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

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

The balance between algorithmic improvements and hardware advancements is crucial, as both are necessary for progress in AI development.

The tech stack must be reimagined from first principles to accommodate new AI workloads.

There's a significant opportunity to accelerate machine learning applications and societal benefits with increased compute power.

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.

The current computing form factors are not optimal for AI; we need devices that are always with us and can understand our context better.

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