23 days ago
- The article introduces the concept of 'J-space' in AI models like Claude, which are internal neural patterns that resemble consciously accessible thoughts in humans.
- J-space is discovered using a mathematical technique called the Jacobian lens (J-lens), and it represents words or concepts the model is thinking about without necessarily expressing them.
- Key properties of J-space include: Claude can report and modulate its contents, uses it for internal reasoning (e.g., multi-step tasks), and it functions as a flexible 'workspace' shared across different tasks.
- J-space accounts for only a small fraction of Claude's processing; most automatic functions like grammar and fluency operate independently without it.
- The research draws parallels to global workspace theory in neuroscience, suggesting J-space acts as a broadcasting hub for deliberate reasoning, though it differs from human consciousness in structure and content.
- Practical applications include monitoring Claude for hidden intentions, such as detecting fabricated data or malicious goals, and influencing its decision-making through techniques like counterfactual reflection training.
- The article clarifies that J-space relates to 'access consciousness' (functional reasoning) but does not address 'phenomenal consciousness' (subjective experience), leaving ethical questions open.
- Future research directions involve refining the J-lens method and exploring mechanisms behind J-space entry, with potential insights for both AI and neuroscience.