Arthur Samue - Computer scientist and pioneer in machine learning
Terms Machine Learning and Artificial Intelligence are often used interchangeably. For marketing and mass media headlines, everything is AI. For academia, machine learning is often seen as a subset of AI.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance so that they can adjust actions accordingly based on previously gathered data.
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In science fiction, AI often has a physical form (e.g. a robot) and can be highly intelligent in similar ways as humans and animals are. Understanding how such a versatile tool would be useful is simple. Current applications of AI are not yet, if ever, on that level. This doesn't stop machine learning from being useful in practical situations.
Machine learning is one of many ways of creating an artificial intelligence system. AI systems can be created without ML by using some non-ML methods. Thus, you can have AI without ML, but you cannot have ML without AI.
Data science practitioners apply the ML algorithms. Considering the substantial overlap between definitions of Artificial Intelligence and Machine Learning, it isn't such a surprise that the words are often seen as synonyms.
Imagine that you have a collection of 10,000 digital images. Sadly, you made a mistake when copying the images and now all of the images are in the same directory "vacationimages". What you would need to do is to split these images into three separate directories: "forestimgs", "mountainimgs" and "museumimgs".
When you copied the files, the files were renamed, so the original file names don't exist anymore. Due to a software bug in the camera, GPS data happened to work only in a small portion of the images. These images are easy to find and organize using the GPS data in your favorite photo catalog app, but the rest is the issue.
For reference, the features that are used in the training process might include an RGB histogram, exposure information from the camera's metadata, raw pixel values, or some other data. Based on the selected features, the model will learn the correlations between the features and the labels. After the training is done, we can use the model to predict the labels of the rest of the 10,000 images. The trained machine learning model doesn't even know what a mountain is; yet, it can label images of mountains in the given dataset. This process, called inference, can be seen in the graph below.
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