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
Jun 22, 2020 • 55min
Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385
Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm AI Research, dives deep into conditional computation for optimizing neural networks. He discusses how channel gating enhances efficiency and accuracy while reducing model size. The conversation explores innovative methods for multitask learning, addressing challenges like catastrophic forgetting in continual learning. Babak also shares insights into practical applications of his research, demonstrating how these advancements transition effectively from the lab to real-world usage.
Jun 18, 2020 • 52min
Machine Learning Commerce at Square with Marsal Gavalda - #384
Marsal Gavalda, the Head of Machine Learning for the Commerce platform at Square, dives into the fascinating world of machine learning applications. He shares how Square's focus on technology from the start has driven success across various areas, particularly in risk management and fraud detection. Gavalda discusses strategies for balancing short-term projects with long-term innovations, the significance of data quality, and the role of cross-team collaboration. He also emphasizes the democratization of ML and the importance of ethical AI practices in today’s landscape.
Jun 15, 2020 • 44min
Cell Exploration with ML at the Allen Institute w/ Jianxu Chen - #383
Jianxu Chen, a scientist at the Allen Institute for Cell Science, shares insights on the transformative Allen Cell Explorer Toolkit. He delves into the challenges of merging machine learning with biology, emphasizing the need for interdisciplinary collaboration. The conversation highlights innovative methods for 3D segmentation of intracellular structures, the importance of GPU computing, and the fascinating role of autoencoders in enhancing microscopy data visualization. Listeners will discover how these advancements are revolutionizing cell image analysis!
Jun 11, 2020 • 32min
Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382
In this fascinating discussion, Andreas Madsen, an independent researcher from Denmark, shares his insights on neural arithmetic units and the challenges of independent research. He emphasizes the importance of collaboration and community support while navigating the competitive landscape of academic publishing. Madsen highlights difficulties in extrapolation with neural networks, proposing innovative benchmarks to enhance performance. His journey from academia to freelancing brings attention to the resource demands and perseverance needed for successful research in machine learning.
Jun 8, 2020 • 1h 2min
2020: A Critical Inflection Point for Responsible AI with Rumman Chowdhury - #381
Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture, dives deep into the critical need for responsible AI at this pivotal moment. He discusses how AI ethics should be personal and offers insights on defining one's ethical approach. The conversation emphasizes the importance of explainability and transparency in AI, addressing current governance gaps. Rumman also highlights the necessity for interdisciplinary collaboration to tackle data bias and the challenges of integrating ethical frameworks into business practices.
Jun 4, 2020 • 1h 7min
Panel: Advancing Your Data Science Career During the Pandemic - #380
In this panel, Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis share invaluable insights for data professionals navigating career shifts during the pandemic. They discuss the importance of upskilling, mentorship, and community support while addressing job market changes, particularly the decline in opportunities. The panelists emphasize crafting effective elevator pitches and building personal brands to attract recruiters. They also tackle biases in hiring and advocate for inclusive hiring practices in the tech industry.
Jun 2, 2020 • 6min
On George Floyd, Empathy, and the Road Ahead
The discussion centers on the emotional impact of systemic racism, ignited by the deaths of George Floyd and others. It underscores the pressing socio-economic issues faced by communities of color. Emphasis is placed on the importance of empathy and the significance of being active participants in advocacy. The conversation also addresses the need for change in America’s racial and class landscape, calling attention to the ongoing fight for justice and equality.
May 28, 2020 • 46min
Engineering a Less Artificial Intelligence with Andreas Tolias - #379
Andreas Tolias, a Professor of Neuroscience at Baylor College of Medicine, dives into the intriguing relationship between brain function and AI. He discusses how traditional AI has limitations that neuroscience can help overcome. The conversation highlights innovative data collection methods, like fMRI and high-density recordings. Tolias emphasizes the significance of understanding behavior outside neural structures and how using biological insights can enhance machine learning models. He also proposes new benchmarks to refine AI capabilities, aiming for systems that better mimic human cognition.
7 snips
May 25, 2020 • 52min
Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378
Joseph Gonzalez, Assistant Professor at UC Berkeley, joins to discuss his innovative approach to model efficiency. He delves into his research on training large models followed by compressing them, questioning the balance between model size and computational resource use. The talk includes insights on rapid architecture iteration and how larger models can still be efficient. He also shares strategies like weight pruning and dynamic querying that enhance performance without excessive resource investment, making advanced AI more accessible.
8 snips
May 21, 2020 • 34min
The Physics of Data with Alpha Lee - #377
Alpha Lee, a Winton Advanced Fellow at Cambridge and co-founder of PostEra, delves into the fascinating intersection of physics, chemistry, and machine learning. He discusses how his startup innovates drug discovery through advanced algorithms and Bayesian approaches to manage uncertainty. The conversation highlights the parallels between physical systems and deep learning, and how transformers are revolutionizing chemical predictions. Lee also touches on recent efforts in developing effective COVID-19 therapies, underscoring the importance of collaboration in science.
