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
Aug 14, 2019 • 48min
The Problem with Black Boxes with Cynthia Rudin - TWIML Talk #290
Cynthia Rudin, a Duke University professor specializing in interpretable machine learning, dives into the contentious topic of black box models in high-stakes decisions. She argues that simpler, interpretable models are essential for accountability, especially when human lives are at stake. The conversation explores the risks and ethical dilemmas posed by opaque algorithms, alongside her research on improving model transparency. Cynthia highlights real-world applications and advocates for a shift towards clarity in predictive modeling, impacting areas like healthcare and criminal justice.
Aug 8, 2019 • 44min
Human-Robot Interaction and Empathy with Kate Darling - TWIML Talk #289
Dr. Kate Darling, a Research Specialist at the MIT Media Lab, dives into the ethics of robot interaction and empathy. She explores how humans relate to lifelike robots and the impact of empathy on behavior, especially in children. The conversation covers innovative uses of robots in therapy, particularly for dementia patients. Darling discusses the trust dynamics between humans and robots, raising questions about automation bias and the societal implications of how we perceive and treat machines.
Aug 5, 2019 • 37min
Automated ML for RNA Design with Danny Stoll - TWIML Talk #288
In this engaging discussion, Danny Stoll, a Research Assistant at the University of Freiburg specializing in automated machine learning for RNA design, reveals his team's innovative work in RNA design. He breaks down the design process through reverse engineering and how deep learning algorithms are applied for sequence training. Key topics include the synergy of machine learning and RNA functionality, challenges of hyperparameter optimization, and the integration of traditional and statistical methods for enhanced efficiency.
Aug 1, 2019 • 37min
Developing a brain atlas using deep learning with Theofanis Karayannis - TWIML Talk #287
Theofanis Karayannis, Assistant Professor at the University of Zurich's Brain Research Institute, specializes in brain circuit development through deep learning. He discusses his path from pharmacy to neuroscience, exploring how neural circuits develop in genetically modified models. The conversation delves into sensory processing and the crucial role of inhibitory neurons. With advanced imaging and deep learning techniques, Theo shares insights on crafting detailed brain atlases and understanding neural connectivity, while also tackling the challenges of data management in this intricate field.
Jul 29, 2019 • 37min
Environmental Impact of Large-Scale NLP Model Training with Emma Strubell - TWIML Talk #286
In this discussion, Emma Strubell, a visiting scientist at Facebook AI Research and future professor at Carnegie Mellon, dives into the environmental costs of NLP model training. She reveals findings from her pivotal paper on the carbon emissions linked to deep learning despite accuracy improvements. Emma also discusses how businesses are responding to environmental concerns and the importance of developing efficient, sustainable NLP practices. Her insights bridge cutting-edge research with real-world applications, offering a vision for greener AI.
Jul 25, 2019 • 1h 15min
“Fairwashing” and the Folly of ML Solutionism with Zachary Lipton - TWIML Talk #285
Zachary Lipton, Assistant Professor at CMU's Tepper School of Business, dives into the intersection of machine learning and healthcare. He highlights the importance of human expertise in AI decision-making and critiques the concept of 'fairwashing' in tech. The conversation touches on the challenges of applying machine learning in medical contexts, discussing the necessity for robust models that account for real-world complexities. Additionally, Lipton explores the ethical dimensions of algorithmic decision-making and the gap between theoretical fairness claims and practical realities.
Jul 22, 2019 • 41min
Retinal Image Generation for Disease Discovery with Stephen Odaibo - TWIML Talk #284
In this discussion, Dr. Stephen Odaibo, Founder and CEO of RETINA-AI Health Inc., shares his unique journey through medicine and AI, merging his expertise in math and ophthalmology. He delves into how his startup uses machine learning for retinal image analysis, tackling diabetic retinopathy and enhancing healthcare. Stephen emphasizes the importance of quality data over quantity in machine learning, explores the evolving landscape of AI and the necessity of domain expertise, and discusses innovation beyond major corporations in the data-driven world.
Jul 18, 2019 • 51min
Real world model explainability with Rayid Ghani - TWiML Talk #283
Rayid Ghani, the Director of the Center for Data Science and Public Policy at the University of Chicago, shares insights on applying machine learning for social good. He explores the crucial role of explainability in AI, emphasizing the need for relevant context in decision-making. Ghani discusses data-driven strategies from political campaigns and the ethical challenges in predictive modeling. He highlights the importance of trust and feedback mechanisms to improve model transparency, particularly in sensitive areas like healthcare and public safety.
Jul 15, 2019 • 26min
Inspiring New Machine Learning Platforms w/ Bioelectric Computation with Michael Levin - TWiML Talk #282
Michael Levin, a professor of biology and director at the Allen Discovery Center, explores groundbreaking concepts in bioelectric computation and machine learning. He discusses how synthetic living machines can revolutionize AI architectures. Levin emphasizes the role of bioelectric processes in regeneration, revealing that DNA isn't the only player in shaping organism behavior. The conversation also touches on the potential of bioelectric medicine for transformative treatments in regenerative health and the intriguing parallels between biological systems and AI.
Jul 9, 2019 • 41min
Simulation and Synthetic Data for Computer Vision with Batu Arisoy - TWiML Talk #281
In this engaging discussion, Batu Arisoy, a Research Manager at Siemens Corporate Technology, shares insights from his work on limited-data computer vision problems. He highlights innovative approaches to generating synthetic data, aiding object recognition for tasks like train maintenance. Batu also discusses collaborative workshops tackling class imbalances in computer vision, and a groundbreaking AI-user collaboration model with the Office of Naval Research that integrates natural language processing. Tune in for fascinating breakthroughs that blend AI, simulation, and user intent!
