Kim Martineau

News

Research

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

IBM crossed a transformer with an SSM and got ‘Bamba’

AI is changing how we work — is it time to change how we credit AI’s involvement?

NAACL 2025

Trustworthy AI

Fairness, Accountability, Transparency

AI Transparency

Computer Vision

Natural Language Processing

Generative AI

Open Source

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

Natural Language Processing

Our work

AI is changing how we work — is it time to change how we credit AI’s involvement?

IBM crossed a transformer with an SSM and got ‘Bamba’

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

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

The Literary Canons of Large-Language Models: An Exploration of the Frequency of Novel and Author Generations Across Gender, Race and Ethnicity, and Nationality

MILU: A Multi-task Indic Language Understanding Benchmark

Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5

Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG

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