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
Jul 27, 2020 • 1h 3min
Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395
Hal Daumé III, a professor at the University of Maryland and a principal researcher at Microsoft, dives into the intricate world of natural language processing. He shares his journey from mathematics to ethics in AI, stressing the importance of reducing bias in NLP. The discussion also highlights the need for inclusive data collection and effective evaluation frameworks. Daumé emphasizes that advancing fairness in AI requires collaboration and systemic change, calling for a commitment to sociolinguistic complexities in technology.
Jul 23, 2020 • 55min
Graph ML Research at Twitter with Michael Bronstein - #394
Michael Bronstein, a Professor at Imperial College London and Head of Graph Machine Learning at Twitter, shares insights into the growth of graph neural networks in machine learning. He discusses challenges like scalability and dynamic graphs, delving into innovative approaches that enhance model training and expressiveness. The conversation highlights real-world applications of graph ML in drug discovery and the importance of understanding molecular properties for advancing therapies. Michael's expertise illuminates the intersection of geometry and deep learning.
28 snips
Jul 20, 2020 • 1h 34min
Panel: The Great ML Language (Un)Debate! - #393
In a lively debate, Chris Nurenberger, a machine learning expert, champions Clojure for its conciseness. Barack Canberr pushes for JavaScript's accessibility, while Huda Nassar highlights Julia's speed and community. Robert Osizu-Aness discusses probabilistic programming's potential in NLP. Catherine Nelson emphasizes Python's flexibility, and Gabriella DeCuroz celebrates R's supportive resources. Avi Bryant discusses Scala's challenges, and Chris Lattner touts Swift's performance. Together, they explore the strengths and weaknesses of various programming languages in the ML landscape.
Jul 16, 2020 • 43min
What the Data Tells Us About COVID-19 with Eric Topol - #392
Eric Topol, Director and Founder of the Scripps Research Translational Institute and author of Deep Medicine, dives into the lessons learned from COVID-19. He highlights the disease's surprising impact on various organs beyond just respiratory issues and the importance of accurate data collection. They discuss the power of AI in enhancing medical decision-making and how deep phenotyping may help personalize responses to treatment. The conversation also touches on advancements in diabetes management and the promise of federated learning for privacy in healthcare.
9 snips
Jul 13, 2020 • 46min
The Case for Hardware-ML Model Co-design with Diana Marculescu - #391
Diana Marculescu, a Professor of Electrical and Computer Engineering at UT Austin, dives into the intriguing world of hardware-aware machine learning. She discusses the necessity of co-designing hardware and ML models for maximizing efficiency. Key topics include optimizing neural networks for edge devices, profiling for power and latency in GPUs, and innovative approaches to architecture search. Diana also emphasizes the critical need for adaptable designs and the future potential of deep learning driven by hardware advancements.
Jul 9, 2020 • 41min
Computer Vision for Remote AR with Flora Tasse - #390
Flora Tasse, Head of Computer Vision & AI Research at Streem, shares her journey from Cameroon to becoming an expert in AR/VR and 3D mesh environments. She discusses the innovative blend of 2D image processing with natural language for 3D modeling and the complexities of remote AR, including real-time 3D meshing challenges. Flora also explores six degrees of freedom in object tracking and highlights the exciting possibilities of AR technology in enhancing consumer experiences and user interactions.
Jul 6, 2020 • 42min
Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389
Julian Quiroga, Computer Vision Team Lead at Genius Sports, dives deep into the world of automated basketball video production. He discusses innovative techniques using Gaussian models for player dynamics and the integration of deep learning to enhance viewer experiences. Challenges like accurate player localization and adapting strategies for different sports are tackled head-on. Quiroga also shares insights into optimizing camera angles and real-time data usage, revolutionizing how basketball games are broadcasted.
Jul 2, 2020 • 1h 21min
How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388
Deb Raji, a Technology Fellow at NYU's AI Now Institute, tackles the pressing issues surrounding facial recognition technology. She shares insights from her work on the Gender Shades project, revealing biases against darker-skinned females. The discussion touches on recent moratoriums from tech giants like IBM and Amazon, highlighting the urgent need for ethical standards and independent audits. Raji also critiques practices like those of Clearview AI, emphasizing the risks of digital surveillance, particularly for marginalized communities.
7 snips
Jun 29, 2020 • 45min
AI for High-Stakes Decision Making with Hima Lakkaraju - #387
Hima Lakkaraju, an Assistant Professor at Harvard University, specializes in fair and interpretable machine learning. In this discussion, she dives into the pitfalls of popular explainability techniques like LIME and SHAP, exposing their vulnerabilities to adversarial attacks. She shares her journey from India to academia, emphasizing the need for transparency in AI, especially in high-stakes areas like healthcare and criminal justice. By examining local and global explanation methods, she reveals critical insights into improving AI fairness and accountability.
10 snips
Jun 25, 2020 • 46min
Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386
In this engaging conversation, Pavan Turaga, an Associate Professor at Arizona State University, shares his groundbreaking work at the intersection of physics and computer vision. He dives into the complexities of invariance and the geometric foundations of deep learning. Pavan highlights the challenges of modeling image variability for object recognition and the innovative use of time constraints in activity classification. His insights into robust loss functions and the integration of artistic elements in technology reveal a fresh perspective on the field.
