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 21, 2021 • 36min
AI for Ecology and Ecosystem Preservation with Bryan Carstens - #449
Join Bryan Carstens, a professor at The Ohio State University and leader in biodiversity research, as he dives into the fascinating intersection of machine learning and ecology. Discover how his lab utilizes ML to tackle species formation, uncover genetic insights, and predict conservation risks amidst a looming extinction crisis. Carstens discusses the shift from traditional data collection to digitized methods, the complexities of ecological modeling, and the future of interdisciplinary collaboration in understanding our planet's critical biodiversity.
Jan 18, 2021 • 1h 2min
Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448
In this discussion, Jason Gauci, a Software Engineering Manager at Facebook AI, dives into the complexities of their Re-Agent reinforcement learning platform. He highlights its role in real-world decision-making, including user engagement strategies for Facebook notifications. The conversation explores counterfactual causality and safety in social network decision-making. Jason also shares insights on differentiating online/offline training models, emphasizing the impact of reinforcement learning on small businesses and the future of AI in eCommerce.
Jan 14, 2021 • 38min
A Future of Work for the Invisible Workers in A.I. with Saiph Savage - #447
In this engaging conversation, Saiph Savage, a visiting professor and director at multiple institutions, discusses the plight of invisible workers in AI who often go unnoticed. She highlights the emotional and economic challenges these workers face and the importance of empowering them through intelligent tools and community support. Saiph also shares insights from her participatory design work in rural Mexico, advocating for solutions that honor cultural relevance and enhance well-being. It's a thought-provoking look at redefining the future workforce.
Jan 11, 2021 • 1h 14min
Trends in Graph Machine Learning with Michael Bronstein - #446
In this engaging discussion, Michael Bronstein, a professor at Imperial College London and Head of Graph Machine Learning at Twitter, dives into the transformative power of graph machine learning. He shares insights on its applications in diverse fields like physics and bioinformatics, especially in predicting chemical properties for drug discovery. The conversation also touches on ethical considerations in AI, advancements in protein structure prediction, and the exciting future of graph ML in molecule discovery and even translating non-human communications. A must-listen for AI enthusiasts!
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.
