The ImageNet dataset, consisting of 1 million images across 1,000 classes, was crucial for the advancement of deep learning in computer vision.

The success of deep learning in AI has been driven by the ability to train models on large datasets, such as ImageNet, which has led to significant improvements in image recognition tasks. In the past few years, the error rate in image recognition has dropped dramatically, with some teams achieving as low as 3% error, which is considered superhuman performance.

The success of deep learning is not just about algorithms; it's also about the availability of large datasets, like ImageNet, which have driven advancements in the field.

While handcrafted models for image denoising are still relevant, deep neural networks are increasingly outperforming them in various scenarios.

The major advances in neural networks over the past 20 years include the development of convolutional neural networks, which efficiently process image data by applying the same computation across different locations.

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.

The breakthroughs in reinforcement learning at DeepMind, particularly in games like Capture the Flag and AlphaStar, have shown that we can achieve human-level performance in complex tasks.

When we started OpenAI, I was convinced that deep learning was genuinely working and that it improved with scale. This realization was crucial because it seemed like others weren't recognizing its potential.

The most cited paper in science is a deep learning paper from 2015, highlighting that significant contributions to AI can come from anywhere, not just Silicon Valley.