The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Tom Szumowski, a Data Scientist at URBN, shares his insights on automating fashion product attribution using custom vision APIs. He discusses the transition from defense to retail, revealing unexpected connections between the two fields. Tom explains the challenges of building machine learning models for image recognition and the significance of data quality in model performance. He also compares various computer vision solutions, highlighting the effectiveness of their homegrown Fast.ai model. Listeners will gain a fascinating glimpse into the intersection of AI and e-commerce.

In this engaging discussion, Peter Wittek, an Assistant Professor at the University of Toronto and a leading expert in quantum-enhanced machine learning, shares insights into the current state and future of quantum computing. He explores the transition from theoretical mathematics to practical applications in machine learning. Peter also highlights the fundamental differences between quantum and classical computers, the promise of hybrid algorithms, and the significance of his new online course for practical learning in this revolutionary field.

In this engaging conversation, Ewin Tang, a PhD student at the University of Washington, takes listeners on a journey through her groundbreaking work on quantum-inspired algorithms for recommendation systems. She reveals how her revolutionary approach challenges traditional notions about the necessity of quantum computing. Ewin dives into the intricacies of quantum superposition, discussing both its potential advantages and the complexities of real-world application. With skepticism swirling in the research community, she advocates for a critical examination of quantum claims.

In this discussion, Alfredo Luque, a software engineer at Airbnb, dives into the challenges of transitioning AI projects from concepts to scalable systems. He shares insights on enhancing Airbnb's image categorization processes using TensorFlow, addressing technical hurdles with a database of half a billion images. Alfredo also introduces BigHead, a library that simplifies model building and integration with TensorFlow, highlighting its unique features like real-time execution and improved visualization tools. The conversation showcases the importance of effective machine learning tooling for streamlined workflows.

Elena Nieddu, a PhD student at Roma Tre University, dives into her fascinating project, "In Codice Ratio," which aims to transcribe and annotate documents from the Vatican Secret Archive using machine learning. She discusses the challenges of traditional OCR and shares innovative strategies for improving accuracy in transcribing medieval manuscripts. Elena also highlights the unique crowdsourcing initiative involving high school students in Italy, empowering them while enriching the project with quality annotations. This intersection of history and technology promises to unlock hidden treasures of knowledge.

In this discussion, Paige Bailey, a TensorFlow developer advocate at Google with a rich background in machine learning, shares insights on TensorFlow 2.0's alpha release. They delve into the evolution of TensorFlow APIs and highlight the benefits of eager execution and Keras integration. Paige emphasizes community collaboration and the significance of tools like tf.function. She also discusses her transformative journey in the field and the opportunities within programs like Google Summer of Code, fostering innovation in tech.

In this engaging discussion, Andrew Trask, a PhD student at the University of Oxford and leader of the OpenMined Project, delves into the intricacies of privacy-preserving AI. He shares his journey from music to machine learning, discussing the importance of differential privacy and secure multi-party computation. Trask highlights the advancements in the OpenMined community, emphasizing future developments in decentralized data science, particularly in finance and healthcare. The conversation underscores the need for robust privacy tools in a data-driven world.

Jos Van Der Westhuizen, a PhD student at Cambridge University, discusses his immersive journey from biomedical engineering to machine learning. He dives into the importance of the forget gate in LSTMs, revealing how it boosts computational efficiency. The conversation also covers his innovative architecture, Janet, which combines attention mechanisms with LSTMs. Jos emphasizes selective learning and how managing what to forget is key in optimizing neural networks. Tune in to hear about the future of simpler, more efficient neural network designs!

Andrew Guldman, VP of Product Engineering and R&D at Fluid, shares insights into creating AI-driven user experiences for online retail. He discusses the innovative Fluid XPS, developed for The North Face to simplify outdoor gear selection for casual shoppers. Guldman delves into using advanced algorithms and graph databases, the challenges of keeping pace with evolving AI, and the balance between programming and flexibility. His anecdotes highlight the importance of emotional connections in product recommendations, making tech more human-centered.

In this insightful talk, Kevin Tran, a PhD student in Chemical Engineering at Carnegie Mellon University, shares his expertise on the intersection of machine learning and renewable energy fuel cells. He discusses the challenges in creating effective electrocatalysts for CO2 reduction and H2 evolution. Kevin dives into the role of active learning in materials design, highlighting its iterative nature and reliance on quantum mechanics-based simulations. He also examines how advanced methodologies, like Bayesian statistics and neural networks, improve predictive accuracy in catalyst performance.