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The goal of innovation technology is to provide fresh, better solutions that solve issues, boost productivity, advance society, and provide value. Using technology to truly change the world is more important than simply creating new devices.
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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
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Découvrez l'évolution et l'impact d'IBM dans le domaine de la technologie et de la cybersécurité. De ses débuts en 1911 à son rôle pionnier avec la machine à cartes perforées, à la révolution de l'informatique personnel avec le premier PC en 1981, IBM a façonné l'industrie technologique. Plongez dans les innovations majeures d'IBM, notamment en cybersécurité avec IBM Security et les technologies d'intelligence artificielle, et explorez l'engagement continu de l'entreprise dans la formation en cybersécurité. Apprenez comment IBM contribue à façonner l'avenir technologique avec des avancées en IA, blockchain et cloud computing.
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