This time recommendation engine takes an arbitrary number of calendar schedules as input and understands the context of the event based on features such as NLP embeddings of event name and description, as well as history of users past events. It output times that worked well for everyone and also made the most sense for the event in question.
Our location recommender utilized natural language embeddings of massive databases of location details to match locations to groups of users and their respective social events. This engine allowed for curated social events to be built automatically with everyone’s preferences in mind.
Computer vision model that takes in an image of a receipt and outputs an itemization of all charges, each tagged with relevant item categories. To make this work properly, we needed to create a custom pipeline comprised of many deep neural networks. The first model performed object recognition, passed the output to an OCR model, then a model that we trained to understand receipt geometry, and a finally an NLP model that performed classification on the types of charges.
This system intakes 3 view mammogram scans of breast tissue and outputs the risk factors associated with the images. It improves on the current clinical accuracy of risk assessment by more than 10% in the majority demographic (caucasian women) and up to 60% improvement in the case of minority demographics. We adapted just released research into a production grade AI system and helped oversee and advise the process for technology acceptance by the FDA.
Utilized ASR to transcribe 20+ years of cataloged audio records from a multimedia company, then created a semantic search engine via natural language embeddings to retrieve relevant recordings and pinpoint the most relevant audio segments down to the second level.