Shital Shah is principal research software engineer at Microsoft Research AI. His interests include simulation, autonomous vehicles, robotics, deep learning and reinforcement learning. He has been working at Microsoft for 14 years architecting, designing and developing large scale distributed machine learning systems. He has contributed in research and engineering in various roles at Microsoft including technical lead, architect, engineering manager and research engineer. Previously at Bing, he founded and led the team to design and develop distributed machine-learned clustering platform for web-scale data. At Microsoft Research, he conceived and lead the development of AirSim, a physically and visually realistic cross-platform simulator for AI research. Most recently, he developed TensorWatch, a new system for debugging and visualizing machine learning.
Microsoft researchers Shital Shah, Ashish Kapoor and Debadeepta Dey are leading development of the Aerial Informatics and Robotics Platform.
Earlier this year, we open-sourced a research project called AirSim, a high-fidelity system for testing the safety of artificial intelligence systems. AirSim provides realistic environments, vehicle dynamics and sensing for research into how autonomous vehicles that use AI that can operate safely in the open world.
Today, we are sharing an update to AirSim: We have extended the system to include car simulation, which will help advance the research and development of self-driving vehicles. The latest version is available now on GitHub as an open-source, cross-platform offering.
The year’s end is an opportunity to reflect on what was achieved and to resolve to aspire to even greater heights in the one that’s about to begin. Looking back on what was accomplished at Microsoft Research in 2018 brings…
At Microsoft, we have a vision and passion to bring artificial intelligence solutions to real-world systems using the power of simulation. We continually strive to accelerate AI advances with the use of realistic simulators, tools, and environments.
Today we are excited to announce AirSim availability on Unity. AirSim developers will be able to leverage the Unity platform and ecosystem when building, training and evaluating autonomous systems in a simulated environment designed for AI.
The rise of deep learning is accompanied by ever-increasing model complexity, larger datasets, and longer training times for models. When working on novel concepts, researchers often need to understand why training metrics are trending the way they are. So far,…