Mike Murphy

Kim Martineau

News

Research

Meet IBM’s new family of AI models for materials discovery

A benchmark for evaluating conversational RAG

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

IBM Granite now has eyes

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

AI and quantum computing: How IBM showed up at SXSW 2025

BeeAI now has multiple agents, and a standardized way for them to talk

MSR 2025

ICASSP 2025

KubeCon EU 2025

Cloud Native + Kubernetes AI Day 2025

Trustworthy AI

Fairness, Accountability, Transparency

Foundation Models

Computer Vision

Natural Language Processing

Generative AI

Granite

Quantum

Materials Discovery

Accelerated Discovery

The world is generating reams of data each day, and the AI systems built to make sense of it all constantly need faster and more robust hardware. We’re developing new devices and architectures to support the tremendous processing power AI requires to realize its full potential.

AI Hardware

Generative AI is revolutionizing how the world computes, but there are still roadblocks for scaling to solve real business problems. At IBM Research, we’re working on trustworthy models, software, and infrastructure to allow any business to harness the power of generative AI.

Machine learning uses data to teach AI systems to imitate the way that humans learn. They can find the signal in the noise of big data, helping businesses improve their operations. We've been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959. 

Machine Learning

Much of the information that can help transform enterprises is locked away in text, like documents, tables, and charts. We’re building advanced AI systems that can parse vast bodies of text to help unlock that data, but also ones flexible enough to be applied to any language problem.

The demand for virtual agents that can handle customer needs has continued to increase dramatically. At IBM Research, we’re building the next generation of artificial intelligence systems that can understand what’s being asked of them and how best to respond as efficiently as possible.

Conversational AI

Generative AI

Our work

BeeAI now has multiple agents, and a standardized way for them to talk

AI and quantum computing: How IBM showed up at SXSW 2025

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

IBM Granite now has eyes

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

A benchmark for evaluating conversational RAG

Publications

Can LLMs Replace Manual Annotation of Software Engineering Artifacts?

Contextual Value Alignment

LLM based Text Generation for Improved Low-resource Speech Recognition Models

Generative AI Model Data Pre-Training on Kubernetes: A Use Case Study

A Practical Guide To Benchmarking AI and GPU Workloads in Kubernetes

Effective cluster management for large scale AI and GPUs: Challenges and opportunities