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
Jan 7, 2021 • 1h 22min
Trends in Natural Language Processing with Sameer Singh - #445
Sameer Singh, Assistant Professor at UC Irvine and an expert in natural language processing, dives into the latest trends in NLP. He discusses the profound impact of GPT-3 and Transformer models on the field. The conversation highlights the complexities of evaluating language models and their practical vulnerabilities. Sameer brings attention to the limitations of current models in achieving true natural language understanding. Additionally, he shares insights on the intersection of language and vision models, shaping the future of NLP.
Jan 4, 2021 • 1h 9min
Trends in Computer Vision with Pavan Turaga - #444
Pavan Turaga, an Associate Professor from Arizona State University, dives into the latest trends in computer vision. He discusses the exciting revival of physics-based scene analysis and the evolution of differentiable rendering, emphasizing its role in 3D structure reconstruction. Turaga highlights the significance of self-supervised learning techniques and innovative network architectures that enhance model performance. He also tackles the real-world evaluation challenges for AI systems, offering insights into assessing model reliability and robustness in practical applications.
Dec 30, 2020 • 1h 27min
Trends in Reinforcement Learning with Pablo Samuel Castro - #443
Pablo Samuel Castro, a Staff Research Software Developer at Google Brain, joins for a deep dive into the evolving world of reinforcement learning. He discusses the latest advancements from major conferences, highlighting key themes like the integration of deep learning and real-world applications. The conversation touches on contrastive loss, the importance of small environments for research, and innovative solutions for disaster connectivity using RL and loon balloons. Expect insights on performance evaluation and the future landscape of deep reinforcement learning.
Dec 28, 2020 • 38min
MOReL: Model-Based Offline Reinforcement Learning with Aravind Rajeswaran - #442
In this conversation with Aravind Rajeswaran, a PhD student at the University of Washington focusing on machine learning and robotics, exciting topics unfold on model-based offline reinforcement learning. They discuss the significance of model-based approaches in improving algorithm efficiency compared to traditional methods. Aravind shares insights on the advances and applications of the MOReL algorithm, explores stateful Markov Decision Processes, and delves into enhancing predictions through ensemble methods. The dialogue highlights how this research shapes the future of reinforcement learning.
9 snips
Dec 23, 2020 • 46min
Machine Learning as a Software Engineering Enterprise with Charles Isbell - #441
In this engaging discussion, Charles Isbell, Dean at Georgia Tech's College of Computing and an expert in interactive machine learning, dives into the transformative power of education in tech. He highlights the success of Georgia Tech's online Master's program, boasting over 11,000 students. The conversation explores the crucial need for diverse voices in AI and reflects on the systemic biases within machine learning. Isbell also emphasizes embedding ethics into engineering education, advocating for a balance between technological advances and human values.
Dec 21, 2020 • 58min
Natural Graph Networks with Taco Cohen - #440
Taco Cohen is a Machine Learning Researcher at Qualcomm Technologies, known for his work on equivariant networks and video compression. In this conversation, he introduces his paper on Natural Graph Networks and the concept of 'naturality,' which proposes that relaxed constraints can lead to more versatile architectures. Taco shares insights on the integration of symmetries from physics in AI, recent advances in efficient GCNNs for mobile, and innovative techniques in neural compression that significantly enhance data efficiency.
Dec 18, 2020 • 41min
Productionizing Time-Series Workloads at Siemens Energy with Edgar Bahilo Rodriguez - #439
Edgar Bahilo Rodriguez, Lead Data Scientist at Siemens Energy, dives into the complexities of productionizing time-series workloads. He shares insights from his journey moving from energy engineering to machine learning, discussing the blend of technologies used to enhance forecasting models. Edgar highlights industrial applications in wind and energy management, emphasizing sustainable solutions. The conversation also touches on the evolution of machine learning in operations, automation in monitoring, and the integration of diverse programming languages in robust AI workflows.
Dec 16, 2020 • 41min
ML Feature Store at Intuit with Srivathsan Canchi - #438
Srivathsan Canchi, Head of Engineering for Intuit's Machine Learning Platform, discusses the groundbreaking role Intuit played in developing the AWS SageMaker Feature Store. He explains how feature stores enhance machine learning by ensuring data consistency and addressing challenges like feature drift. The conversation also touches on the exploding interest in feature stores, their importance in scalable AI, and the implementation challenges faced during their establishment, including the benefits of GraphQL integration. Tune in for insights on the future of machine learning!
Dec 14, 2020 • 49min
re:Invent Roundup 2020 with Swami Sivasubramanian - #437
Swami Sivasubramanian, VP of Artificial Intelligence at AWS, shares insights from the recent re:Invent conference. He highlights the introduction of groundbreaking tools like Tranium for efficient machine learning training and SageMaker enhancements for better workflow management. The conversation dives into the significance of integrating DevOps with machine learning processes and showcases tailored AI solutions for industrial applications. With anecdotes reflecting the field's evolution, it underscores the push towards making machine learning accessible to all.
Dec 11, 2020 • 40min
Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436
Subarna Sinha, a leader in Machine Learning Engineering at 23andMe, dives into the world of genomic data and disease prediction. She discusses the development of polygenic risk scores and the complexities involved in predicting disease likelihood from genetic variations. The conversation highlights the technological innovations behind their ML platform, including tools like AWS and Jenkins. Subarna also addresses the challenges of data drift and the importance of team dynamics in refining predictive models, ensuring more accurate health assessments.
