The importance of transparency and user control in machine learning
The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.
The O'Reilly Data Show Podcast explores the opportunities and techniques driving big data, data science, and AI. Subscribe on iTunes, Stitcher, SoundCloud, and RSS.
The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.
The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.
The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.
The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.
The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.
The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.
The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
The O’Reilly Data Show Podcast: Neha Narkhede on data integration, microservices, and Kafka’s roadmap.
The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production.
The O’Reilly Data Show Podcast: Rhea Liu on technology trends in China.
The O’Reilly Data Show Podcast: Bruno Fernandez-Ruiz on the importance of building the ground control center of the future.
The O’Reilly Data Show Podcast: Carme Artigas on helping enterprises transform themselves with big data tools and technologies.
The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark.
The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.
The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.
The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and on deep learning within Facebook.
The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.
The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.
The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.
The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.
The O’Reilly Data Show Podcast: Parvez Ahammad on minimal supervision, and the importance of explainability, interpretability, and security.
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks.
The O’Reilly Data Show Podcast: Adam Gibson on the importance of ROI, integration, and the JVM.
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
The O’Reilly Data Show Podcast: A look at some trends we’re watching in 2017.
The O’Reilly Data Show Podcast: Ion Stoica on building intelligent and secure applications on live data.
The O’Reilly Data Show Podcast: Vikash Mansinghka on recent developments in probabilistic programming.
The O’Reilly Data Show Podcast: Michael Franklin on the lasting legacy of AMPLab.
The O’Reilly Data Show Podcast: Dafna Shahaf on information cartography and AI, and Sam Wang on probabilistic methods for forecasting political elections.
The O’Reilly Data Show Podcast: Christopher Nguyen on the early days of Apache Spark, deep learning for time-series and transactional data, innovation in China, and AI.
The O’Reilly Data Show Podcast: Natalino Busa on developments in feature engineering and predictive techniques across industries.
The O’Reilly Data Show Podcast: Shaoshan Liu on perception, knowledge, reasoning, and planning for autonomous cars.
The O’Reilly Data Show Podcast: Dean Wampler on streaming data applications, Scala and Spark, and cloud computing.
The O’Reilly Data Show Podcast: Michael Li on the state of data engineering and data science training programs.
The O’Reilly Data Show Podcast: Rana el Kaliouby on deep learning, emotion detection, and user engagement in an attention economy.
The O’Reilly Data Show Podcast: Adam Marcus on intelligent systems and human-in-the-loop computing.
The O’Reilly Data Show Podcast: Jana Eggers on building applications that rely on synaptic intelligence.
The O’Reilly Data Show Podcast: John Akred on building data platforms and enterprise data strategies.
The O’Reilly Data Show Podcast: Yishay Carmiel on applications of deep learning in text and speech.
The O’Reilly Data Show Podcast: Rajat Monga on the current state of TensorFlow and training large-scale deep neural networks.
The O’Reilly Data Show Podcast: Rohit Jain on the challenges of hybrid data management systems.
The O’Reilly Data Show Podcast: Mike Tung on large-scale structured data extraction, intelligent systems, and the importance of knowledge databases.
The O’Reilly Data Show Podcast: Michael Armbrust on enabling users to perform streaming analytics, without having to reason about streaming.
The O’Reilly Data Show Podcast: Danny Bickson on recommenders, data science, and applications of machine learning.
The O’Reilly Data Show Podcast: Ira Cohen on developing machine learning tools for a broad range of real-time applications.