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 KarunaratneThis AI could likely beat you at an IQ test
ResearchAbbas Rahimi and Michael HerscheHow IBM Research is accelerating discoveries in the fight against COVID-19
ResearchMike Murphy10 minute readAI, you have a lot of explaining to do
ReleaseDinesh Garg, Parag Singla, Dinesh Khandelwal, Shourya Aggarwal, Divyanshu Mandowara, and Vishwajeet Agrawal5 minute readIBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
ReleaseDan Gutfreund, Abhishek Bhandwaldar, and Chuang Gan6 minute readMimicking the brain: Deep learning meets vector-symbolic AI
ResearchAbu Sebastian and Abbas Rahimi4 minute read- See more of our work on Neuro-symbolic AI
Projects
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
Publications
Epigraph Based Multilevel Optimization (EMO) for Enhancing Chain-of-Thought Reasoning Capabilities
- Songtao Lu
- Yanna Ding
- et al.
- 2025
- ICASSP 2025
On Representations of Mean-Field Variational Inference
- Soumyadip Ghosh
- Yingdong Lu
- et al.
- 2025
- Journal of Applied Analysis
CogSys: Efficient and Scalable Neurosymbolic Cognition System via Algorithm-Hardware Co-Design
- Zishen Wan
- Hanchen Yang
- et al.
- 2025
- HPCA 2025
Bridging the Gap Between AI Planning and Reinforcement Learning
- Zlatan Ajanovi{\'c}
- Timo Gros
- et al.
- 2025
- AAAI 2025
Neural Reasoning Networks: Efficient interpretable neural networks with automatic textual explanations
- Steve Carrow
- Kyle Harper Erwin
- et al.
- 2025
- AAAI 2025
UNIFIEDGT: Towards a Universal Framework of Transformers in Large-Scale Graph Learning
- Lin Junhong
- Xiaojie Guo
- et al.
- 2024
- Big Data 2024
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.