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
May 31, 2021 • 43min
Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488
Nir Bar-Lev, co-founder and CEO of ClearML, shares insights from his extensive tech background including time at Google. He discusses the evolving landscape of machine learning platforms and the critical decision between building versus buying solutions. Nir emphasizes the importance of effective experiment management, the risks of relying solely on cloud vendors, and the balance needed to combat overfitting. He also touches on advancements in federated learning and how ClearML integrates innovative techniques to empower businesses in their AI journeys.
May 27, 2021 • 56min
Applied AI Research at AWS with Alex Smola - #487
In this engaging discussion, Alex Smola, Vice President and Distinguished Scientist at AWS AI, explores cutting-edge AI research. He delves into deep learning on graphs, highlighting its role in enhancing data interpretation and applications like fraud detection. Alex also discusses the significance of AutoML, designed to make machine learning more accessible. He introduces Granger causality in causal modeling and shares insights about the growing AWS ML Summit, showcasing speaker highlights and exciting trends in AI.
May 24, 2021 • 40min
Causal Models in Practice at Lyft with Sean Taylor - #486
Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs, shares his journey from lab director to hands-on innovator. He dives into the moonshot approaches his team takes towards marketplace experimentation and forecasting. The conversation highlights the significance of causality in their modeling efforts and the challenges of balancing supply and demand. Moreover, he discusses the application of neural networks for decision-making, emphasizing collaboration and the transformation of traditional statistical methods to drive business insights.
May 20, 2021 • 42min
Using AI to Map the Human Immune System w/ Jabran Zahid - #485
Jabran Zahid, a Senior Researcher at Microsoft Research, dives into the fascinating world of mapping the human immune system using AI. With a unique background in astrophysics, he discusses the Antigen Map Project and its adaptation during the COVID-19 pandemic. Jabran shares insights into the complexities of T cell development and the challenges faced in using machine learning for immunology. He highlights the importance of model interpretability and the progress made towards developing FDA-approved diagnostic tools for enhanced health understanding.
May 17, 2021 • 38min
Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch - #484
In this discussion, Konstantin Rusch, a PhD student at ETH Zurich, dives into innovative recurrent neural networks (RNNs) aimed at tackling long-time dependencies. He shares insights from his papers on coRNN and uniCORNN, inspired by neuroscience, and how these architectures compare to traditional models like LSTMs. Konstantin also reveals challenges in ensuring gradient stability and innovative techniques that enhance RNNs' expressive power. Plus, he discusses his ambitions for future advancements in memory efficiency and performance.
May 13, 2021 • 38min
What the Human Brain Can Tell Us About NLP Models with Allyson Ettinger - #483
In this engaging discussion, Allyson Ettinger, an Assistant Professor at the University of Chicago, dives into the intriguing intersection of machine learning and neuroscience. She shares insights on how brain research can enhance AI, particularly in natural language processing (NLP). The conversation highlights the importance of controlled evaluation methods and the challenges AI faces in truly understanding language. Ettinger also touches on the predictive abilities of NLP models and how they compare to human cognitive processing, revealing the ongoing quest to mimic brain functionality in AI.
May 10, 2021 • 41min
Probabilistic Numeric CNNs with Roberto Bondesan - #482
Roberto Bondesan, an AI researcher at Qualcomm, shares his groundbreaking work on probabilistic numeric CNNs, which leverage Gaussian processes for enhanced error correction. He delves into innovative adaptive neural compression techniques that optimize data transmission efficiency. The conversation also touches on the exciting intersection of quantum computing and AI, where Bondesan discusses the future potential of combinatorial optimization in revolutionizing logistics and design. His insights bridge physics and advanced AI applications, highlighting a promising frontier in technology.
May 6, 2021 • 35min
Building a Unified NLP Framework at LinkedIn with Huiji Gao - #481
Huiji Gao, Senior Engineering Manager at LinkedIn, shares his passion for building sophisticated NLP tools, like the open-source DeText framework. He discusses how DeText revolutionized LinkedIn’s approach to model training and its broad applications across the company. The conversation highlights the synergy between DeText and LiBERT, optimized for LinkedIn's data. They delve into the challenges of model evaluation, the importance of user interaction in enhancing performance, and techniques for document ranking optimization.
May 3, 2021 • 35min
Dask + Data Science Careers with Jacqueline Nolis - #480
Jacqueline Nolis, Head of Data Science at Saturn Cloud and co-host of the Build a Career in Data Science Podcast, shares her expertise on navigating data science careers. She discusses essential insights for newcomers and strategies for effectively signaling their skills. Jacqueline also delves into Dask, highlighting its advantages for distributed computing in Python and contrasting its user-friendliness with other tools. The conversation emphasizes the importance of understanding modern software development practices and community engagement in advancing data science.
Apr 29, 2021 • 37min
Machine Learning for Equitable Healthcare Outcomes with Irene Chen - #479
Irene Chen, a Ph.D. student at MIT, is on a mission to ensure fair healthcare outcomes through machine learning. She discusses innovative projects like the early detection of intimate partner violence, aiming to improve patient care. Irene dives into the importance of risk stratification and the ethical challenges of AI in healthcare. She emphasizes the need for collaboration between clinicians and ML researchers to create algorithms that address disparities and enhance predictive accuracy.
