Technologies that fuel the AI craze: artificial intelligence, deep/machine learning, etc.
Smart machines can, thanks to Artificial Intelligence (AI), learn from experience. They, then, adjust to new inputs and though carry out human-like tasks.
Most examples of AI technologies (e.g., chess-playing computers, self-driving cars, etc.) strongly lean on deep learning and depends very much on natural language processing. When using those, systems can be specialized to fulfill specific tasks. Therefore, they will process large quantities of data and identify patterns in the data.
This method of data analysis automatizes analytical model building. This subcategory of artificial intelligence relies on the idea that systems can learn from data just like they can also recognize patterns and decide with minimal human involvement.
Artificial intelligence (AI) can be determined as the broad science of mimicking human abilities. On contrary, machine learning represents a specific subset of Artificial Intelligence that teaches machines the learning process.
Another type of machine learning is ‘Deep learning‘. It trains a computer to complete human-like tasks. Among others, this method can recognize speech, ascertain images, or make forecasts. Deep learning establishes basic parameters to then teach computers, systems, and machines how to learn on their own. Rather than spending time to organize and structure the data to run through predefined equations, it relies on acknowledged patterns using many layers of processing.
Natural Language Processing
Natural language processing (NLP) belongs to artificial intelligence and supports systems in their understanding, interpretation, and manipulation of human language.
This technology enables machines to communicate with humans in their language. It utilizes several features to let systems interact: They can read texts, hear, and interpret speeches, measure sentiments, and identify the most important parts.
As a field of artificial intelligence computer vision trains systems to interpret and understand the visual world. On one hand, it’s based on digital images such as photos and videos; on the other hand, it depends on deep learning models. After a precise determination and categorization of objects, computers can react to what they “see.”
Computer vision not only competes with but also outstrips human visual abilities in a lot of areas: starting from face recognition up to transactions of live actions of a football game.