AI, artificial intelligence, data analytics, Deep Learning, Machine Learning, Predictive Analytics; 23 Views. Related. AI-Generated Imagery is the New …

Tech giant Microsoft made the decision to announce that one of the top-tier cross-platform frameworks for model-based machine learning is open to one and all worldwide. "We're extremely excited today to open source Infer.NET on GitHub under the permissive MIT license for free use in commercial applications," wrote Yordan Zaykov, Principal Research Software Engineering Lead at Microsoft in an official statement. Open sourcing Infer.NET represents the culmination of a long and ambitious journey. The Microsoft Research Team in Cambridge embarked on developing the framework back in 2004. The statement said that they had learned a lot along the way about making ML solutions that are scalable and interpretable.

One of the biggest challenges in building autonomous vehicles is dealing with the irregularities of physical spaces outdoors: From electric poles to winding roads and bumpy terrain, there are lots of things that can trip up your creation. With that in mind, a team of researchers at Microsoft have built and open sourced a simulation tool to help people train autonomous cars, drones and robots learn how to avoid obstacles just like they would in the real world. It's called the Aerial Informatics and Robotics Platform, or AirSim for short. Available for Linux and Windows, it lets you generate a random environment to train your bot or vehicle, experiment with various models and test in a range of scenarios right in your workshop. It's worth noting that the software isn't designed to entirely replace real-world testing, but rather to complement it by allowing you to simulate a wide range of experiments on demand and as many times as necessary. In addition to creating detailed environments that mimic the real world, AirSim also incorporates realistic physics systems to accurately simulate various lighting conditions and object clusters (such as a bunch of trees) to help train robotic models in understanding how to compute depth, avoid obstacles and deal with shadows and glare when navigating through outdoor spaces.