Better AI Interpretability

Powerful and opaque systems are changing our world.

Humans need to understand them better – so we can learn from them, so we can ensure they don't inherit our biases, and so we can make them safe.

Leap Labs is a research-driven interpretability startup. We develop state of the art interpretability techniques that make it possible for humans to understand complex AI systems in new ways.

ML developers work hard to train reliable, accurate models, spending enormous amounts of time on data prep, model training, testing, and troubleshooting  – but most of the time we’re working in the dark, with our understanding of what our models are learning limited to a few quantitative metrics and evaluations on a fixed dataset. Even if a model’s performance on a test set is good, there’s no guarantee that all possible failure modes and edge cases are contained in that test set.

We can’t be sure our models won’t fail in the real world, because we don’t understand them.

Enter interpretability – the science of understanding what neural networks have learned. Existing interpretability methods are mostly academic in nature and not up to the task of interpreting today’s increasingly powerful and complex models. Besides, most of these methods are data-dependent, meaning that they provide explanations based on data that we already have – this doesn’t tell us much about what a model has learned overall.

Leap Labs, 10min.mp4

We need better interpretability.

This is exactly what we're building at Leap Labs.

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