Peter Hess

Kim Martineau

News

Research

Explainer

Teaching AI models to improve themselves 

Why we’re teaching LLMs to forget things 

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 

IBM Granite has new experimental features for developers to test

Can a personalized AI be more useful?

NeurIPS 2024

Trustworthy AI

Trustworthy Generation

Foundation Models

Natural Language Processing

Generative AI

Computer Science

Explainable AI

Most enterprises rely on large volumes of aging code that are a challenge to debug, maintain, and update. At IBM Research, we’re using AI to help modernize software stacks, designing automated programming and refactoring systems to help businesses keep up with the speed of modern life. 

AI for Code

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.

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

As more of the world moves online, the demand for systems that can understand users and speak to them in natural language is growing exponentially. At IBM Research, we're working on next-generation AI that learns to decipher and replicate the way humans speak.

Speech

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

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

Screenshot of the App Builder interface from the BeeAI application