The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Episodes
Mentioned books
Feb 19, 2020 • 51min
How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350
Kamran Khan, founder and CEO of BlueDot and a professor at the University of Toronto, shares fascinating insights on how AI can predict infectious disease outbreaks. He discusses how BlueDot's algorithms were the first to warn about the coronavirus from Wuhan. The conversation also covers lessons learned from the SARS outbreak, the importance of human mobility in disease spread, and the collaboration needed among diverse experts to enhance public health responses. Khan highlights both the power and limitations of AI in outbreak detection.
9 snips
Feb 17, 2020 • 42min
Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
Emmanuel Ameisen, a machine learning engineer at Stripe and author of "Building Machine Learning Powered Applications," dives deep into the journey of turning ideas into ML products. He shares insights on structuring end-to-end projects and stresses the importance of explainability for model success. The conversation covers practical approaches to debugging, ethical considerations in deployment, and the necessity of post-deployment monitoring. Ameisen also emphasizes user feedback's role in refining ML applications, advocating for flexible development practices.
9 snips
Feb 13, 2020 • 41min
Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348
In this conversation, Abeba Birhane, a PhD student from University College Dublin and author of a notable paper on algorithmic injustices, dives into the ethics of AI. She discusses the 'harm of categorization' and how traditional fairness metrics overlook marginalized communities. Birhane advocates for relational ethics, arguing for a focus on societal impacts rather than mere algorithmic fairness. The talk also touches on the complexities of language in machine learning and critiques the notion of 'robot rights' in favor of prioritizing human welfare.
Feb 10, 2020 • 1h 4min
AI for Agriculture and Global Food Security with Nemo Semret - #347
Nemo Semret, CTO of Gro Intelligence, shares his expertise on using AI to tackle global food security challenges. He highlights the importance of data-driven strategies in agriculture, particularly in addressing issues like climate change and locust outbreaks. The discussion delves into precision agriculture's impact on land use and crop selection, and the role of advanced machine learning in yield predictions. Nemo also addresses the complexities of agricultural data management, including automation and the challenges of maintaining data quality.
8 snips
Feb 7, 2020 • 34min
Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
Ryan Rogers, a Senior Software Engineer at LinkedIn specializing in differential privacy, shares insights on user data privacy in analytics. He delves into his innovative paper on differential privacy and top-k selection, highlighting how LinkedIn balances user anonymity while providing aggregate insights. The discussion covers challenges in real-world applications, the role of Gumbel noise in algorithm performance, and the significant collaboration in advancing differential privacy in the tech industry.
9 snips
Feb 5, 2020 • 32min
Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345
Erez Cohen, VP of CloudX & AI at Mellanox (now part of NVIDIA), dives into the vital role of networking in deep learning. He discusses how advancements like RDMA and GPU Direct are enhancing multi-node deep learning on Kubernetes. Erez highlights the acquisition of Mellanox by NVIDIA and shares insights on optimizing network switch configurability. Moreover, the integration of frameworks like TensorFlow and how they interact with advanced networking technologies are explored, pushing the boundaries of performance in AI applications.
12 snips
Feb 3, 2020 • 25min
Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
Bob Killen, Research Cloud Administrator at the University of Michigan, shares insights on deploying Kubernetes to enhance research capabilities. He discusses how Kubernetes is transforming user experiences in diverse research areas and supports tools like Jupyter notebooks. Bob addresses concerns about balancing ML/AI needs amid broader usage and explores the challenges of managing long-running AI workloads. The conversation highlights the ongoing evolution of Kubernetes to support various applications, including collaborative efforts to improve usability.
7 snips
Jan 30, 2020 • 45min
Scalable and Maintainable Workflows at Lyft with Flyte w/ Haytham AbuelFutuh and Ketan Umare - #343
In this discussion, Ketan Umare and Haytham AbuelFutuh, both software engineers at Lyft, dive into the innovative Flyte project they contribute to. Ketan shares the motivation behind developing Flyte, while Haytham highlights its Kubernetes-native design. They explore strong typing's role in improving user experience, the challenges of managing machine learning workflows, and Flyte's open-source journey to foster community engagement. The conversation also touches on data provenance and optimizing computational efficiency in large-scale data processing.
10 snips
Jan 27, 2020 • 40min
Causality 101 with Robert Osazuwa Ness - #342
Robert Osazuwa Ness, an ML Research Engineer at Gamalon and an instructor at Northeastern University, dives into the intriguing world of causality. They discuss how understanding causal relationships can enhance model accuracy and increase algorithmic fairness. Ness explains disentangled representations and their importance in causal inference through examples like variational autoencoders. They also share exciting details about a new collaborative study group focused on causal modeling, inviting community participation to deepen knowledge in this essential area.
Jan 23, 2020 • 42min
PaccMann^RL: Designing Anticancer Drugs with Reinforcement Learning w/ Jannis Born - #341
In this insightful discussion, Jannis Born, a PhD student at ETH Zurich and IBM Research Zurich, dives into his groundbreaking work with 'PaccMann^RL.' He explains how his background in computational neuroscience informs anticancer drug discovery and the role of reinforcement learning in tailoring treatments. Jannis also explores the complexities of RNA sequencing, gene expression, and innovative drug prediction methods using deep learning. Listeners gain a glimpse into the future of personalized medicine and the integration of AI in revolutionizing cancer treatment.
