As our world becomes more and more digitized, organizations need to leverage behavior data to provide a better customer experience and detect potential problems before they arise. This can be achieved by analyzing behavior patterns from digital platforms customers operate.
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.
Fantasy sports represent a rich and exciting world of modeling and analytic possibilities. With the advent of modern computer vision, statistics tracking, and the general embrace of the sporting community of a “data-centric” view to the game, there is a wealth of information available about each player, their performances, and various metadata.
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.
The business was investigating how to incorporate modern data science techniques into their budgeting decisions. Since they didn’t have this expertise in-house, they needed an analytics partner that could provide the right capabilities to accurately model and forecast revenue and serve these results to users in the proper context.
Anytime you wish to predict the transient state(s) of something or someone constantly monitored by sensors, time series classifications are the right tool. This article will explain some basic concepts of using deep learning models for TSC and finish with a brief discussion of ways to improve the performance to save on cost and speed.
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.