Summary of Neural Networks For Abstraction and Reasoning: Towards Broad Generalization in Machines, by Mikel Bober-irizar et al.
Neural networks for abstraction and reasoning: Towards broad generalization in machines
by Mikel Bober-Irizar, Soumya Banerjee
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning researchers have long sought to create AI systems that can learn new concepts from minimal examples, just like humans do. While specific neural networks excel at solving various problems, they struggle with broad generalization to situations outside their training data. This paper explores novel approaches for tackling the Abstraction & Reasoning Corpus (ARC), a dataset of abstract visual reasoning tasks designed to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve most ARC tasks and rely on complex hand-crafted rules, without using machine learning at all. This paper revisits whether recent advances in neural networks allow progress on this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence researchers have been trying for a long time to make computers smarter like humans. They want computers to learn new things easily from just a few examples. Some computer systems are very good at solving certain problems, but they struggle with understanding situations that are completely new to them. This paper looks at some new ways to solve puzzles that test how well algorithms can understand and reason about abstract concepts. Despite many competitions with big prizes, the best algorithms still don’t do a great job on these puzzles and use special rules instead of learning like humans do. The paper asks if recent advances in computer systems will help them get better at solving these puzzles. |
Keywords
* Artificial intelligence * Generalization * Machine learning
