Summary of Symbolicai: a Framework For Logic-based Approaches Combining Generative Models and Solvers, by Marius-constantin Dinu and Claudiu Leoveanu-condrei and Markus Holzleitner and Werner Zellinger and Sepp Hochreiter
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
by Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner, Werner Zellinger, Sepp Hochreiter
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC); Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI framework, SymbolicAI, is introduced that combines symbolic reasoning with generative processes using a logic-based approach. This modular framework seamlessly integrates large language models (LLMs) as semantic parsers to execute tasks based on both natural and formal language instructions. The framework utilizes probabilistic programming principles to tackle complex tasks, leveraging differentiable and classical programming paradigms for strengths in specific domains. SymbolicAI introduces polymorphic, compositional, and self-referential operations for multi-modal data, connecting multi-step generative processes and aligning their outputs with user objectives in complex workflows. This enables the creation and evaluation of explainable computational graphs. The framework also includes a quality measure, referred to as the VERTEX score, which evaluates these graphs empirically. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SymbolicAI is a new way for AI machines to learn and work together using logic and reasoning. It combines different kinds of language models with other tools to help them understand complex tasks and generate new ideas. This framework uses special programming principles to solve hard problems and creates a new type of graph that can be used to explain how the AI machine came up with its answers. |
Keywords
* Artificial intelligence * Multi modal
