AI Bible
As we navigate this novel frontier, we aim to equip you with the tools to decode the intricate realm of artificial intelligence: introducing the AI Bible.
Genesis
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems.
Bias in AI
Pre-existing, often unconscious beliefs that can influence the decision-making process in AI systems.
Deep Learning
A type of machine learning that uses neural networks with many layers (hence “deep”) to analyze various factors with a structure similar to the human brain.
Machine Learning (ML)
A subset of AI that enables computers to learn from and make decisions based on data.
Natural Language Processing (NLP)
A branch of AI that focuses on the interaction between computers and humans through language.
Neural Network
A computational model inspired by the human brain’s neural structure, used in machine learning and deep learning.
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Human Interaction
Algorithm
A set of rules or steps for a computer to follow in calculations or problem-solving.
Chatbot
A software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.
Computer Vision
An AI field that trains computers to interpret and understand the visual world.
Data Mining
The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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AI Techniques
Big Data
Extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
Fuzzy Logic
A form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, often used in AI to handle uncertain or vague data.
Generative Adversarial Network (GAN)
A class of machine learning systems where two neural networks compete with each other to become more accurate in their predictions.
Predictive Analytics
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Reinforcement Learning
A type of machine learning where an agent learns to behave in an environment by performing actions and seeing the results.
Semi-Supervised Learning
A type of Machine Learning that uses a combination of a small amount of labeled data and a large amount of unlabeled data for training.
Supervised Learning
A type of Machine Learning where the model is provided with labeled training data.
Unsupervised Learning
A type of Machine Learning where the model learns from data without any labels.
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Applications
Internet of Things (IoT)
The network of physical objects (“things”) that are embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet.
Knowledge Graph
A knowledge base used by Google to enhance its search engine’s search results with semantic-search information gathered from a wide variety of sources.
Robotics
The field of AI and engineering focused on creating machines that can move and react to sensory input.
Sentiment Analysis
The use of natural language processing to identify, extract, quantify, and study affective states and subjective information.
Speech Recognition
The ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format.
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AI Representation
Autonomous Vehicles
Vehicles capable of sensing their environment and operating without human involvement.
Turing Test
A measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.