What’s Next in AI
is foundation models at scale
The AI landscape today is dominated by purpose-built models deployed for dedicated tasks. But enterprises need a large corpus of labeled data, significant resources, and teams of skilled data scientists to train and maintain these models. Foundation models represent a generational opportunity for enterprise. They’re general-purpose, pre-trained models that can be fine-tuned to accomplish a wide set of tasks. We’re developing software, middleware, and hardware to bring frictionless, cloud-native development and use of foundation models to enterprise AI.
Introducing watsonx.ai
Explore our next-generation enterprise platform, powered by IBM's full technology stack and designed to enable enterprises to train, tune, and deploy AI models.
Our work
How open source paved the way for computing from anywhere
NewsKim Martineau- AI
- Hybrid Cloud Platform
- Machine Learning
- Scaling AI
An open-source toolkit for debugging AI models of all data types
Technical noteKevin Eykholt and Taesung Lee- Adversarial Robustness and Privacy
- AI Testing
- Data and AI Security
AI transformers shed light on the brain’s mysterious astrocytes
ResearchKim Martineau- AI
- Foundation Models
- Life Sciences
- Machine Learning
- Natural Language Processing
- Science
IBM Research’s newest prototype chips use drastically less power to solve AI tasks
NewsMike Murphy- AI
- AI Hardware
- Semiconductors
What is retrieval-augmented generation?
ExplainerKim Martineau- AI
- Explainable AI
- Generative AI
- Natural Language Processing
- Trustworthy Generation
IBM Research's latest analog AI chip for deep learning inference
NewsAbu Sebastian, Manuel Le Gallo-Bourdeau, and Vijay Narayanan- AI
- AI Hardware
- Semiconductors
- See more of our work on AI
Tools + code
IBM Analog Hardware Acceleration Kit
An open source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence.
View project →Project Debater for Academic Use
The technologies underlying Project Debater available as cloud services. Includes core natural language understanding capabilities, argument mining, and narrative generation.
View project →AI FactSheets 360
Toolkit to create factsheets outlining the details about how an AI service operates, how it was trained and tested, its performance metrics, fairness and robustness checks, intended uses, maintenance, and other critical details.
View project →GT4SD
An open-source library to accelerate hypothesis generation in the scientific discovery process.
View project →
MIT-IBM Watson AI Lab
We’re partnering with the sharpest minds at MIT to advance AI research in areas like healthcare, security, and finance.
Publication collections
KDD 2023
12
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
06 OctIJCAI 2023
39
International Joint Conference on Artificial Intelligence
19 AugICML 2023
44
International Conference on Machine Learning
24 JulACL 2023
46
Annual Meeting of the Association for Computational Linguistics
09 Jul
Topics
- Adversarial Robustness and Privacy
- AI for Asset Management
- AI for Business Automation
- AI for Code
- AI for Supply Chain
- AI Testing
- Automated AI
- Causality
- Computer Vision
- Conversational AI
- Explainable AI
- Fairness, Accountability, Transparency
- Foundation Models
- Human-Centered AI
- Knowledge and Reasoning
- Machine Learning
- Natural Language Processing
- Neuro-symbolic AI
- Speech
- Trustworthy AI
- Trustworthy Generation
- Uncertainty Quantification
