Summary
- Trillion-parameter AI models and the growing usage of AI in applications are pushing the boundaries of computing.
- Advancements in machine learning and hardware/silicon design are helping to achieve increased efficiency and smaller computational demands from AI systems.
- New system architectures, like retrieval-augmented generation (RAG), are being used to streamline data access during training and inference to reduce computational costs and overhead.
- Emerging technologies such as photonic computing solutions and quantum computing could transform the AI hardware space.
- To become truly seamless and ubiquitous, AI systems require the right hardware to handle inference and other tasks efficiently, meaning the integration of different processing units and orchestrating AI applications are crucial to delivering impact and value.
standardised frameworks, platforms and security measures are required to ensure the smooth implementation of AI going forward.