Why we need to think differently about AI
General intelligence or creativity can only be properly imagined if we peel away the layers of abstractions.
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).
5 findings from O’Reilly’s machine learning adoption survey companies should know
New survey results highlight the ways organizations are handling machine learning's move to the mainstream.
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.
Data’s day of reckoning
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
How companies can get started with AI
The program for our Artificial Intelligence Conference in London is structured to help companies that are still very much in the early stages of AI adoption.
The five Cs
Five framing guidelines to help you think about building data products.
How to take machine learning from exploration to implementation
Recognizing the interest in ML, the Strata Data Conference program is designed to help companies adopt ML across large sections of their existing operations.
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.
Recognizing cultural bias in AI
Camille Eddy explains what we can do to create culturally sensitive computer intelligence and why that's important for the future of AI.
Open source and open standards in the age of cloud AI
Tim O'Reilly looks at how we can extend the values and practices of open source in the age of AI, big data, and cloud computing.
Of oaths and checklists
Oaths have their value, but checklists will help put principles into practice.
What machine learning means for software development
“Human in the loop” software development will be a big part of the future.
Doing good data science
Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.
Data regulations and privacy discussions are still in the early stages
The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy.
AI’s desire
It’s easy to imagine an AI winning a game of Go, but can you imagine an AI wanting to play a game of Go?
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.
How to think about AI and machine learning technologies, and their roles in automation
An overview and framework, including tools that can be used to enable automation.
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.
Lessons learned turning machine learning models into real products and services
Why model development does not equal software development.
The evolution of data science, data engineering, and AI
The O’Reilly Data Show Podcast: A special episode to mark the 100th episode.
Charting a data journey to the cloud
Mick Hollison, Sven Löffler, and Robert Neumann explain how Deutsche Telekom is harnessing machine learning and analytics in the cloud to build Europe’s largest IoT data marketplace.
Highlights from the Strata Data Conference in London 2018
Watch highlights covering machine learning, GDPR, data protection, and more. From the Strata Data Conference in London 2018.
How will the GDPR impact machine learning?
Answers to the three most commonly asked questions about maintaining GDPR-compliant machine learning programs.
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.