2414 SW ANDOVER ST, STE D-101, SEATTLE, WA 98106 
 info@mxtreality.com | Tel: 1-844-697-2333
         The Courtyard, 4 Evelyn Road, London, W4 5JL
infoUK@mxtreality.com | Tel: +44 203-633-5450 
© 2019 by MXTreality, a  MYPAD3D company
  • White Facebook Icon
  • White YouTube Icon
  • White Twitter Icon

ARTIFICIAL INTELLIGENCE

(ai)

Agent - A physical or virtual entity that can act, perceive its environment (in a partial way), and communicate with others, is autonomous, and has skills to achieve its goals and tendencies

Artificial General Intelligence (AGI) - General purpose systems with intelligence comparable to that of the human mind

Artificial Intelligence (AI)- The capacity of computers or other machines to exhibit or simulate intelligent behavior

Algorithm – A systematic and precise, step-by-step procedure (such as a recipe, a program, or a set of programs) for solving certain kinds of problems or accomplishing a task

Automation - The conversion of a work process, a procedure, or equipment to automatic rather than human operation or control

Black Box: A device, system or object which can be viewed in terms of its inputs and outputs, without any knowledge of its internal workings

Big Data - Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations

Computer Vision - An AI labeling technique whereby computers process training images and videos in order to classify components from new imagery.

Data Mining - The practice of examining large data sets in order to generate new information

Data Science - A multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data Deep Learning - A class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input


Deep Learning: A subset of Machine Learning that typically uses unstructured data to self solve complex problems.

Deepfake: Manipulated photos and videos of people doing or saying something they never did, sometimes for

nefarious reasons, using deep learning

Explainable A.I.: A system that can detail factors that went into a prediction in a way that humans can interpret

Genetic Algorithm: An optimization algorithm based on principles of genetics and natural selection

Image Processing - The analysis and manipulation of a digitized image, especially in order to improve its quality

Image Classification – Systems that sort images into predefined categories

Inference - Predicting an outcome based on a mathematical model given input conditions

Machine Learning - A subset of artificial intelligence that gives computer systems the ability to perform tasks without using explicit instructions.  Input data is usually structured.

Model – A mathematical or schematic description that describes the behavior of a system

 

Natural Language Processing - A branch of artificial intelligence that deals with the interaction between computers and humans using human language syntax and semantics, rather than structured computer language

 

Neural Network – A supervised learning model inspired by the human brain made up units (neurons) connected in multiple layers

 

Predictive Analytics - A variety of statistical techniques that analyze data to make predictions about future events.

Reinforcement Learning - The subfield of machine learning that learns from a series of reinforcements -- rewards or punishments

 

Supervised Learning - The subfield of machine learning that learns a function that maps an input to an output based on example input-output pairs

 

Training – The process by which supervised learning algorithms take input and tune a model to reflect the given data

 

Transfer Learning - A machine learning technique that uses pre-trained models from one task to solve a different but related problem

 

Turing Test: A test for AI to determine if it can fool a person into believing they’re seeing or interacting with a real person.

Unsupervised Learning - The subfield of machine learning that learns structure in the input without explicit feedback (like labels)