50+ Artificial Intelligence & Machine Learning Terms

The AI industry is booming! With every new company jumping on the bandwagon, it’s hard to keep up with all of the terms. But don’t worry – we’ve got your back. We have compiled a list of every term you need to know before entering this world and are here to explain them in plain English. If you want to be an A+ player in the AI game, then get ready for your vocabulary lesson!

  • Algorithm: an automated process that performs a specific task.
  • Artificial intelligence (AI): artificial intelligence refers to any one of many technologies developed in order for computers or machines to mimic human cognitive abilities such as knowledge representation, natural language processing, perception and planning. The term “artificial” implies it was created by humans who would otherwise not exist without technology. 
  • Autonomous: a self-governing; being under no outside control. 
  • Backward chaining: A teaching strategy where the teacher gives instructions to the student and then waits for them to carry out those instructions.
  • Bias: a deviation from an objective or rational basis. 
  • Big data: large datasets that are beyond the ability of typical database software tools to capture, store, manage, and process. 
  • Bounding box: used in computer graphics to define the limits of what will be shown on-screen; it defines where objects appear as well as how they interact with other elements such as touch devices. It is also called a Viewport (in OpenGL) or Window (on Microsoft Windows). 
  • Chatbot: A chatbot is artificial intelligence which appears through instant messages via text messaging platforms (e.g., Facebook Messenger), over voice chats within apps like Alexa Echo/Google Home, and over video chats like Skype or Facetime.
  • Cognitive computing: the engineering discipline of designing computer systems that can perform tasks normally requiring human intelligence such as reasoning, learning, understanding language, predicting outcomes from limited data sets (e.g., weather), and self-correction of errors in learned knowledge representations. 
  • Computational learning theory: a mathematical theory about how people learn to make decisions based on experience; it is used in AI research for supervised machine learning algorithms with neural networks.
  • Corpus: corpus linguistics uses quantitative methods to analyze large bodies of written texts. It was originally developed by philologists studying Latin grammar before spreading to other languages including English where it shows up under names such as ‘text analysis’.
  • Data Mining: Term used in a variety of contexts, including the extraction of patterns from large quantities of data.
  • Data Science: A scientific field that applies statistics and machine learning to extract knowledge or insights from raw data. Data science is considered an “extension of traditional business intelligence into new territories.” 
  • Dataset: datasets are collections of records/rows with different variables (columns). The columns may represent any variable about the case being studied, such as age (different for each individual), gender, number-of-children, occupation type etc. Datasets typically come in two forms – fixed format where all rows have exactly the same layout; or free form where cases vary extensively on how they are organized. 
  • Deep Learning: Deep Learning is a subset of machine learning. It utilizes many layers (or levels) of non-linear processing units for input data to create more complex and abstract outputs as it goes through the layers, which is where deep comes from. 
  • Entity Annotation: The process by which entities are identified in texts – typically human readable sentences or documents composed using natural languages – with respect to their reference types, properties, and relations to other entities. 
  • Entity Extraction: In Natural Language Processing (NLP), extraction is “the act of extracting linguistic information that pertains to some entity.” Entity extraction can be supervised or unsupervised. Supervised extractions infer knowledge about how two related words refer to the same thing; unsupervised extractions use statistical methods to discover and organize entities without prior knowledge.
  • Forward Chaining: Forward chaining is a type of reasoning where the desired conclusion is reached by first tracing implications from premises that are known or assumed to be true. 
  • General AI: General Artificial Intelligence, also referred to as Strong AI, refers broadly to any artificial intelligence that matches human cognitive capabilities in terms of general-purpose intelligence (i.e., not limited in domain). 
  • Hyperparameter: Hyperparameters are parameters for machine learning algorithms which cannot be automatically tuned but need tuning through manual experimentation with different values before being selected. They may refer either directly or indirectly to configuration settings for various software components such as neural network layers; these can affect training time/accuracy tradeoffs and may be selected by cross-validation.
  • Intent: The desired goal of a user when using a system or application, also called “the why.” 
  • Label: A variable that is applied to an entity in order to describe its properties or features. Labels are assigned by humans who classify the entities they encounter according to some criteria (e.g., gender). 
  • Linguistic Annotation: Linguistics annotations denote linguistic phenomena in language data such as parts of speech, syntactic structures, semantic relations between words etc with formal rules for their use established beforehand. They can be used for example in Machine Translation systems which need information about how two languages relate at word level before translating automatically from one into another.
  • Machine Intelligence: Machine intelligence is the ability of a machine to act and behave in ways that are similar to humans. 
  • Machine Learning: Machine learning is “the science of getting computers to act without being explicitly programmed.” It offers a lot more advantages than human-programmed software as it can automatically detect patterns, find correlations with data sets, deal with large amounts of information etc. In other words, it reduces dependence on time consuming manual programming from experts while giving machines the capability for making decisions based on what they learn through experience or interaction. 
  • Model: Models are statistical representations of one or more phenomena via mathematical equations (e.g., population models). They provide an abstract representation which may be tested against reality by comparing its predictions about future events with observations from the real world.
  • Neural Network: Neural networks, also referred to as artificial neural networks (ANNs), are computing systems that are inspired by how biological neurons interact with each other in animal brains. They consist of interconnected nodes or units which represent abstract entities such as words 
  • Natural Language Generation : Natural language generation refers broadly to any computer-generated text which is meant for humans and/or computers to read; it can take the form of prose or even poetry, either from a natural language parser or an imagined voice generator. 
  • Natural Language Processing: Natural language processing (NLP) is “the study and development of computational methods involved in understanding human speech.” It relies on machine learning algorithms like deep neural nets in order to produce meaningful results. 
  • Natural Language Understanding: Natural language understanding (NLU) techniques are used to process natural languages, often with the goal of making a semantic representation which can be passed on to other systems such as an NLG system or a machine translation system.
  • Overfitting : When training data is too small for the complexity of models being trained and not enough testing has been done thoroughly in order to ensure that they generalize well when deployed. This leads to overfitted models that do not work well outside their particular environment, meaning it will take much more time before any improvements are seen.
  • Parameter: One type of parameter is called tuning parameters because they usually need adjusting during model fitting; these include hyperparameters like λ values in logistic regression or variance in generalized linear models (GLMs).
  • Pattern Recognition: Pattern recognition is the process of finding a regularity, discovering hidden relationships and predicting future patterns from data. It can be used for many applications like identifying music by its melody or speech by analyzing sound waves. 
  • Predictive Analytics: Predictive analytics “uses statistics-based approaches to determine which variables are likely to affect other ones.” This approach helps identify the risk factors associated with an outcome based on what has happened before so that appropriate measures can be taken to prevent it happening again. 
  • Python : Python is one of the most popular programming languages today — especially among programmers who specialize in machine learning tasks such as designing neural networks. The syntax of this language is relatively simple, so the code can be understood easily by human beings.
  • Random Forest: A random forest is a tree-based predictive model that belongs to ensemble learning methods for classification and regression problems. It uses bootstrap aggregation which means that many trees are created at different nodes in order to reduce variance and bias as much as possible— this prevents overfitting too. 
  • Reinforcement Learning (RL) : Reinforcement learning involves “learning from experience,” usually without being explicitly programmed — it offers more advantages than programs written by humans because of its ability to learn automatically through interacting with data or making decisions based on what it’s learned before. 
  • Robot : Robots have been around since 1948 when they were first introduced into the industrial world. They are a type of machine that has the ability to carry out tasks typically done by human beings, often in hazardous environments or for long periods without tiring. 
  • Scala: Scala is an object-oriented programming language which was designed as a successor to Java and Python. It’s one of the most popular languages used by data scientists including those who focus on deep learning because it offers strong performance with features like higher order functions and implicit conversions not found in other languages.
  • Scripting Languages : Scripts are computer programs written using a scripting language, such as JavaScript or PHP. These types of scripts can be executed directly from their source code rather than first having to be compiled into another form before execution (as you would have to do with C++ or Java).
  • Semantic Segmentation: Semantic segmentation is a computer vision technique for mapping and labeling different semantic objects in an image. It can be used to detect specific sets of features like humans, cars, buildings, trucks, roads and so on — effectively dividing the images into regions that correspond to these various classes. 
  • Sentiment Analysis : Sentiment analysis includes techniques where people review customer feedback which has been collected from social media sites such as Twitter or Facebook in order to determine their sentiment about your products. Surveys are also often conducted by asking customers how they feel about what you offer; this information then gets compiled together and analyzed statistically using methods like regression models. 
  • Strong AI: Strong AI is an artificial intelligence that has been programmed to think like a human being. It is able to learn, understand natural languages and use logic.
  • Supervised Learning: Supervised learning refers to the process of training machine-learning models by feeding them with input data (called “instructions”) and desired output data or response variables. The algorithm then uses this information to identify patterns in order for it make predictions about future inputs based on what they’ve seen before; these are known as supervised learning methods because there’s always someone providing guidance along the way either implicitly or explicitly. 
  • Test Data: Test sets are groups of records which are used in computer systems tests such as A/B testing where we want to know how the system will respond to different types of inputs. It’s what we use in order to validate the results which our models produce so that they can be considered reliable. 
  • Training Data: Training data is a set of input records and corresponding responses variables or output values for those records. These sets are used by machine-learning algorithms (algorithms designed with the goal of improving themselves) when training them in supervised learning methods — this involves showing them instances from their past where they were close, but not quite accurate enough yet; these datasets allow it to identify patterns within each specific dataset and then generalize that knowledge across other related datasets as well.
  • Transfer Learning : Transfer learning refers to using information gathered during one task/application towards another unrelated problem. So if for example an AI had been trained to solve a problem from one domain, such as natural language processing (NLP) or computer vision and it was then given a related task that didn’t make use of the data provided during its initial training phase — but rather dealt with another subset of problems such as those encountered in voice recognition tasks. 
  • Turing Test: The Turing test is designed to determine whether artificial intelligence has become strong enough so that we can no longer tell what’s human versus machine. It consists of having judges speak to both humans and machines; they are not told which one is which nor do they know who will be talking back. If the judge cannot reliably identify the machine-generated responses more than 50% of the time, the machine is said to have passed and we can assume it has become close enough in intelligence to that of humans. 
  • Unsupervised Learning: Unsupervised learning refers to methods where a model uses input data which does not come with any desired output variables or response values — so there’s no one telling it what should happen.
  • Validation Data : Validation data are sets used for estimating how well an algorithm might do on another problem when compared against some benchmark; these datasets are typically quite different from those given during training. For example, if you were working on building predictive models that could predict whether someone will go online and purchase something at your company’s site after being presented with various offers (thus generating revenue) then you can use validation datasets to estimate how well these models might work on other companies.
  • Validation Set: Validation sets are used in order to assess the accuracy of an algorithm’s performance — it is a set of records which has been withheld from training so that we can gauge its efficacy at providing accurate predictions when applied towards new, previously unseen data; this usually involves splitting your dataset into two parts and only using one part for training while saving the rest for testing purposes. This helps you avoid overfitting (where algorithms tends to memorize rather than generalizing) by not relying exclusively on information they’ve already seen as input during their initial stages. 
  • Variation: Variation is a term used to refer to an instance where the same input can produce different outputs and so it’s what we use in order to explore how capable our AI systems are at responding to various types of inputs. It’s also what helps us validate their reliability by examining if they have responded consistently across multiple instances. 
  • Weak AI: Weak artificial intelligence, or weak AIs for short, refers to artificially intelligent entities which don’t possess human-level intellect nor do they even come close; these include computer programs that solve problems like chess-playing algorithms — but nothing more sophisticated than this.

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