The growth of artificial intelligence (AI) is driving a shift towards distributed computing, where processing power and data storage are spread out across a network rather than centralized in the cloud.
This trend is being fueled by the need for faster processing speeds and lower latency as AI is increasingly integrated into real-time applications such as autonomous vehicles, drones, and industrial robotics
Key Points:
- The rise of edge computing, which moves data processing and storage closer to the source of the data, rather than relying on cloud-based servers.
- The increasing importance of low-latency, real-time processing for AI applications in areas such as healthcare, transportation, and manufacturing.
- The challenges of building and maintaining distributed computing systems, including security, reliability, and scalability.
- The potential for new business models and revenue streams as organizations invest in edge computing infrastructure and services.
- The need for collaboration and standardization across the industry to ensure that distributed computing systems can work together seamlessly and efficiently.
Personal Insights:
- This is a very smart article on how compute has moved from local, to the cloud and now back to the edge.
- I think there will be a few new, and proprietary providers, of compute capacity (chips) that will be special purpose built for edge AI work… Think NVIDIA, Google, etc.
Article Highlights:
- An old school video on “What is a Tensor?” – 🙂 TensorFlow
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