Summary of System 2 Reasoning Via Generality and Adaptation, by Sejin Kim et al.
System 2 Reasoning via Generality and Adaptation
by Sejin Kim, Sundong Kim
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This research paper aims to bridge the gap between current AI systems and Artificial General Intelligence (AGI) by exploring the limitations of existing approaches in achieving deep reasoning, generality, and adaptation. Specifically, it highlights the importance of System 2 reasoning, which is crucial for AGI, but often lacking in current models such as program synthesis, language models, and transformers. The paper proposes four key research directions to address these gaps: learning human intentions from action sequences, combining symbolic and neural models, meta-learning for unfamiliar environments, and reinforcement learning to reason multi-step. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence is getting smarter, but it’s not quite as smart as humans yet. This paper talks about why current AI systems struggle with things like understanding human behavior and adapting to new situations. The researchers think that this is because AI models don’t really understand what we mean by “deep reasoning”. They’re proposing some new ways to make AI more human-like, such as learning from action sequences or combining different types of AI models. The goal is to make AI better at figuring things out and making decisions on its own. |
Keywords
» Artificial intelligence » Meta learning » Reinforcement learning
