Kim Martineau

News

Research

Can a personalized AI be more useful?

A benchmark for evaluating conversational RAG

IBM Granite now has eyes

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

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

ICLR 2025

CHI 2025

CAIN 2025

NAACL 2025

Trustworthy AI

Fairness, Accountability, Transparency

AI Transparency

Computer Vision

Natural Language Processing

Generative AI

Granite

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

Screenshot of the App Builder interface from the BeeAI application

Natural Language Processing

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

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

IBM Granite now has eyes

A benchmark for evaluating conversational RAG

Publications

Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study

Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation

Designing and implementing LLM guardrails components in production environments

Diagnosing and Prioritizing Issues in Automated Order-Taking Systems: A Machine-Assisted Error Discovery Approach

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

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