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Let's get to the basics.


We work on some of the most challenging and interesting challenges in Machine Learning

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{ Challenges that we are solving }



When you have terabytes of unstructured data, it is important to think about how content can be consumed more efficiently. Our semantic model can help us understand what users are looking for when they search for information.



When humans read, our experiences tell us which words and sentences we should focus on. We teach our model to do the same by using self-attention mechanisms to find out where the A.I. should pay more attention to.



Our machines read documents on the pixel level, allowing us to reconstruct tables and map data across an ocean of content. This unlocks limitless possibilities in transforming unstructured information into structured data like no other.



We employ dozens of data annotators to read and label a wide spectrum of information. Our annotation tool is so efficient that data scientists can do the annotation themselves, radically changing the way we teach machines.

We actively work with university faculty members and students to advance our research.

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