide up-to-date, context-aware, and personalized responses. Specialized Hardware: The development of AI-optimized chips (like Google's TPU, IBM's North Pole chip, and Nvidia's GPUs) will continue to improve the efficiency and reduce the energy costs associated with running complex AI models. Applications and Impact Physical AI and Robotics: Generative AI capabilities will be integrated into robots, enabling them to perform a wider range of physical tasks in manufacturing, logistics, and even everyday life, moving beyond purely cognitive automation. Hyper-Personalization: AI will deliver highly tailored user experiences in education, entertainment, and e-commerce by adapting content, recommendations, and service. #AgenticAi #LLM #futureofwork #smartagents #GenerativeAi

Reality of AI Today:- Building AI solutions that truly solve business problems isn’t easy. It’s not just about deploying a model it’s about endless testing, validation, and refinement. Every step must ensure customers feel empowered, not like they’re getting a downgraded or a robotic experience. The Future of AI:- Smarter, more capable models are on the horizon but testing, safety, and human oversight will remain just as critical. "Human in the loop" will be the safeguard that keeps AI aligned with our values and intentions. As trust in AI summaries, and agentic action grows, the mundane processes will be accelerated dramatically. Humans will have to shift from doing the grunt work to guiding the intelligence via prompting and design and focusing on overall strategy, and goals. The real future of AI isn’t replacing humans, it’s amplifying what a single person can achieve. Think how calculators and CAD systems have boosted what a single engineer can achieve, the same is going to happen with software.

80% of companies reported that AI had no significant impact on earnings in 2025, despite rapid adoption. This is because businesses lack a clear strategy for AI integration. According to this author, the answer lies in an experimentation approach. https://lnkd.in/gWQdKxW8

$20B → $90B by 2033. And that's not AI coding tools That's the projected growth in industrial AI automation according to GVR. That's a 4.5x growth driven by predictive maintenance, machine vision, quality control, and autonomous robotics hitting factory floors. When we talk about AI on LinkedIn, it's usually code generation or content writing. Industrial AI? Completely different ball game with a huge effect on our everyday lives. This is where AI stops being a nice-to-have productivity boost and becomes the thing that keeps entire production lines from breaking. We're talking real-time sensor data analysis, defect detection at scale, equipment failure prediction before it happens. The shift isn't just technical, it's economic. Manufacturers adopting this stuff aren't chasing hype. They're solving actual problems: downtime costs millions, quality issues tank margins, and labor for repetitive inspections is expensive and error-prone. AI isn't replacing factory workers. It's giving them leverage to do their jobs better and catch problems humans physically can't see at speed. People sleep on industrial use cases because they're not sexy. But while everyone's building the 47th AI chatbot, some companies are quietly using vision models to detect microscopic defects in real-time production. That's where the real impact is.

How does AI enhance the “Measure” phase of process improvement? In process engineering, one principle always stands true: you can’t improve what you don’t measure. Accurate data collection is the foundation of every Six Sigma or Lean improvement effort — and today, AI is taking that foundation to a new level. A great example comes from Domtar, where artificial intelligence is helping to scan lumber, detect defects, and collect massive amounts of process data in real time. This isn’t about replacing human expertise — it’s about expanding our ability to see and act on what’s really happening in our processes. AI tools can accelerate the “Measure” and “Analyze” phases of Six Sigma by automatically identifying variation and trends that would take hours to detect manually. When integrated with tools like Pareto charts or Statistical Process Control, this creates a more proactive, data-driven approach — one that catches deviations before they become costly problems. I believe the real power lies in combining AI’s speed with human insight and disciplined methods. Technology gives us the data; our expertise turns it into improvement. How do you see AI changing the way we measure and control performance in your industry? #AI #SixSigma #SPC #DataDriven #ProcessEngineering #ContinuousImprovement #OperationalExcellence #LeanManufacturing