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
Apr 26, 2019 • 52min
Organizing for Successful Data Science at Stitch Fix with Eric Colson - TWiML Talk #257
Eric Colson, Chief Algorithms Officer at Stitch Fix, shares insights from his journey in data science, emphasizing the need for a robust organizational framework. He reveals three key principles for structuring data science teams that enhance decision-making. The conversation dives into the role of data science in recommendation systems, inventory management, and demand forecasting, showcasing how over 800 algorithms optimize operations. Colson also discusses the importance of collaboration and adaptability in developing innovative solutions.
Apr 24, 2019 • 49min
End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256
Burcu Baran, a Senior Data Scientist at LinkedIn, shares her insights on using data science to influence business decisions. She discusses the end-to-end data science process, covering essential aspects like problem formulation, collaborative strategies, and model performance monitoring. Burcu highlights the critical role of A/B testing and feature monitoring in maintaining model effectiveness. She also emphasizes the transition from theoretical knowledge to practical applications in data science, and the importance of simplifying processes for enhanced team efficiency.
Apr 22, 2019 • 44min
Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255
In this conversation, Shioulin Sam, a Research Engineer at Cloudera Fast Forward Labs, sheds light on the challenges of machine learning with limited labeled data. She discusses active learning as a powerful approach to enhance model accuracy while reducing costly manual labeling. The discussion delves into measuring uncertainty in deep neural networks and effective strategies for implementing active learning across enterprises. Shioulin also highlights the significance of integrating labeled data into models to drive optimal performance in real-world applications.
Apr 19, 2019 • 38min
cuDF, cuML & RAPIDS: GPU Accelerated Data Science with Paul Mahler - TWiML Talk #254
Join Paul Mahler, a senior data scientist at NVIDIA, discussing the RAPIDS open-source project designed to enhance GPU acceleration in data science. He shares his journey from philosophy to machine learning, highlighting tools like cuDF and cuML that boost data processing efficiency. Discover how GPUs revolutionize algorithms like ridge regression with impressive speed and explore innovations in natural language processing and graph algorithms. Paul also emphasizes the importance of community involvement in shaping the future of these pivotal technologies.
Apr 18, 2019 • 39min
Edge AI for Smart Manufacturing with Trista Chen - TWiML Talk #253
Trista Chen, Chief Scientist of Machine Learning at Inventec, brings her extensive experience in AI and smart manufacturing to the discussion. She dives into the challenges and innovations within Industry 4.0, particularly focusing on defect detection and operational optimization. Trista explains the advantages of edge AI over cloud computing and discusses the complexities of ROI in AI projects. Her insights on integrating digital and physical systems highlight the transformative potential of automation in enhancing efficiency and production processes.
Apr 16, 2019 • 42min
Machine Learning for Security and Security for Machine Learning with Nicole Nichols - TWiML Talk #252
Nicole Nichols is a senior research scientist at Pacific Northwest National Lab, specializing in the intersection of machine learning and security. In this discussion, she shares insights from her presentation on using machine learning for cybersecurity, including detecting insider threats through advanced language models. The conversation also dives into software fuzz testing enhanced by deep learning, issues with algorithm efficiency, and the vulnerabilities in object recognition systems, highlighting the critical balance between detection accuracy and human analysis.
Apr 15, 2019 • 32min
Domain Adaptation and Generative Models for Single Cell Genomics with Gerald Quon - TWiML Talk #251
Gerald Quon, an assistant professor at UC Davis, specializes in leveraging deep learning for genomics and single-cell data analysis. He shares his insights on how deep domain adaptation and generative models are revolutionizing disease identification and treatment. Discover the novel methodologies he's developing to analyze complex diseases like schizophrenia and cancer. Quon also discusses the challenges of integrating multimodal data to enhance genomic analysis and the importance of user-friendly tools for biologists in this intricate field.
Apr 11, 2019 • 34min
Mapping Dark Matter with Bayesian Neural Networks w/ Yashar Hezaveh - TWiML Talk #250
Yashar Hezaveh, an Assistant Professor at the University of Montreal and a research fellow at the Center for Computational Astrophysics, dives into the fascinating intersection of machine learning and astrophysics. He discusses the challenges of gravitational lensing, where light bends due to gravity, and how ML enhances image reconstruction of distant galaxies. Yashar explores simulation-based approaches, the critical role of 'priors' in analysis, and the future potential of integrating deep learning techniques to transform our understanding of the universe.
Apr 9, 2019 • 49min
Deep Learning for Population Genetic Inference with Dan Schrider - TWiML Talk #249
In this discussion, Dan Schrider, an assistant professor at UNC Chapel Hill, reveals how machine learning is revolutionizing population genetics. He highlights the effectiveness of convolutional neural networks in outperforming traditional statistical methods. Dan shares insights on the integration of bioinformatics and discusses the importance of reference genomes in understanding genetic variation. Furthermore, he explores the challenges of estimating human population dynamics and the need for innovative approaches to bridge machine learning with genetic research.
Apr 5, 2019 • 41min
Empathy in AI with Rob Walker - TWiML Talk #248
Rob Walker, Vice President of Decision Management at Pegasystems, shares his expertise on the importance of empathy in AI. He discusses how empathy enhances customer engagement and differentiates it from ethics. The conversation explores the integration of empathy metrics into AI decision-making, balancing profitability with customer welfare. Walker also highlights the impact of irrelevant engagements on brand reputation and the emerging role of a chief empathy officer in corporate structures.
