The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.

Scaling and training very large neural networks has proven effective, as demonstrated by our training of a neural network 60 times larger than previous models using 16,000 CPU cores.

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.

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

The era of scaling pre-training may not be completely over, but reasoning now appears to offer the most potential for maximizing gains from scaling compute.

We were confident that larger neural networks would yield better results, even before we had the data to prove it.

The influential paper on scaling laws for neural language models published in January 2020 established that increasing model size, data, and compute leads to consistent improvements in performance, emphasizing the importance of scale over algorithm.

In AI, every approach has performed better when more scale is applied, especially as foundational problems are addressed over time.

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