Kim Martineau

Hironobu Takagi

Masaki Kuribayashi

Seita Kayukawa

Chieko Asakawa

News

Research

IBM and NASA team up to spur new discoveries about our planet 

Debugging foundation models for bias 

What is prompt-tuning?

Environmental analysis made easier with IBM’s Geospatial Studio

IBM’s new benchmark changes monthly to avoid teaching to the test

IBM Granite now has eyes

New smartphone app to navigate blind people to stand in lines with distances

AAAI 2025

ICASSP 2025

ICLR 2025

Fairness, Accountability, Transparency

Foundation Models

Hybrid Cloud Platform

Computer Vision

Human-Centered AI

Machine Learning

Natural Language Processing

Scaling AI

Generative AI

Granite

Computer Science

Climate and Sustainability

Explainable AI

Accelerated Discovery

We’re helping reinvent how enterprises manage their assets, including automating anomaly detection, failure patterns, and sensor data. We’re creating AI that fuses noisy sensor data, images, and text from operator logs and repair manuals to enable intelligent workflows with natural-language explanations.

AI for Asset Management

Modern computer vision systems have superhuman accuracy when it comes to image recognition and analysis, but they don’t really understand what they see. At IBM Research, we’re designing AI systems with the ability to see the world like we do.

To respond to the global climate emergency, we're using AI and hybrid cloud to accelerate discovery of climate mitigation and adaption solutions. We're improving carbon emission performance through optimization and capture, and preparing enterprises for the impact of climate change.

Using AI and the hybrid cloud to accelerate the discovery of climate mitigation and adaption solutions to respond to the growing global climate emergency.

The combination of increasingly powerful computers and AI offers the possibility to be able to detect, diagnose, and cure diseases like never before. At IBM Research, we’re working on creating software and AI systems that can convert reams of health data into useable information for clinicians the world over.

Healthcare and Life Sciences

We're building systems that can convert reams of health data into useable information for clinicians the world over.

We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we're aiming to create a revolution in AI, rather than an evolution. 

Neuro-symbolic AI

Debugging AI for Fairness NeurIPS -Cool Gray 20.jpg

Computer Vision

Our work

IBM Granite now has eyes

IBM’s new benchmark changes monthly to avoid teaching to the test

Environmental analysis made easier with IBM’s Geospatial Studio

What is prompt-tuning?

IBM and NASA team up to spur new discoveries about our planet

Debugging foundation models for bias

Publications

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching

Self-supervised learning of monocular depth estimators in autonomous vehicles with federated learning

Foundation models for materials discovery – current state and future directions

LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

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