The article also discusses the challenges of scaling this method to larger, more complex AI systems, such as GPT-4. The researchers suggest that in order to interpret an AI of this size, an interpreter-AI of similar size would be required, which would be a costly and complex process. The article concludes by suggesting that this research could also have implications for understanding how the human brain works, as it also uses neural networks to reason about concepts.
Key takeaways:
- AI has been considered a "black box" due to the difficulty in understanding how it works. However, a recent study from Anthropic, a big AI company/research lab, claims to have looked inside an AI and understood its workings.
- The study suggests that AIs use a method called "superposition" to represent more concepts than they have neurons. This involves using a few neurons to represent multiple concepts, which can lead to complex and abstract representations.
- Anthropic's team trained a simple AI and found that it used various geometric shapes to represent concepts, depending on how many concepts it needed to understand at a time. This suggests that AIs might be simulating more powerful AIs to perform their tasks.
- However, interpreting these simulated AIs is challenging as they exist in abstract hyperdimensional spaces. The team managed to dissect one of these simulated AIs, finding that its simulated neurons were monosemantic, meaning they represented one specific thing. This could potentially allow for better understanding and interpretation of AI systems in the future.