Artificial Intelligence (AI) is the study of how computer systems can simulate intelligent processes such as learning, reasoning, and understanding symbolic information in context. AI is inherently a multi-disciplinary field. Although it is most commonly viewed as a subfield of computer science, and draws upon work in algorithms, databases, and theoretical computer science, AI also has close connections to the neurosciences, cognitive science and cognitive psychology, mathematical logic, and engineering.
IBM has been a leader in AI since AI's earliest days, when Arthur Samuels (in the 1950s) developed an expert checkers-playing program that learned from experience. Forty years later, IBM Research's chess-playing program Deep Blue made history by beating world chess champion Gary Kasparov.
AI Research at IBM goes far beyond game-playing programs and is at the forefront of many of the hottest areas of Artificial Intelligence. Research in AI at IBM can be characterized by the AI techniques or methodologies used in a particular project, or by the motivating application for which AI is used. AI techniques and methodologies include learning, Bayesian Reasoning, intelligent agents, knowledge representation, logic programming, and planning. AI applications include electronic commerce, intelligent tutoring systems, knowledge management, performance management, and exploratory vision.
Projects
- Agent Building and Learning Environment
A Java framework, component library, and productivity tool kit for building intelligent agents using machine learning and reasoning.
- Anti-Spam Research
Research into algorithms for spam filtering.
- Business process integration and automation (BPIA)
Technologies that help businesses to implement a service-oriented architecture by adopting a model-driven approach.
- Data Analytics group at Tokyo Research Lab
Research into fundamental analysis methods such as anomaly detection and risk-sensitive data analytics.
- Data Analytics Research
Research activities in machine learning, predictive modeling, high dimensional data mining, and related business intelligence infrastructure and solutions.
- Exploratory Computer Vision
- Haifa Lab Machine Learning group
Development of algorithms for automatic pattern recognition, prediction, analysis, classification, and learning of structures.
- Machine Learning for Coverage Directed test Generation
Automatic solutions for hardware verification
- Performance Management
Technologies and methodologies for managing change in computing systems.
- Personal Wizards
The Personal Wizards project aims to make it easy for end users to capture and disseminate procedural knowledge: knowledge about how to accomplish tasks in their computing environment.
- Pyr.mea.IT - Permeating IT towards the Base of the Pyramid
Create technologies and solutions that would help deliver IT to the billions of under-privileged people in the developing regions of the world.
- Real-time Active Inference and Learning (RAIL)
Development of efficient techniques for real-time inference (diagnosis and prognosis) and learning (model adaptation to system changes) in complex distributed systems.
- SwiftFile
An intelligent assistant for Lotus Notes that helps users organize their e-mail.
Publications and presentations
- Books and Book Chapters
- Journal Articles
- Conference and Workshop Papers
- Innovation Matters: How we can predict patient response to anti-HIV treatment
- Innovation Matters: Melody summarizes computer system descriptions automatically
- Innovation Matters: Article index
- Podium: Conversations with visiting researchers