We aim to establish TensorFlow as a common platform for expressing various machine learning ideas, ensuring it can be used for deep learning, perception problems, and language understanding.

TensorFlow is our second-generation system designed to tackle deep learning and machine learning problems, built to be an open-source platform for global collaboration.

We believe that open-source models will drive more progress in AI, as contributions will come from various labs, not just the largest ones.

The growth in interest for TensorFlow has led to nearly a thousand non-Google contributors globally, enhancing the system through feature additions and bug fixes.

The open-source ecosystem is evolving, and while we lead in this space, we must be cautious about relying on others to maintain the momentum we've built in open-source AI.

We started building Deep Research over a year ago, driven by progress in reinforcement learning and reasoning models, initially focusing on math, science, and coding tasks.

At OpenAI, we pursued robotics not for commercial motives, but to demonstrate the power of machine learning, which later influenced large language models.

Open-source technology is the safest option for building AI systems, as it allows for greater transparency and scrutiny from the community.

We have developed a self-serve platform that allows developers to easily tune AI applications for speed and quality, facilitating rapid scaling and experimentation.