Product Categorization API Part 3: Creating an API
Or how to put machine learning models into production.
Search the full wire by company, model, lab, or keyword. Every story we have ever aggregated.
Or how to put machine learning models into production.
The OpenAI team is now 45 people. Together, we’re pushing the frontier of AI capabilities—whether by validating novel ideas, creating new software systems, or deploying machine learning on robots.
A web app to find similar products based on image.
Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.
Cleaning up text and messing with ascii (urgh!)
How Lazada ranks products to improve customer experience and conversion at Strata 2016.
We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.
A simple web app to classify fashion images into Amazon categories.
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
Got accepted into Georgia Tech's Computer Science Masters!
A card sorting game to discover youl passion by identifying skills you like and dislike.
Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
Parsing json and formatting product titles and categories.
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
We’ve hired more great people to help us achieve our goals. Welcome, everyone!
Learning Scala from Martin Odersky, father of Scala.
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.