Peter Hess

Kim Martineau

News

Research

Explainer

The chips of tomorrow may well take inspiration from the architecture of our brains 

An air traffic controller for LLMs 

Serving customized AI models at scale with LoRA

A new tool to unlock data from enterprise documents for generative AI 

Can a personalized AI be more useful?

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

A benchmark for evaluating conversational RAG

AAAI 2025

IUI 2025

MSR 2025

Foundation Models

Edge Computing

Natural Language Processing

Generative AI

Hardware Technology

Semiconductors

Materials Discovery

AI Hardware

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.

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

A conceptual image of neuromorphic computing, showing a symmetrical mandala-like array of blue dots

Screenshot of the App Builder interface from the BeeAI application

Generative AI

Our work

A benchmark for evaluating conversational RAG

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

Can a personalized AI be more useful?

A new tool to unlock data from enterprise documents for generative AI

Serving customized AI models at scale with LoRA

The chips of tomorrow may well take inspiration from the architecture of our brains

Publications

Can LLMs Replace Manual Annotation of Software Engineering Artifacts?

Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach

HAI-GEN 2025: 6th Workshop on Human-AI Co-Creation with Generative Models

Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data)

Agentic AI for Digital Twin

Question-guided Insights Generation for Automated Exploratory Data Analysis