Glossary of terms and acronyms for #Blockchain and #Cryptocurrency




Blockchain: The foundational technology behind the blockchain and cryptocurrency sector. It is a virtual, immutable (unchangeable), distributed store of data stored on servers around the world. This is a new way of distributing both trust and data. It is an alternative to traditional systems where a central organization holds all the data.
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Glossary of Terms for Healthcare Data Analytics



BALANCED SCORECARD:
A framework developed by Robert Kaplan and David Norton that suggests four perspectives of performance measurement to provide a comprehensive view of an organisation. These are service user perspective, internal management perspective, continuous improvement perspective and financial perspective.

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Glossary of Healthcare & HealthIT Terms and Acronyms



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Glossary of Terms & Acronyms for Artificial Intelligence and Machine Learning


AI & Machine Learning Terms



Artificial intelligence
The development of computers capable of tasks that typically require human intelligence. A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior.

Machine learning
Using example data or experience to refine how computers make predictions or perform a task. A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data. 

Deep learning
A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain. The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.

Supervised learning
Showing software labeled example data, such as photographs, to teach a computer what to do. 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.

Unsupervised learning
Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet. 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.

Reinforcement learning
Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.

Artificial general intelligence
As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.

Large-scale Machine Learning Design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.

Deep Learning Model composed of inputs such as image or audio and several hidden layers of sub-models that serve as input for the next layer and ultimately an output or activation function.

Natural Language Processing (NLP) Algorithms that process human language input and convert it into understandable representations. The ability for a program to recognize human communication as it is meant to be understood. 

Collaborative Systems Models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.

Computer Vision (Image Analytics) The process of pulling relevant information from an image or sets of images for advanced classification and analysis.

Algorithmic Game Theory and Computational Social Choice Systems that address the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.

Soft Robotics (Robotic Process Automation - RPA) Automation of repetitive tasks and common processes such as IT, customer servicing and sales without the need to transform existing IT system maps.

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.

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 solve.

Autonomic computing: A system's capacity for adaptive self-management of its own resources for high-level computing functions without user input.

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.

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; clusters are modeled with a measure of similarity defined by metrics such as 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.

Convolutional neural network (CNN): A type of neural networks that identifies and makes sense of images.

Data mining: The examination of data sets to discover and mine patterns from that data that can be of further use.

Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data.

Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.

Fluent: A type of condition that can change over time.

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.

Genetic algorithm: An evolutionary algorithm based on principles of genetics and natural selection that is used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.

Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating options that are incorrect.

Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.

Logic programming: A type of programming paradigm in which computation is carried out based on the knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.

Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.

Machine perception: The ability for 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 software is also usable.

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.

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.


Terminology
Definition
Automated communications
Also known as an interactive agent, or artificial conversational entity, these are computer programs which conduct a conversation via auditory or textual methods. For example, chatbots, mailbots.
Automated data analyst
AI solutions aimed at performing the job of data analysts and data scientists and bridging the gap between such roles and business imperatives. For example, these might include programs that are able to develop a deep understanding of customer preferences from data, identify high-risk customer groups and tailor interaction touch points in a manner personalised to such customers.
Automated operational and efficiency analyst
AI solutions targeted at increasing operational efficiency and reducing costs. These include AI programs and bots aimed at automating repetitive manual tasks such as identifying and correcting data and formatting mistakes, performing back office tasks and automating repetitive interactions with customers.
Automated research and information aggregation
Applications of AI that involve aggregating and processing large volumes of information on a topic so as to generate meaningful insights. For example, aggregating information from research papers or medical journals for diagnosis support, identifying online hoax, bad reporting and statistics, and identifying plagiarised publications.
Automated sales analyst
AI-powered digital analysts for sales and marketing decisions. These programs are able to test a range of scenarios using internal and external data to predict the impact of marketing strategies such as promotions and campaigns, simulate ‘what if’ scenarios against multiple hypotheses and perform root cause analyses against business results.
Business decision makers/influencers
A sub-set of participants in the survey who have identified themselves to be either in a decision making role or an influencing role in their current organisations. Some of the survey questions had been specifically targeted towards this group.
Decision support systems
Decision support systems (DSS) are a specific class of computerised information systems that support business and organisational decision-making activities.
Machine learning
Machine learning is concerned with computer programs that automatically improve their performance through experience.
Predictive analytics
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behaviour patterns–for example, sales forecasts, predicting customer churn and industrial
machine failure.

Robotics
Robotics deals with the design, construction, operation and use of robots, as well as computer systems for their control, sensory feedback and information processing. Environmental information such as imagery and sound are captured using a group of sensors and the same are processed using various computerised techniques for the robot to respond.
Virtual personal assistants
Virtual assistants use natural language processing (NLP) to match user text or voice input to executable commands. Many continually learn using AI techniques, including machine learning. For example, Apple’s Siri, Amazon’s Alexa, Google Now.
AI advisors
AI advisors are machines or systems that monitor employees’ progress and performance. They are responsible for the growth of the employee in the organisation and for the delivery of projects.
AI assistants
AI assistants are machines or systems or application programming interfaces ([APIs] a set of subroutine definitions, protocols and tools for building application software) that perform non-value adding services such as scheduling and email management.
Source: 

References


[1]: pwc AI: https://www.pwc.com/ai

[2]: AI: The Complete Guide: 
https://www.wired.com/story/guide-artificial-intelligence/
[3]: https://dzone.com/articles/ai-glossary








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