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 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.
Jun 22, 2020 • 55min
Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385
Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm AI Research, dives deep into conditional computation for optimizing neural networks. He discusses how channel gating enhances efficiency and accuracy while reducing model size. The conversation explores innovative methods for multitask learning, addressing challenges like catastrophic forgetting in continual learning. Babak also shares insights into practical applications of his research, demonstrating how these advancements transition effectively from the lab to real-world usage.
Jun 18, 2020 • 52min
Machine Learning Commerce at Square with Marsal Gavalda - #384
Marsal Gavalda, the Head of Machine Learning for the Commerce platform at Square, dives into the fascinating world of machine learning applications. He shares how Square's focus on technology from the start has driven success across various areas, particularly in risk management and fraud detection. Gavalda discusses strategies for balancing short-term projects with long-term innovations, the significance of data quality, and the role of cross-team collaboration. He also emphasizes the democratization of ML and the importance of ethical AI practices in today’s landscape.
Jun 15, 2020 • 44min
Cell Exploration with ML at the Allen Institute w/ Jianxu Chen - #383
Jianxu Chen, a scientist at the Allen Institute for Cell Science, shares insights on the transformative Allen Cell Explorer Toolkit. He delves into the challenges of merging machine learning with biology, emphasizing the need for interdisciplinary collaboration. The conversation highlights innovative methods for 3D segmentation of intracellular structures, the importance of GPU computing, and the fascinating role of autoencoders in enhancing microscopy data visualization. Listeners will discover how these advancements are revolutionizing cell image analysis!
Jun 11, 2020 • 32min
Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382
In this fascinating discussion, Andreas Madsen, an independent researcher from Denmark, shares his insights on neural arithmetic units and the challenges of independent research. He emphasizes the importance of collaboration and community support while navigating the competitive landscape of academic publishing. Madsen highlights difficulties in extrapolation with neural networks, proposing innovative benchmarks to enhance performance. His journey from academia to freelancing brings attention to the resource demands and perseverance needed for successful research in machine learning.
Jun 8, 2020 • 1h 2min
2020: A Critical Inflection Point for Responsible AI with Rumman Chowdhury - #381
Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture, dives deep into the critical need for responsible AI at this pivotal moment. He discusses how AI ethics should be personal and offers insights on defining one's ethical approach. The conversation emphasizes the importance of explainability and transparency in AI, addressing current governance gaps. Rumman also highlights the necessity for interdisciplinary collaboration to tackle data bias and the challenges of integrating ethical frameworks into business practices.
Jun 4, 2020 • 1h 7min
Panel: Advancing Your Data Science Career During the Pandemic - #380
In this panel, Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis share invaluable insights for data professionals navigating career shifts during the pandemic. They discuss the importance of upskilling, mentorship, and community support while addressing job market changes, particularly the decline in opportunities. The panelists emphasize crafting effective elevator pitches and building personal brands to attract recruiters. They also tackle biases in hiring and advocate for inclusive hiring practices in the tech industry.
