What does AI stand for? Simple glossary of AI terms and concepts
To describe simple artificial intelligence (AI) terms is like eating an elephant bit by bit. What does AI mean then? Do you know what it actually stands for?
The idea of ‘a machine that thinks’ dates back to Ancient Greece, but according to Enterprise Project (link below), there are 5 AI assumptions and truths. What does AI mean?
- An AI strategy is not one size fits all …
- AI and machine learning are not interchangeable …
- Algorithms are not more important than data …
- AI lacks an important human trait: Empathy …
- Expect a cultural and skills shift …
Because life today as we know it is inevitable without AI, thus, what does AI mean?
“It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”John McCarthy
What does AI mean in cybersecurity? Dive into this blog post to understand: What exactly is cybersecurity?
- History of AI
- What is the meaning of AI for Businesses?
- Glossary of AI Key Terms
- What does AI mean for startups? Everything you need to know about AI!
- What does AI mean and how does it work?
- Machines’ controlled learning
- Ways of human intelligence simulation answer what does AI mean?
- What does AI mean? – Explanation in simple terms
- Why AI?
History of AI
Before taking a deep dive into ‘what does AI mean,’ here is a run down of the history of artificial intelligence. It’s FREE. Get it now here.
What does AI mean for you?
Martin Minsky, the founding father of artificial intelligence (AI) described it as,
“the science of making machines do things that would require intelligence if done by men.”– Brittanica
What does AI mean for its researchers? They faced a problem: It was difficult to capture the external world in the cold, hard syntax of even the most powerful computer programming languages. To solve this, Minsky developed the concept of “frames” in 1975. He was able to identify the precise programmed into a computer before considering specific directions.
Hence, ability to make decisions like those a human would make given the same information is artificial intelligence.
What is the meaning of AI for Businesses?
From the onset, use of AI in corporate processes was still in its infancy, with only a theoretical promise. AI technologies and applications have progressed to the point where they now bring value to enterprises.
What does AI mean for businesses today? There isn’t a single company that hasn’t reaped from the ever-expanding variety of measurable benefits from AI-powered technologies in one form or another.
AI technology spending by 2024 will have more than doubled to $110 billion– just since 2020– IDC Forecasts
Defining Artificial Intelligence
What does AI mean to a modern computer scientist? Scientists of today define AI as a system that is able to perceive its environment and take actions to maximize the chance of successfully achieving its goals. Notwithstanding, the system interprets and analyzes data in such a way that it learns and adapts as it goes.
Thus, what does AI mean? Follow closely as other Artificial Intelligence related terms that you must know are defined henceforth!
Abductive reasoning is a form of logical inference which starts with an observation then seeks to find the simplest and most likely explanation.
A machine’s ability to make decisions and perform tasks that simulate human knowledge and behavior.
Action Model Learning
Action model learning (action learning) is an area of machine learning concerned with software agent knowledge creation and modification. It looks at the effects and preconditions of the actions that are executable within its environment.
An adaptive algorithm is an algorithm that changes behavior when run. It’s basis is on information available and a priori defined reward mechanism
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.
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.
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 get the error rate of the ML model. Most common validation techniques: Resubstitution, K-fold cross-validation, Random subsampling, Bootstrapping.
Artificial Neural Network (ANN)
This is a learning model created to act like a human brain. Its solves tasks that are too difficult for traditional computer systems to answer.
Artificial Narrow Intelligence (ANI)
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).
Read our post on When Facebook changes its algorithm
Glossary of AI Key Terms
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.
Bayesian networks (causal networks, belief network, or 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.
Big Data is a term for data sets. These are so large or complex that traditional data processing applications are inadequate to deal with them.
Black Box: A description of some deep learning systems. They take an input and provide an output. However, the calculations that occur in between are not easy for humans to interpret.
Concepts of AI answer the question ‘what does AI mean’
- Capsule Network: A nested set of neural layers is a capsule. Thus, 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.
- Chatbots (chat robots or chatbot): They simulate a conversation with human users by communicating through text chats, voice commands, or both. The most commonly used interface for computer programs that include AI capabilities is a chatbot.
- Classification: Classification algorithms let machines assign a category to a data point based on training data.
- Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; Similarity measures model clusters defined by metrics, e.g., Euclidean or probabilistic distance.
- Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.
- 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.
- 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.
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Most used AI terms and their meanings
- 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).
- 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.
- 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.
- Confidence Interval (new): An interval about a point estimate that quantifies the statistical uncertainty in the real value being estimated due to variability.
- 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.
- Convolutional neural network (CNN): A type of neural networks that identifies and makes sense of images.
Updated examples tackle ‘what does AI mean?’
What does AI mean in small businesses? Big data is today’s most valuable currency. Do you know what the following AI terms mean?
It 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. It encompasses diverse techniques under a variety of names, in different business, science, and social science domains.
The examination of data sets to discover and mine patterns from that data that can be of further use.
Data science is an interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science. The primary aim is to provide insight into phenomenon via either structured or unstructured data.
A tree and branch-based model used to map decisions and their possible consequences, similar to a flowchart.
The ability of machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.
- 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.
- 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.
What does AI mean in other applications?
- False Negative (New): A test result that does not detect the condition when the condition is present.
- False Positive (New): A test result that detects the condition when the condition is absent.
- 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 few-shot (and even one-shot) learning is to create a system that dramatically reduces the amount of training needed to learn.
- 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.
- 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.
I highly recommend you have a look at this post to better understand the dark side of AI.
What does AI mean for startups? Everything you need to know about 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.
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.
This networks comprise a system of two competing neural network models (generative models that use supervised learning). GANs compete with each other and can analyze, capture, and copy the variations within a dataset.
The evolutionary algorithm based on principles of genetics and natural selection is a genetic algorithm. It finds optimal or near-optimal solutions to severe problems that would otherwise take decades to solve.
What does AI mean and how does it work?
- Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating incorrect options.
- 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.
What does the full form of artificial intelligence (AI) mean?
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.
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.
Thus 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.
Artificial superintelligence (Intelligence Explosion)
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.
Other terminologies that give answers on what does AI mean
- Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.
- Limited memory (New): systems with short-term memory limited to a given timeframe.
- 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.
Watch Neil Sahota Ted Talk on Transforming Today with Cognitive Computing
Neil is an IBM Master Inventor, United Nations AI subject-matter expert, and professor at UC Irvine. He co-authored the book “Own the A.I. Revolution: Unlock Your AI Strategy to Disrupt Your Competition”
Machines’ controlled learning
Is it increasingly becoming a challenge to understand all the AI terms? What does AI mean in adoption of machine learning?
is an umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.
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 AI. 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.
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 attached hardware, though the software is also usable.
Machine translation (MT) is an automated translation. It is a process by which computer software translates a text from one natural language (like English) to another (such as Spanish).
Extra AI Glossary
Let’s attempt to get the best definitions of what does AI mean in the market out there.
- Narrow Intelligences: The programmed to perform a single task is Narrow AI. It could be checking the weather, being able to play chess, or analyzing raw data to write journalistic reports.
- Natural language processing (NLP) recognizes human communication as it is understood.
- Neuromorphic Chip: A computer chip designed to act as a neural network. It can be analog, digital, or a combination.
- 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.
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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.
An early 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.
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.
Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.
What does AI mean and related terms in tech world?
- 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.
- Recommendation Algorithms (New): Algorithms that help machines suggest a choice based on their commonality with historical data.
- 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.
- 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.
“If intelligence was 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.
In 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.
To tell how far away the agent is from achieving the end goal, its put through a few iterations, once. From the start, the input and output are already well defined.
The branch of technology that deals with the design, construction, operation, and application of robots. Today, most robots are do repetitive actions or jobs considered too dangerous for humans. A great example is a robot going into a building that has a possible bomb.
Robots are also used in factories. They build things like cars, candy bars, and electronics.
Robotic process automation (RPA)
It uses software with AI and ML capabilities to perform repetitive tasks once completed by humans.
Ways of human intelligence simulation answer what does AI mean?
- 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.
- 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.
- Strong AI: AI that 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.
- Superintelligence (New): A superintelligence is a hypothetical agent that possesses intelligence far surpassing a level of general intelligence that massively exceeds our own.
- 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.
- Swarm behavior is an emergent behavior arising from simple rules followed by individuals. Swarm behavior does not involve any central coordination. Its a mathematical modeler.
- 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.
- 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.
- True Negative (New): A test result that does not detect the condition when the condition is absent.
- True Positive (New): A test result that detects the condition when the condition is present.
- Turing Test: A test of AI’s ability to pass as human. For example, the ability to converse through written text (Alan Turing’s original conception).
What does AI mean? – Explanation in simple terms
- Unfriendly Artificial knowledge: artificial general intelligence capable of causing great harm to humanity, and having goals that make it useful for the AI to do so.
- 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.
So, what does AI mean? If you know other vital AI terms to include in this list, please share in the comments section below.
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