Jigsaw fixes bugs in machine-written software
Large pre-trained language models such as GPT-3, Codex, and others can be tuned to generate code from natural language specifications of programmer intent. Such automated models have the potential to improve productivity for every programmer…
Teachable AI Experiences (Tai X)
The Teachable AI Experiences team (Tai X) aims to innovate teachable AI systems that allow people near or far from the norm to create meaningful personalized experiences for themselves. What we ALL have in common…
AI for Testing
AI-driven test case generation for Visual Studio and VSCode This project aims at developing AI-based systems with the goal of automating software testing activities. We train large transformer models to learn from developers’ code how…
Ekya: Continuous Learning on the Edge
Ekya is a system which enables continuous learning on resource constrained devices. Given a set of video streams and pre-trained models, Ekya can continuously fine-tune the models to maximize accuracy by intelligently allocating resources between…
IntelliCode Completions
AI-driven code auto-completion in Visual Studio and VSCode The IntelliCode completions project started in 2018 when we tried to improve developer productivity through recommending class members based on user’s code context in Visual Studio. The…
KID: Knowledge Infused Decoding
Knowledge Infused Decoding (KID) is a decoding algorithm that infuses knowledge (from Wikipedia) into each step decoding of text generation.
Jigsaw Datasets
Jigsaw Dataset: Natural language to Python Pandas code. Two datasets (PandasEval1 and PandasEval2) described in our paper, “Jigsaw: Large Language Models meet Program Synthesis”.
Swiss Joint Research Center Workshop 2022
This two-day Workshop brought together PhD students and postdocs working on collaborative research projects between academia and Microsoft via the Swiss Joint Research Center, Mixed Reality & AI Zurich Lab, Mixed Reality & AI Cambridge Lab (opens in…