We live in a rapidly changing world.
Demographic shifts, climate change, political dynamics, and many other forces are creating urgent challenges in critical areas like global health and scientific discovery, environmental sustainability, food security, and societal resilience.
In this new AI era, technology is changing even faster than before, and the transition from research to reality, from concept to solution, now takes days or weeks rather than months or years.
“Today we are seeing so much AI research happening at the speed of conversation, to the point where even our top researchers feel that their heads are spinning, but working together, providing openness, providing greater access, we can see that we’ve made tremendous progress.”
– Peter Lee, President, Microsoft Research
In 2024, Microsoft Research continued its foundational research (opens in new tab) to expand the capabilities of large language models, but we also explored more deeply how smaller models (opens in new tab) can be trained for specific tasks. We discovered that by using smaller datasets and fewer compute resources, some small language models can demonstrate enhanced reasoning and other complex capabilities that were once considered the exclusive province of large-scale models.
Microsoft Research and its external collaborators used AI to enable earlier detection and treatment of esophageal cancer, which could lead to dramatically improved survival rates, and to accelerate the discovery of new drugs needed to treat infectious diseases that kill millions of people every year. And we continued to use AI to develop new tools for scientific discovery so that we and others in the scientific community can confront some of humanity’s most important challenges.
One team of Microsoft researchers created the world’s first large-scale model of the atmosphere, which could transform weather forecasting and our ability to predict and mitigate the effects of extreme weather events. Another team worked with global experts to create a generative AI tool that empowers non-governmental organizations (NGOs) to fight human trafficking.
We also opened a new research lab in Tokyo this year. It joins our other labs in Europe, Asia, Africa, and North America. And we launched a series of quarterly Research Forums to help update the global research community about some of the pivotal work we’re doing at Microsoft Research. Register for future episodes, view presentations from previous forums, and explore our briefing book content.
This post highlights some of the work that Microsoft Research has done in 2024, along with academic and industry colleagues, to help drive real-world benefits for people worldwide.
AI for Business Transformation: Multimodal Models
Top social posts of 2024
Accelerating Foundation Models Research
Microsoft Research program encourages AI development by academics, not just industry.
Ece Kamar on the future of AI agents
Introducing BitNet b1.58
1.58-bit LLMs that rival full-precision Transformer LLMs in performance while significantly boosting efficiency—in terms of latency, throughput, memory and energy consumption.
Phi-4
A small language model that performs as well as (and often better than) large models on certain types of complex reasoning tasks.
Q-Sparse
A breakthrough in training fully sparsely-activated LLMs supports both full-precision and 1-bit LLMs.
You Only Cache Once (YOCO)
A novel decoder-decoder architecture for LLMs, enhancing memory efficiency by caching key-value pairs only once.
Differential Transformer
A new foundation architecture for LLMs that enhances focus on relevant information while canceling attention noise.
AI Controller Interface
Helping researchers and developers efficiently implement existing strategies for controlling LLMs and invent new ones.
GraphRAG 1.0
Advancing AI use in complex domains like science.
MatterSimV1
A deep learning atomistic model across elements, temperatures, and pressures.
Top stories of 2024
GHDDI and Microsoft Research use AI to achieve progress in new drug discovery for global infectious diseases
The joint team designed several chemical compounds that are effective in inhibiting these pathogens’ essential target proteins, accelerating the structure-based drug discovery process.
GraphRAG: Unlocking LLM discovery on narrative private data
GraphRAG is a significant advance in enhancing the capability of LLMs and enables us to answer important classes of questions that we cannot attempt with baseline RAG alone.
Scaling early detection of esophageal cancer with AI
Our collaboration with Cyted demonstrates the transformative potential of integrating advanced AI models into clinical workflows. Earlier detection of cancer and earlier start of treatment mean that more than 9 in 10 patients survive 5 years after diagnosis.
SIGMA: An open-source mixed-reality system for research on physical task assistance
Imagine if every time you needed to complete a complex physical task you had a world-class expert standing over your shoulder and guiding you through the process. What would it take to build an interactive AI system that could assist you with any task in the physical world?
MatterSim: A deep-learning model for materials under real-world conditions
The model efficiently handles simulations for a variety of materials, including metals, oxides, sulfides, halides, and their various states such as crystals, amorphous solids, and liquids.
Aurora: The first large-scale foundation model of the atmosphere
Aurora presents a new approach to weather forecasting that could transform our ability to predict and mitigate the impacts of extreme events. The model can forecast a broad range of atmospheric variables, from temperature and wind speed to air-pollution levels and concentrations of greenhouse gases.
Data-driven model improves accuracy in predicting EV battery degradation
Microsoft Research collaborated with Nissan Motor Corporation to develop a new machine learning method that predicts battery degradation with an average error rate of just 0.94%, significantly bolstering Nissan’s battery recycling efforts.
Large-scale pathology foundation models show promise on a variety of cancer-related tasks
Imagine if pathologists had tools that could help predict therapeutic responses just by analyzing images of cancer tissue. By leveraging AI and machine learning, researchers are now able to analyze digitized tissue samples with unprecedented accuracy and scale, potentially transforming how we understand and treat cancer.
Find My Things: New teachable AI tool helps blind and low-vision people locate lost personal items
Find My Things makes it easy for people with vision disabilities to use their phones to recognize and locate the personal items they use every day.
Data Formulator: Exploring how AI can help analysts create rich data visualizations
Data Formulator’s architecture separates data transformation from chart configuration, improving both the user experience and AI performance. Refining how users interact with AI-powered tools is essential for improving how they communicate their requirements, paving the way for more efficient and effective collaboration.
Microsoft establishes a new lab, Microsoft Research Asia – Tokyo (opens in new tab)
The Tokyo lab will focus on critical areas that reflect Japan’s socioeconomic priorities, including embodied AI, well-being and neuroscience, societal AI, and industry innovation. These research efforts aim to leverage advanced technologies to foster societal progress and contribute to the region’s innovation ecosystem.
MarS: A unified financial market simulation engine in the era of generative foundation models
These innovations have the potential to empower financial researchers to customize generative models for diverse scenarios, establishing a new paradigm for applying generative models to downstream tasks in financial markets.
Thank you for reading, watching, and listening
In 2024, contributions across the research community at Microsoft continued to advance the company’s vision of what technology can and should be: a means for empowering every person and every organization on the planet to achieve more.
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