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

Julie Shah, a professor at MIT, specializes in interactive robotics. In this engaging discussion, she explains how robots can predict human behavior, vital for effective collaboration. Shah highlights her ambitious vision for a field robot that operates independently of human control. She also delves into the nuances of cross-training robots and humans for improved teamwork and the challenges of adapting to human unpredictability in dynamic environments. The conversation is a captivating glimpse into the future of human-robot partnerships.

In a lively discussion, Yunyao Li, a Senior Research Manager at IBM Research, tackles the intricacies of natural language processing in enterprise settings. She shares insights on productizing NLP, balancing customer needs with research rigor. Yunyao dives into the complexities of document discovery and the synergy of deep learning techniques. Highlighting the importance of human involvement, she discusses innovative data augmentation strategies to create high-quality datasets. Her unique perspective reveals how IBM empowers users to enhance AI transparency and accuracy.

Kim Branson, SVP and global head of AI and machine learning at GSK, shares insights on leveraging machine learning in pharmaceuticals. He discusses the integration of genetics data for drug discovery and the impressive 500 billion node knowledge graph designed to mine scientific literature. Kim also highlights their recent collaboration with King’s College, focusing on personalized cancer research using AI. The conversation dives into the challenges of building scalable AI infrastructures and the critical need for robust evaluation programs in real-world applications.

David Ha, a research scientist at Google Brain, shares his insights on how constraints and biological bottlenecks can revolutionize AI training. He discusses the evolution of generative adversarial networks, highlighting their journey from basic image generation to sophisticated applications. The conversation dives into neuroevolution, sensory substitution, and adaptive learning techniques, showcasing how these innovations can enhance AI systems. David also explores the importance of collective intelligence and self-organization in neural networks, making profound connections to both biology and technology.

Join Luke Stark, an ethics and AI researcher at Western University, as he critiques facial recognition technology, likening it to plutonium. He discusses his paper on physiognomic AI, highlighting the inherent racism in using facial features for judgments. Luke delves into Facebook's recent announcement to shut down its facial recognition system, suggesting it's not as groundbreaking as it seems. They explore the biases affecting marginalized communities and the urgent need for a regulatory framework to address ethical concerns surrounding this controversial technology.

Francesc Joan Riera, an applied machine learning engineer at LEGO, dives into the intricacies of machine learning within the company. He discusses innovative content moderation techniques tailored for child safety. Riera also shares insights about integrating human feedback with algorithms and implementing a serverless A-B testing framework using AWS. He highlights the importance of efficient data management strategies and the evolution of machine learning infrastructure, ultimately aiming to enhance user engagement through theme detection in LEGO apps.

Hamel Husain, a Staff Machine Learning Engineer at GitHub, has contributed to pivotal open-source projects like FastAI and nbdev. He shares insights from his journey through Silicon Valley and discusses the ML tooling gaps he encountered. The conversation highlights innovative tools like MBDev that integrate coding and documentation, while also exploring how FastAI enhances data science workflows. Hamel expresses excitement for future ML tools and the seamless automation capabilities of GitHub Actions, promising a more efficient coding experience.

Julianna Ianni, VP of AI Research & Development at Proscia, discusses groundbreaking advancements in using deep learning for melanoma detection. She highlights the development of a multitask classifier, improving accuracy in distinguishing low-risk from high-risk cases. Julianna also elaborates on the challenges of achieving consensus among pathologists and the complexities of training AI to handle image artifacts. As AI transforms cancer diagnostics, she shares insights into the promising future for integrating these technologies into clinical practices.

In this engaging discussion, Akshat Kaul, Head of Data Science and Machine Learning at Redfin, shares his expertise in leveraging machine learning to transform the real estate industry. He delves into the evolution of Redfin’s ML capabilities, emphasizing innovative applications like price estimation and property recommendations. Akshat also introduces Redeye, their internal platform for modeling, and discusses how cloud services are reshaping their ML deployment. Additionally, he reflects on how the pandemic has influenced buyer behavior and future ML strategies.

Edward Raff, Chief Scientist at Booz Allen Hamilton, dives deep into the interface of machine learning and cybersecurity, focusing on malware detection. He discusses the evolution of adversarial ML and insights from his recent paper on adversarial transfer attacks. Edward highlights the unique challenges malware poses and the ethical implications of adversarial strategies. The conversation also explores innovative techniques like graph neural networks and the future directions for enhancing malicious software detection. A fascinating look at the tech battle between defenders and attackers!