Artificial Intelligence Terminology and Concepts You Need To Know

What We Have Covered in This Article

1 Machine learning
2 Supervised learning
3 Unsupervised learning
4 Deep learning
5 Reinforcement learning
6 Data science
- 6.1 NLP
7 Computer vision
8 AI chatbots
9 Robotics

Last Updated on September 11, 2018 by Editor Futurescope

The film industry has taken fancy to the theme of artificial intelligence (AI) long ago. Nowadays, people can experience benefits of this innovation in real life. The entire technology world is talking about AI, and many confusing terms like data science, machine learning algorithms, neural networks and others have stricken root in the web spaces. Therefore, in this article, we want to look into the top hottest terms in artificial intelligence to make them clear.

Machine learning

Machine learning (ML) denotes the idea of the computer’s capability to learn in a way the humans do. The software you use on a daily basis contains algorithms programmed by humans to perform particular tasks. Machine learning algorithms are also created by humans, but they go by their own logic to make decisions. Media-streaming services like Spotify or Netflix are a good example of ML implementation in real life. The services make recommendations to you based on ML algorithms that analyze your likes/dislikes and other metrics. The technology is already in the inventory of numerous software development companies like Railsware, as well as different cutting-edge startups involved in education, healthcare, marketing, and transportation industries.

Supervised learning

Talking about ML algorithms, we cannot omit to mention its two major categories. So, let’s start with the most widespread one – supervised learning or SL. The idea of this concept lies in the algorithm’s learning from the training dataset that contains both input and output variables. So, the point is to find a mapping function or relationship between them. The algorithm analyzes the given dataset, which acts as a kind of supervisor to make predictions or solve a specific set of problems like understanding risk factors for a disease or learning customers’ preferences. SL algorithms are usually applied in tasks associated with regression and classification like visual recognition, sorting, decision support, etc. For example, SL is used in versatile fintech solutions to determine fraudulent loan applications.

Unsupervised learning

This ML category in that the dataset lacks the output values. We have unlabeled input data only, and the algorithm’s task is to learn a pattern or an underlying structure in the data. Unsupervised learning (UL) is mostly used in tasks that require density estimation, representation learning, dimensionality reduction, and clustering. A hands-on example is always a key to understanding. Airbnb leverages UL algorithms for grouping the housing options into neighborhoods to ease user’s navigation.

Deep learning

Deep learning (DL) is an ML technique that teaches machines to learn through examples. Let’s try to get at DL in the context of image recognition. Algorithms make the future prediction by analyzing the training data (“true” or “false” pictures) through numerous hierarchically-structured processing layers. The program builds the recognition feature set by itself, i.e., unsupervised. Image recognition is just a tip of the iceberg because the technology is widely used in the defense industry (identification of safe/unsafe areas for troops), self-driving vehicles (detection of signs and traffic lights), medicine (cancer cells detection), and many others.

Reinforcement learning

Unlike supervised learning, where algorithms have the training data as a supervisor, reinforcement learning (RL) stipulates the decision making based on the collected training examples through the cut-and-try method. Do you remember CD rats that look for cheese in a maze filled with electric shock traps? RL’s computational approach is similar – it learns from actions (avoid traps) to achieve the maximum reward (cheese). The value of RL algorithms is expressed in numerous hands-on applications including robotics, education and training, industrial automation, dialogue systems, advertising, finances and many others.

Data science

In recent years, data science (DS) became a buzzword in different industries and even got a special recognition by Harvard…as the sexiest job in the 21st century. DS involves not only a simple analysis of data but combines versatile methods and approaches including machine learning algorithms to discover the data insight. P&G leverages data science techniques to foresee future demand, Youtube builds its viewing patterns using DS to grasp what attracts users, and even your favorite search engine gives your desired results with the help of data science algorithms. The range of applications also includes image-speech recognition, digital advertising, video games, planning of airline routes, logistics, fraud detection, and many others.

NLP

This acronym has two popular full form versions – neuro-linguistic programming and natural language processing. The first one is the study of successful communication with people and has no relation to computer science. We are interested in the second one, which is a form of AI that looks into human language. The essence of NLP is to make a human speech or text comprehendible for a machine to enable interaction. This technology is employed when Alexa transcripts your voice message into text. Other application cases include translation from one language into another, different sorts of SEO analytics, social media monitoring, affective and cognitive computing, simplifying clinical documentation and others.

Computer vision

As you see, most of the technologies described in the article aim at acquiring some human-like features. As for computer vision, its goal is to let computers see and comprehend what they see in the way that human beings do. This technology brings many benefits to numerous areas of human life. Vehicles equipped with computer vision can respond faster to different signs and objects on the road; banking applications apply the technology to improve visual recognition of customers; production facilities use computer vision to monitor hazardous equipment or areas, etc. The future of this innovation is more than just high-potential.

AI chatbots

Using chatbots is an immense trend of the second decade of the 21st century. Banking institutions, medical facilities, educational establishments and other organizations actively incorporate this technology into their customer service. In general, an AI chatbot is a set of algorithms that provide a human-like conversation between a user and a machine. The scope of their activities is not limited to customer service. Due to artificial intelligence capabilities, these digital assistants can understand what they are asked and make a profound analysis to provide the most accurate outcome. On that account, such industries as sales, marketing, human resources, finance, insurance, and others can benefit from AI chatbots.

Robotics

You know what a robot is, don’t you? It is a machine that is programmed to perform particular tasks. Robotics is a branch of engineering that deals with building robots. So, where is AI in this scope? Artificial intelligence and robotics are separate terms, but they overlap in creating autonomous or semi-autonomous robots. Sophia is the most famous AI robot example so far. It can engage in conversation and animate different facial expressions. Artificially intelligent robots are not a gimmick in the modern world. Their development is accelerating, and many experts forecast a wide application of AI robots in replacing human jobs like customer support specialists, drivers and even financial analysts.

The modern life realities show a fast growth of innovations that pop up like mushrooms after a spring rain. Now, you became more versed in artificial intelligence and its terminology. Get ready for new endeavors!

Did you like this?
Tip Editor Futurescope with Cryptocurrency