Mike Murphy

Kim Martineau

News

Research

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

IBM’s safety checkers top a new AI benchmark

A new kind of adapter helps LLMs get their words out faster

NAACL 2025

ODSC East 2025

DAC 2025

OSSNA 2025

Trustworthy AI

Fairness, Accountability, Transparency

AI Transparency

Computer Vision

Natural Language Processing

Generative AI

Granite

Quantum

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

A new kind of adapter helps LLMs get their words out faster

IBM’s safety checkers top a new AI benchmark

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

Publications

Lightning Talk: Serving guardrail detectors on vLLM

CIRCUITSYNTH-RL: LLM-Based Circuit Topology Synthesis with RL Refinement

Guardrails in generative AI workflows via orchestration

InspectorRAGet: An Introspection Platform for RAG Evaluation

DAMAGeR: Deploying Automatic and Manual Approaches to GenAI Red-teaming

The Literary Canons of Large-Language Models: An Exploration of the Frequency of Novel and Author Generations Across Gender, Race and Ethnicity, and Nationality