Robotics to AI Adoption + Debugging the Limits of Code LLMs | Two 30 Min. Talks

Two Talks:
1. Robotics to AI Adoption
2. Debugging the Limits of Code LLMs

Speakers

1. Alex Kalish from Amazon Fulfillment Technologies & Robotics
2. George Mazzeo from BlackLocus

Summaries
1. Alex Kalish will share lessons from a decade at Amazon, where he transformed networks, scaled robotics, and recently works on AI adoption programs that touch thousands of employees all while he drove measurable cost savings and quality improvements. His talk will explore today’s robotics challenges, from the over-creation of tools to the real struggle of grounding innovation in user needs. Drawing on his experience leading multi-million-dollar AI transformation programs and now consulting with organizations on generative AI strategy, Alex will highlight why connecting technology to people where they are is the critical unlock for progress, and what that means for startups, enterprises, and investors in Austin’s ecosystem.

2. George Mazzeo will deep dive on the paper: "The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs"
Link to paper: https://arxiv.org/abs/2506.18403
Abstract:
The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strategic points in the debugging process, demonstrating that well-timed interventions can rescue the effectiveness of debugging. DDI reveals a fundamental limitation in current AI debugging and provides the first quantitative framework for optimising iterative code generation strategies.

Info
Austin Deep Learning Journal Club is group for committed machine learning practitioners and researchers alike. The group typically meets every first Tuesday of each month to discuss research publications. The publications are usually the ones that laid foundation to ML/DL or explore novel promising ideas and are selected by a vote. Participants are expected to read the publications to be able to contribute to discussion and learn from others. This is also a great opportunity to showcase your implementations to get feedback from other experts.

Sponsors:
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