Preference Model is building the next generation of training data to power the future of AI.

Today's models are powerful but fail to reach their potential across diverse use cases because so many of the tasks that we want to use these models for are outside of their training distribution. Preference Model creates reinforcement learning environments that encapsulate real-world use cases, enabling AI systems to practice, adapt, and learn from feedback grounded in reality.

We raised $16M led by a16z with participation from SignalFire, South Park Commons, Scale Angel Group, and amazing angels like Dr. Fei-Fei Li, Ian Goodfellow, Julian Schrittwieser.

Path attribution methods are a gradient-based way of explaining deep models. These methods require choosing a hyperparameter known as the baseline input. What does this hyperparameter mean, and how important is it? In this article, we investigate these questions using image classification networks as a case study. We discuss several different ways to choose a baseline input and the assumptions that are implicit in each baseline. Although we focus here on path attribution methods, our discussion of baselines is closely connected with the concept of missingness in the feature space - a concept that is critical to interpretability research.

Read More