Krishna Rangasayee’s Post

Exciting times at SiMa.ai! 🎉 We recently hit a major milestone with the release of Palette Edgematic (now available on AWS Marketplace!)—a low-code, no-code platform designed to make AI deployment effortless. And now, we’re taking things even further with MLSoC Modalix—the first purpose-built multi-modal and GenAI edge product family, now sampling to customers! 🚀 Optimized for real-time vision, language, and sensor fusion workloads, Modalix is redefining what’s possible at the power-efficient edge. At SiMa.ai, we’re committed to pushing the boundaries of scalable, efficient AI at the edge. Can’t wait to see what innovators build with these technologies! #AI #EdgeAI #GenAI #SiMaAI #MachineLearning #Innovation

Exciting Video Alert: Dive into 𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐕𝐢𝐬𝐢𝐨𝐧 !!🤖👀 If you’ve ever wondered how robots "see" and interpret the world around them, this video is for you! Join us as we break down the cool technologies behind robotic vision, from cameras and sensors to the algorithms that help robots make sense of their surroundings. We had a blast creating this workshop, and we can’t wait for you to check out the next events !! #IEEE #RoboticVision #Innovation #Video #Technology #AI

🔍 𝐀 𝐁𝐫𝐢𝐞𝐟 𝐇𝐢𝐬𝐭𝐨𝐫𝐲 𝐨𝐟 𝐘𝐎𝐋𝐎: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 🚀 Since its inception by Joseph Redmon and Ali Farhadi in 2015, YOLO (You Only Look Once) has redefined object detection with speed, accuracy, and versatility. Here’s how it evolved into one of the most powerful tools in computer vision: 1️⃣ 𝐘𝐎𝐋𝐎𝐯𝟏 (𝟐𝟎𝟏𝟓) - The original, enabling real-time object detection and quickly gaining popularity. 2️⃣ 𝐘𝐎𝐋𝐎𝐯𝟐 (𝟐𝟎𝟏𝟔) - Improved with batch normalization, anchor boxes, and dimension clusters, setting a new standard in detection efficiency. 3️⃣ 𝐘𝐎𝐋𝐎𝐯𝟑 (𝟐𝟎𝟏𝟖) - Enhanced performance with a more efficient backbone, multiple anchors, and spatial pyramid pooling, further increasing detection reliability. 4️⃣ 𝐘𝐎𝐋𝐎𝐯𝟒 (𝟐𝟎𝟐𝟎) - Introduced innovations like Mosaic data augmentation, anchor-free detection, and a refined loss function, making it robust for complex scenes. 5️⃣ 𝐘𝐎𝐋𝐎𝐯𝟓 (𝟐𝟎𝟐𝟎) - Focused on hyperparameter optimization, experiment tracking, and seamless export capabilities, this version gained immense popularity for its user-friendly features. 6️⃣ 𝐘𝐎𝐋𝐎𝐯𝟔 (𝟐𝟎𝟐𝟐) - Open-sourced by Meituan, with widespread usage in autonomous delivery robots. 7️⃣ 𝐘𝐎𝐋𝐎𝐯𝟕 - Added pose estimation capabilities using the COCO keypoints dataset, pushing beyond standard object detection. 8️⃣ 𝐘𝐎𝐋𝐎𝐯𝟖 (𝟐𝟎𝟐𝟑) - Ultralytics introduced YOLOv8 with enhanced performance and flexibility, supporting detection, segmentation, and various vision tasks. 9️⃣ 𝐘𝐎𝐋𝐎𝐯𝟗 - Innovated with Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for even better feature aggregation. 🔟 𝐘𝐎𝐋𝐎𝐯𝟏𝟎 - A leap in real-time detection with an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements. 1️⃣1️⃣ 𝐘𝐎𝐋𝐎𝐯𝟏𝟏 (𝟐𝟎𝟐𝟒) - Ultralytics' latest, delivering state-of-the-art performance across detection, segmentation, tracking, and more, showcasing YOLO’s adaptability in diverse AI applications. From real-time surveillance to autonomous vehicles 🚗, YOLO’s advancements continue to make it a go-to in computer vision, driving innovation and enabling high-performance applications across industries. 🌐 #YOLO #ComputerVision #ObjectDetection #AI #MachineLearning #DeepLearning #Innovation #TechAdvancements

🚨 Safer! Smarter! Obstacle Auto-Avoiding in Action! 🚨 Check out this amazing demonstration of the #X30 robot dog’s lightning-fast reflexes! 🐕⚡ When faced with sudden obstacles or approaching humans, the X30 reacts instantly, autonomously navigating its way around to avoid collisions. It's as if this robot "gets scared" and leaps into action to ensure everyone's safety! 🤖💨 Equipped with state-of-the-art sensors, advanced AI, and cutting-edge navigation algorithms, the X30 robot dog is designed to thrive in dynamic environments: ✅ Obstacle Detection: Senses sudden changes like approaching humans or unexpected objects. ✅ Collision Avoidance: Smart maneuvers to ensure safety and seamless interaction. ✅ Autonomous Navigation: Effortlessly navigates crowded or unpredictable spaces, adapting in real time. ✅ Human-Robot Interaction: Recognizes humans and responds safely, making it ideal for public and industrial use. Whether it’s delivering goods in warehouses, guiding individuals, or patrolling for security, the X30 is setting new standards in robotics. 🤩🚀 With deep learning technology, this robotic marvel keeps learning, adapting, and improving its reflexes for even smarter performance. This is the future of robotics—intelligent, responsive, and built to work with humans. 💡 What do you think of this lifelike reaction? Let us know in the comments! 👇 Stay ahead in AI! Follow AIPOOOL and subscribe to our free newsletter for the latest updates! #AI #robotics #ai #chatgpt #aitools #openai #aitips #machinelearning #artificialintelligence #deeplearning #generativeai #gpt4o

How Network APIs Bridge AI Applications and AI-RAN The AI-on-RAN goals focus on enhancing the integration of AI applications with the RAN. For a given AI application, the intent analyzer identify key requirements for the RAN to enable this AI application. This involves determining which RAN slice, facilitated via a network API, is best suited. For example, low-latency slices may be needed for real-time AI functions such as connected drone or autonomous driving, while high-throughput slices might be required for data-intensive applications like intelligent video analytics. Secondly, it is important to monitor RAN performance, such as delay, throughput, and reliability, to optimize the deployment of AI applications. For machine learning-based tasks like video analytics, this requires careful consideration of the model splitting point between the User Equipment (UE) and the server. Splitting the model at the optimal point allows parts of the AI processing to happen locally on the UE, reducing latency and bandwidth demands, while more computationally intensive processes can be executed on edge servers or in the cloud. The RAN network API will serve as the critical interface between AI applications and the RAN system, facilitating real-time interaction between the two. It will provide essential metrics and capabilities such as slicing configurations and Quality of Service (QoS) parameters, allowing AI applications to adapt dynamically to changing network conditions. This approach ensures that AI tasks can be seamlessly integrated into the RAN environment while optimizing resource allocation and network performance. #AIonRAN #AIRAN #NetworkAPI #RANSlicing #AIRAN_Alliance

Robots are getting smarter. Could a robot replace you at work? Boston Dynamics just took a giant leap. Their Atlas robot now works autonomously in real-world scenarios. The mind-blowing part? When Atlas fails, it adapts instantly. Using advanced sensors for vision, force, and proprioception, it recalibrates and keeps going without human intervention. If your job involves moving items from one place to another, pay close attention. Here’s why: → Atlas can see and understand its environment. → It can sense the force needed to handle objects. → It knows its position and movement at all times. → It recalibrates on the fly, without stopping. This future could be closer than you think. Like and follow for more AI updates.

Robots are getting smarter. Could a robot replace you at work? Boston Dynamics just took a giant leap. Their Atlas robot now works autonomously in real-world scenarios. The mind-blowing part? When Atlas fails, it adapts instantly. Using advanced sensors for vision, force, and proprioception, it recalibrates and keeps going without human intervention. If your job involves moving items from one place to another, pay close attention. Here’s why: → Atlas can see and understand its environment. → It can sense the force needed to handle objects. → It knows its position and movement at all times. → It recalibrates on the fly, without stopping. This future could be closer than you think. Like and follow for more AI updates.

Octo: A Revolutionary Open-Source Solution for Generalist Robot Policies Leveraging 800k Trajectories from the Open X-Embodiment Dataset In the realm of robotic learning, conventional wisdom dictates the use of tailored datasets to train policies specific to each robot and task. However, this approach demands significant data collection efforts for each activity, often resulting in policies with limited generalizability. Theoretically, leveraging data from diverse robotic contexts could offer a solution, enhancing models’ ability to generalize across tasks. Yet, creating a truly versatile “general-purpose robot model” capable of controlling various robots remains a formidable challenge. Unique factors such as robot embodiments, sensor setups, action spaces, task specifics, and computational constraints present significant hurdles in developing a unified control strategy for robotics. https://is.gd/D3RzuC #AI #artificialintelligence #generalistrobotpolicies #llm #machinelearning #Octo #Robotics

When we talk about the relationship between #symbolicAI and #embodiedAI with #industrial #production and #output, with #robotics, #smartcities, #aerospace and #smartvehicles, with #integratedsystems, we are primarily discussing Symbolic AI involvement with using #logic and #rulebased systems to #encode structured knowledge and facilitate problem-solving and reasoning. Embodied AI, on the one hand, refers to #AIsystems #embedded in physical entities, such as #robots, #drones, #smartdevices, and other systems, enabling them to interact with and adapt to the #realworld. The integration of these two #AI approaches offers significant advancements across various industries, enhancing #industrialproduction, smart cities, aerospace, and smart vehicles. In industrial production, the combination of symbolic AI and embodied AI enhances robotics and #automation. Symbolic AI can provide the rules and logic needed for robots to perform highly precise tasks, while embodied AI allows these robots to adapt to changing conditions in #realtime. Additionally, embodied AI systems equipped with sensors can monitor machinery and #predict when maintenance is needed. Symbolic AI can analyse this data and provide actionable insights, reducing downtime and increasing productivity. By integrating symbolic AI and embodied AI, #manufacturing processes can be optimised with adaptive production lines and real-time quality control. When we connect these two processes with #edgecomputing, we further enhance their capabilities by bringing computation closer to the #datasource. Edge computing allows real-time #dataprocessing and decision-making at the source of #datageneration. This means that in industrial settings, robots and #machinery equipped with embodied AI can process #sensordata locally, enabling faster and more responsive actions. Symbolic AI can then utilise this processed data to refine rules and logic dynamically, leading to more efficient and adaptive #productionsystems. When we further connect the integration of symbolic AI, embodied AI, and edge computing with #transformers, particularly #GraphAttention Transformers, we enhance the capabilities of these systems significantly. Transformers, known for their powerful representation learning and #attentionmechanisms, are particularly effective in processing and analysing complex #datastructures. Graph Attention Transformers, specifically, excel in handling graph-structured data, which is crucial for many advanced applications in industrial production, smart cities, aerospace, and smart vehicles.

🚀 Figure 02, represents a monumental leap in AI-driven automation and human-machine collaboration. 🤖 Powered by custom AI models from our collaboration with OpenAI, the F02 boasts three times the computational power of its predecessor, enabling fully autonomous operations. 📷 Equipped with six RGB cameras and a vision language model, the F02 excels in real-world perception and obstacle avoidance, making it ideal for dynamic environments. ⚙️ Featuring custom motors, a 2.25-kWh battery for extended runtime, and hands with 16 degrees of freedom, the F02 is designed for precision and endurance. 🏭 Already proving its capabilities in BMW’s manufacturing plant, the F02 is poised to revolutionize sectors from logistics to retail. This innovation isn’t just about technology—it’s about redefining the future of work and human potential. Will F02 be able to address global labor challenges through cutting-edge robotics. Learn more about Figure 02 and the journey towards a more automated and efficient future: 👉 https://www.figure.ai —— 👍 Liked this post? ♻️ REPOST to your network 👉 Follow Me for more #AI #Robotics #Innovation #FutureOfWork #Automation #TechInnovation #FigureAI