The Future of AI and Everything Else is Hybrid
Qualcomm released a paper entitled \”The Future of AI Is Hybrid\” in which they argue that AI must be processed on both the cloud and edge to reach its full potential. Computing at the edge could improve cost, reliability, latency, and privacy issues. All of these things make scaling and growing technology difficult. They’re right, too: For AI to optimize fully it needs more partners, more solutions. The greater lesson is that this holds true for all future technology.
Many of us associate the word \”hybrid\” with cars that use both gasoline and electric power. In the tech world, we eventually used the term hybrid cloud to describe situations where companies might process some of their information on the cloud in a mix of public, private, and data centers. These hybrid technology models were created with the same goal as hybrid cars: to reduce energy consumption, increase performance, and improve costs.
Hybrid cars are popular because they combine the best of both gas and electric cars. The gas engines enable the hybrids to travel longer distances and refuel more quickly. Electricity helps reduce emissions and saves money. AI is no different. AI requires a powerful, stable environment for inference and model training. These tasks require a lot of processing power and space. Cloud computing is the answer. AI must also be fast. It must be able to process data closer to the actual action, which is at the edge of a smartphone.
Source:
https://www.forbes.com/sites/danielnewman/2023/07/10/the-future-of-ai-and-everything-else-is-hybrid/?sh=3a0830b72b76
