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Artificial Intelligence Terms

as Defined by ChatGPT

Artificial Intelligence (AI) has revolutionized various industries and is transforming the way we live and work. With its rapid advancements and growing impact, it is helpful to familiarize oneself with key AI concepts and terminologies.

Table of Contents

Adversarial Attacks:

Techniques where malicious actors intentionally manipulate input data to mislead or deceive AI systems, exploiting vulnerabilities and causing incorrect predictions or decisions.

Algorithm:

A step-by-step set of instructions or rules followed by a computer program to solve a specific problem or perform a particular task.

Artificial General Intelligence (AGI)

The hypothetical concept of AI systems that possess the same level of general intelligence and cognitive capabilities as humans, able to understand, learn, and perform any intellectual task that a human can do.

Artificial Intelligence (AI)

The field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence.

AutoML (Automated Machine Learning):

The use of AI algorithms and tools to automate the process of building, optimizing, and deploying machine learning models, reducing the need for manual intervention.

Bayesian Networks:

Probabilistic graphical models that represent relationships between variables using directed acyclic graphs, enabling reasoning under uncertainty and facilitating decision-making.

Bias:

In AI, bias refers to systematic errors or prejudices in data, algorithms, or decision-making processes that can lead to unfair or discriminatory outcomes.

Big Data:

Extremely large and complex datasets that cannot be effectively managed, processed, or analyzed using traditional data processing techniques.

Chatbot:

A computer program or AI application designed to simulate human conversation, typically used for customer service, information retrieval, or personal assistance.

Cognitive Computing:

A field of AI that aims to simulate human thought processes and cognition, enabling machines to perceive, understand, reason, and learn from complex and unstructured data.

Computer Vision:

The field of AI that focuses on enabling computers to extract information and understand visual data from images or videos.

Convolutional Neural Network (CNN):

A type of neural network commonly used in computer vision tasks, designed to automatically and efficiently learn visual hierarchies from input data.

Data Augmentation:

The technique of artificially increasing the size and diversity of a training dataset by applying transformations or modifications to the existing data, improving the generalization and robustness of AI models.

Data Mining:

The process of extracting patterns or knowledge from large datasets using various AI and statistical techniques.

Deep Learning:

A subfield of machine learning that uses artificial neural networks with multiple layers to learn and extract patterns from large amounts of data.

Edge AI:

The deployment of AI algorithms and models on edge devices, such as smartphones, IoT devices, or embedded systems, enabling real-time processing and analysis without relying on cloud infrastructure.

Edge Computing:

A distributed computing paradigm where computation and data storage are performed closer to the source of data generation, reducing latency and bandwidth requirements.

Edge-to-Cloud AI:

A hybrid approach that combines edge computing and cloud computing, where AI tasks are distributed between edge devices and cloud servers based on computational requirements, data privacy, and network conditions.

Ethical AI:

The study and practice of ensuring that AI systems are designed, developed, and used in a manner that aligns with ethical principles, respects human rights, and minimizes biases and risks.

Explainable AI (XAI):

The field of AI concerned with developing models and techniques that can explain the reasoning and decision-making processes of AI systems in human-understandable ways.

Feature Extraction:

The process of transforming raw data into a format suitable for machine learning algorithms by selecting or creating relevant features that capture the important information.

Generative Adversarial Networks (GANs):

A class of machine learning models consisting of two neural networks—the generator and the discriminator—competing against each other to create realistic outputs, such as images or text.

Generative Ai

Generative AI refers to AI systems that generate original content resembling a given dataset. It uses training data to learn patterns and structures and creates novel outputs. It employs techniques like GANs and VAEs and has applications in art, design, entertainment, and research. It fosters creativity by producing unique content, making it valuable for various fields.

Hyperparameters:

Parameters in machine learning algorithms that are set prior to training and affect the model’s learning process, such as learning rate, number of hidden layers, or batch size.

Internet of Things (IoT):

The network of interconnected physical devices embedded with sensors, software, and other technologies that enable them to collect and exchange data.

Large Language Model (LLM):

Advanced AI system trained on vast text data to generate human-like responses and understand natural language. Used in various applications like chatbots and content generation.

Machine Learning (ML):

A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.

Natural Language Processing (NLP):

The branch of AI that enables computers to understand, interpret, and generate human language, facilitating interactions between humans and machines.

Neural Network:

A computational model inspired by the structure and functioning of the human brain, composed of interconnected artificial neurons that process and transmit information.

Overfitting:

A situation in machine learning where a model becomes too specialized in the training data and performs poorly on new, unseen data.

Quantum Computing:

A computing paradigm that leverages the principles of quantum mechanics to perform complex computations, offering potential advantages for certain AI tasks, such as optimization or machine learning.

Recurrent Neural Network (RNN):

A type of neural network suitable for sequential data analysis, where the output depends on both the current input and the previous computations, enabling it to model temporal dependencies.

Reinforcement Learning:

A type of machine learning where an agent learns to make decisions or take actions in an environment, maximizing rewards or minimizing penalties through trial and error.

Robotics:

The interdisciplinary field involving the design, development, and use of robots to perform tasks autonomously or with human interaction, often incorporating AI techniques.

Supervised Learning:

A machine learning technique where algorithms are trained on labeled data, with input-output pairs, to make predictions or classify new, unseen data.

Swarm Intelligence:

An AI approach inspired by the collective behavior of social insects, where multiple agents or robots work together to solve complex problems or accomplish tasks.

Synthetic Data:

Artificially generated data that mimics the statistical properties and characteristics of real-world data, used to supplement or replace actual data for training and testing AI models.

Transfer Learning:

A technique in machine learning where knowledge gained from solving one problem is applied to a different but related problem, enabling faster learning and improved performance.

Underfitting:

The opposite of overfitting, where a machine learning model fails to capture the underlying patterns and complexity of the data, leading to poor performance.

Unsupervised Learning:

A machine learning technique where algorithms learn patterns and structures from unlabeled data, without specific input-output pairs.

XAI (Explainable AI):

The field of AI concerned with developing models and techniques that can explain the reasoning and decision-making processes of AI systems in human-understandable ways.

Zero-Shot Learning:

Zero-Shot Learning: A machine learning approach where a model can generalize and make predictions on classes or tasks it has never seen before, without explicit training on those classes or tasks.