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
Sep 3, 2019 • 35min
Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296
Lotte Bransen, a Scientific Researcher at SciSports specializing in soccer analytics, joins the discussion on how mental pressure impacts player performance. She delves into her research, 'Choke or Shine?', revealing insights into how players respond under stress. The conversation highlights the merging of mathematics with sports, the complexities of measuring player contributions, and the development of models to quantify pressure levels. Coaches can benefit from understanding which players excel when the stakes are high, shaping team strategies and player recruitment.
Aug 29, 2019 • 42min
Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
Lukas Biewald, CEO and co-founder of Weights & Biases, dives into the world of deep learning experiments. He shares how his company addresses the need for reproducibility and effective evaluation frameworks. The conversation covers the chaos of machine learning management and the importance of collaboration in improving workflows. Lukas also teases an upcoming ebook on data management and highlights the significance of version control in community-driven projects. Learn how his tools are reshaping the experimentation landscape in AI!
Aug 26, 2019 • 49min
Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294
Angela Bassa, Director of Data Science at iRobot, shares her journey from academia to robotics, revealing how she built a data-driven culture at iRobot. She discusses the company's re-architecture and the innovative ways they taxonomize data science. Angela highlights the transition to Python, enhancing team collaboration and product development. The conversation also covers the crucial role of DevOps in integrating hardware, software, and cloud solutions, and the importance of rigorous testing for new technologies like SLAM in robots.
Aug 22, 2019 • 43min
Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293
Olivier Bachem, a research scientist at Google AI's Brain team, dives into exciting advancements in reinforcement learning. He shares insights on Google Research Football, a unique environment designed for AI training that outshines traditional platforms like OpenAI Gym. Olivier discusses the intricacies of disentangled representations in high-dimensional data and the innovative challenges of developing soccer simulations. He also touches on recent updates, emphasizing collaborative learning and realistic gameplay dynamics in this pioneering research project.
Aug 19, 2019 • 50min
Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292
In this discussion, Tijmen Blankevoort, a staff engineer at Qualcomm, delves into the fascinating world of neural network compression and quantization. He explains how much ML models can be compressed without losing efficiency and outlines the best strategies for achieving this. The conversation also touches on the lottery ticket hypothesis, exploring how feature selection can optimize neural networks. Tijmen reveals challenges in automating compression, like error propagation, and introduces innovative data-free quantization techniques that enhance performance across various models.
Aug 15, 2019 • 40min
Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291
Join Anubhav Jain, a Staff Scientist at Lawrence Berkeley National Lab and leader of the Hacking Materials Research Group, as he dives into the intersection of materials science and natural language processing. He discusses his groundbreaking paper on using unsupervised word embeddings to analyze scientific literature, enabling advanced material discovery. Anubhav highlights innovative predictive methods and the significant role of NLP in identifying new functional materials, as well as the exciting potential of validating predictions with real experimental data.
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.
