Turning Complex AI Models into Searchable Graphs

The problem of extracting human-understandable information from large, complex, and noisy text or image data is one of the fundamental challenges facing artificial intelligence. Modern AI models (esp. LLMs) can deliver amazing outputs, but their decision-making processes often remain hidden, creating a “black box” that makes it difficult to know why models fail or succeed.

BluelightAI’s flagship interpretability platform, Cobalt, directly addresses this AI black box problem by leveraging Topological Data Analysis (“TDA”) as a foundational technology, a critical differentiator unmatched by existing evaluation platforms. The fundamental idea behind TDA is that for many kinds of data, traditional algebraic tools are not flexible enough to represent data as effectively as we would like.

How smart feature engineering improves speech recognition

In our earlier blog we showed that topological techniques can be used to improve the performance of convolutional neural networks being used for image classification.  Specifically, we used features parametrized by a geometric object called a Klein bottle as well as an architecture guided by the same object to drastically speed up the learning rate, but more importantly to improve generalization.  We believe that generalization is a good measure of progress toward Artificial General Intelligence.

Go beyond the leaderboard: How TDA uncovers what benchmark scores miss in model evaluation.

The evaluation of models is absolutely critical to the artificial intelligence enterprise.  Without an array of evaluation methods, we will not be able to understand whether the models are doing what we want them to do, or what measures we should take to improve them.  Another reason for the need for good evaluation measures is that once an AI model is deployed, we will find that the input data, the interaction of users with the model, and the user reactions to the output of the model will change over time.  This means that not only do we need evaluation at the time of construction of the model, we will need to evaluate continually throughout the deployment lifecycle of the model.