NLP? AI? ML? In many places, on the web, we can come across articles using the abbreviations mentioned. In nomenclature related to artificial intelligence, without knowing some of the terms, we can't understand the whole statement. Whether you are a recruiter, a novice programmer, or a director at a company that has set itself the task of increasing technological awareness and implementing artificial intelligence, we have compiled a list of basic terms and explained them in a friendly way for non-technical people. The list is enriched with examples to facilitate understanding.
An umbrella term for any field of research that involves processing large amounts of data to provide insights into real-world problems. Data engineering - A field of research that applies data science techniques to engineering problems. It often involves collecting large amounts of data about the object under study and then using that data to develop computer models to analyze and improve the object. design and functionality
Is a solution that learns to recognize and respond to patterns, emulating traditional human tasks such as understanding language, recommending business actions, and synthesizing large amounts of information. AI works best when it helps humans by learning highly repetitive tasks that depend on large amounts of information.
A field of artificial intelligence involving computer algorithms that can "learn" by finding patterns in sample data. The algorithms then typically apply these findings to new data to make predictions or provide other useful results, such as translating text or guiding a robot in a new environment. Medicine is one area of promise: machine learning algorithms can, for example, identify tumors on scans that doctors may have overlooked./.
A subfield of artificial intelligence that often uses statistical techniques to give computers the ability to "learn," i.e., incrementally improve the performance of a specific task using data, without explicit programming.
A broad field of research dealing with large data sets. It has grown rapidly over the past few decades as computers began storing and capturing huge amounts of data collected about our lives and our planet. A key challenge with big data is figuring out how to generate useful insights from the data without inappropriately compromising the privacy of data subjects.
A form of machine learning that uses computational structures known as "neural networks" to automatically recognize patterns in data and provide relevant output, such as predictions or evidence for decisions. Deep learning neural networks are loosely inspired by the way neurons in animal brains are organized, which consist of multiple layers of simple computational units ("neurons") and are suitable for complex learning tasks such as selecting features in images and speech. Deep learning thus underpins voice control in our phones and smart speakers and enables autonomous cars to identify pedestrians and stop signs.
An area of artificial intelligence that studies the interactions between computers and human languages, particularly the processing and analysis of large amounts of natural language data.
A field of research that uses computers to extract useful information from digital images or videos. Applications include object recognition (e.g., identifying animal species in photos), facial recognition (intelligent passport checking), medical imaging (detecting tumors in scans), navigation (autonomous cars), and video surveillance (monitoring crowd levels at events).
Software as a Service (or SaaS) is a way of delivering applications over the Internet as a service. Instead of installing and maintaining the software, you simply access it over the Internet, freeing you from complex software and hardware management.
DevOps is a combination of developers (dev) and operations (ops). It is defined as a software engineering methodology that aims to integrate the work of software development and software operations teams by facilitating a culture of collaboration and shared responsibility.
The above definitions do not, of course, exhaust all the terms with which we are inundated by industry insiders. We invite you to explore the knowledge we share on our blog
As a continuation of theoretical knowledge, we invite you to read : AI, Big Data, ML, let us explain