Truly general intelligence requires the ability to build an unbounded number of solutions. For a finite system to achieve this, it needs a mechanism for unbounded generation, like recursion (similar to how a Turing machine operates). General intelligence also requires constant adaptation. So how do you get both? The paper proposes it arises from the dynamic interaction between an adaptive continuous substrate (like the human brain or an ANN) and an internalized symbolic framework (like human language).
The "engine" of this process, according to the ESC framework, is recursive symbolic generation. The substrate learns to:
1. Sequentially generate symbolic sequences (like words forming thoughts or sentences).
2. Process these sequences.
3. Evaluate them based on internal rules and goals.
This recursive loop allows the system to effectively function as a powerful, discrete symbolic processor, capable of navigating vast combinatorial spaces and constructing structured solutions for diverse problems—essentially, to think and reason in a general-purpose way.
Why this might be interesting:
- It tries to bridge the gap between connectionist learning (like in ANNs) and symbolic competence (rule-based reasoning).
- It offers a lens on why language seems so crucial for human thought.
- It sheds light on the surprising abilities emerging in LLMs (which learn only from text) as a key piece of evidence.
- It defines GI functionally, focusing on what it does (generates novel information to solve problems across unbounded domains).
fraggler•8mo ago
Truly general intelligence requires the ability to build an unbounded number of solutions. For a finite system to achieve this, it needs a mechanism for unbounded generation, like recursion (similar to how a Turing machine operates). General intelligence also requires constant adaptation. So how do you get both? The paper proposes it arises from the dynamic interaction between an adaptive continuous substrate (like the human brain or an ANN) and an internalized symbolic framework (like human language).
The "engine" of this process, according to the ESC framework, is recursive symbolic generation. The substrate learns to: 1. Sequentially generate symbolic sequences (like words forming thoughts or sentences). 2. Process these sequences. 3. Evaluate them based on internal rules and goals.
This recursive loop allows the system to effectively function as a powerful, discrete symbolic processor, capable of navigating vast combinatorial spaces and constructing structured solutions for diverse problems—essentially, to think and reason in a general-purpose way.
Why this might be interesting: - It tries to bridge the gap between connectionist learning (like in ANNs) and symbolic competence (rule-based reasoning). - It offers a lens on why language seems so crucial for human thought. - It sheds light on the surprising abilities emerging in LLMs (which learn only from text) as a key piece of evidence. - It defines GI functionally, focusing on what it does (generates novel information to solve problems across unbounded domains).