Artificial intelligence (AI) has been a fascinating topic for decades. There is a huge focus on it right now, and many people are wondering how the subject will impact them today. However, there aren’t many simple explanations of AI available. I’m here to shed some light on this area by introducing some basic AI terms every beginner should know.
AI Terms Everyone Should Know
An algorithm is like the recipe for making an elaborate dish, which you can then use to cook any number of servings. Algorithms are used for computer programming, designing websites, playing games, and many other things.
Artificial Intelligence is an artificial process that mimics human thought. It is pertinent to note that some people will refer to Artificial General Intelligence (AGI) when talking about AI.
Autonomous is defined as capable of thinking, deciding, or acting independently. It is also referred to as being autonomous, which is represented by an autonomous agent.
Backward chaining is a technique of creating a rule-based expert system. In backward chaining, the results are arrived at first, and then the appropriate conditions or events that lead up to that result are determined. In other words, it starts from the end and works its way backward.
Bias also known as prejudice is a cognitive or motivational tendency to make unfavorable or unfavorable judgments or decisions in favor of (or against) certain people, ideas, and/or groups and against (or for) other people, ideas, and/or groups.
Big data is a term that describes techniques and technologies used to handle large and complex data sets and their analysis. The related terms “data mining”, “data warehouse”, “spy databases”, and “knowledge discovery in databases” refer to the broader task of using unstructured data, such as text, images, sound files, and videos.
The bounding box is a term used in computer vision. It is a rectangular or elliptical region in space around an object, which contains the entire object and no other. The bounding box is calculated using barycenter coordinates of all corners of the object.
Chatbots help you talk to a computer and can be used in a variety of ways. They can either conduct simple dialogs or simulate elaborate conversations based on the input provided. Chatbots are often used in situations where it is economically expensive or inconvenient to have a real person do it.
Cognitive computing is the next-generation paradigm of computing that has the potential to revolutionize entire industries and alter users’ lives.
A database is an electronic system that uses software to store and arrange data. The corpus contains the texts stored together in a database or spread across multiple databases.
Data mining is a technology that finds valuable patterns or trends from data to extract valuable conclusions.
Data science is an interdisciplinary field of study that brings together techniques and methods from varied fields including computing, statistics, machine learning, data engineering, pattern recognition, and knowledge representation.
The data set refers to the sample of information and examples used for training an AI model.
Deep Learning (DL) is a revolutionary type of Machine Learning (ML) algorithm used for Artificial General Intelligence (AGI), meaning that it can learn to solve any problem like a human. Unlike traditional AI algorithms like Tree-based and Logic-based, Deep Learning works through multiple layers of connections called Artificial Neural Networks (ANNs).
Entity annotation is used in natural language processing (NLP) for representing the context (which can be sentences or texts ) of the entities (person, place, or thing ).
Entity Extraction is related to textual data and information extraction.
Forward chaining is a way of structuring knowledge so that it can be used efficiently for inferencing. It is related to the concept of query answering, but differs in that forward chaining can be thought of as computing what you want rather than searching for it.
General AI is the branch of machine learning that is concerned with designing general, or wide-applicability, computational methods for artificial intelligence. Developing machines capable of intelligent behavior has long been an important research field.
Hyperparameter is a technology that enables artificial intelligence (AI) programs to make decision-making autonomously. For example, in machine learning, it’s the value used to fine-tune automatic features synthesis parameters for a decision model.
The intent is the ability to understand how an entity (machine or human) will act in a given situation.”
Labels are used to identify elements in your neural network. A label is used to tell the neural network that this particular feature belongs together.
Linguistic annotation is the study of linguistic phenomena by applying a set of symbols to textual data to represent, in a precise and standardized manner, the structural properties of this data.
Machine intelligence is a buzzword these days, and for good reason. AI has the potential to create incredible outcomes and improve all aspects of our lives, but it’s also a scary topic we know very little about. So we need to be proactive and meaningfully digest and learn as much as we can about this emerging field so we can navigate it wisely and understand the changes taking place around us.
Machine Learning (ML) is described as a branch of artificial intelligence (AI) that deals with the development of computer programs to facilitate automated data analysis and predictive modeling.
Machine translation is a complex software program that translates text from one human language into another.
A model is the main object in a machine learning system. A model is trained to learn features from input data by using methods such as backpropagation. The learned features are then used to make predictions or decisions about new inputs.
A neural network is a computer model system that has been inspired by the biological nervous system. The neural network is made up of interconnected processing units, which are known as neurons or neurons, in an artificial neural network.
Natural Language Generation
NLP involves applying algorithms to natural language to generate meaningful output. It’s a field focusing on the creation of a computer program that can understand human language and create natural language sentences, texts, or emails.
Natural Language Processing
Natural language processing is the task of computer systems to understand, produce, and interpret human speech in natural languages such as English in real-time. The technology is used in many areas, including Internet search engines, speech recognition, call center applications, content management systems, etc.
Natural Language Understanding
Natural language understanding (NLU) is an artificial intelligence subfield concerned with enabling machines to understand natural language, particularly to enable dialog or question-and-answering type interactions.
Overfitting is over-reliance on one or two feature selection techniques to the exclusion of others. For example, in the context of linear interpolation where you are fitting a line with N points to a set of data with K sample instances, you will underfit your training data if your features are not linearly dependent.
The parameter is a term mostly used in computer programming to refer to “a parameter” or, more specifically, an imaginary number/variable used to hold any value.
Pattern recognition is a branch of artificial intelligence that’s designed to be faster and more efficient than humans. A program that recognizes patterns in data is called a pattern recognition system, or a pattern recognizer.
Predictive analytics combines statistics, machine learning, and artificial intelligence to simulate the future. The techniques are used to create a forecast of a specific situation based on data linked to the present or the past. It is a common method for a business or a company to achieve success in the modern context. Predictive analytics helps makers of products, devices, and systems improve existing products, while also helping them anticipate new transformations and consumer behavior.
Python includes modules, sub-processes, and built-in functions that allow interacting with the operating system. Its simple syntax enables you to write programs to solve problems or develop new ideas fast. Python programs are portable across many operating systems and many hardware platforms because their design emphasizes readability and simplicity.
A type of machine learning called reinforcement learning is a subset of the broader machine learning umbrella. It’s based on the idea that we can reward or punish an AI for its actions or decisions, and through this process, it will learn what to do. Reinforcement learning is used in several applications, but automotive is a particularly interesting industry where it’s used to train a network to drive a car. An example of this is Roborace, a company that aims to “bring motorsport back to the public roads” with driverless cars designed for racing.
A semantic annotation refers to the use of ontologies and other semantic web technologies to capture and express semantics in a structured way, using formal tools and programs (artificial intelligence). The purpose of the semantic annotation is to allow machines to process the information in a document along with the context of that information, for example, time or location.
Semantic analysis is a kind of problem where we want to not just understand the text but want to understand the meaning behind mapping the words and phrases with some logical structure. This structure is also called a knowledge base and this process of forming the knowledge base is called Knowledge Representation.
A strong AI is a system that can outperform all human players in almost any intellectual task, such as playing chess or Go. It would then be capable of defeating human intelligence and surpassing it.
Supervised learning (also known as supervised segmentation and anomaly detection) is a method using algorithms and machine learning to automatically detect an object in an image. The algorithm finds a value(s) that the human eye would not be able to find on its own.
Test Data is a dataset used to test algorithms. In Artificial Intelligence, it is used to evaluate program learning performance and the accuracy of prediction.
Machine learning is based on the use of training data and algorithms. It’s what allows the algorithm to learn how to make predictions.
Transfer learning is a technique for training a classifier from one set of data to another, to generate a classifier that performs as well as, or better than, the original, using a smaller amount of labeled data.
Turing’s test determines if a machine can exhibit intelligent behavior equivalent to that of a human. The test is based on the idea that the only credible test for intelligence is conversation.
Unsupervised learning is learning where no teacher, teacher, or human intervention is involved.
Validation data is a term referring to the values used in a neural network, application of machine learning, or any other type of artificial intelligence system. It’s data that’s been verified by a human review process beforehand.
Variance is a common problem in machine learning, and for a good reason – it’s simply hard to have a perfect algorithm – no matter how good the algorithm is, there will always be some amount of error if you want a perfect solution. Variance is a way of preventing algorithm errors from being too big.
Variations are one of the most important concepts in machine learning. Variation is very important to distinguish between your model performance and your data’s performance. Prerequisite knowledge is essential both for beginners and experienced practitioners.
Weak AI is considered an approach for automated agents that attempt to simulate human thought processes to answer questions, solve problems and achieve goals in a “human-like” fashion. As any other term in the science of AI, weak AI is used with quite a large number of varying and often contradicting meanings in both pieces of research and in the press.