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
Disentangling visual attributes with neuro-vector-symbolic architectures, in-memory computing, and device noise
Technical noteAbu Sebastian, Abbas Rahimi, and Geethan Karunaratne- AI
- AI Hardware
- Neuro-symbolic AI
This AI could likely beat you at an IQ test
ResearchAbbas Rahimi and Michael Hersche- AI
- Neuro-symbolic AI
How IBM Research is accelerating discoveries in the fight against COVID-19
ResearchMike Murphy10 minute read- Exploratory Science
- Healthcare
- Impact Science
- Neuro-symbolic AI
AI, you have a lot of explaining to do
ReleaseDinesh Garg, Parag Singla, Dinesh Khandelwal, Shourya Aggarwal, Divyanshu Mandowara, and Vishwajeet Agrawal5 minute read- Explainable AI
- Generative AI
- Knowledge and Reasoning
- Natural Language Processing
- Neuro-symbolic AI
- Trustworthy AI
IBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
ReleaseDan Gutfreund, Abhishek Bhandwaldar, and Chuang Gan6 minute read- AI
- Knowledge and Reasoning
- Neuro-symbolic AI
Mimicking the brain: Deep learning meets vector-symbolic AI
ResearchAbu Sebastian and Abbas Rahimi4 minute read- AI
- Neuro-symbolic AI
- See more of our work on Neuro-symbolic AI
Projects
Accelerator Technologies
Publications
- Harsha Kokel
- Junkyu Lee
- et al.
- 2023
- IJCAI 2023
- Radu Marinescu
- Haifeng Qian
- et al.
- 2023
- IJCAI 2023
- Shreyas Basavatia
- Shivam Ratnakar
- et al.
- 2023
- IJCAI 2023
- Achille Fokoue
- Ibrahim Abdelaziz
- et al.
- 2023
- IJCAI 2023
- Parikshit Ram
- Tim Klinger
- et al.
- 2023
- IJCAI 2023
- Cameron Allen
- Timo Gros
- et al.
- 2023
- IJCAI 2023
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.
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 →