Machine Learning (ML) is a famous innovation that almost all people are aware of . A have a look at uncovers that 77% of gadgets that...
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Artificial intelligence and machine learning in the enterprise level.
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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 | 72 comments on LinkedIn
𝐂𝐈/𝐂𝐃 𝐟𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: A must know for AI/ML Engineers ---- Real-Time AI, Vector Search, Gen AI - Simplified and a good way to… | 33 comments on LinkedIn
Deep Learning requires much more of an ARCHITECT mindset than traditional Machine Learning. In a sense, part of the feature engineering work has been moved to… | 14 comments on LinkedIn
🎯 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐓𝐨 𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬 ⬇…
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This nice illustration or visualization of the Data Pipeline! The data pipeline refers to the end-to-end process of collecting, transforming, and delivering… | 16 comments on LinkedIn
📌 A Comprehensive Visual Guide to Key AI Terms 📌 🔵 Artificial Intelligence (AI) The broad discipline of creating smart machines capable of performing tasks…
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