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

Luke Marsden, Founder and CEO of Dotscience, shares insights on streamlining MLOps for machine learning projects. He discusses the integration of MLOps and DevOps, highlighting the challenges faced in collaboration and reproducibility. The conversation dives into a manifesto that promotes software engineering practices in ML, aiming for better accountability and continuous deployment. Luke also explores features enhancing collaborative workflows and the benefits of using Jupyter for data science, along with containerized deployment strategies using Docker for optimized model performance.

Mike Tung, Founder and CEO of Diffbot, dives into the unique world of autonomous knowledge graphs. He explains how Diffbot's approach differs from traditional search engines like Google and Bing. The conversation highlights the importance of structured data in AI, challenges of knowledge fusion, and the developer experience with tools like Extraction API and Crawlbot. Tung also discusses their dual role in research and commercial viability, offering insights into their subscription model for accessing the knowledge graph.

Eleven-year-old Aaron Ma, a prodigious machine learning engineer in training, shares his incredible journey through the world of AI. With over 80 Coursera courses under his belt, he discusses his passion for reinforcement learning and self-driving cars. Aaron reflects on his experiences in Kaggle competitions, the challenges of bridging complex concepts without a math-heavy background, and the innovations driving self-driving technologies. His insights offer a unique perspective on the future of technology as he balances academics with his coding adventures.

Terrence Sejnowski, a pioneer in computational neuroscience and head of the Computational Neurobiology Lab at the Salk Institute, joins to unravel the complexities of spiking neural networks. He discusses how these networks mimic biological brain functions, boosting energy efficiency in machine learning. The conversation also delves into the challenges of training these networks, the synergy between neuroscience and AI, and their transformative potential in robotics. Sejnowski shares insights on the future of neuromorphic hardware and its implications for technology.

In this engaging discussion, Xavier Amatriain, Co-founder and CTO of Curai, shares insights into revolutionizing primary healthcare through AI and machine learning. He tackles the shortcomings of traditional care and highlights how his startup enhances patient-physician communication. The conversation delves into the integration of expert systems with modern ML tools and the use of synthetic data to improve diagnostics. Amatriain also discusses the application of transformers like BERT and GPT-2 in crafting intelligent chatbots that support healthcare interactions.

In this discussion, Thomas Dietterich, Distinguished Professor Emeritus at Oregon State University and expert in AI, dives into the nuances of machine understanding. He critiques current systems, advocating for a richer definition of 'understanding' in AI. The conversation touches on the hype surrounding AI advancements and the path toward artificial general intelligence (AGI), proposing that AGI should be viewed as a collection of specialized components. Dietterich also explores the philosophical implications of understanding through the Chinese Room argument, challenging perceptions of true comprehension.

Jonathan Hung, a Sr. Software Engineer at LinkedIn, shares insights on scaling TensorFlow within their infrastructure. He discusses leveraging existing Hadoop clusters for deep learning, introducing TonY, a framework that runs TensorFlow jobs natively on Hadoop. The conversation delves into the challenges of resource management and fault tolerance, particularly in GPU allocation. Hung also highlights LinkedIn's transition to Kubernetes to enhance machine learning workloads and improve the experience for engineers navigating complex AI systems.

Omoju Miller, a Senior Machine Learning Engineer at GitHub, shares her insights on enhancing developer collaboration and open-source participation. She discusses her dissertation on computer science education and her pioneering work on GitHub's machine learning team. The conversation dives into the trends of GitHub stars for ML packages, reflecting on the shifts in the industry. Omoju also highlights the challenges of integrating new technologies and the exciting potential of open-source innovation, inspiring collaboration and creativity in the coding community.

Archana Venkataraman, a John C. Malone Assistant Professor at Johns Hopkins University, specializes in using machine learning to tackle neurological disorders. In this discussion, she shares insights on predicting clinical severity in autism using fMRI data. Venkataraman highlights innovative tools for enhancing emotional understanding in individuals with autism and touches on combining EEG and MRI data for diagnoses. She emphasizes the transformative potential of AI in clinical decision-making and improving patient outcomes.

Phoebe DeVries, a postdoctoral fellow at Harvard, and Brendan Meade, a professor there, delve into the groundbreaking fusion of deep learning and earthquake prediction. They discuss how machine learning can analyze GPS data to forecast aftershock patterns. The conversation highlights the shift to neural networks for faster modeling, revealing surprising insights into seismic activities. Their collaborative efforts aim to create advanced computational tools that integrate tectonic dynamics, enhancing the accuracy of aftershock forecasts and understanding earthquake mechanisms.