wilzy123
Founding Member
Key discussion points
- Evolution of AI Models:
Discussion on how AI models have evolved, focusing on optimisation for edge devices.
Ian Bratt mentions, "There will be a breakthrough... and then you see kind of a phase of optimisation and that makes the model much smaller... more optimised and more amiable for deployment on the edge”.
- Balance of Cloud and Edge Computing:
Insights on the shifting dynamics between cloud computing and edge computing in the AI landscape.
Nandan Nayampally states, "While most of the buzz and investment seems to have been on the cloud... smaller more compact versions [of AI models] will emerge rapidly that are more amable to the edge".
- Demand for Edge AI:
Exploration of factors driving the increasing demand for AI capabilities at the edge, such as latency and privacy concerns.
Ian Bratt explains, "If you can do it on the edge then it will be done on the edge, so there's huge demand to enable significant AI workloads on the edge".
- Challenges in AI Development:
Addressing the current challenges faced in the field of AI, including those related to large language models.
Ian Bratt discusses the complexities in AI development, noting the ongoing process of model breakthroughs and subsequent optimisation phases.
- Future of AI and AGI:
Predictions and expectations for the future of AI, including the concept and potential realization of Artificial General Intelligence (AGI).
In the closing discussion, Ian Bratt expresses optimism about reaching AGI (Artificial General Intelligence), aligning with a positive future vision.
NVIDIA TAO mentioned in podcast - https://www.edge-ai-vision.com/2023...els-for-trillions-of-devices-with-nvidia-tao/