E99: Developing AI Agents with Generally Intelligent

Challenges

Building AI agents presents significant challenges, particularly in making them function effectively in real-world environments. Kanjun Qiu emphasizes the importance of reasoning, which involves understanding when to ask questions, clarifying them, and projecting outcomes. She explains, "The hardest part is making them work," highlighting the complexity of integrating reasoning into AI systems 1. Generally Intelligent focuses on training foundation models from scratch, allowing agents to generate data that feeds back into these models, enhancing their reasoning capabilities 2.

   

Reasoning

Reasoning is a crucial capability for AI agents, enabling them to make informed decisions and adapt to new situations. Kanjun Qiu notes that while language models can predict the next word, they often lack the judgment needed for effective action. She states, "These models are intelligent in that way, but they don't make for good agents," underscoring the need for systems that can reason and act appropriately 3. Generally Intelligent's research explores reasoning in real environments, such as code editors, to enhance AI performance 4.

   

Optimization

Training AI models involves optimizing hyperparameters and utilizing data effectively. Kanjun Qiu discusses the use of Avalon, a faster alternative to Minecraft, for training agents, allowing for more efficient experimentation. She highlights the importance of tuning, stating, "Tuning really matters," as it significantly impacts model performance 5. Generally Intelligent employs automatic hyperparameter optimization to streamline training processes and improve AI capabilities 6.