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.
Ideas | Data Show Podcast
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.
What machine learning engineers need to know
The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.
How to train and deploy deep learning at scale
The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.
Using machine learning to monitor and optimize chatbots
The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.
Unleashing the potential of reinforcement learning
The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.
Graphs as the front end for machine learning
The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.
Machine learning needs machine teaching
The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.
How machine learning can be used to write more secure computer programs
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
Bringing AI into the enterprise
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
How machine learning will accelerate data management systems
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
Machine learning at Spotify: You are what you stream
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
The current state of Apache Kafka
The O’Reilly Data Show Podcast: Neha Narkhede on data integration, microservices, and Kafka’s roadmap.
Building a natural language processing library for Apache Spark
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.
Machine intelligence for content distribution, logistics, smarter cities, and more
The O’Reilly Data Show Podcast: Rhea Liu on technology trends in China.
Vehicle-to-vehicle communication networks can help fuel smart cities
The O’Reilly Data Show Podcast: Bruno Fernandez-Ruiz on the importance of building the ground control center of the future.
Transforming organizations through analytics centers of excellence
The O’Reilly Data Show Podcast: Carme Artigas on helping enterprises transform themselves with big data tools and technologies.
The state of machine learning in Apache Spark
The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark.
Effective mechanisms for searching the space of machine learning algorithms
The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.
How Ray makes continuous learning accessible and easy to scale
The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.
Why AI and machine learning researchers are beginning to embrace PyTorch
The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and on deep learning within Facebook.
How big data and AI will reshape the automotive industry
The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.
A framework for building and evaluating data products
The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.
Building a next-generation platform for deep learning
The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.
A scalable time-series database that supports SQL
The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.
Programming collective intelligence for financial trading
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
Creating large training data sets quickly
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
Data science and deep learning in retail
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
Language understanding remains one of AI’s grand challenges
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Data preparation in the age of deep learning
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Scaling machine learning
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Architecting and building end-to-end streaming applications
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Becoming a machine learning engineer
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
Natural language analysis using Hierarchical Temporal Memory
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
Saving the world—or at least the world’s scientific and government data
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
Deep learning that's easy to implement and easy to scale
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.
Building machine learning solutions that can withstand adversarial attacks
The O’Reilly Data Show Podcast: Parvez Ahammad on minimal supervision, and the importance of explainability, interpretability, and security.
Deep learning for Apache Spark
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks.
The key to building deep learning solutions for large enterprises
The O’Reilly Data Show Podcast: Adam Gibson on the importance of ROI, integration, and the JVM.
How big compute is powering the deep learning rocket ship
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
2017 will be the year the data science and big data community engage with AI technologies
The O’Reilly Data Show Podcast: A look at some trends we’re watching in 2017.
Data is only as valuable as the decisions it enables
The O’Reilly Data Show Podcast: Ion Stoica on building intelligent and secure applications on live data.
Introducing model-based thinking into AI systems
The O’Reilly Data Show Podcast: Vikash Mansinghka on recent developments in probabilistic programming.
Building the next-generation big data analytics stack
The O’Reilly Data Show Podcast: Michael Franklin on the lasting legacy of AMPLab.
Visual tools for overcoming information overload
The O’Reilly Data Show Podcast: Dafna Shahaf on information cartography and AI, and Sam Wang on probabilistic methods for forecasting political elections.
Why businesses should pay attention to deep learning
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.
Understanding predictive analytics
The O’Reilly Data Show Podcast: Natalino Busa on developments in feature engineering and predictive techniques across industries.
The technology behind self-driving vehicles
The O’Reilly Data Show Podcast: Shaoshan Liu on perception, knowledge, reasoning, and planning for autonomous cars.
Data architectures for streaming applications
The O’Reilly Data Show Podcast: Dean Wampler on streaming data applications, Scala and Spark, and cloud computing.
Data science for humans and data science for machines
The O’Reilly Data Show Podcast: Michael Li on the state of data engineering and data science training programs.
The importance of emotion in AI systems
The O’Reilly Data Show Podcast: Rana el Kaliouby on deep learning, emotion detection, and user engagement in an attention economy.
Building human-assisted AI applications
The O’Reilly Data Show Podcast: Adam Marcus on intelligent systems and human-in-the-loop computing.
Enabling enterprise adoption of AI technologies
The O’Reilly Data Show Podcast: Jana Eggers on building applications that rely on synaptic intelligence.
Using Agile development techniques for data science projects
The O’Reilly Data Show Podcast: John Akred on building data platforms and enterprise data strategies.
Commercial speech recognition systems in the age of big data and deep learning
The O’Reilly Data Show Podcast: Yishay Carmiel on applications of deep learning in text and speech.
Building intelligent applications with deep learning and TensorFlow
The O’Reilly Data Show Podcast: Rajat Monga on the current state of TensorFlow and training large-scale deep neural networks.
Hybrid transactional/analytic systems and the quest for database nirvana
The O’Reilly Data Show Podcast: Rohit Jain on the challenges of hybrid data management systems.
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Using AI to build a comprehensive database of knowledge
The O’Reilly Data Show Podcast: Mike Tung on large-scale structured data extraction, intelligent systems, and the importance of knowledge databases.
Structured streaming comes to Apache Spark 2.0
The O’Reilly Data Show Podcast: Michael Armbrust on enabling users to perform streaming analytics, without having to reason about streaming.
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Building and deploying large-scale machine learning applications
The O’Reilly Data Show Podcast: Danny Bickson on recommenders, data science, and applications of machine learning.
Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
The O’Reilly Data Show Podcast: Ira Cohen on developing machine learning tools for a broad range of real-time applications.