Neuro-symbolic AI
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.
Our work
How IBM Research is accelerating discoveries in the fight against COVID-19
ResearchAI, you have a lot of explaining to do
ReleaseIBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
ReleaseMimicking the brain: Deep learning meets vector-symbolic AI
ResearchIBM-Stanford team’s solution of a longstanding problem could greatly boost AI
ResearchGetting AI to reason: using neuro-symbolic AI for knowledge-based question answering
Research
Tools + code
Transition-based AMR Parser
Transition-based parser for Abstract Meaning Representation (AMR) in Pytorch.
View project →IBM Hyperlinked Knowledge Graph
A set of libraries to provide an abstraction layer to access the endpoints of databases that store IBM Hyperlinked Knowledge Graphs.
View project →Forbid-Iterative Planner
Forbid-Iterative Planner is an automated PDDL-based planner that includes planners for top-k, top-quality, and diverse computational tasks.
View project →ITOPS: an ontology for IT Operations
Ontology source files for the IT Operations Ontology derived from Wikidata, DBPedia and Wikipedia.
View project →Classical planner FD-Novelty-PO
Code for IJCAI 2021 paper “The Fewer the Merrier: Pruning Preferred Operators with Novelty”
View project →
Publications
- 2022
- EMNLP 2022
- 2022
- EMNLP 2022
- 2022
- EMNLP 2022
- 2022
- NeurIPS 2022
- 2022
- NeurIPS 2022
- 2022
- NeurIPS 2022
Neuro-symbolic AI research at the MIT-IBM Watson AI Lab
Read more about our work in neuro-symbolic AI from the MIT-IBM Watson AI Lab. Our researchers are working to usher in a new era of AI where machines can learn more like the way humans do, by connecting words with images and mastering abstract concepts.