Understand AI-related terms
There are several related terms that people use when talking about artificial intelligence, so it's useful to have clear definitions for each.
Data science
Data science is a discipline that focuses on the processing and analysis of data; applying statistical techniques to uncover and visualize relationships and patterns in the data, and defining experimental models that help explore those patterns.
For example, a data scientist might gather samples of data about the population of an endangered species in a geographical area, and combine it with data about levels of industrialization and economic demographics in the same area. The data can then be analyzed, using statistical techniques to extrapolate from the samples to understand trends and relationships between human activities and wildlife, and test hypotheses using models that show the likely impact of human activity on the wildlife population. By doing so, the data scientists may help determine optimal policies that balance the need for economic wellbeing for the human population with the need for conservation of the endangered wildlife.
Machine learning
Machine learning is a subset of data science that deals with the training and validation of predictive models. Typically, a data scientist prepares the data and then uses it to train a model based on an algorithm that exploits the relationships between the features in the data to predict values for unknown labels.
For example, a data scientist might use the data they have collected to train a model that predicts the annual growth or decline in population of a species based on factors such as the number of nesting sites observed, the area of land designated as protected, the human population in the local area, the daily volume of traffic on local roads, and so on. This predictive model can then be used as a tool to evaluate plans for housing, infrastructure, and industrial development in the local area and assess their likely impact on the local wildlife.
Artificial intelligence
Artificial intelligence usually (but not always) builds on machine learning to create software that emulates one or more characteristics of human intelligence.
For example, balancing the need for wildlife conservation against economic development requires accurate monitoring of the population of the endangered species being protected. It may not be feasible to rely on human experts who can positively identify the animal in question, or to monitor a large area over a sufficient period of time to get an accurate count. Indeed, the presence of human observers may deter animals and prevent their detection. In this case, a predictive model could be trained to analyze image data taken by motion-activated cameras in remote locations, and predict whether a photograph contains a sighting of the animal. The model could then be used in a software application that responds to automated identification of animals to track animal sightings across a large geographical area, identifying areas with dense animal populations that may be candidates for protected status.