- How innovation works in academia, startups, and large enterprise
- Why it often makes sense to build, not buy, AI products
- How to predict that a new AI technology will be impactful
- Technologies we’re excited about, including demos of our prototypes for natural language generation, deep learning for image analysis, automated text summarization, and, coming soon, probabilistic programming!
We’re running a holiday promotion on the research reports and prototypes Hilary introduces in her talk. Our research is a great resource to educate your organization on what’s truly possible in contemporary machine learning. We cover a range of technologies to help our clients make informed choices on which algorithms will work best for their data and problems. Write to us at email@example.com to learn more!
More from the Blog
Dec 12 2016
by — We at Fast Forward Labs have long been interested in speech recognition technologies. This year’s chatbot craze has seen growing interest in machines that interface with users in friendly, accessible language. Bots, however, only rarely understand the complexity of colloquial conversation: many practical customer service bots are trained on a very constrained set of queries (”I lost my passwo...
Jan 3 2017
by with — React and Redux helped us keep application state manageable in our probabilistic programming prototypes For every topic we research at Fast Forward Labs, we create prototypes to show how the technology can be applied to make great products. Finite, stand-alone projects, our prototype web applications are great opportunities to experiment with new front-end tech. In our latest report on prob...
Nov 14 2018
by — We’re excited to release Federated Learning, the latest report and prototype from Cloudera Fast Forward Labs. Federated learning makes it possible to build machine learning systems without direct access to training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs. This article is about the technical side of federated learning. If ...