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 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.
9 snips
Apr 26, 2021 • 41min
AI Storytelling Systems with Mark Riedl - #478
Mark Riedl, a Professor at Georgia Tech, discusses his pioneering work in AI storytelling systems. He explains how AI can predict what happens next in a story by leveraging large language models like GPT-3. The conversation dives into the art of creating suspense and emotional resonance in narratives, as well as the challenges of aligning AI with human thought processes. Riedl also highlights the importance of model explainability and the potential of integrating symbolic systems with neural networks to enhance narrative coherence.
7 snips
Apr 21, 2021 • 40min
Creating Robust Language Representations with Jamie Macbeth - #477
Jamie Macbeth, an assistant professor at Smith College focusing on cognitive systems and natural language understanding, dives into his unique approach to language representation. He critiques misconceptions in AI while advocating for using handcrafted models to understand human intelligence. The conversation touches on the limitations of deep learning in grasping linguistic nuance and the need for innovative evaluation metrics. Jamie also explores how pre-linguistic structures contribute to common sense knowledge and discusses the future of AI in enhancing reasoning through episodic memories.
Apr 19, 2021 • 58min
Reinforcement Learning for Industrial AI with Pieter Abbeel - #476
Pieter Abbeel, a leading Professor at UC Berkeley and Co-founder of Covariant, dives into the cutting-edge world of AI and robotics. He discusses the challenges of transforming AI concepts into practical applications, especially in warehousing. Abbeel highlights the unique blend of unsupervised and reinforcement learning methods that foster curiosity-driven learning. He also unveils his research on pre-trained transformers as versatile computation tools and introduces his new podcast, Robot Brains, focused on bridging AI research with real-world applications.
