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Important Artificial Intelligence Terms for Every AI Enthusiast


Although AI isn’t new as a concept, it’s still very much in its infancy, and for the first time, as a society, we’re beginning to shift towards an AI-first realm. With endless possibilities and so much unchartered territory to explore, it’s no wonder that the race for AI supremacy is on.

For driven industry professionals in all fields, AI presents an exciting challenge to develop new technologies, set industry standards, and create new processes and workflows.

However, more and more, it’s increasingly becoming a challenge to understand all of the AI terms out there in the market. So let me put together some of the essential terms in the world of artificial intelligence for you in this article.

  1. Abductive Reasoning: Abductive reasoning is a form of logical inference that starts with an observation then seeks to find the simplest and most likely explanation.
  2. Action Model Learning: Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of software agent’s knowledge about effects and preconditions of the actions that can be executed within its environment.
  3. Adaptive Algorithm: An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and a priori defined reward mechanism
  4. Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own; classification, clustering, recommendation, and regression are four of the most popular types.
  5. Analytical Validation (new): The measure of the ability of a task to accurately and reliably generate the intended technical output from the input data. Validation techniques in machine learning are used to get the error rate of the ML model. Most common validation techniques: Resubstitution, K-fold cross-validation, Random subsampling, Bootstrapping.
  6. Artificial Intelligence: A machine’s ability to make decisions and perform tasks that simulate human knowledge and behavior.
  7. Artificial General Intelligence: Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and a common topic in science fiction and futurism.
  8. Artificial Narrow Intelligence (ANI) (New): Artificial narrow intelligence (ANI), also known as weak AI, is a general-purpose AI that refers to a computer’s ability to perform a single task extremely well, such as crawling a webpage or playing chess. Many currently existing AI-powered systems are likely operating as a weak AI focused on a narrowly defined specific problem (it used in building virtual assistants like Siri).
  9. Artificial Neural Network (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to answer.
  10. Back Propagation: The way many neural nets learn. They find the difference between their output and the desired output, then adjust the calculations in reverse order of execution.
  11. Bayesian networks (New): Also known as causal networks, belief network, and decision network, Bayesian Networks are graphical models for representing multivariate probability distributions. They aim to model conditional dependence, and therefore causation, by describing limited dependence by edges in a directed graph.
  12. Bias: While you may think of machines as objective, fair, and consistent, they often adopt the same unconscious biases as the humans who built them. That’s why companies must recognize the importance of normalizing data — meaning adjusting values measured on different scales to a common scale — to ensure that human biases aren’t unintentionally introduced into the algorithm. Take hiring as an example: If you give a computer a data set with 100 female candidates and 300 male candidates and ask it to predict the best person for the job, it is going to surface more male candidates because the volume of men is three times the size of women in the data set. Building technology that is fair and equitable may be challenging but will ensure that the algorithms informing our decisions and insights are not perpetuating the very biases we are trying to undo as a society.
  13. Big Data: Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.
  14. Black Box: A description of some deep learning systems. They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret.
  15. Capsule Network: Capsule is a nested set of neural layers. So in a regular neural network, you keep on adding more layers. In a capsule network, you would add more layers inside a single layer. Alternatively, in other words, nest a neural layer inside another.
  16. Chatbots: A chat robot (chatbot for short) that is designed to simulate a conversation with human users by communicating through text chats, voice commands, or both. They are a commonly used interface for computer programs that include AI capabilities.
  17. Classification: Classification algorithms let machines assign a category to a data point based on training data.
  18. Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; clusters are modeled with a measure of similarity defined by metrics such as Euclidean or probabilistic distance.
  19. Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.
  20. Cognitive computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition.
  21. Computer-Aided Detection (CADe) (New): Belongs to pattern recognition software that classifies suspicious features on the image and brings them to the attention of the radiologist, to decrease false-negative readings.
  22. Computer-Aided Diagnosis (CADx) (New): Belongs to software that examines a radiographic finding to determine the likelihood that the feature renders a specific disease process (e.g., benign versus malignant).
  23. Computer Vision: Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
  24. Computational Learning Theory: In computer science, computational learning theory (or just learning theory) is a subfield of Artificial Intelligence devoted to studying the design and analysis of machine learning algorithms.
  25. Confidence Interval (new): An interval about a point estimate that quantifies the statistical uncertainty in the real value being estimated due to variability.
  26. Continuous Learning Systems (CLS) (New): Systems that are inherently capable of learning from the real-world data and can update themselves automatically over time while in public use.
  27. Convolution: Convolution is a mathematical operation that does the integral of the product of 2 functions(signals), with one of the signals flipped. For example below we convolve 2 signals f(t) and g(t).
  28. Convolutional neural network (CNN): A type of neural network that identifies and makes sense of images.
  29. Data Analysis: Analysis of data is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
  30. Data mining: The examination of data sets to discover and mine patterns from that data that can be of further use.

  31. Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomena via either structured or unstructured data.
  32. Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flowchart.
  33. Deep Learning: The ability of machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.
  34. Embodied AI (New): The idea of embodied AI comes from that of embodied cognition, which suggests that intelligence is as much a part of the body as it is a part of the brain. With this in mind, embodied AI (for example, bringing sensory input and robotics into the equation) has a beneficial effect on the cognitive function of the AI, allowing it to better understand its situation and surroundings for more thorough data analysis and response processing.
  35. Expert System: A form of AI that attempts to replicate a human’s expertise in an area, such as medical diagnosis. It combines a knowledge base with a set of hand-coded rules for applying that knowledge. Machine-learning techniques are increasingly replacing hand-coding.
  36. Explainability: Explainability is knowing why AI rejects your credit card charge as fraud, denies your insurance claim, or confuses the side of a truck with a cloudy sky. Explainability is necessary to build trust and transparency into AI-powered software. The power and complexity of AI deep learning can make predictions and decisions challenging to explain to both customers and regulators. As our understanding of potential bias in data sets used to train AI algorithms grows, so does our need for greater explainability in our AI systems. To meet this challenge, enterprises can use tools like Low Code Platforms to put a human in the loop and govern how AI is used in important decisions.
  37. False Negative (New): A test result that does not detect the condition when the condition is present.
  38. False Positive (New): A test result that detects the condition when the condition is absent.
  39. Few-Shot Learning (New): Normally, machine learning tasks like computer vision require the input of massive amounts of image data to train a system. However, the goal of a few-shot (and even one-shot) learning is to create a system that dramatically reduces the amount of training needed to learn.
  40. Forward Chaining (New): A method where AI looks back and analyzes the rule-based system to find the “if” rules, and to determine which rules to use to find a solution.
  41. Friendly Artificial Intelligence (FIA)(new): If the values of artificial general intelligence are aligned with our own, then it is known as friendly AI. In this hypothetical scenario, a benevolent artificial intelligence would have a positive benefit on humanity. See also unfriendly artificial intelligence.
  42. Game AI: A form of AI-specific to gaming that uses an algorithm to replace randomness. It is a computational behavior used in non-player characters to generate human-like intelligence and reaction-based actions taken by the player.
  43. Generative Adversarial Networks: GAN is a type of neural network that can generate seemingly authentic photographs on a superficial scale to human eyes. GAN-generated images take elements of photographic data and shape them into realistic-looking images of people, animals, and places. GANs are made up of a system of two competing neural network models (generative models that use supervised learning). They compete with each other and can analyze, capture, and copy the variations within a dataset.
  44. Generative Models: The idea of generative models, is to be able to learn the probability distribution of the training set. This important idea could have the following use cases: 1) A super dataset augmenting system. (Able to create more data from the original data; 2) Reinforcement Learning systems where the Generator could be a simulator of the environment, simulating possible futures when planning a decision and reasoning
  45. Genetic algorithm: An evolutionary algorithm based on principles of genetics and natural selection that is used to find optimal or near-optimal solutions to severe problems that would otherwise take decades to solve.
  46. Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating incorrect options.
  47. Human-Computer Interaction: Human-computer interaction (commonly referred to as HCI) researches the design and use of computer technology, focused on the interfaces between people (users) and computers.
  48. Image recognition (New): Image recognition is the ability of a system or software to identify objects, people, places, and actions in images. It uses machine vision technologies with artificial intelligence and trained algorithms to recognize images through a camera system.
  49. Inductive reasoning (New): A logical process where multiple premises that are true or true most of the time, are combined to form a conclusion and often used in prediction and forecasting.
  50. Intelligence: Intelligence has been defined in many different ways, including as one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity, and problem-solving.
  51. Intelligence Explosion (New): A term coined for describing the eventual results of work on general artificial intelligence, which theorizes that this work will lead to a singularity in artificial intelligence where an “artificial superintelligence” surpasses the capabilities of human cognition.
  52. Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.
  53. Limited memory (New): systems with short-term memory limited to a given timeframe
  54. Linear Algebra: Linear algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces, but is also concerned with properties common to all vector spaces.
  55. Linear classification: A linear classifier does classification decisions based on the value of a linear combination of the characteristics. Imagine that the linear classifier will merge into its weights all the characteristics that define a particular class. (Like merge all samples of the class cars together)
  56. Loss Function: the Loss/Cost functions are mathematical functions that will answer how well your classifier is doing its job with the current set of parameters (Weights and Bias). One important step in supervised learning is the choice of the right loss function for the job/task.
  57. Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.
  58. Machine learning: Machine learning is a set of algorithms that can be fed only with structured data to complete a task without being programmed how to do so. All those algorithms build a mathematical model known as “training data” to make predictions or decisions. While AI is a technique that enables machines to mimic human behavior, Machine Learning is a technique used to implement Artificial Intelligence. It is a specific process during which devices (computers) are learning by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so. So all-in-all, Machine Learning is the meat and potatoes of AI.
  59. Machine perception: The ability of a system to receive and interpret data from the outside world similarly to how humans use our senses. This is typically done with the attached hardware, though the software is also usable.
  60. Machine translation (New): Machine translation (MT) is an automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).

  61. Markov Decision Process (MDP): is a framework used to help to make decisions on a stochastic environment. the goal is to find a policy, which is a map that gives us all optimal actions on each state on our environment. MDP is somehow more powerful than simple planning because your policy will allow you to do optimal actions even if something went wrong along the way. Simple planning just follows the plan after you find the best strategy.
  62. Narrow Intelligence (New): Narrow AI is AI that is programmed to perform a single task — whether it’s checking the weather, being able to play chess, or analyzing raw data to write journalistic reports.
  63. Natural language processing (NLP): The ability of a program to recognize human communication as it is meant to be understood.
  64. Neural Networks: Neural networks are examples of the Non-Linear hypothesis, where the model can learn to classify much more complex relations. Also, it scales better than Logistic Regression for a large number of features.
    It’s formed by artificial neurons, where those neurons are organized in layers. We have 3 types of layers: the input layer, the hidden layers, and the output layer.
  65. Neuromorphic Chip: A computer chip designed to act as a neural network. It can be analog, digital, or a combination.
  66. Optical Character Recognition (OCR) (New): Optical character recognition or optical character reader is the conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image.
  67. Pattern Recognition: Pattern recognition is a branch of machine learning that focuses on the identification of patterns and regularities in data, although it is, in some cases, considered to be nearly synonymous with machine learning.
  68. Perceptron: A new type of neural network, developed in the 1950s. It received considerable hype but was then shown to have limitations, suppressing interest in neural nets for years.
  69. Predictive Analytics: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
  70. Principal Component Analysis (PCA): is a tool used to make dimensionality reduction. This is useful because it makes the job of classifiers easier in terms of speed, or to aid data visualization. The principal components are the underlying structure in the data. They are the directions where there is the most variance on your data, the directions where the data is most spread out.
  71. Python: Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.
  72. Real-Time Health Systems (RTHS)(New): a next-gen care delivery system, wherein, the providers can share, adapt, and apply their medical mastery in real-time. It involves a collection of relevant information from different sources (devices, applications, e-records), which can, therefore, make decision making, fast.
  73. Recommendation Algorithms (New): Algorithms that help machines suggest a choice based on their commonality with historical data.
  74. Recurrent neural network (RNN): A type of neural network that makes sense of sequential information and recognizes patterns, and creates outputs based on those calculations.
  75. Regression (New): A statistical approach that estimates the relationships among variables and predicts future outcomes or items in a continuous data set by solving for the pattern of past inputs, such as linear regression in statistics. Regression is foundational to machine learning and artificial intelligence.
  76. Reinforcement Learning: “If intelligence were a cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake.” — Yann LeCun, Founding Father of Convolutional Nets RL, is a type of Machine Learning algorithms which allows software agents and machines to automatically determine the ideal behavior within a specific context, to maximize its performance. Reinforcement algorithms are not given explicit goals; instead, they are forced to learn these optimal goals by trial and error. RL, we have an agent that is moving around in an environment with the ability to take actions (like moving in a specific direction). This agent could be an algorithm, or a person, or an object. The action takes effect on the input that comes from the environment. Only once the agent is put through a few iterations can we tell how far away it is from achieving the end goal. When it comes to supervised learning, the input and output are already very well defined from the start.
  77. Robotics (New): the branch of technology that deals with the design, construction, operation, and application of robots. Most robots today are used to do repetitive actions or jobs considered too dangerous for humans. A robot is ideal for going into a building that has a possible bomb. Robots are also used in factories to build things like cars, candy bars, and electronics.
  78. Robotic process automation (RPA) (New): uses software with AI and ML capabilities to perform repetitive tasks once completed by humans.
  79. Shadow learning (New): A term used to describe a simplified form of deep learning, in which their processing precedes the search for key features of data by a person and entering into the system specific to the sphere to which this data relates. Such models are more “transparent” (in the sense of obtaining results) and high-performance due to the increase in time invested in the design of the system.
  80. Singularity (New): The technological singularity is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization.
  81. Strong AI: AI is as smart and well-rounded as a human. Some say it’s impossible. Current AI is weak, or narrow. It can play chess or drive but not both, and lacks common sense.
  82. Superintelligence (New): A superintelligence is a hypothetical agent that possesses intelligence far surpassing a level of general intelligence that massively exceeds our own.
  83. Supervised learning: A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student, more common than unsupervised learning.
  84. Swarm behavior: From the perspective of the mathematical modeler, it is an emergent behavior arising from simple rules that are followed by individuals and does not involve any central coordination.
  85. TensorFlow: A collection of software tools developed by Google for use in deep learning. It is open-source, meaning anyone can use or improve it. Similar projects include Torch and Theano.
  86. Transfer Learning: A technique in machine learning in which an algorithm learns to perform one task, such as recognizing cars, and builds on that knowledge when learning a different but related task, such as recognizing cats.
  87. True Negative (New): A test result that does not detect the condition when the condition is absent.
  88. True Positive (New): A test result that detects the condition when the condition is present.
  89. Turing Test: A test of AI’s ability to pass as human. In Alan Turing’s original conception, an AI would be judged by its ability to converse through written text.
  90. Unfriendly Artificial Intelligence (New): artificial general intelligence capable of causing great harm to humanity, and having goals that make it useful for the AI to do so.
  91. Unsupervised learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis.