The power of continuous learning

OpenAI

Lilian Weng works on Applied AI Research at OpenAI.

What excites you most about the future of AI?

Artificial general intelligence (AGI) should outperform humans at most economically valuable work. I’m looking forward to seeing AGI help human society in these ways:

1. Fully automate or significantly reduce human efforts on tasks that are repetitive and non-innovative. In other words, AGI should drastically boost human productivity.

2. Greatly expedite the discovery of new scientific breakthroughs, including but not limited to facilitating human decision making process by providing additional analyses and information.

3. Understand and interact with the physical world effectively, efficiently and safely.

What projects are you most proud of that you’ve worked on at OpenAI?

During my first 2.5 years at OpenAI, I worked on the Robotics team on a moonshot idea: we wanted to teach a single, human-like robot hand to solve Rubik’s cube. It was a tremendously exciting, challenging, and emotional experience. We solved⁠ the challenge with deep reinforcement learning (RL), crazy amounts of domain randomization, and no real-world training data. More importantly, we conquered the challenge as a team.

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:

“Ideas in different topics or fields can often inspire new ideas and broaden the potential solution space.”