Mosaic Data Science’s whitepapers are authored by our AI/ML experts, diving into groundbreaking research, meticulous analysis, and forward-thinking in AI and machine learning. Whether you’re seeking to comprehend the latest trends, demystify intricate technologies, or grasp the far-reaching implications of these innovations, our whitepapers are your gateway to informed decision-making.
Contextual search can be labeled as a search capability that focuses on the context of the user-generated query including the original intent of the user to show the most relevant set of results. It is quite different than traditional search technologies which focus only on keyword matching.
Mosaic used NLP algorithms to automatically extract various insights about people calling into a customer call center. We created a process to identify at-risk customers that called the previous day based on the transcript of the call.
In our whitepaper, Mosaic explores deep learning , including when to use deep learning over machine learning using practical examples.
Mosaic developed a proof-of-concept (POC) system that uses computer vision technologies to infer human subjects’ positions, proximity, and gaze directions from video feeds. Future human missions to Mars will pose mental and behavioral challenges for crew members.
This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Operating in today’s conditions requires creative thinking, agile decision making and embracing change. AI & Digital Transformation can support all these disciplines.
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.
Having an autonomous artificial intelligence (AI) system that can monitor individuals via facial mood recognition, vocal tonality analysis, proximity to one another, performance, biosensors, surveys, and more, and predict conflict before it is problematic could improve a unit’s cohesion and performance in missions both in space and in isolated environments on Earth.
Machine learning provides an excellent avenue for predicting future energy consumption. Accurate insights can provide critical insights into variables affecting the demand, providing decision-makers with an opportunity to address these levers. Forecasts also provide a benchmark to identify anomalous behavior, either high/low consumption, and alert managers to faults within the building.