SO, Lets start with Asking what is machine learning and artificial intellegence.
- “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.” ML it’s one of the ways we expect to achieve AI. Machine learning relies on working with small to large data-sets, by examining and comparing the data to find common patterns and explore nuances.
- For instance, if you provide a machine learning model with a lot of songs that you enjoy, along their corresponding audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate (depending of the supervised machine learning model used) and generate a recommender system as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do . In a simple example, if you load a machine learning program with a considerable large data-set of x-ray pictures along with their description (symptoms, items to consider, etc.), it will have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on. The machine learning model will look at each one of the pictures in the diverse data-set, and find common patterns found in pictures that have been labeled with comparable indications. Furthermore, (assuming that we use a good ML algorithm for images) when you load the model with new pictures it will compare its parameters with the examples it has gathered before in order to disclose to you how likely the pictures contain any of the indications it has analyzed previously.
- The type of machine learning from our previous example is called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous data-sets  fed. Unsupervised learning, another type of machine learning are the family of machine learning algorithms, which are mainly used in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model is trained with unlabeled data).
- “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.” That is a great way to define AI in a single sentence; however, it still shows how broad and vague the field is. Fifty years ago, a chess-playing program was considered a form of AI , since game theory, along with game strategies, were capabilities that only a human brain could perform. Nowadays, a Chess game would be considered dull and antiquated, due to the fact that it can be found on almost every computer’s OS , therefore, “until recently” is something that progresses with time .