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Below are several key terms that are important for developing a comprehensive understanding of Artificial Intelligence, as well as the broad spectrum of tools and functions that exist within this field.

Artificial Intelligence (AI)

The capacity of machines to mimic human cognitive functions such as learning, problem-solving, and pattern recognition, enabling them to perform tasks that normally require human intelligence. It includes various subfields, such as machine learning and natural language processing.

Example: Virtual assistants like Siri or Alexa use AI to understand and respond to voice commands.

Algorithm

A step-by-step procedure or a set of rules followed by a computer to solve a problem or accomplish a task. In the context of AI, algorithms often refer to the methods used to train a machine learning model.

Example: Google Maps uses an algorithm to calculate the fastest route from one location to another based on factors such as traffic and road conditions.

Bias in AI

Inherent or learned biases in AI systems that may lead to unfair outcomes or decisions. Such biases often reflect existing societal biases present in the data used to train the AI.

Example: A resume screening AI might unfairly favor certain demographic groups if it was trained on a dataset containing biases.

Chatbot

A software application that communicates with users through text or voice interactions, simulating human conversation to a certain degree.

Example: Customer service chatbots can resolve common queries, provide information, and direct users to relevant resources.

Data Science

An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines aspects of mathematics, statistics, computer science, and domain expertise.

Example: Companies use data science to analyze customer behavior and optimize their marketing strategies.

Natural Language Processing (NLP)

A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.

Example: Google Translate uses NLP to translate text from one language to another.

Large Language Model (LLM)

An AI model that can understand, generate, and interpret human-like text based on the input it receives.

Example: OpenAI's GPT-4 can write essays, summarize texts, and answer complex questions.

Machine Learning (ML)

A subfield of AI where computer systems are given the ability to learn and improve from experience without being explicitly programmed. This is usually achieved by training the system with large amounts of data.

Example: Recommendation systems, like those used by Amazon, suggest products based on a user's browsing and purchasing history.

The following terms, while not fundamental for an initial understanding of Artificial Intelligence, can provide additional insights for those looking to understand specialized aspects and applications of AI.

Adaptive Learning Platforms

Systems that leverage AI to tailor educational content according to the specific needs of each learner, improving the efficiency of the learning process. 

Example: DreamBox Learning provides an adaptive online math program that adjusts to each student's skills and learning speed.

AI-based Assessment Tools

Tools that utilize AI to evaluate a learner's performance, often providing real-time feedback, which enables timely and personalized adjustments to the learning process.

Example: Grammarly uses AI to provide real-time feedback and suggestions to improve writing skills.

Intelligent Tutoring Systems (ITS)

AI systems built to offer customized instruction and feedback to learners, often operating independently of human supervision.

Example: Carnegie Learning’s MATHia platform acts as a personal math tutor, providing tailored instruction and immediate feedback to students.

AI Image Generation

AI systems capable of generating images. Techniques used may include Generative Adversarial Networks (GANs), which can produce realistic images from random input data.

Example: DeepArt uses AI to transform photos into works of art in the style of famous painters.

Speech Recognition

AI technologies that transcribe spoken language into written text, enabling human-computer interactions through voice.

Example: Google's Voice Typing tool converts spoken words into written text in real time.

Computer Vision

The field within AI that enables computers to interpret and understand visual data from the real world, mimicking human vision but often with greater accuracy or speed.

Example: Facebook uses computer vision to automatically identify and tag people in photos.

Recommendation Systems

AI systems designed to predict a user's preferences and suggest items or content accordingly, commonly seen in online shopping and streaming platforms.

Example: Spotify uses a recommendation system to suggest songs and playlists based on a user's listening habits.

Predictive Analytics

The use of AI to analyze current and historical data to make predictions about future events, often used in forecasting and decision-making processes.

Example: Credit scoring companies use predictive analytics to determine a person's credit risk based on their past financial behavior.

AI in Autonomous Vehicles

AI technologies, such as computer vision, sensor data processing, and control systems, that enable the operation of self-driving vehicles.

Example: Waymo, Google's self-driving car project, uses AI to interpret sensor data, identify objects, and make driving decisions.

Emotion AI or Affective Computing

AI that is capable of recognizing, interpreting, processing, and even simulating human emotions. This can include sentiment analysis in text or emotion detection in faces.

Example: Affectiva's Emotion AI is used in market research to improve customer experiences by detecting and analyzing emotional reactions.

Swarm Intelligence

AI algorithms inspired by the collective behavior of decentralized, self-organized systems, typically seen in nature, such as ant colonies or bird flocking.

Example: The PowerSwarm platform uses swarm intelligence for drone fleet management, allowing drones to work together to complete tasks more efficiently.

Supervised Learning

A type of machine learning where the model is trained on a labeled dataset, i.e., a dataset where the "correct" answers are provided.

Example: Spam filters use supervised learning to classify emails as spam or not-spam based on past examples.

Unsupervised Learning

A type of machine learning where the model identifies patterns and relationships in a dataset without any labels or predefined notions of what to look for.

Example: Clustering algorithms group customers with similar purchase patterns without being explicitly told what the groups are.

Decision Tree

A flowchart-like model in AI that helps in decision-making processes, where each node represents a choice, and each branch represents a possible outcome of that choice.

Example: Financial institutions use decision trees to assess the risk of granting loans based on factors like employment status, credit history, and income.

Game AI

A form of AI designed to create responsive, adaptive, or intelligent behaviors primarily in non-player characters within video games.

Example: In the game Fortnite, AI-powered characters can engage in combat and interact with players.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.

Example: AlphaGo, developed by DeepMind, learned to play the game Go by reinforcement learning, beating world champions.

Transfer Learning

A machine learning method where a pre-trained model is used on a new, related problem. It allows the model to apply knowledge learned from one task to a different but related task.

Example: A neural network trained to recognize dogs can be adapted to recognize cats with minimal additional training.

Overfitting

Occurs when a model is trained too well on the training data and becomes too complex, performing poorly when presented with new, unseen data. Essentially, the model memorizes the training data rather than learning to generalize from it.

Example: A model that perfectly predicts stock market prices based on past data might fail when applied to future data.

Deep Learning (DL)

A subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

Example: Voice recognition systems, like Apple's Siri, employ deep learning to understand spoken language.

Artificial Neural Network (ANN)

A computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence and solves complex problems.

Example: Optical Character Recognition (OCR) uses ANN to recognize handwritten or printed text in digital images.

Convolutional Neural Network (CNN)

A type of deep learning model particularly effective for image processing, object detection, and other tasks involving spatial data.

Example: CNNs are used in facial recognition systems to identify individuals.

Generative Adversarial Networks (GANs)

A class of AI algorithms used in unsupervised machine learning. Two neural networks contest with each other in a zero-sum game framework.

Example: GANs can be used to create realistic artificial images that resemble a set of training photos.

Explainable AI (XAI)

An area of AI focused on creating transparent models that provide clear and understandable explanations of their decisions.

Example: A healthcare AI diagnosing a disease can provide an explanation of its decision based on symptoms and patient history.

The following sources were referenced and used in generating and compiling this resource: 

Misha. (2023, April 23). ChatGPT, Bing Chat, Google's Bard—AI is infiltrating the lives of billions. The 1% who understand it will run the world. Here's a list of key terms to jumpstart your learning. []. 

OpenAI. (2021). ChatGPT, version 4. Retrieved May 30, 2023, from