AI

By Robin Bordoli, December 29, 2016

7 AI predictions for 2017

2016 was a busy year for the emergence of AI, but 2017 is shaping up to be an even busier […] [...]

AI

By Tyler Schnoebelen, November 16, 2016

Budgeting for Training Data

Organizations build machine learning systems so that they can predict and categorize data. But to get a system to do […] [...]

AI

News

Research and Insights

By Lukas Biewald, November 9, 2016

Announcing our Scientific Advisory Board

One of the challenges of working at the forefront of an exciting and fast moving field is the amount of […] [...]

AI

By Nick Gaylord, October 18, 2016

Understanding your model statistics

Two weeks ago, we were very happy to announce the launch of CrowdFlower AI in partnership with Microsoft Azure Machine […] [...]

AI

By Tyler Schnoebelen, October 13, 2016

U.S. Presidential Debates Through the Eyes of a Computer

This post wraps up a series I’ve been doing on using machine learning models to understand recent American political debates […] [...]

AI

By Tyler Schnoebelen, October 10, 2016

The most Clintonian and Trumpian moments of the VP Debate

Last week, I wrote about the most Clintonian and Trumpesque moments of the first presidential debate. In this post, I’m […] [...]

AI

By Robin Bordoli, September 29, 2016

The 7 Myths of AI

If you’re a business executive (rather than a data scientist or machine learning expert), you’ve probably been exposed to the […] [...]

AI

By Robin Bordoli, September 26, 2016

CrowdFlower and Microsoft, Better Together

About a year ago the teams at CrowdFlower and Microsoft Azure Machine Learning got together to discuss a shared vision. […] [...]

AI

By Tyler Schnoebelen, September 23, 2016

More data beats better algorithms

Most academic papers and blogs about machine learning focus on improvements to algorithms and features. At the same time, the […] [...]

AI

By Tyler Schnoebelen, September 8, 2016

Nattering Nabobs of Negativity: Bigrams, “Nots,” and Text Classification

You can get pretty far in text classification just by treating documents as bags of words where word order doesn’t […] [...]