From simulation and RL training to vision perception and hardware firmware, we collaborated so closely and cohesively. It was an amazing experiment and during that time, I often thought of Steve Jobs’ reality distortion field⁠(opens in a new window): when you believe in something so strongly and keep on pushing it so persistently, somehow you can make the impossible possible.

Since the beginning of 2021, I started leading the Applied AI Research team. Managing a team presents a different set of challenges and requires working style changes. I’m most proud of several projects related to language model safety within Applied AI:

As the Applied AI team is practicing the best way to deploy cutting-edge AI techniques, such as large pre-trained language models, we see how powerful and useful they are for real-world tasks. We are also aware of the importance of safely deploying the techniques, as emphasized in our Charter⁠.

Current deep learning models are not perfect. They are trained with a gigantic amount of data created by humans (e.g., on the Internet, curated, and literature) and unavoidably absorb a lot of flaws and biases that long exist in our society. For example, when DALL·E was asked to portray a nurse, it would only generate female characters, or for a professor, it would only generate white people. The model captures biases in real world statistics or biases in our training data.