Practical AI

Mosaic Data Science has deployed Practical AI to bridge the gap between AI research and practical implementation by creating AI-driven tools, systems, and applications that address specific challenges and tasks. Our solutions are designed to enhance efficiency, automate processes, improve decision-making, and deliver measurable benefits to our customers.

Integrated Machine Learning and Mathematical Optimization

For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.

By Sel Gerosa, ago

Similarity Learning for Image Geolocation

We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.

By Drew Clancy, ago

Few Shot Learning for Computer Vision

Object detection in video has become a matter of routine, however, expanding these models to detect an object of your choosing requires many thousands, if not tens of thousands, of training examples. Few shot learners seek to make this process cheaper and easier by learning to detect new objects with only a small handful of examples (i.e. 1-30).

By Drew Clancy, ago
Privacy Policy
Cookie Policy