The technical, societal, and cultural challenges that come with the rise of fake media
The O’Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.
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.
Using machine learning and analytics to attract and retain employees
The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit.
How machine learning impacts information security
The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.
In the age of AI, fundamental value resides in data
The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.
Trends in data, machine learning, and AI
The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.
Tools for generating deep neural networks with efficient network architectures
The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.
Building tools for enterprise data science
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.
Lessons learned while helping enterprises adopt machine learning
The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.
Machine learning on encrypted data
The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.
How social science research can inform the design of AI systems
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
Why it’s hard to design fair machine learning models
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Using machine learning to improve dialog flow in conversational applications
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
Building accessible tools for large-scale computation and machine learning
The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning.
Simplifying machine learning lifecycle management
The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.
How privacy-preserving techniques can lead to more robust machine learning models
The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.
Specialized hardware for deep learning will unleash innovation
The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.
Data regulations and privacy discussions are still in the early stages
The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy.
Managing risk in machine learning models
The O’Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain.
The real value of data requires a holistic view of the end-to-end data pipeline
The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.
The evolution of data science, data engineering, and AI
The O’Reilly Data Show Podcast: A special episode to mark the 100th episode.
Companies in China are moving quickly to embrace AI technologies
The O’Reilly Data Show Podcast: Jason Dai on the first year of BigDL and AI in China.
Teaching and implementing data science and AI in the enterprise
The O’Reilly Data Show Podcast: Jerry Overton on organizing data teams, agile experimentation, and the importance of ethics in data science.
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.
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.