Checking in on TensorFlow 2.0: Keras, API cleanup, and more
Paige Bailey, TensorFlow product manager at Google, highlights notable features of TensorFlow 2.0 and looks ahead to near-term updates.
Artificial Intelligence
The latest insights, ideas, and tools for building solutions that rely on machine intelligence.
2019 Emerging AI Pioneers Showcase winners
AI startups vied for awards at the O’Reilly Artificial Intelligence Conference in San Jose.
How graph algorithms improve machine learning
A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.
One simple graphic: Researchers love PyTorch and TensorFlow
Interest in PyTorch among researchers is growing rapidly.
Proposals for model vulnerability and security
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
You created a machine learning application. Now make sure it’s secure.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
3 reasons to add deep learning to your time series toolkit
The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.
What is neural architecture search?
An overview of NAS and a discussion on how it compares to hyperparameter optimization.
Assessing progress in automation technologies
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
AI and machine learning at Amazon
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Trust and transparency of AI for the enterprise
Ruchir Puri explains why trust and transparency are essential to AI adoption.
Connected arms
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
Four success factors for building your AI business journey
Manish Goyal shows you how to best unlock the value of enterprise AI.
Customized ML for the enterprise
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Raising AI to benefit business and society
Kishore Durg explains why deploying AI requires raising it to act as a responsible representative of the business and a contributing member of society.
Using machine learning in workload automation
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days.
AI at scale at Coinbase
Soups Ranjan describes the machine learning system that Coinbase built to detect potential fraud and fake identities.
Enabling reliable, secure collaboration on data science and machine learning projects
A conversation with Paul Taylor, chief architect in Watson Data and AI, and IBM fellow.
Notes from the first Ray meetup
Ray is beginning to be used to power large-scale, real-time AI applications.
What do computers see?
Tricks to visualize and understand how neural networks see.
MLflow: A platform for managing the machine learning lifecycle
An overview of the challenges MLflow tackles and a primer on how to get started.
Learn about AI with these books, videos, and tutorials
This collection of AI resources will get you up to speed on the basics, best practices, and latest techniques.
Winner of the Top Innovator Award at AI NY 2018: temi
The personal robot temi refactors robotic human behaviors we encounter in the “iPhone Slump,” and moves those back to actual robots.
Setting benchmarks in machine learning
Dave Patterson and other industry leaders discuss how MLPerf will define an entire suite of benchmarks to measure performance of software, hardware, and cloud systems.