Machine learning (ML) is an essential part of modern society. It allows various computer and artificial intelligence systems to constantly learn from the information it is given. For example, technology companies such as Apple and Google improve systems using the data analytics that users provide. When you begin to type something into Google’s search engine, it anticipates what you’ll type next. This process utilizes ML to base the system’s predictions of a user’s preferences on previous searches. YouTube also uses this concept to recommend users videos based on their predicted interests. Once again, these predictions are from the ML process that feeds on previous searches and views.
Recreationally, people have used ML to solve minor inconveniences in their daily lives. For instance, have you ever cooked a meal that didn’t taste as well as you had hoped? Well, ML has the potential of aiding with that problem. If trained on an annotated dataset, an ML algorithm can use pattern recognition to accomplish the task at hand. In this case, if one were to collect or find a dataset full of rated, online recipes, they would be able to train an ML algorithm to predict the rating of an unrated recipe based on the combination and ratio of ingredients. However, ML is also used for more pending problems such as hate speech detection. Specifically, there is a presence of cultural bias in current hate speech detection software. Through the manipulation of the datasets that ML algorithms are given to learn, it is possible to mitigate this inherent bias with these systems. The versatility of ML will only grow in the future and it is important for us, as a society, to understand its concepts and applications.
