Architecting the Edge for AI and ML

Believe it or not, the Raspberry Pi came out 11 years ago. In that time, single board computers (SBCs) have gotten unbelievably powerful. During this same decade every major telecom provider started rolling out 5G services. Oh, and by the way, AlexNet, the neural network that completely changed they way we process imagery, landed on the computer vision scene in 2012.

This convolution (ha) of small, powerful computers, fast network access, and practical neural networks created the perfect conditions for edge computing to blossom. We live in the golden age of small, cheap computers capable of running software that didn’t and couldn’t have existed 10 years ago. It’s a great time to be alive! Read More

#devops

Addressing the Security Risks of AI

In recent weeks, there have been urgent warnings about the risks of rapid developments in artificial intelligence (AI). The current obsession is with large language models (LLMs) such as GPT-4, the generative AI system that Microsoft has incorporated into its Bing search engine. However, despite all the concerns about LLMs hallucinating and trying to break up marriages (the former quite real, the latter more on the amusing side), little has been written lately about the vulnerability of many AI-based systems to adversarial attack. A new Stanford and Georgetown report offers stark reminders that the security risks for AI-based systems are real. Moreover, the report—which I signed, along with 16 others from policy research, law, industry, and government—recommends immediately achievable actions that developers and policymakers can take to address the issue. Read More

#adversarial