Microsoft Research Podcast

Ashley Llorens hosts Chris Bishop to discuss the state of deep learning, their new textbook on the subject, and the potential for 'super copilots' in scientific breakthroughs. They explore advancements in deep learning, including pre-training and zero-shot learning, and highlight the applications of AI in healthcare and materials, such as molecule design and sustainable energy solutions.

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements. In this episode, Senior Principal Research Manager Tao Qin and Senior Researcher Lijun Wu discuss “FABind: Fast and Accurate Protein-Ligand Binding.” The paper, accepted at the 2023 Conference on Neural Information Processing Systems (NeurIPS), introduces a new method for predicting the binding structures of proteins and ligands during drug development. The method demonstrates improved speed and accuracy over current methods.Learn more:FABind: Fast and Accurate Protein-Ligand BindingFABind code on GitHub

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.In this episode, Principal Researcher Alessandro Sordoni joins host Gretchen Huizinga to discuss “Joint Prompt Optimization of Stacked LLMs using Variational Inference.” In the paper, which was accepted at the 2023 Conference on Neural Information Processing Systems (NeurIPS), Sordoni and his coauthors introduce Deep Language Networks, or DLNs, an architecture that treats large language models as layers within a network and natural language prompts as each layer’s learnable parameters.Read the paper

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.In this episode, Xing Xie, a Senior Principal Research Manager of Microsoft Research Asia, joins host Dr. Gretchen Huizinga to discuss “Evaluating General-Purpose AI with Psychometrics.” As AI capabilities move from task specific to more general purpose, the paper explores psychometrics, a subfield of psychology, as an alternative to traditional methods for evaluating model performance and for supporting consistent and reliable systems.Read the paper: Evaluating General-Purpose AI with Psychometrics

Transforming research ideas into meaningful impact is no small feat. It often requires the knowledge and experience of individuals from across disciplines and institutions. Collaborators, a Microsoft Research Podcast series, explores the relationships—both expected and unexpected—behind the projects, products, and services being pursued and delivered by researchers at Microsoft and the diverse range of people they’re teaming up with.In this episode, Dr. Gretchen Huizinga speaks with Cecily Morrison, MBE, a Senior Principal Research Manager at Microsoft Research, and Karolina Pakėnaitė, who also goes by Caroline, a PhD student and member of the citizen design team working with Morrison on the research project Find My Things. An AI phone application designed to help people who are blind or have low vision locate their personal items, Find My Things is an example of a broader research approach known as Teachable AI. Morrison and Pakėnaitė explore the Teachable AI goal of empowering people to make an AI experience work for them. They also discuss how “designing for one” when it comes to inclusive design leads to innovative solutions and what they learned about optimizing these types of systems for real-world use (spoiler: it’s not necessarily more or higher-quality data).Learn more:Teachable AI Experiences (Tai X) | Project pageUnderstanding Personalized Accessibility through Teachable AI: Designing and Evaluating Find My Things for People who are Blind or Low Vision | Publication, October 2023Microsoft Inclusive Design | Inclusive design resource centerDeafBlind Everest Project | Karolina (Caroline) Pakėnaitė personal website

Explore the challenges of generative AI in communication and privacy, including disinformation and social engineering. Learn about the methodology and framework for contextual confidence in addressing these challenges. Discover the importance of protecting context for effective communication and the real-world impact of contextual confidence and generative AI.

In this new Microsoft Research Podcast series What’s Your Story, Johannes Gehrke explores the who behind the technical and scientific advancements helping to reshape the world. A systems expert whose 10 years with Microsoft spans research and product, Gehrke talks to members of the company’s research community about what motivates their work and how they got where they are today.Across his time at Microsoft, Desney Tan, Managing Director of Microsoft Research Redmond, has had the experience of shepherding research ideas into products multiple times, and much like the trajectory of research, his life journey has been far from linear. In this episode, Tan shares how he moved to the United States from Singapore as a teenager, how his self-described “brashness” as a Microsoft intern helped shift the course of his career, and how human impact has been a guiding force in his work.

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.In this episode, Andy Gordon, a Partner Research Manager, and Carina Negreanu, a Senior Researcher, both at Microsoft Research, join host Dr. Gretchen Huizinga to discuss “Co-audit: Tools to help humans double-check AI-generated content.” This paper brings together current understanding of generative AI performance to explore the need and context for tools to help people using the technology find and fix mistakes in AI output.View the paper

In this new Microsoft Research Podcast series What’s Your Story, Lab Director Johannes Gehrke explores the who behind the technical and scientific advancements helping to reshape the world. He talks to members of the research community at Microsoft about what motivates their work and how they got where they are today.Ranveer Chandra is Managing Director of Research for Industry and CTO of Agri-Food. He is also Head of Networking Research at Microsoft Research Redmond. His work in systems and networking is helping to bring more internet connectivity to more people and is yielding tools designed to help farmers increase food production more affordably and sustainably. In this episode, he shares what it was like growing up in Jamshedpur, India; why he focuses his efforts in the areas he does; and where the joy in his work comes from.Learn more:Ranveer Chandra at Microsoft ResearchFarmBeats: AI, Edge & IoT for AgricultureProject FarmVibes6G | Space

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements. In this episode, Dr. Sheng Zhang, a Senior Researcher at Microsoft Research, joins host Dr. Gretchen Huizinga to discuss “UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition.” In this paper, Zhang and his coauthors present mission-focused instruction tuning, a method for distilling large language models into smaller, more efficient ones for a broad application class. Their UniversalNER models achieved state-of-the-art performance in named entity recognition, an important natural language processing (NLP) task. Model distillation has the potential to make NLP and other capabilities more accessible, particularly in specialized domains such as biomedicine, which could benefit from more resource-efficient and transparent options. Learn more:View the paperUniversalNER project website with demoCode on GitHubDataset and models on Hugging Face