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.

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 breakthrough moment in computer vision for deep learning was the 2012 AlexNet paper, which significantly outperformed previous algorithms in the ImageNet challenge.

Deep learning applications at Google include Google Photos, which effectively utilizes computer vision to understand the content of images.

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.

In the medical domain, we applied deep learning to ophthalmology, specifically to detect diabetic retinopathy in retinal images.

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

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.

Neural networks can struggle with images they haven't been trained on, highlighting the continued importance of handcrafted models in certain contexts.