Intentionally Designing the Future of AI
goodfire.ai ● Covered by 4 sources
Goodfire proposes intentional design, an approach using interpretability techniques to guide AI model training by decomposing neural networks into semantically meaningful components and selectively controlling what models learn from each data point. The company aims to move from current trial-and-error training methods to closed-loop control systems where practitioners can steer learning during training rather than only evaluating afterward. This would enable sample-efficient learning from natural language feedback and better alignment of models with desired values during the training process itself.
Why it matters
An overview of Goodfire's goal of moving model training from guess-and-check toward systems researchers can inspect, steer, and improve deliberately.