Part 9/14:
The speaker emphasizes that we are still in the early innings of gen AI's potential. The current hype cycle is reminiscent of past tech bubbles, but the core technology is here to stay. The rapid pace of innovation—such as the surge of open-source models—means that within months, new tools emerge that challenge before consolidating.
Key areas of ongoing research include:
Reducing the hardware footprint required to train and operate large models.
Developing cost-effective hardware, moving away from expensive GPU-based systems.
Advancing hardware efficiency with techniques like integer-based calculations to lower energy consumption, which currently is a significant concern due to the enormous energy needs of AI data centers.