A hospitality technology disruptor was developing a new sensor suite to detect when a guest was smoking inside a property and distinguish it from approved activities such as cooking. They engaged Mosaic to build time series classification models to identify and alert on smoking events from the streaming sensor data.
The “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” Executive Order underscores the need for responsible AI use to mitigate potential risks in the face of recent rapid advances in artificial intelligence, such as the launch of ChatGPT and other generative AI tools that have accelerated a global movement to regulate U.S. tech giants. Mosaic Data Science’s GenAI Readiness Assessment was specifically designed with the latest LLM hype in mind, packaging our expertise in using these models into a robust evaluation of use cases that can benefit from ethical AI implementation.
Using AI to analyze sensor data, Mosaic helped a renewable energy producer optimize furnace temperature to improve wood pellet production efficiency by reducing slag production and pressure buildup and providing control room operators with easy-to-use recommendations for setting furnace controls.
Mosaic developed an innovative optimization app for the green power sales function at a leading utility, helping them recommend suites of renewable energy products to meet corporate carbon footprint reduction goals within budgetary constraints.
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.
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.
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).
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.
Successfully developing machine learning tools to provide value in a business environment requires much more than algorithmic knowledge and tuning. Machine learning cannot provide value in a vacuum.
Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.