Current AI systems excel in supervised learning, which is the only paradigm that has shown remarkable success in business applications, while other paradigms like unsupervised and reinforcement learning are still developing.

The majority of financial value from AI today comes from supervised learning, which can generate over 100 billion US dollars a year for companies like Google.

Supervised learning is effective for labeling and recognizing data, such as identifying whether an email is spam or not.

We aimed to create a useful model by training it directly on tasks that users perform in their daily lives, with online browsing being a key focus due to its broad applicability.

The last decade focused on large-scale supervised learning, where increasing data and model size led to improved performance.

General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.

I've been really interested in machine learning, particularly its creative applications, and how it can transform the economy and interact with words, pictures, and sounds.

Social media platforms monetize user data by analyzing behavioral patterns and preferences to target advertisements effectively.

Supervised learning is also useful in automated visual inspection, such as identifying defects in manufactured products.