20 Artificial Intelligence Vocabularies - banner
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Lately there have been some differences among experts concerning the future of Artificial Intelligence and its everyday life implications. At one end we can see optimism regarding the prospects of self learning machines and robotics.  In contrast, popular figures like Elon Musk and Stephen Hawkings have raised concerns over the existential threat posed by intelligent machines on humans.

Taking both perspectives into consideration, there is no denying that Artificial Intelligence has started to transform businesses across a wide variety of sectors like healthcare, sales, education, automobile, etc. The ever-growing impact of AI on these industries makes it clear that the use of AI will only increase in the upcoming years.

As AI is set to grow exponentially and impact our day-to-day lives, it is highly beneficial for everyone to have a basic understanding of it. For your convenience, we have enlisted 20 Artificial Intelligence vocabularies, buzzwords, and terminologies that will help you have a better understanding of AI. By no means is this list a comprehensive resource to AI, but we do hope this list will expand your knowledge in AI.

 

1. Algorithm

In general, an algorithm is a set of rules or specific mathematical steps that are used to solve a problem. In AI, algorithms are used in machine learning to make predictions from the data sets they analyze. For example, social media platform like Facebook use algorithms to generate personalized newsfeeds based on the user’s past behavior.

2. Artificial intelligence

Artificial Intelligence deals with the study of computer software making intelligent decisions, reasoning, and problem solving. It refers to the concept that makes machines do things intelligently without or with little help from humans. It is a broad field of computer science which includes subfields such as machine learning, data science and big data.

3. Augmented reality

If you’ve ever played Pokémon Go, you’d understand what augmented reality is. Pokémon Go, a mobile app based game took the world by storm with its first-hand adventure experience. Although it isn’t the most appropriate examples of augmented reality, it tries to integrate virtual objects with real-world surroundings. Augmented reality is the real-time integration of digital information with the user’s environment. Unlike virtual reality, which creates a totally artificial environment, augmented reality uses the existing environment and overlays new information on top of it.

4. Autonomous car

Only in the last decade, an autonomous car was science fiction to many people. With the help of artificial intelligence and technological innovations, autonomous cars are now a part of reality. An autonomous car is simply a vehicle capable of sensing its environment and navigating itself without human input. It uses a variety of techniques such as GPS, computer vision and lidar to detect its surroundings. It has further advanced control systems capable of analyzing sensory data that interprets sensory information to identify navigation paths, other cars and roads.

5. Big Data

Big data is a huge amount of structured and unstructured data that is so large that it’s difficult if not impossible to process them using traditional databases and software techniques. Big data often represents the intelligent analytics and processing tools that is used in analysis, storage, visualization and curation of data. Many companies are using big data to improve their operations and make faster, intelligent, data driven decisions.

6. Chatbot

A Chatbot is an artificial intelligence program that helps users interact with computer via a chat interface.. It uses machine learning and natural language processing technologies to mimic human conversation and responds to a user as a conversational partner. Chatbots are mostly used in customer service and marketing sectors through messaging apps and social media platforms for instant messaging and conversation with the users. Apple’s Siri is one of the most popular chatbots that interacts with its users to answer questions, drive conversations, and even suggest similar topics.

7. Cognitive Computing

If you’re familiar with IBM Watson, you’d probably have a brief understanding of cognitive computing. IBM Watson leverages natural language and human interactions to generate evidence based on the hypothesis. It further learns from user responses and communicates further based on the history with the user. In general, cognitive computing is the simulation of human thought processes in a computerized model. It involves self-learning systems that use data mining, natural language processing and pattern recognition to impersonate human brain workings.

8. Computer vision

Computer vision is a broad field of science and technology that deals with building artificial systems for obtaining information from digital images or videos. From a perspective of artificial intelligence, computer vision is used to deal with autonomous planning or deliberation for systems which can perform actions such as moving a robot through an environment. One of the prominent application fields of computer vision is autonomous cars. Autonomous cars use computer vision for navigation and detecting environment.

9. Data mining

Data mining is a process of analyzing data from many dimensions and discovering patterns to extract meaningful information. It combines techniques from AI, machine learning and statistics to leverage data-processing. Various industries use data mining tools to help determine an organization’s current progressive status, level of market competition, market trend, and the overall decision making process. For example, finance and accounting industries use data mining to get insight on the preliminary financial status, detect management fraud and flaws, estimate credit risks, and predict overall corporate performance.

10. Data science

Data science is a blend of data mining, machine learning, statistics and other data analytic technologies to extract insights from structured and unstructured data. It is the process and method of studying where data comes from, what value it has and how it can be turned into valuable insights to generate business value. Many companies use it to explore new market opportunities, increase effectivity, and increase the company’s competitive edge. For example, Netflix uses data science to understand movie viewing patterns and interest of their users to produce their own original series.

11. Deep learning

Deep learning is a subfield of machine learning based on a set of algorithms that use artificial neural networks to model high level data abstractions. As to how they are different from normal “shallow” machine learning, a simplified explanation is that they make use of more layers both architecturally as well as conceptually so that they are able to learn both low and high level features and how to use them. It is based on recent developments in neural net training techniques.  

12. Existential risk

The rise of AI has brought many ideas forward including the existential risk of humans. Technology pioneers such as Elon Musk and Bill Gates have been vocal about the threat AI can bring if not developed safely. AI is making human lives better and there is even greater opportunity to improve lives with AI, but the question will always remain if AI could surpass human intelligence and become difficult to control.

13. Internet of Things

Internet of Things (IoT) is a network of connected devices that can manage, communicate and transfer real time data between devices autonomously. IoT is distinguished by four basic attributes: objects or things, sensor technology, wireless connectivity networks, and data computing abilities. The concept of IoT is to usher automation in all fields enabling advanced applications like smart grid and even expanding to the areas such as smart cities.

14. Machine Learning

Machine learning is A subfield of artificial intelligence, that provides machines with the ability to learn without being explicitly programmed. They learn from previous data to generate reliable decisions and predictions. Evolved from pattern recognition, machine learning is gaining a lot of prominence lately for its ability to automatically learn and predict big data. Many industries that work with large amounts of data use machine learning to generate faster and more accurate insights. For example, e-Commerce websites Amazon use machine learning to analyze its users buying history to feeds similar products.

15. Natural Language Processing

Natural Language Processing (NLP) is the field of artificial intelligence that focuses on the interactions between human language and computers. It is a way for machines to understand, analyze, and decipher meaning from human language or natural language input. NLP is used to perform tasks such as machine translation, natural language understanding, speech recognition and automatic summarization. One of the most prominent examples of NLP is Google Translate. The complex programming of Google Translate automatically translates text from one human language to another.

16. Pattern recognition

Pattern recognition is a branch of machine learning that deals with recognizing visual and sound patterns in data. It extracts and classifies data based on a priori knowledge and statistical information. Some of the applications of pattern recognition techniques are automatic image recognition, automatic speech recognition, and text classification.

17. Probabilistic programming language

A probabilistic programming language is a high-level programming language designed to describe probability models and then solve these models automatically. It is a machine learning technology that deals with the reduction of time and effort in understanding data and implementing new models. It is used for image detection, detecting cyber securities and predicting stock prices.

18. Speech recognition

Speech recognition deals with the recognition and translation of spoken language into machine readable format. It uses algorithms through acoustic and language modeling. The most prominent applications of speech recognition include speech-to-text processing, voice dialing and voice search. Android phone’s “OK Google” is a popular example of voice search.

19. Supervised and Unsupervised Learning

Supervised and unsupervised learning are types of machine learning which differ only in the casual structure of the model. In supervised learning, machine learning tasks are trained for every input with corresponding target which will be able to provide target for any new input after sufficient training. In unsupervised learning, machine learning tasks are trained only with a set of inputs, which will be able to find the structure or relationships between different inputs.

20. Turing Test

Turing Test is the standard for qualifying computer programs (chatbots) as intelligent. Developed by Alan Turing in 1950, the test involves a series of 5 minute-long text conversations with judges. During the test, the program must convince that it is human on average at least 30% of the time. Eugene Goostman, a chatbot that simulates a 13 year old boy, is considered as the first program that passed the Turing test. However, there is a lot of debate as many developers consider that he cheated the test in a lot of ways.