PocketSkills Open Source Release
Mobile web application shell for educational content and interactive skill practices using a chat-like interface.
Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
Mobile web application shell for educational content and interactive skill practices using a chat-like interface.
Project Frigatebird is aimed at enabling small fixed-wing UAVs to travel long distances by soaring — taking advantage of rising air regions the way human sailplane pilots and bird species like frigatebirds do — using…
A deep learning project in cooperation with the NOAA Marine Mammal Lab to detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
This data contains our custom Mad Libs that accompany the EMNLP 2017 paper called “Filling the Blanks for Mad Libs Humor”. There are 50 Mad Libs in total. For each, we provide our original Mad…
Z3 is a theorem prover from Microsoft Research. It is licensed under the MIT license (opens in new tab). Z3 can be built using Visual Studio, a Makefile or using CMake. It provides bindings for…
KreMLin is a tool that extracts an F* program to readable C code. If the F* program verifies against a low-level memory model that talks about the stack and the heap; if it is first-order;…
Lean is a functional programming language that makes it easy to write correct and maintainable code. You can also use Lean as an interactive theorem prover. Lean programming primarily involves defining types and functions. This…
Boogie is an intermediate verification language (IVL), intended as a layer on which to build program verifiers for other languages. Several program verifiers have been built in this way, including the VCC and HAVOC verifiers…
A Python package that implements a variety of algorithms that mitigate unfairness in supervised machine learning.