AI & Robotics

In a paper published this week on the preprint server Arxiv.org, a team of researchers from Google Brain, Google X, and the University of Calfornia at Berkeley describe an extension to existing AI methods that enable an agent — for instance, a robot — to decide which action to take while performing a previous action. The idea is that modeling an agent’s behavior after that of a person or…

In a recent paper published on the preprint server Arxiv.org, researchers at Carnegie Mellon, Facebook, and the University of Illinois Urbana-Champaign propose Active Neural Simultaneous Localization and Mapping (Active Neural SLAM), a hierarchical approach for teaching AI agents to explore environments. They say that it leverages the strength of both classical and AI-based path- and goal-planning…

In an academic paper published this week on the preprint server Arxiv.org, a team of researchers from Princeton, Microsoft, the nonprofit Algorand Foundation, and Technion propose Falcon, an end-to-end framework for secure computation of AI models on distributed systems. They claim that it’s the first secure C++ framework to support high-capacity AI models and batch normalization, a technique…

If, like many people these days, you’re trying to get a firmer understanding of what AI is and how it works but are secretly panicking a little because you’re struggling with terminology so opaque that you’re lost before you get to Markov chains, you may want to crack open Janelle Shane’s new book. She recently sat down with VentureBeat to talk about the book, whose title, You Look Like a…