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
Jul 8, 2019 • 53min
Spiking Neural Nets and ML as a Systems Challenge with Jeff Gehlhaar - TWIML Talk #280
Jeff Gehlhaar, VP of Technology and Head of AI Software Platforms at Qualcomm, dives into the world of AI and machine learning. He shares his journey from engineering to leading AI innovations, focusing on spiking neural networks that mimic biological processes. The discussion highlights Qualcomm's advancements in AI software development, the role of federated learning in enhancing privacy, and the importance of interoperability among frameworks like ONNX. Gehlhaar emphasizes a holistic approach to AI, integrating hardware and algorithms for future innovations.
Jul 1, 2019 • 46min
Transforming Oil & Gas with AI with Adi Bhashyam and Daniel Jeavons - TWIML Talk #279
Join Daniel Jeavons, General Manager of Data Science at Shell, and Adi Bhashyam, VP at C3 AI, as they dive into the transformative power of AI in the oil and gas industry. They discuss advancements in Shell's AI platforms, the complexities of data replication, and the challenges of scaling machine learning solutions across multiple sites. The conversation also highlights a new reward engine aimed at enhancing customer experiences and the innovative use of machine learning in optimizing energy production while addressing environmental concerns.
Jun 27, 2019 • 39min
Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278
Yunfan Gerry Zhang, a PhD student at UC Berkeley and SETI research center affiliate, dives into the fascinating intersection of machine learning and astrophysics. He discusses his groundbreaking paper on detecting fast radio bursts using innovative techniques. Highlights include the use of Generative Adversarial Networks for predicting cosmic signals and the challenges of processing immense astronomical datasets. Gerry also shares insights on detecting periodicity in signals and the transformative impact of AI in analyzing radio frequency data.
Jun 24, 2019 • 42min
Tracking CO2 Emissions with Machine Learning with Laurence Watson - TWIML Talk #277
In this conversation, Laurence Watson, Co-Founder and CTO of Plentiful Energy and former data scientist at Carbon Tracker, dives into innovative methods for tracking CO2 emissions using machine learning and satellite imagery. He shares insights from Carbon Tracker's research on fossil fuel power plants and discusses the challenges of quantifying emissions accurately. The talk also highlights advancements in cloud solutions for data integration and how they enhance monitoring efforts, making environmental data more accessible and actionable.
Jun 20, 2019 • 45min
Topic Modeling for Customer Insights at USAA with William Fehlman - TWIML Talk #276
William Fehlman, Director of Data Science at USAA, shares his expertise in topic modeling and natural language processing. He explains how USAA leverages these techniques to enhance customer service by analyzing both structured and unstructured data from communication channels. Fehlman delves into methodologies like latent semantic indexing and non-negative matrix factorization, discussing their effectiveness in uncovering customer insights and optimizing operational strategies. Insights on coherence scoring and term frequency provide a deeper understanding of the topic modeling process.
Jun 18, 2019 • 44min
Phronesis of AI in Radiology with Judy Gichoya - TWIML Talk #275
Judy Gichoya, an interventional radiology fellow and co-organizer of Black in AI, shares her insights on the interplay of AI and radiology. She examines the claims of superhuman AI, emphasizing its true potential in enhancing radiological diagnostics. Judy humorously debunks the myth of AI replacing radiologists, calling for collaboration rather than competition. The conversation dives into the biases that can skew AI performance and the need for practical education to effectively integrate AI within the field.
Jun 14, 2019 • 43min
The Ethics of AI-Enabled Surveillance with Karen Levy - TWIML Talk #274
In this engaging conversation with Karen Levy, an assistant professor at Cornell University, the focus is on the ethics of AI-enabled surveillance. She highlights how surveillance technology can disproportionately affect marginalized groups, especially truck drivers, whose dignity and autonomy are compromised by constant monitoring. Karen advocates for a critical perspective on AI, emphasizing that technology should enhance, rather than undermine, quality of life. The discussion encourages directly engaging with affected communities to grasp the nuanced impacts of these systems.
Jun 11, 2019 • 46min
Supporting Rapid Model Development at Two Sigma with Matt Adereth & Scott Clark - TWIML Talk #273
In this engaging discussion, Matt Adereth, Managing Director at Two Sigma, and Scott Clark, CEO of SigOpt, dive into the complexities of model development in finance. They explore Two Sigma's innovative end-to-end modeling platform and the challenges of production. The conversation highlights how optimization and experimentation tools like SigOpt can transform machine learning processes. The speakers also discuss the motivation behind companies adopting automation, the significance of domain expertise, and the ongoing evolution of programming languages in data science.
Jun 6, 2019 • 42min
Scaling Model Training with Kubernetes at Stripe with Kelley Rivoire - TWIML Talk #272
Kelley Rivoire, an engineering manager at Stripe with expertise in machine learning infrastructure, shares her insights on scaling model training. She discusses Stripe's journey from production-focused systems to building the Railyard API for efficient model management on Kubernetes. Kelley highlights the importance of collaboration across teams, custom parameters for hyperparameter optimization, and the significance of an infrastructure team to support machine learning advancements. Tune in to discover how Stripe is navigating the complexities of AI implementation!
Jun 3, 2019 • 46min
Productizing ML at Scale at Twitter with Yi Zhuang - TWIML Talk #271
In this enlightening conversation, Yi Zhuang, a Senior Staff Engineer at Twitter, shares his insights from leading the machine learning core environment on the Cortex team. He discusses the evolution of Twitter's machine learning landscape and the role of the DeepBird framework, which integrates TensorFlow for enhanced model training. Zhuang also highlights the newly formed 'Meta' team focused on bias, fairness, and accountability in algorithms, stressing the importance of ethical AI practices amid technological advancements.
