Wouldn’t it be amazing if we could create an algorithm that could independently write billions of lines of code and generate a variety of nearly error-free applications with only minimal direction, and without any programming knowledge? All we’d have to provide as input are just one or two already existing sample application codebases! The information technology industry could see enormous long-term profits through the use of such algorithms. Not only that, if such an algorithm were developed, there would hardly be any distinction left between service-based and product-based IT companies! This trend of creating such algorithms or software applications is currently the stuff of imaginary technology, but even now, some of these algorithms already exist and are dominating fields like image recognition, speech recognition, social network filtering, playing board and video games, medical diagnosis, and even tasks that were once thought possible only for the human mind, such as painting!
Have you ever wondered why children dream more than adults while sleeping? This is because their brains are more interested in real-world learning than in theoretical learning. The common characteristic of the algorithms used in the fields mentioned above is that they resemble the minds of children; in other words, they learn by following examples, not instructions! These algorithms are called “feature learning algorithms” or “representation learning algorithms,” whose performance improves as they are used—in other words, as these algorithms accumulate experience over time, they keep learning from that experience, becoming ever more refined and effective! The ability to learn independently—self-learning—is a special trait of these algorithms!
Artificial self-learning can be mainly of two types— “supervised feature learning” and “unsupervised feature learning”.
“Supervised feature learning” is learning from labeled data. For example, an image recognizer or an algorithm capable of identifying images learns to recognize any image containing a person by analyzing images labeled as either “person” or “not person,” where the labels indicate the presence or absence of a person.
In the case of “unsupervised feature learning,” an algorithm first learns from unlabeled data and is then deployed in an environment with labeled data to improve its performance. Since the initial input data lacks labels, the algorithm learns to identify the underlying lower-dimensional features or distinguishing characteristics, instead of high-dimensional features, within the data.
“Feature learning algorithms” or “representation learning algorithms” actually discover ways—representations—necessary for identifying the distinguishing features of input data and for classifying these features. They then help the computer learn these distinguishing features and apply them to specific tasks. In the past, this was limited to only recognition, but today, such algorithms have been developed to such an advanced degree that they have given computers the power to imagine!
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