There's a significant gap between the current compute capabilities and what could be achieved with ten to a hundred times more compute, which would allow for much greater AI services.

The demand for computing power is growing rapidly, and we can't ramp up spending fast enough to keep up with use cases.

The advancements in AI depend significantly on the amount of computing power we allocate and the timing of unpredictable breakthroughs.

The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

The adaptation of compute platforms is essential for running capable models in low power environments, such as smartphones and autonomous vehicles, as well as in large-scale data centers.

Both algorithmic improvements and computational advancements, such as better chips and larger data centers, are essential for progress in AI.

We need to develop scaling science alongside our compute scaling efforts to ensure effective advancements in AI capabilities.

The future of computing will involve asynchronous workloads where AI systems perform extensive research before returning results, necessitating robust infrastructure to handle increased loads.

Our group investigates the hypothesis that large amounts of compute can solve interesting problems using neural networks, which has led to significant advancements in unsupervised learning at scale.