Accelerate Foundation Models Research: Supporting a global academic research ecosystem for AI

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The latest advances in artificial intelligence have sparked broad public interest and excitement, and the sciences are no exception. Increasingly capable foundation models are fuelling a fundamental shift in computing research, natural sciences, social sciences, and even computing education itself. As industry-led advances in AI continue to reach new heights, Microsoft Research believes that a vibrant and diverse research ecosystem is essential to realizing the promise of AI. This means ensuring that the academic research community, and especially researchers working outside computer science, can tap into these capabilities. Their depth and breadth of expertise across disciplines, cultures and languages can contribute meaningfully to our ability to use AI to address some of the world’s greatest technical, scientific, and societal challenges.

To this end, Microsoft Research has established Accelerate Foundation Models Research (AFMR), a new initiative that brings together an interdisciplinary research community to pursue three goals:

  • Aligning AI with shared human goals, values, and preferences via research on models, which enhances safety, robustness, sustainability, responsibility, and transparency, while also exploring new evaluation methods to measure the rapidly growing capabilities of new models.
  • Improving human interactions via sociotechnical research, which enables AI to extend human ingenuity, creativity and productivity, while also working to reduce inequities of access and working to ensure positive benefits for people and societies worldwide.
  • Accelerating scientific discovery in natural sciences through proactive knowledge discovery, hypothesis generation, and multiscale multimodal data generation.

AFMR is a global research network and a resource platform that enables researchers in computer science and many other disciplines to engage with some of the greatest technical and societal challenges of our time. This includes a grant program that provides access to state-of-the-art foundation models hosted through Microsoft Azure AI.

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The goal is to foster more collaborations across disciplines, institutions, and sectors, and to unleash the full potential of AI for a wide range of research questions, applications, and societal contexts.

Following a successful pilot program and initial call for proposals (CFP), details of which are provided below, we are committed to continuing this work and can expect to solicit additional proposals throughout the coming year. Visit the AFMR site to learn more about upcoming programs and events, read peer-reviewed work that has resulted from the program and find resources to accelerate research and collaborations. 

Inspiring research in the era of AI

When ChatGPT was released in the fall of 2022, it quickly became clear that this new technology and tool would play a central role in AI computing research and applications.

“As a natural language processing (NLP) researcher, I was excited at first by ChatGPT’s potential to stimulate an AI revolution,” said Evelyne Viegas, senior director of research engagement at Microsoft Research. “Soon, I became concerned about a potential lack of access to this resource outside of industry, which could delay important progress in academic settings.”

When Microsoft enabled access to OpenAI models (Embeddings series, GPT-3.5-Turbo series, and GPT-4 series) via the Azure AI services, it created an opportunity to engage with the academic community to learn about their needs and aspirations and start enabling them. A team at Microsoft Research conducted a pilot program offering model access to a small number of participants, and the success of this effort inspired a broader and more sustained program.

Research topics undertaken as part of the pilot reflect the ambitions of AI research at Microsoft in understanding general AI, driving model innovation, ensuring social benefit, transforming scientific discovery, and extending human capabilities across different domains (e.g., astronomy, education, health, law, society).

Although the research supported by this pilot is still underway, the examples below illustrate the possibilities of opening access to leading-edge models to a diverse group of researchers:

Integrating ChatGPT into English as a Foreign Language (EFL) Writing Education – Korea Advanced Institute of Science and Technology (KAIST)

This project explores how students can utilize generative AI for interactive revision in EFL writing. Because the majority of KAIST courses are given in English, the sooner non-English speakers can learn the language the better they will be able to participate in their classes. While earlier chatbots have been used for EFL, language learners found them unengaging. With Azure OpenAI Service, the KAIST team is gathering data to show how the unique capabilities of a GPT-4-based chatbot are accelerating learning while making the learner’s experience more engaging.

Lightweight Adaptation of LLMs for Healthcare Applications – Stanford University

This work focuses on accelerating the task of report summarization for radiologists to improve workflow and decrease the time needed to generate an accurate report. It uses domain adaptation via pretraining on biomedical text, or clinical text and discrete prompting or fine-tuning. Initial results are promising, showing the added value of using foundation models for some clinical tasks.

AI-Based Traffic Monitoring System using Physics-Informed Neural Networks and GPT Models – North Carolina A&T State University

Researchers are creating a traffic monitoring system using data collected from unmanned aerial vehicles (UAVs) to fine-tune foundation models for video analysis and traffic state estimation. This work can directly benefit transportation agencies and city planners, helping them understand traffic patterns, congestion, and safety hazards.

Forging New Horizons in Astronomy – Harvard University

This project seeks to enhance human interaction with astronomy literature utilizing the capabilities of the large language models (LLM), particularly GPT-4. This work employs in-context prompting techniques to expose the model to astronomy papers to build an astronomy-focused chat application to engage the broader community.

Expanding AFMR

Much experimentation remains to be done with foundation models. The AFMR CFP invited the community to develop proposals focused on the goals and questions below:

  • Aligning AI systems with human goals and preferences
  • Advancing beneficial applications of AI
  • Accelerating scientific discovery in the natural and life sciences

The response to the AFMR Fall CFP has been phenomenal, with close to 400 proposals from 170 universities across 33 countries.

“Research undertaken by the principal investigators brings the promise to advance research across a greater breadth of research pursuits, application domains, and societal contexts than we could have imagined,” Viegas said. “It covers a vast range of scientific and sociotechnical topics: creativity, culture, economy, education, finance, health, causality, evaluation, augmentation and adaptation, multimodal, responsible AI, robotics, scientific discovery, software and society. It is inspiring to see experts from different countries with different cultures, languages, institutions, and departments, including computer science, social science, natural sciences, humanities, medicine, music, all come together to work on democratizing AI and work on solving some of the greatest technical and societal challenges of tomorrow.”

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