8 AI trends we’re watching in 2020
Roger Magoulas looks at developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.
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).
AI is computer science disguised as hard work
Rob Thomas and Tim O’Reilly discuss the AI Ladder framework.
Why debugging ML models matters
Understanding and fixing problems in ML models is critical for widespread adoption.
AI and the road to Software 2.0
It’s clear that AI can and will have a big influence on how we develop software.
Moving AI and ML from research into production
Dean Wampler discusses the challenges and opportunities businesses face when moving AI from discussions to production.
There’s a path to an AI ROI
Ankur Patel discusses challenges and opportunities in enterprise machine learning and AI applications.
“AI is a lie”
Eric Jonas on AI hype and questions of ethics.
Dealing with a world of deepfakes
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Highlights from TensorFlow World 2019
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Sticker recommendations and AI-driven innovations on the Hike messaging platform
Ankur Narang discusses sticker recommendations with multilingual support, a key innovation driven by sophisticated natural language processing (NLP) algorithms.
“Human error”: How can we help people build models that do what they expect
Anna Roth discusses human and technical factors and suggests future directions for training machine learning models.
Personalization of Spotify Home and TensorFlow
Tony Jebara explains how Spotify improved user satisfaction by building components of the TFX ecosystem into its core ML infrastructure.
TensorFlow.js: Bringing machine learning to JavaScript
Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library.
TFX: An end-to-end ML platform for everyone
Konstantinos Katsiapis and Anusha Ramesh dive into the insights and approach that helped TensorFlow Extended (TFX) reach its current popularity within Alphabet.
MLIR: Accelerating AI
Chris Lattner and Tatiana Shpeisman explain how MLIR addresses the complexity caused by software and hardware fragmentation.
TensorFlow Hub: The platform to share and discover pretrained models for TensorFlow
Mike Liang discusses TensorFlow Hub, a platform where developers can share and discover pretrained models and benefit from transfer learning.
TensorFlow Lite: ML for mobile and IoT devices
Jared Duke and Sarah Sirajuddin explore on-device machine learning and the latest updates to TensorFlow Lite.
Accelerating ML at Twitter
Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.
The latest from TensorFlow
Megan Kacholia explains how Google’s latest innovations provide an ecosystem of tools for developers, enterprises, and researchers who want to build scalable ML-powered applications.
TensorFlow World 2019 opening keynote
Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.
TensorFlow community announcements
Kemal El Moujahid reveals new developments for the TensorFlow community.
Highlights from O'Reilly AI London 2019
Experts explore AI's most promising developments, emerging technologies, and profitable use cases.
When to trust AI
Marta Kwiatkowska provides an overview of techniques being developed to help improve the robustness, safety, and trust in AI systems.
When flying is cheaper than standing still
Raffaello D’Andrea presents his vision of how autonomous indoor drones will drive the next wave of robotics development.