Minisymposium Presentation

As the landscape of machine learning (ML) continues to evolve, the integration of generative AI has become a focal point for automating code generation. While it is perfectly suitable to generate text for humans such as the abstract you're reading, this approach often falls short in ensuring the correctness of the generated code, leading to potential pitfalls in robust ML applications. Recent cases exemplify this challenge, where the use of AI to accelerate coding processes resulted in faster but incorrect code, something unacceptable in scientific computing. In response to this critical need, we introduce TADASHI—a novel library designed to bridge the gap between speed and correctness in code transformations. TADASHI offers a user-friendly Python interface that can seamlessly integrate into existing ML scripts. It empowers developers by not only expediting code modifications but also enforcing rigorous correctness checks on these transformations. By ensuring that code remains valid and reliable, TADASHI enhances the stability of ML workflows and fosters confidence in automated processes. Join us as we delve into TADASHI's capabilities, showcasing its potential to revolutionize the code transformation landscape in ML, ensuring that efficiency does not compromise correctness.