Understanding AI: Opening the Black Box
It's often said that AI operates like a black box: inputs go in, and outputs come out, but the reasoning behind the output remains unknown. This is because AIs are not programmed in the traditional sense; instead, they are trained, and during this training, they develop their own strategies for problem-solving. To maximize the usefulness, reliability, and security of AI, it's crucial to understand why they make the decisions they do.
The Challenge of Interpretation
However, simply "opening" the black box isn't enough. Even with access to the inner workings, interpreting the data remains a challenge. This situation is analogous to a neuroscientist studying the brain; specific tools and techniques are required to decipher the processes at play. We need methods to understand how an AI model connects concepts and uses those connections to answer questions.
Observing Internal Thought Processes
New methods have been developed to observe an AI model's internal thought processes. These allow us to see how concepts are connected, forming logical circuits within the AI.
Example: Claude and Poetry
Consider an example where the AI, Claude, was asked to write the second line of a poem. The first line was: "He saw a carrot and had to grab it." Research revealed that Claude was planning a rhyme even before completing the line.
-
Claude recognizes "a carrot" and "grab it."
-
The AI then considers "rabbit" as a word that rhymes and makes sense.
-
Claude then completes the line: "His hunger was like a starving rabbit."
By examining the model's processing when considering the word "rabbit," researchers could see other ideas it had for the poem, including the word "habit."
Intervention and Modification
These new methods allow intervention within these circuits. In this example, the influence of "rabbit" was reduced while the model was planning the second line. When asked to complete the line again, Claude responded: "His hunger was a powerful habit."
This demonstrates that the model can consider different ways to complete a poem based on the beginning and plan toward those completions. The ability to cause these changes before the final line is written provides evidence that the model is planning ahead.
Implications and Future Directions
This poetry planning result, along with other findings, supports the idea that AI models are genuinely "thinking" about what they say. Just as neuroscience helps improve human health, this deeper understanding of AI can contribute to making models safer and more reliable. The ability to "read" the model's mind increases confidence that it's operating as intended.
More examples of Claude's internal thoughts can be found in the researchers' paper at anthropic.com/research.