Paige Bailey, TensorFlow product manager at Google, highlights notable features of TensorFlow 2.0 and looks ahead to near-term updates.
AI startups vied for awards at the O’Reilly Artificial Intelligence Conference in San Jose.
A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.
Interest in PyTorch among researchers is growing rapidly.
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
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.
An overview of NAS and a discussion on how it compares to hyperparameter optimization.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Ruchir Puri explains why trust and transparency are essential to AI adoption.
Manish Goyal shows you how to best unlock the value of enterprise AI.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
Kishore Durg explains why deploying AI requires raising it to act as a responsible representative of the business and a contributing member of society.
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days.
Soups Ranjan describes the machine learning system that Coinbase built to detect potential fraud and fake identities.
A conversation with Paul Taylor, chief architect in Watson Data and AI, and IBM fellow.
Ray is beginning to be used to power large-scale, real-time AI applications.
Tricks to visualize and understand how neural networks see.
An overview of the challenges MLflow tackles and a primer on how to get started.
This collection of AI resources will get you up to speed on the basics, best practices, and latest techniques.
The personal robot temi refactors robotic human behaviors we encounter in the “iPhone Slump,” and moves those back to actual robots.
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.