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
Aug 10, 2020 • 45min
Human-AI Collaboration for Creativity with Devi Parikh - #399
Devi Parikh, an Associate Professor at Georgia Tech and research scientist at Facebook AI Research, dives into the fascinating intersection of AI and creativity. She shares her insights on how AI can enhance the creative process for artists. From discussing the nuances of creativity to exploring innovative tools like casual creation for art generation, she highlights how AI can foster collaboration and inspire unique artistic expressions. Devi also touches on transforming personal journaling into abstract visuals, showcasing the potential of AI in everyday creativity.
Aug 6, 2020 • 49min
Neural Augmentation for Wireless Communication with Max Welling - #398
In this engaging conversation, Max Welling, Vice President of Technologies at Qualcomm Netherlands and Professor at the University of Amsterdam, delves into neural augmentation and its real-world applications. He discusses federated learning as a means to enhance data privacy for users, while exploring the exciting intersection of quantum mechanics and neural networks. Max also highlights innovative chip design approaches that merge traditional engineering with machine learning, showcasing the potential of these advancements in tackling complex challenges.
Aug 4, 2020 • 1h
Quantum Machine Learning: The Next Frontier? with Iordanis Kerenidis - #397
Iordanis Kerenidis, a Research Director at CNRS Paris and Head of Quantum Algorithms at QC Ware, discusses the groundbreaking realm of quantum machine learning. He shares insights from his keynote at ICML, delving into the evolution of quantum algorithms and their historical milestones. The conversation navigates the intricacies of quantum computing fundamentals, showcasing the power of superposition and its applications in recommendation systems. Iordanis also tackles challenges in integrating quantum methods with classical techniques, revealing the exciting potential and complexities ahead.
Jul 30, 2020 • 47min
ML and Epidemiology with Elaine Nsoesie - #396
Elaine Nsoesie, an assistant professor of global health at Boston University, brings her expertise in machine learning and epidemiology to discuss innovative approaches in public health. She delves into how data from social media and search engines can track health behaviors, particularly in African countries. The conversation also highlights the application of satellite imagery to assess obesity rates and the challenges faced by epidemiologists during the COVID-19 pandemic, focusing on health disparities among marginalized communities.
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.
