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 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.
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.
Apr 15, 2021 • 36min
AutoML for Natural Language Processing with Abhishek Thakur - #475
Abhishek Thakur, a machine learning engineer at Hugging Face and the world’s first quadruple Kaggle Grandmaster, shares insights from his fascinating journey. He discusses his evolution in Kaggle competitions, emphasizing practical skills gained along the way. Abhishek dives into his work on AutoNLP, revealing its goals and how it stacks up against handcrafted models. He also highlights key lessons in NLP techniques and the importance of blending theory with practice, alongside his experiences writing his book, Approaching (Almost) Any Machine Learning Problem.
Apr 12, 2021 • 36min
Inclusive Design for Seeing AI with Saqib Shaikh - #474
Saqib Shaikh, a Software Engineer at Microsoft and the lead for the Seeing AI Project, shares insights on his groundbreaking app that narrates the world for the visually impaired. He discusses its evolution from a hackathon project to a powerful tool, the technical challenges behind real-time machine learning, and the significance of user intent in enhancing interaction. Saqib also explores future innovations like smart glasses and the role of AI in promoting accessibility, emphasizing the balance between automation and user trust.
8 snips
Apr 8, 2021 • 34min
Theory of Computation with Jelani Nelson - #473
Jelani Nelson, a professor in the Theory Group at UC Berkeley, dives into the fascinating realms of computational theory, streaming algorithms, and dimensionality reduction. He explores the delicate balance between innovating new algorithms and optimizing existing ones. Listeners will discover practical applications of random projections in machine learning and essential theoretical tools for practitioners. Additionally, Jelani discusses his nonprofit, AddisCoder, which empowers Ethiopian high school students through programming and algorithm education.
Apr 5, 2021 • 41min
Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472
Stevie Chancellor, an Assistant Professor at the University of Minnesota, tackles the intersection of human-centered machine learning and high-risk behaviors. She shares insights on using machine learning to assess mental illness severity and discusses how convolutional graph neural networks can reveal new behaviors in opioid use disorder. Chancellor also delves into the ethical challenges of mining social media data for mental health research, underscores the importance of clear communication in mental health, and emphasizes ethical considerations in AI-driven crisis detection.
Apr 1, 2021 • 24min
Operationalizing AI at Dataiku with Conor Jensen - #471
Conor Jensen, Director of Data Science at Dataiku and an expert in AI operationalization, shares his unique journey from a military background to leading data science teams. He discusses the hurdles of managing real-world data inputs and the importance of a strong evaluation program. Jensen highlights the need for a cultural shift in organizations to embrace data-driven decision-making. He also explores strategies for effectively implementing AI across product lines and the significance of collaboration in transforming data science practices.
