Noam Slonim

Kim Martineau

Roy Bar-Haim

Yoav Katz

Elad Venezian

News

Release

IBM Granite led open-source LLMs on API calling 

Project Debater argues for key point analysis of surveys, reviews, and social media

Putting more knowledge at the fingertips of non-English speakers

IBM Solution: AI for Customer Service

EMNLP 2024

NeurIPS 2024

DREAM 2024

AAAI 2025

IUI 2025

Foundation Models

Natural Language Processing

Generative AI

Conversational AI

Granite

Explainable AI

Modern computing architectures are revolutionizing the way business gets done. We’re working on tools to help businesses automate much of their daily processes, to save time and drive new insights through trusted, safe, and explainable AI systems.

AI for Business Automation

For AI to be useful, it needs to understand what’s being asked of it, and to know how to respond. At IBM Research, we’re working on systems to help AI better reason with the tasks it’s presented, such as understanding context and analogies, comprehension, and planning through scenarios. 

Knowledge and Reasoning

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.

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.

Project Debater argues for key point analysis of surveys, reviews, social media

Conversational AI

Our work

IBM Granite led open-source LLMs on API calling

Putting more knowledge at the fingertips of non-English speakers

Project Debater argues for key point analysis of surveys, reviews, and social media

Publications

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

Bridging the Gap Between AI Planning and Reinforcement Learning

FineWeb-Conv: A Method for Finding Good Conversation Data

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Generative AI in Digital Health

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

IBM Solution: AI for Customer Service

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