Artificial intelligence (AI) is a field that is rich in terminology. With the advancement of technology, many people have become interested in AI. Having a working knowledge of AI terminology will help you advance your AI career.
Below you’ll find a comprehensive glossary of AI terminology as well as an AI terminology cheat sheet to provide you with adequate knowledge. If you master these fundamentals, this list of AI terms will help you as you study AI and begin your pursuit of a career in the field.
What is AI?
AI is an advanced technology that allows robots or machines to perform tasks that are usually done by humans. These tasks often require intellectual processes such as reasoning, learning from past experiences, and generalizing. You can classify artificial systems into Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence.
AI may possess aspects of human intelligence such as speech recognition, visual perception, and decision-making. As such, artificial intelligence is used as a substitution for natural intelligence. Some uses of AI include facial recognition, speech recognition, self-driving cars, image recognition, chatbots, virtual assistants, feature detection, and autocomplete.
Who Uses AI Terminology?
AI terminology is mainly used in the information technology industry, and there is a wide range of job opportunities in AI. Professionals such as software engineers, research scientists, machine learning engineers, AI engineers, data scientists, and data analysts use AI terminology.
List of AI Terms: Things Every Research Scientist Should Know
- Affective Computing
- Backward chaining
- Big Data
- Cognitive Computing
- Data Science
- Deep Learning
- Forward Chaining
- Kernel Method
- Linguistic Annotation
- Machine Intelligence
- Machine Learning
- Machine Vision
- Natural Language Processing (NLP)
- Neural Network
- Transfer Learning
Glossary of AI Terminology: 5 Common AI Terms
To appreciate the impact of artificial intelligence on our lives, we should be familiar with basic AI terminology. Below are five of the most common AI terms that an aspiring research scientist should know.
An algorithm is a simple set of commands that a computer can follow to understand how to perform and complete a task. For example, machine learning programs use a machine-learning algorithm to make predictions.
Why a Research Scientist Needs to Know About Algorithms
Algorithms are essential for a range of tasks such as studying data, getting insight, and generating predictions. Therefore, understanding the principles of algorithms and task-specific algorithms as a research scientist will help you implement them better.
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A chatbot is an AI system that engages with a user via audio or text channels to assist them with simple tasks. Examples of chatbots include smartbot, talkbot, chatterbot, bot, IM bot, interactive agent, conversational interface, or artificial conversational entity.
Why a Research Scientist Needs to Know About Chatbots
A research scientist should understand that chatbots are far more than conversational tools. They can also automate time-consuming and repetitive processes such as emailing customers, responding to FAQs, and completing surveys.
This advanced AI function mimics the way humans gain knowledge by using artificial neural networks. Deep learning is often considered a type of machine learning and it can take the form of supervised, semi-supervised, or unsupervised learning.
Why a Research Scientist Needs to Know About Deep Learning
Deep learning is extremely beneficial to research scientists as they are responsible for collecting, analyzing, and interpreting large amounts of data. Deep learning makes this process far more efficient.
Machine learning is a branch of AI that focuses on the use of algorithms and data to imitate the way humans learn. This allows machines to teach themselves to perform tasks more effectively without human involvement through the use of patterns and inference.
Why a Research Scientist Needs to Know About Machine Learning
Machine learning research scientists carry out data engineering and modeling jobs. Machine learning researchers frequently use each other’s models to save time and computational resources. Hence, research is vital.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is the umbrella term for the ability of computers to execute conversational functions. For example speech recognition, comprehending meaning, and replying intelligibly.
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Why a Research Scientist Needs to Know About Natural Language Processing
NLP is necessary for research scientists since it helps resolve uncertainty in common language and adds helpful numerical structure to data for many application areas such as human speech recognition or text analytics.
AI Terminology Cheat Sheet: 5 Advanced AI Terms
The following advanced AI terms may be used less frequently in the workplace, but they are essential to anyone looking to work with AI. Knowing this advanced AI terminology will give you a comprehensive understanding of machine language and artificial intelligence concepts.
A machine is said to be an autonomous entity if it completes a task without human assistance. Autonomous cars and autonomous robots are examples of autonomous entities. This branch of AI brings in billions of dollars. For example, the autonomous car market is predicted to be worth $400 billion in 2025.
Why a Research Scientist Should Know About Autonomous Entities
Autonomous entities are beneficial to various industries because of their flexibility to multiple conditions and environments. It is also an incredibly lucrative branch of AI that is vital for the future of AI.
Backward chaining, also known as backward reasoning, is a method in which the model starts with the desired outcome and then works backward to find the data that supports it. Machine learning applications make use of backward chaining. The opposite of backward chaining is forward chaining.
Why a Research Scientist Should Know About Backward Chaining
A research scientist can generate a potential solution using backward chaining if the inference engine follows predetermined rules. It is easy to know the outcome with backward chaining, making it simple for a research scientist to conclude their research.
Cognitive Computing is another way of saying “artificial intelligence”. The term refers to new hardware or software that simulates human language and brain function to assist human decision-making. Sentiment analysis, speech recognition, facial recognition, image recognition, fraud detection, and risk assessment are cognitive computing applications.
Why a Research Scientist Should Know About Cognitive Computing
Cognitive computing should be known to research scientists because, as cognitive science advances, humans will broaden the subject and merge it with computer science to create artificial intelligence. Understanding brain neurons and synapses, for example, aids the development of artificial neurons in artificial neural networks.
Machine vision allows a computer system to evaluate data in various industries by using image-capture technology. It is one of the most basic technologies used by self-guiding robots and it allows automatic inspections and process controls. It is a collection of interconnected systems that work together to tackle specific challenges in the real world.
Why a Research Scientist Should Know About Machine Vision
The main benefit of machine vision is enhanced flexibility. This is a vital tool for research scientists to use. A vision-enabled robot can do the tasks of numerous blind robots. If preprogrammed, this robot can easily switch between activities with little to no downtime to assist research scientists.
An artificial neural network is a computer system in machine learning that mimics the functions of the human mind. Although researchers are still working on developing a machine model of the human brain, existing neural networks can perform a variety of functions such as natural language generation, facial recognition, pattern recognition, and board game strategy.
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Why a Research Scientist Should Know about Neural Networks
Research scientists need to study neural networks because various applications use neural networks for pattern recognition and classification, complex systems modeling, control, language processing, optimization, and prediction.
How Can I Learn AI Terminology in 2022?
You can learn AI terminology by taking online artificial intelligence courses, classes, and training. These resources will help you to earn an online education in artificial intelligence. Alternatively, you can enroll in AI bootcamps. These bootcamps will offer you in-demand AI skills in a shorter space of time.
AI Terminology FAQ
The main goals of artificial intelligence (AI) research are reasoning, knowledge representation, perception, planning, learning, natural language understanding, and moving and manipulating objects. General intelligence is also among the project’s long-term goals.
You should learn a programming language like Python, understand the conceptual terms of machine learning, and know about data structures and algorithms. It is also important to have strong mathematical skills and an understanding of scientific methods.
Yes, AI is an excellent career choice. More organizations each year turn to AI and machine learning technology to use intelligent systems in their business operations. The US Bureau of Labor Statistics predicts that job openings in AI will increase by 22 percent between 2020 to 2030. Furthermore, the average AI salary is about $125,000 a year, making it an in-demand and lucrative career choice.
The founding fathers of AI are considered to be John McCarthy, Alan Turing, Marvin Minksy, Allen Newell, and Herbert A. Simon. However, it was John McCarthy and his team who coined the term “artificial intelligence” in 1956.
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