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.
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.
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.
Traditional lending practices are a prime candidate for machine learning improvements. Lenders can make more accurate and faster decisions by shifting decision-making from analysis of individuals to analysis of trends and patterns.
Decision processes in support of jobs that either cannot be or are very difficult to automate are frequently overlooked by out of the box software providers. One such process is the creation of optimal staffing plans for outbound teams loading cartons onto trucks.