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"The Edge AI-sle" brings you to the forefront of artificial intelligence and edge computing, powered by Hailo.ai. In this podcast, we explore how edge AI is reshaping industries from smart cities and intelligent video analytics to autonomous vehicles and retail innovation. Join industry experts and thought leaders as they share insights into how Hailo’s AI processors are transforming the way industries function, enabling real-time deep learning and AI-driven applications directly on edge devices.
This isn’t just a podcast about technology—it's about how AI is empowering industries and improving lives across the globe. Whether it’s smart cameras making our cities safer or AI accelerators driving innovation in autonomous vehicles, the possibilities are endless.
If you're passionate about the nuts and bolts of AI processors and how they integrate with edge platforms to deliver unparalleled performance, "The Edge AI-sle" is the podcast for you. Expect detailed analysis and a peek behind the curtain at the future of edge computing AI.
Welcome to another blog by halo. Bringing you innovations and insights into AI on the edge. Now let's get started.
Host 2:Hi. Today, we'll talk about the fascinating advancements in AI on the edge. Exploring its journey from machine vision to the creative power of generative AI and what it means for the future. In the vast landscape of artificial intelligence, one of the most intriguing journeys has been the evolution of AI on the edge. This journey has taken us from classic machine vision to the realms of perceptive AI, enhancer AI, and now the groundbreaking frontier of generative AI.
Host 2:Each step has brought us closer to a future where intelligent systems seamlessly integrate with our daily lives, offering an immersive experience of not just perception, but also creation at the palm of our hand. Let's review the evolution of AI starting from perceptive AI through enhancing AI to generative AI. The journey began with machine vision, enabling computers to perceive and interpret the visual world around them. However, it was the advent of AI powered video analytics that truly revolutionized this field. Perceptive AI empowered machines to not only recognize objects and scenes, but also understand them.
Host 2:Tasks like object detection and instance segmentation have long surpassed human performance level, thereby enabling machines to identify individuals, vehicles, animals, and other objects reliably, and trigger real time activity accordingly. This is successfully put into use in surveillance, safety monitoring, and law enforcement applications. With the growing understanding of the theory of neural network operation and the successful results they have yielded, its application widened beyond just information retrieval and extraction. Using semantic understanding of the physical nature of the scene, neural networks can be leveraged to enhance the visual quality of images for more pleasing results, as well as to further enhance perception and analysis. A slow but steady shift is observed from classic vision related functionality to AI powered video enhancement features ranging from low light performance through high dynamic range, digital zoom, local tone mapping, and more.
Host 2:These advancements made visual data clearer, more detailed, and more reliable. We refer to this set of features by the general name, enhancive AI. However, the true paradigm shift came with the rise of generative AI inspired by the progress in natural language understanding and its adoption to any data modality. The ability to create new content, images, and videos that are indistinguishable from reality has managed to transform industries in less than a year. Welcome to the age of generative AI in which machines have the ability to generate content at near human level.
Host 2:To realize the full potential of generative AI and integrate it seamlessly into our daily lives, it must become fully immersive, always available, and therefore an inseparable part of edge devices. In hindsight, the 3 paradigms can be viewed from an information theoretical perspective as different manipulations on the source entropy. Perceptive tasks are targeted to reduce entropy. Entropy. Enhancer tasks are more or less maintaining accuracy, whereas generative tasks are mostly increasing entropy.
Host 2:Several factors drive AI to be implemented on the edge. Firstly, cost. Monthly subscriptions to cloud based generative AI tools can be prohibitive for many organizations. The multitude of tools serving different user needs, such as chat, search engine, and image slash video creation can add up to significant monthly expense per user, making it even more financially challenging for organizations. Moving generative AI to the edge and making it preintegrated offers a more cost effective solution in which the user is the owner of the tools, and no monthly subscription or long term commitments are needed.
Host 2:The second driver is latency. Instantaneous results are crucial in various applications, from autonomous vehicles to real time translation or content creation. Edge deployment reduces latency and enhances user experience. The third factor is connectivity. The more generative AI based applications become commonplace.
Host 2:Their continuous availability will become a necessity. It will no longer be best effort and users will be less tolerant to not always having full access. Edge based generative AI ensures continuous access to essential tools and services. The 4th driving factor is privacy. Concerns about data privacy and security are paramount, especially when it comes to native language, context aware interactions, which practically reveal vast knowledge about the owner or engaged party.
Host 2:Performing generative AI tasks on the edge mitigates both personal and business related risks associated with delivering sensitive data without control. And finally, the 5th driving force of enabling AI on edge devices is sustainability. The environmental impact of cloud based AI processing is immense and usually overlooked by the end users. In a recent study, it is estimated that generating an image using a powerful AI model takes as much energy as fully charging a smartphone. The c o two emitted by generation of a 1 minute real time video is the equivalent of the emissions resulted by 4 years of charging a single cellular phone each day.
Host 2:Cloud computing by millions of users daily not only requires processing power, but also the energy and water resources required to cool the servers, resulting in massive pollution. Edge devices consume less power, reduce cooling requirements, and minimize carbon footprint, making them a more sustainable option for generative AI in a widespread deployment and high usage rates. Among the leaders in this frontier is Halo, with its high performance, low power, and low carbon footprint Edge AI accelerators, which broaden the possibilities for a range of Edge AI applications. The halo accelerators enable generative AI to run on a multitude of Edge machines like personal computers, vehicles, infotainment center, and more. With generative AI poised to revolutionize our interaction with technology, the edge has become the next frontier for innovation and empowerment.
Host 2:Through advancements in hardware and software, we are witnessing the dawn of a new era where intelligence becomes immersive and creativity flourishes at the tip of our fingers.
Host 1:Thank you for listening to the Halo audio blog. If you enjoyed this episode, don't forget to sign up and check out more information at halo.ai. Keep the conversation going by sharing this with your peers and never stop exploring the future of AI.