This podcast episode explores the development and potential of AI agents for high-level reasoning. The co-founders of EMBU, Kanjun Qiu and Josh Albrecht, share their journey and focus on training large foundation models for high-level reasoning. They highlight the significance of agents that can reason and code, envisioning a future where computers can understand instructions and assist users in completing tasks. The episode also discusses the range of difficulty in agent tasks and the trend towards more general and autonomous agents. The challenges of building real-world performing agents are addressed, emphasizing the potential to revolutionize human-computer interaction. The importance of reasoning and error correction in enhancing system reliability, as well as the role of code in achieving robustness, is emphasized. Furthermore, the concept of coding agents is introduced, along with insights into the research process. The evaluation and assessment of coding agents are also covered. The speakers stress the need to balance functionality and research in a product company, as well as the allocation of capital and compute resources for AI companies.
Takeaways
• The development of AI agents for high-level reasoning is a key focus for EMBU.
• Knowledge gained from building an AI recruiting company played a role in the development of reasoning agents.
• Language models have potential in various modalities besides text, such as video and images.
• Agents that can act on behalf of humans and free them up for other tasks are important.
• Agent tasks vary in difficulty, with a shift towards more general and autonomous agents.
• The challenges in building real-world performing agents are seen as a spectrum of difficulty.
• Reasoning and error correction techniques are essential for system reliability.
• The use of code and language models in combination can create more robust reasoning agents.
• The evaluation process for coding agents includes criteria such as speed, code quality, and reliability.
• Balancing functionality and research is crucial in a product company.
• Capital allocation and compute resources play a significant role in the development of AI agents.
• Training large models requires sufficient computing power and data, with a focus on coding.
• Coding agents have the potential to enhance software quality and user experience.
