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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 audio blog by halo. Bringing you innovations and insights into AI on the edge. Now let's get started.
Host 2:Balancing personal privacy and public safety with edge AI. In today's world, rising urbanization, increasing crime rates, and the threat of terrorism are putting public safety at risk. As cities expand and their population grows denser, the challenge of ensuring public safety becomes even more complex, especially considering constrained law enforcement resources. Advances in technology have led to the deployment of monitoring devices and cameras to make public spaces safer. With an installed base of over 600,000,000 surveillance cameras, China has almost 1 camera per 2 people.
Host 2:The top most surveilled cities outside of China include Delhi, Moscow, New York, and London among others. However, this increase in surveillance comes at a significant cost, the erosion of personal privacy. People value their right to remain anonymous and free from constant monitoring. The feeling that the big brother is watching at all times is leading to a complex clash between safety and privacy. This triggers a vivid debate among policy makers that often leads to legislation to regulate or prohibit the use of monitoring devices in the public domain.
Host 2:AI has been playing a growing role in maintaining public safety recently through integration into security systems at the camera or the video management system level. Advancement in technology, especially around generative AI, makes AI even more attractive for public safety monitoring. The most common AI use cases in surveillance systems include perimeter protection and access control. These applications leverage AI tasks such as object detection, segmentation, video metadata, and re identification to rapidly and accurately identify legitimate versus suspicious or abnormal people or behavior and trigger response in real time. AI powered surveillance systems offer more sophisticated and nuanced surveillance capabilities, enabling detection, identification, and response to security events in real time and high accuracy.
Host 2:However, while enhancing security and ensuring public safety, these technologies also raise concerns about privacy and potential misuse of personally identifiable information, highlighting the need for robust data protection measures. Traditional Cloud based AI solutions offer powerful processing capabilities by leveraging centralized data centers. However, they also introduce certain vulnerabilities, particularly concerning data privacy. The initial vulnerability lies in data at rest. Centralized storage of vast amounts of data makes Cloud systems attractive targets for cyberattacks.
Host 2:Hackers, whether private individuals, organized crime syndicates, or even hostile governments, can exploit these systems, leading to massive data breaches. Distributing the data processing to the edges of the network makes any breach limited to the specific node being hacked, making massive data breach more challenging. Additionally, regulations regarding data privacy impose limitation on how and which raw data can be analyzed. Cloud based systems must navigate these complex legal landscapes, often resulting in limited insights, compliance challenges, and even potential legal liabilities. Edge processing, on the other hand, enables harvesting profound insights while only storing and transmitting the minimum required information.
Host 2:Another potential vulnerability involves data in transit. Transmitting data from devices to the cloud creates multiple points of vulnerability. Intercepting data during transmission can expose sensitive information, undermining the security of the system. A third vulnerability lies in the trusted execution environment. A cloud center is a single point of failure that might impact a large number of cameras while if distributed, each system is free to adopt different algorithms and capabilities that scale and accuracy based on owners and integrators decision.
Host 2:Edge AI offers a compelling solution to these challenges by processing data locally on the device itself rather than transmitting it to a centralized cloud. This approach presents several advantages. Firstly, by processing data on the device, edge AI minimizes the need to transmit sensitive information over the Internet, significantly reducing the volume of data transmitted, and therefore the risk of interception and breaches. Another advantage is the localization of data storage. Edge devices store data locally, which limits the exposure in case of a cyber attack.
Host 2:Even if a device is compromised, the scope of the breach is contained to that specific device rather than an entire network. Another advantage lies in anonymizing data storage. If anonymization takes place locally, data stored on the Edge device or in the cloud can be anonymized, maintaining the essence of the data without exposing personally identifiable information. One more advantage of processing data locally on the device itself is data selectivity. Edge AI can be designed to focus only on relevant events, such as identifying instances of violence or suspicious behavior without recording continuous footage.
Host 2:This selective recording helps maintain the privacy of individuals in public spaces. To effectively balance safety and privacy, edge AI systems can be designed with specific limitations that inherently protect personal data. For example, bandwidth limitation, which restricts the transmission capabilities of cameras to ensure that video files are not continuously sent to the cloud. This reduces the risk of data breaches and preserves the privacy of individuals. Another native technology limitation can be applying selective recording to limit the amount of stored data and focus on capturing only what is necessary for public safety.
Host 2:For Edge AI to be effective, it must be both powerful and efficient. Devices need to process complex algorithms quickly to identify threats in real time while remaining cost efficient and power efficient. While ISVs are optimizing algorithms to ensure that edge AI can perform sophisticated tasks without draining compute resources, advances in AI hardware, such as specialized AI processors and low power high performance chips are making edge AI possible. Edge AI presents a promising solution to the challenge of balancing public safety with personal privacy By processing data locally and imposing inherent limitations on data transmission and storage, Edge AI reduces the risks associated with cloud based systems. As these technologies continue to evolve, Edge AI will play a crucial role in creating safer public spaces while respecting individuals right to remain anonymous.
Host 2:This approach not only enhances security, but also builds trust in systems designed to protect us.
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.