When AI systems impact people’s lives, it is critically important that people understand their behavior. By understanding their behavior, data scientists can properly debug their models. If able to reason how models behave, designers can convey that information to end users. If doctors, judges and…
Everyone is familiar by now with recommendation systems such as on Netflix for movies, Amazon for products, Yelp for restaurants and TripAdvisor for travel. Indeed, quality recommendations are a crucial part of the value provided by these businesses. Recommendation systems encourage users to share feedback…
Lots of important information, including job openings and other kinds of advertising, are often transmitted by word-of-mouth in online settings. For example, social networks like LinkedIn are increasingly used as a way of spreading information about job opportunities, which can greatly affect people’s career development.…
Deep learning algorithms, supported by the availability of powerful Azure computing infrastructure and massive training data, constitutes the most significant driving force in our AI evolution journey. In the past three years, Microsoft reached several historical AI milestones being the first to achieve human parity…
“Here’s my conference paper—what do you think?” After hours of agonizing over words and illustrations, sharing a draft document is a moment of great pride. All too often, this is shortly followed by embarrassment when your colleague gets back to you with reams of edits.…
As researchers continue to apply machine learning to more complex real-world problems, they’ll need to rely less on algorithms that require annotation. This is not only because labels are expensive, but also because supervised learners trained only to predict annotations tend not to generalize beyond…
Recent successes in machine intelligence hinge on core computation ability to efficiently search through billions of possibilities in order to make decisions. Sequences of decisions, if successful, often suggest that perhaps computation is catching up to–or even surpassing–human intelligence. Human intelligence, on the other hand,…
Designing artificial intelligence systems and features poses new challenges for user experience (UX) practitioners. Traditionally, UX designers rely on sketching and low-fidelity, fast prototyping to imagine and test their ideas. The usual design tools and techniques, however, can’t always meet the demands of designing AI-infused…
There is a growing interest in exploring the use of variational auto-encoders (VAE), a deep latent variable model, for text generation. Compared to the standard RNN-based language model that generates sentences one word at a time without the explicit guidance of a global sentence representation,…
Episode 71, April 10, 2019 - On today’s podcast, Dr. Pollefeys brings us up to speed on the latest in computer vision research, including his innovative work with Azure Spatial Anchors, tells us how devices like Kinect and HoloLens may have cut their teeth in…
Autonomous Systems have attracted a lot of attention as they promise to improve efficiency, reduce cost and most importantly take on tasks that are too dangerous for humans. However, building a real-world autonomous system that would operate safely at scale is a very difficult task.…
We’re excited about the formal launch of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) on Monday, March 18th. We resonate with Fei-Fei Li, John Etchemendy, and other leaders at Stanford on the promise of taking an interdisciplinary approach to AI, on a pathway guided…
