Peter Hess

Kim Martineau

News

Research

Explainer

IBM Granite led open-source LLMs on API calling 

Teaching AI models to improve themselves 

Breaking down the AI transformer

How memory augmentation can improve large language model efficiency and flexibility

For LLMs, IBM’s NorthPole chip overcomes the tradeoff between speed and efficiency

Why we’re teaching LLMs to forget things 

An air traffic controller for LLMs 

ECAI 2024

EMNLP 2024

NeurIPS 2024

Trustworthy AI

Trustworthy Generation

Foundation Models

Natural Language Processing

Generative AI

Conversational AI

Exploratory Science

Granite

Computer Science

Semiconductors

AI Hardware

Explainable AI

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.

IBM Research scientists are working on different approaches to teach AI models how to improve themselves

an abstract image of connected purple dots

A 3x3 grid of concentric circles and squares

A 2U server blade containing NorthPole cards