How Autoresearch is changing ML research — Zhengyao Jiang, Weco

This talk argues that the core challenge in software engineering is shifting from writing code to managing complexity, designing systems, and ensuring correctness. While AI models now excel at generating code syntax, the future of software development lies in leveraging AI for higher-level tasks like architectural design, complex problem decomposition, and inductive reasoning. The economics of code production are changing, making code generation nearly free and emphasizing the need for new processes and evaluation methods.

Benoit Schillings, VP of Research at Google DeepMind, outlines the frontier of AI engineering research, emphasizing the shift from foundational model development to sophisticated engineering for product integration and real-world impact. The agenda focuses on building robust, scalable, and reliable AI systems that can be productized and deployed effectively. This involves a deep dive into the engineering challenges and opportunities at the forefront of AI research.

This talk addresses the challenge of transitioning frontier machine learning research into production-ready software. It proposes a three-pronged approach focusing on research legibility, code structure, and decomposition strategies to improve the velocity and efficiency of teams bridging the gap between ML research and software engineering. The core idea is to treat this transition as a systems and process problem.

This talk addresses the ambiguity of "done" in the context of AI agents, arguing that a simple boolean check is insufficient. It proposes that "done" is a bundle of claims requiring verification across multiple dimensions, including artifact production, evidence of completion, adherence to a rubric, clear ownership for next steps, and survival of real-world conditions. The core thesis is that agent systems need a liveness model to manage the tension between keeping work moving and ensuring its quality.

This talk challenges the assumption that advanced frontier models are necessary for building effective voice agents. The presenters argue that simpler, more focused models can achieve comparable or even superior results for specific voice agent tasks, emphasizing practicality and efficiency over raw model power. They advocate for a pragmatic approach to agent development, prioritizing task-specific capabilities.