Categories: Artificial Intelligence
Supervised learning is a commonly used technique in AI-based machine learning. This type of algorithm has two main capabilities: prediction and classification. Prediction occurs when the algorithm receives labeled datasets to predict an outcome. For example, a large number of images labeled as either a cat or a dog is provided as input to an algorithm, and the algorithm builds a model of what a cat is supposed to look like and what a dog is supposed to look like. Now the algorithm can accurately classify a new unlabeled image as a cat or dog. The capability is beyond simply matching, as the software learns all variations to arrive at the correct result.
Think of a project as an image. All the characteristics of a completed project are input to an algorithm, and the project is labeled either a success or failure. The algorithm builds an image of what a successful project looks like. A new project can then be evaluated to verify how close it is to the image of a successful project. Typically, the output is a probability. If the probability is above 90 percent, the project is set up for success. If the probability is lower, several actions can be taken. An organization may want to add the probability of success as another project selection criterion. The starting image of the project does not guarantee success since projects can encounter variances very quickly. Supervised learning can also be used to proactively minimize variances or determine how to resolve them.
There are many situations when an accurate prediction is useful. A machine learning algorithm can predict whether a risk will occur on a project or, even more importantly, whether the risk will cause project failure. An algorithm can predict communication issues, stakeholder management problems, and numerous other potentially failure-inducing situations. Supervised learning allows proactive measures to be taken to keep the project on a path to success.
As evident from the descriptions above, historical data is crucial for an algorithm to create a model. Supervised learning normally requires a significant amount of data to perform accurately. In the field of medicine, vast amounts of datasets are collected. In the world of project management, it is yet to be determined what amount of data is sufficient. Some academic research suggests that as few as 50 datasets are acceptable. However, the number of characteristics captured in each dataset is also essential. The theory that AI requires large amounts of data is based on supervised learning, yet it is not necessarily true for other AI capabilities.
Computers have surpassed humans in the ability to store and process data. Supervised learning is a clever concept that can be effectively deployed to improve project performance.
