Hybrid bio-opto-electronics for AI
George Church discusses the IARPA MICrONS project, which aims to revolutionize machine learning by reverse-engineering the algorithms of the brain.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
The physics of AI
Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.
Intel AI for the enterprise ecosystem
Fiaz Mohamed explains how Intel AI solves today’s business problems.
Fireside chat with Peter Norvig and Kavya Kopparapu
Kavya Kopparapu shares her inspiration for starting GirlsComputingLeague.
The frontiers of machine learning and AI
Zoubin Ghahramani discusses recent advances in artificial intelligence, highlighting research in deep learning, probabilistic programming, Bayesian optimization, and AI for data science.
Highlights from the Artificial Intelligence Conference in New York 2018
Watch highlights covering artificial intelligence, machine learning, automation, and more. From the Artificial Intelligence Conference in New York 2018.
Increasing business results through AI in the entertainment industry
Fiaz Mohammed and Justin Herz discuss how artificial intelligence can improve content discovery and monetization
Understanding automation
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
Autonomy and human-AI interaction
Manuela Veloso looks at the role humans can play in autonomy-based AI interactions and the underlying challenges to AI.
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.
Data engineers vs. data scientists
The two positions are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity.
4 things business leaders should know as they explore AI and deep learning
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
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.
Sprouted clams and stanky bean: When machine learning makes mistakes
Using silly data sets as examples, Janelle Shane talks about ways that algorithms fail.
Privacy in the age of machine learning
Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products.
Highlights from the Strata Data Conference in San Jose 2018
Watch highlights covering machine learning, business intelligence, data privacy, and more. From the Strata Data Conference in San Jose 2018.
Merging human and machine learning for everyday solutions
Li Fan shows how Pinterest is using AI to predict what’s in an image, what a user wants, and what they’ll want next.
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.
The working relationship between AIs and humans isn’t master/slave
We need a new model for how AI systems and humans interact.