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 29, 2021 • 55min
Semantic Folding for Natural Language Understanding with Francisco Weber - #451
Francisco Weber, CEO and co-founder of Cortical.io, shares insights into his company's innovative use of semantic folding for natural language processing. He discusses the evolution from traditional NLP methods to a biologically inspired approach, emphasizing data efficiency. The conversation critiques GPT-3's inefficiencies in business applications and highlights the development of contract intelligence tools for lawyers. Francisco also touches on advancements in sentiment analysis and the challenges of automating machine learning workflows.
Jan 25, 2021 • 53min
The Future of Autonomous Systems with Gurdeep Pall - #450
Gurdeep Pall, Corporate Vice President at Microsoft, shares his impressive 31-year journey at the company, contributing to projects like Skype for Business. He dives into Microsoft's acquisition of Bonsai, detailing its role in machine teaching for autonomous systems. The conversation highlights the challenges of real-world simulation and decision-making, along with innovative applications in HVAC and aviation. Gurdeep also discusses strategies for achieving ROI on autonomous system investments and emphasizes the importance of safety in advancing these technologies.
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.
