Highlights from the O’Reilly Artificial Intelligence Conference in San Jose 2019
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
Srinivas Narayanan takes a deep look into the next change we’re seeing in AI—going beyond fully supervised learning techniques.
Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.
Dinesh Nirmal examines how organizations can unlock the value of their data for AI with a unified, prescriptive information architecture.
Distributed Consistency, Face Anonymization, Game Mechanic Discovery, and Images of Images
Mapping Values, Crawlers are Legal, Laser Tripwire, and Coercion-Resistant Design
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
Secure Android, Group Chats, Ethical Location Data, and Philosophy of Computer Science
Code Reviews, Dogfooding, Deobfuscation, and Differential Privacy
Cultural Competency, Computer-Generated Sound, Bottom-Up CS, and Continuous Compliance
iOS Security, IOT Wifi Attacks, Interactive SSH Programs, and Replacing Faces in Video
Crummy Translations, Synthetic Datasets, Building Communities, and Deleting Accounts
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
As organizations embrace machine learning, the need for new deployment tools and strategies grows.
Growing adoption of microservices, cloud, containers, and orchestration signal a paradigm shift that we're calling Next Architecture.
Enigma Simulator, Robot Startups, Code as Type, and Conversational Modeling
Multi-Language Teams, AI Release Models, Security Myth, and The Internet is for End Users
Debugging a Scale Problem, Verifying Cryptographic Protocols, Remote Team Stress, and PAC-MAN Source