Coram AI’s Post

Weigh in on a debate for us: Simply put, where in the video surveillance system architecture should the the AI or "brains" of the camera reside? There are two main architectures: – In-Camera AI: The AI is inside the camera, near the imaging sensor. – Centralized AI: The AI is in a central video recorder or processing unit, with the camera only capturing data and sending it to the central unit. When the AI is inside the camera, you don’t need a central server. This setup is neat and has its pros. However, you are locked into that camera provider ecosystem and hardware, limiting flexibility. On the other hand, having the AI in a central server means the camera's job is just to capture data and send it to the central unit. This method works with any camera type, making it easier to integrate different kinds of cameras like thermal, PTZ, or multi-sensor cameras. Upgrading to advanced AI capabilities is also simpler with a centralized approach — replacing a server only takes like 10-20 minutes. One significant consideration is future-proofing. AI technology evolves rapidly. In-camera AI can quickly become outdated, much like using an old smartphone. With centralized AI, you can continuously upgrade and leverage powerful GPUs for advanced algorithms without replacing physical cameras. This approach ensures you stay current with the latest advancements, leading to better performance, cost savings, and enhanced security. Are we biased? Yeah, probably. The one thing we can all agree on is that as AI becomes more integral to operations, every organization needs to adopt an AI-first mindset to save time, reduce costs, and enhance safety. Being stuck with outdated technology will certainly hinder progress.

In the context of my vision for a factory controlled by AI agents, one of the primary challenges that arose in my initial idea was the issue of managing the vast amount of data and imaging required to implement such a system. Transitioning from the existing system of RFIDs working alongside scanners and connected devices to a more advanced setup leveraging computer vision alongside sensors posed a significant increase in data and imaging demands. The shift to computer vision technology offered tremendous potential for enhancing automation and efficiency in the factory environment. However, the sheer volume of data and imaging generated by this approach presented a daunting obstacle. Unlike RFIDs, which provide discrete data points, computer vision systems produce continuous streams of visual data that require substantial processing and analysis. To address this challenge, I began exploring strategies to reduce the data and imaging requirements while maintaining the integrity and effectiveness of the system. One approach involved implementing compression techniques to optimise the storage, transmission, and processing of visual data. I talked about this subject last year. By compressing image feeds and sensor data streams, I could minimise bandwidth usage and alleviate the computational burden on the AI agent Additionally, I investigated the potential for leveraging advancements in machine learning and computer vision algorithms to enhance data efficiency. Techniques such as feature extraction, object detection, and anomaly detection could help distill relevant information from the raw data, enabling more streamlined analysis and decision-making. Furthermore, I considered the importance of sensor placement and configuration in minimising redundant data collection. By strategically deploying sensors and cameras throughout the factory environment, I could focus on capturing only the essential information needed for effective operation and monitoring. The next scenario in this vision concerned the communication involved, computer vision has in its entirety and the burden that would entail. With computer vision systems generating continuous streams of visual data, efficient communication between sensors, cameras, and AI agents becomes paramount. However, the sheer volume of data and the real-time processing demands that would impose substantial burden on communication networks would inevitably raise the cost of such a build and traditional communication protocols and infrastructures often struggled to handle the influx of data and maintain responsiveness. #smartfactories #autonomoussystems #computervision #sensors #cameras #smartdevices #datastreaming #realtimedata #advanceddataprocessing #telecommunication #algorithms

You can now read your notes while still making eye contact with the camera during your meetings! NVIDIA's Eye Contact feature is an exciting new tool that uses artificial intelligence (AI) to make video calls even better. It creates the impression that you are looking directly at the camera, even if you are actually looking at your notes or another screen. This means you can stay engaged with the people you are talking to while easily accessing the information you need. The technology behind this feature involves advanced algorithms that work to maintain a natural appearance. For instance, it includes subtle eye movements that make it seem like you are genuinely focused on the camera. Additionally, it allows for the occasional simulated glance away, making the experience feel more realistic and less robotic. This innovation can be particularly helpful during virtual meetings, where it’s important to connect with others. Making eye contact can convey confidence and engagement, but it's often tricky if you need to refer to notes or documents. With NVIDIA's Eye Contact feature, you can strike a balance between reading your materials and keeping your audience's attention. What do you think about this technology? Is it a cool advancement in video conferencing, or does it feel a bit creepy to you? If you want to stay updated on the latest developments in artificial intelligence and technology, be sure to follow me! #ai #nvidia #tech

Computer vision sees the world the same way as we do. It has its own set of eyes such as sensors, cameras, and radars to collect visual data and perceive information. But the real magic is what happens after this visual data is collected. Learn more in our latest blog "The Evolving Landscape of Computer Vision and Its Business Implications" Link: https://lnkd.in/giJnfEhP #ArtificialIntelligence #Tech #Innovation #ComputerVision #Technology #DDD

Check out the latest report from IMIR Market Research Pvt. Ltd. on the 𝗔𝗜 𝗖𝗮𝗺𝗲𝗿𝗮 𝗠𝗮𝗿𝗸𝗲𝘁 Outlook and Geography Forecast till 2028 Don't miss out on this opportunity to stay informed about the latest trends in the industry. AI Camera Market Size, Share & Trends Analysis Report By Technology (Image/Face Recognition, Computer Vision, Emotion Recognition, DSLR Cameras, Network Cameras Security, Cameras Others (Wi-Fi Camera)), By Camera Type (PTZ Camera, Dome Camera, Bullet Camera, Box Camera, Others), By End User (BFSI, Healthcare, Automotive, Consumer Electronics, Smartphones & Tablets CCTV Camera Digital Camera Others, Retail, Government, Logistics & Transportation, Military and Defense, Commercial Spaces, Media and Entertainment Others (Residential, Oil & Gas)), Global Economy Insights, Regional Outlook, Growth Potential, Price Trends, Competitive Market Share & Forecast, 2023-2031 The AI Camera Market refers to the market for cameras that incorporate artificial intelligence (ai) technology for various applications such as security and surveillance, traffic management, healthcare, retail, industrial automation, and others. Ai cameras use computer vision and machine learning algorithms to analyze video data and detect, identify, and track objects and people. The scope of the ai camera market includes both hardware and software components, including the cameras themselves, processors, and software algorithms for ai-based video analysis. The market includes various types of cameras such as ip cameras, thermal cameras, and others. It also includes various deployment models such as cloud-based, on-premise, and hybrid deployments. 📚 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 𝘀𝗮𝗺𝗽𝗹𝗲 𝗥𝗲𝗽𝗼𝗿𝘁:👇 https://lnkd.in/djJSE8bs Mars Rover Manipal Metaspectral MiiCare Milesight Security Mirasys Neurolabs Nevalabs NexOptic Technology Corp. NFS Technologies LLC Nikon Research Corporation of America Nirovision Noteworthy AI Observit Ocucon Oculo Optomed Plc OxBlue Pano AI PhotoGAUGE Photoneo Playform Plexonics PowerArena promiseQ Pushpak AI RAVIN.AI RetiSpec Scutum France Scylla SECURE VISION   Seervision SENSIA SENSIVIC SewerAI ShelfPix SightBit Signatrix Sixth Energy Technologies AiBO (Formely SkyREC)

🚀 The rise of specialized AI/ML hardware is revolutionizing tech, introducing a new era of enhanced computing efficiency. 🔍 Understanding this hardware's importance is key for innovators and businesses eager to fully exploit AI and ML's transformative potential. 🌟 What is specialized hardware for AI/ML, and why is it crucial for advancing technological innovation and operational efficiency? Specialized hardware for AI/ML includes processors and computing systems specifically designed to accelerate the training and deployment of AI and ML models. These technologies, such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Field-Programmable Gate Arrays (FPGAs), offer unparalleled processing power and efficiency, tailored to handle the complex calculations and data processing tasks inherent in AI and ML workloads. The benefits of adopting specialized AI/ML hardware are multifold: 1. Increased Efficiency and Speed: These dedicated processors can significantly reduce the time required for AI model training and inference, enabling faster iterations and deployments of AI solutions. 2. Energy Savings: Specialized hardware is often more energy-efficient than general-purpose computing systems when performing AI/ML tasks, contributing to greener computing practices and reduced operational costs. 3. Enhanced Capabilities: With the ability to process large datasets and perform complex computations more efficiently, specialized hardware supports more sophisticated and accurate AI models, pushing the boundaries of what AI can achieve. 4. Economic and Competitive Advantage: Organizations leveraging specialized AI/ML hardware can gain a competitive edge through improved product offerings and operational efficiencies, driving growth and innovation in their respective fields. 5. Democratization of AI: By making AI more accessible and affordable, specialized hardware helps democratize AI technology, enabling a wider range of businesses and researchers to explore and implement AI solutions. In conclusion, specialized hardware for AI/ML is a cornerstone of the next generation of technological advancements. It plays a pivotal role in enhancing the performance and capabilities of AI systems, facilitating breakthroughs across various domains, including healthcare, autonomous vehicles, finance, and beyond. For businesses and innovators, investing in specialized AI/ML hardware is a strategic move towards achieving greater efficiency, innovation, and competitive advantage in a rapidly evolving digital landscape. #AI #MachineLearning #TechnologyInnovation #SpecializedHardware

AIoTs or Artificial Intelligence Internet of Things and their real time Data (Data is the real ''Gold'') Streams flown through an IA (Intelligent Automation, or Hyperatomation) Goledn Edge Gate that supervises, manages and distributes them among a rapidly growing population of AIoT sensors, Smart Mobile Devices, and Virtual Machines (expected to explode to trillions in less than a decade, on one hand and one or more Data Centers (we call them Gold Vaults), and/or Cloud-Native Edge (assuring Mobile Edge Computing), cum, Application Servers, on the other undr a Secure. & Remote Neural Network rapidly evolving into an end-to-end software managed network. IA Golden Edge Gate is a design by S2N's Chameleon Automation endgineering team developing sustems and integrated IT/OT Solutions based on IA and AI.

AI Trend in Manufacturing ft. #NVIDIA & Spingence In this Innotalks session, speakers from Advantech, NVIDIA, and #Spingence delve into how #AI solutions are revolutionizing defect inspection and #automation in #manufacturing. They explore how AI enables rapid responses to customer demands, supply chain disruptions, and safety concerns while driving #operational efficiency and #sustainability. The panel also showcases real-life use cases of AI applications in industries like semiconductor inspection and electronics component manufacturing, highlighting the transformative potential of AI in manufacturing. Watch now to learn how AI technology can enhance manufacturing efficiency and drive business innovation and growth!https://ow.ly/LMW650SOOt5

AI for staff devices, that could save your business countless hours 🤖   NVIDIA has just unveiled an exciting tech demo called Chat with RTX, empowering users to create custom chatbots right on their Windows RTX PCs.   Unlike cloud-based solutions, this chatbot runs locally, ensuring speed and data privacy. It’s trained on users unique content, including local files and even YouTube videos.   Some simple ways this product can be of benefit; ➡️ You can create a personalised chatbot to handle customer inquiries, troubleshoot common issues, and provide instant responses. ➡️ Data Analysis: Analyse large volumes of text data, legal documents, or reports. ➡️ Generate creative content for marketing campaigns, social media, or blog posts. Chat with RTX can assist in drafting engaging content tailored to your audience - Great for any marketing team! ➡️ Internal Communication: Develop an internal chatbot to answer employee queries, provide onboarding information, or assist with HR-related tasks. There could be endless benefits for your company and countless hours saved improving efficiency. With NVIDIA leading the way for AI innovation for desktops it will be interesting to see how the market reacts! Drop me a DM to see how Nviron Ltd could help find the perfect infrastructure to run your desired technology. #AI #DesktopAI #Infrastructure