Automatic Agent Learning from Scratch via Self-Planning

February 1, 2024

A new paper, “AUTOACT: Autonomous Agent Creation for Task Completion,"1 by Shuofei Qiao et al., introduces the AUTOACT automatic agent learning framework. This framework stands out by eschewing the traditional reliance on extensive annotated data and synthetic trajectories, a stark contrast to models like GPT-4. AUTOACT’s strength lies in its ability to synthesize planning trajectories and implement a division-of-labor strategy. This facilitates the creation of sub-agent groups that work in tandem, showing promise in complex question-answering tasks and potentially surpassing the capabilities of established models like GPT-3.5-Turbo.

While the AUTOACT framework currently demonstrates impressive capabilities, I’m intrigued by the possibility of its future evolution, especially in terms of swarm behavior. Swarm behavior involves numerous agents operating together, much like a flock of birds or a colony of ants, to achieve complex goals beyond the capability of a single agent. This self-organizing behavior, characterized by simplicity in individual actions but complexity in collective outcomes, could revolutionize AI’s approach to problem-solving. Just imagine AI agents collaborating like cells in Conway’s Game of Life, leading to emergent, sophisticated solutions for real-world challenges.

I’m also curious about the more immediately practical application of the division-of-labor applied to stronger models like GPT-4. This blend could enable automatic agents that breeze through complex tasks that currently stump the best of them.

While the advancements and possibilities presented by AUTOACT are vast, they warrant a cautious approach. Sadly, I suspect that unless this technology is kept in the hands of individuals, the institutional imperative of corporations will use these advancements to break human brains and sell them things. In an optimistic future, they could become the computer from Star Trek. It necessitates a strong focus on ethical considerations and regulatory measures. Additionally, I think it is key that we ensure open-source AI development flourishes. This is the Luddite equivalent of making sure every home has a loom.

Responsible development and application of these technologies are essential to harness their full potential while safeguarding against potential societal risks. The future of AI, abundant with opportunities, must be navigated with an emphasis on innovation balanced by ethical stewardship.