Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter

Open-Endedness

Open-endedness in AI refers to systems capable of generating infinite data, continuously increasing in complexity and interest. Marc Rigter discusses the potential of these systems to self-improve by generating new data, which can be used to further train AI models. However, he acknowledges the challenge of ensuring the data is genuinely novel, as it often originates from previously trained models 1. Tim Scarfe highlights the importance of creativity in AI, suggesting that the next frontier is designing systems that not only answer questions but also ask them 2.

The next frontier of AI is really, how do we design systems that don't just answer questions, but they actually are the ones that start to ask the questions.

--- Marc Rigter

This shift could bring AI closer to traditional notions of strong artificial general intelligence (AGI).

   

New Knowledge

AI's potential to generate new knowledge lies in its ability to explore and creatively combine existing models. Minqi Jiang explains that AI can use synthetic data and reinforcement learning to optimize behavior, building on the foundation of existing knowledge 3. Tim Scarfe describes GPT-4 as a memetic intelligence, emphasizing its role in distilling cultural knowledge and acting as an automated scientist 4.

Creativity happens through knowledge. New knowledge doesn't come from the ether; it's on the trodden path of existing knowledge.

--- Tim Scarfe

This approach highlights the importance of cultural and historical context in AI's creative processes.

   

Creativity & Robustness

Balancing creativity with robustness in AI systems is crucial for developing reliable models. Minqi Jiang discusses the evaluation of algorithms in synthetic domains, emphasizing the importance of robustness in handling diverse environments 5. He explains that model-based reinforcement learning separates dynamics from value models, allowing for explicit planning and simulation 6.

We achieve this robustness property which we talked about in terms of mini max regret.

--- Minqi Jiang

This separation enhances the system's ability to generalize across tasks, ensuring stability while fostering creative exploration.