Title:
Logical Neural Networks: Towards Unifying Statistical and Symbolic AI
Logical Neural Networks: Towards Unifying Statistical and Symbolic AI
| dc.contributor.author | Gray, Alexander | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Data Engineering and Science | en_US |
| dc.contributor.corporatename | IBM Research | en_US |
| dc.date.accessioned | 2021-01-26T22:32:19Z | |
| dc.date.available | 2021-01-26T22:32:19Z | |
| dc.date.issued | 2021-01-15 | |
| dc.description | Presented online on January 15, 2021 at 2:00 p.m. | en_US |
| dc.description | Alexander Gray serves as VP of Foundations of AI at IBM, and currently leads a global research program in Neuro-Symbolic AI at IBM. His current interests generally revolve around the injection of non-mainstream ideas into ML/AI to attempt to break through long-standing bottlenecks of the field. | en_US |
| dc.description | Runtime: 59:55 minutes | en_US |
| dc.description.abstract | Recently there has been renewed interest in the long-standing goal of somehow unifying the capabilities of both statistical AI (learning and prediction) and symbolic AI (knowledge representation and reasoning). We introduce Logical Neural Networks, a new neuro-symbolic framework which identifies and leverages a 1-to-1 correspondence between an artificial neuron and a logic gate in a weighted form of real-valued logic. With a few key modifications of the standard modern neural network, we construct a model which performs the equivalent of logical inference rules such as modus ponens within the message-passing paradigm of neural networks, and utilizes a new form of loss, contradiction loss, which maximizes logical consistency in the face of imperfect and inconsistent knowledge. The result differs significantly from other neuro-symbolic ideas in that 1) the model is fully disentangled and understandable since every neuron has a meaning, 2) the model can perform both classical logical deduction and its real-valued generalization (which allows for the representation and propagation of uncertainty) exactly, as special cases, as opposed to approximately as in nearly all other approaches, and 3) the model is compositional and modular, allowing for fully reusable knowledge across talks. The framework has already enabled state-of-the-art results in several problems, including question answering. | en_US |
| dc.format.extent | 59:55 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/64243 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | IDEaS-AI Seminar Series | en_US |
| dc.subject | Artificial intelligence (AI) | en_US |
| dc.subject | Logical Neural Networks | en_US |
| dc.title | Logical Neural Networks: Towards Unifying Statistical and Symbolic AI | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Institute for Data Engineering and Science | |
| local.relation.ispartofseries | IDEaS Seminar Series | |
| relation.isOrgUnitOfPublication | 2c237926-6861-4bfb-95dd-03ba605f1f3b | |
| relation.isSeriesOfPublication | 315185f2-d0ec-4ea2-8fdc-822ed04da3a8 |
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