Katia Moskvitch

Syed Yousaf Shah

 Dhaval Patel

Wesley Gifford

Thomas Parnell

Haris Pozidis

Łukasz Ćmielowski

Daniel Ryszka

Kim Martineau

Michelle Brachman

Kartik Talamadupula

News

Technical note

Case study

Q & A

Release

Explainer

Simplifying data: IBM’s AutoAI automates time series forecasting

Goal-oriented flow assist: supporting low code data flow automation with natural language

How IBM is helping a major retailer stay ahead of the holiday crunch

What is AI alignment?

Software has eaten the world. What now?

Generative AI could offer a faster way to test theories of how the universe works

Snap ML pushes AutoAI to deliver 4x-faster automated machine learning on IBM Cloud

Tech Preview: IBM Federated Learning

IBM Solution: AutoAI on IBM Watson Studio

ICASSP 2025

KubeCon EU 2025

ICSE 2025

ICLR 2025

DAC 2025

Fairness, Accountability, Transparency

AI for Asset Management

Foundation Models

AI for Business Automation

AI for Code

Automated AI

Human-Centered AI

Machine Learning

Natural Language Processing

Generative AI

Granite

AI for IT

Application Modernization

AI can help sift through the noise to identify significant patterns in the increasingly large pools of data generated by enterprises. We’re reimagining IT operations, using AI to automate core processes, better detection and diagnosis, preformance monitoring, and explainable action recommendations.

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.

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. 

There’s an ever-increasing demand on business systems to keep up with the speed of the web. We’re designing systems to help organizations modernize their applications, while reducing the time it takes businesses to rebuild their products to take advantage of the benefits of the hybrid cloud. 

We're building tools to help AI creators reduce the time they spend designing their models. Our goal is to allow non-experts across industries to build their own AI solutions, without writing complex code or performing tedious tuning and optimization.

A woman shopping for clothes in a department store.

Automated AI

Our work

Generative AI could offer a faster way to test theories of how the universe works

Software has eaten the world. What now?

What is AI alignment?

How IBM is helping a major retailer stay ahead of the holiday crunch

Goal-oriented flow assist: supporting low code data flow automation with natural language

Snap ML pushes AutoAI to deliver 4x-faster automated machine learning on IBM Cloud

IBM Solution: AutoAI on IBM Watson Studio

Publications

CIRCUITSYNTH-RL: LLM-Based Circuit Topology Synthesis with RL Refinement

ASTER: Natural and Multi-language Unit Test Generation with LLMs

Rationalization Models for Text-to-SQL

RAC-GAN: Iterative Dual-Objective Over and Under Sampling for Imbalanced Datasets

Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching

Generative AI Model Data Pre-Training on Kubernetes: A Use Case Study

Tech Preview: IBM Federated Learning

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