Introduction: AI for Everyone Practice Flashcards

These vocabulary flashcards cover the fundamental definitions, historical milestones, types, domains, and career roles of Artificial Intelligence as presented in the lecture notes.

Artificial Intelligence (AI)

A technology and field of computer science that combines robust datasets to enable problem-solving by allowing machines to learn patterns and make predictions.

Alan Turing

A mathematician whose 1950 paper "Computing Machinery and Intelligence" proposed the "imitation game," later known as the Turing test.

John McCarthy

The computer scientist who organized the Dartmouth Conference in 1956 and coined the term "Artificial Intelligence."

AI Winter

A period between 1980 and 1990 characterized by mixed optimism and skepticism toward breakthroughs in machine learning and neural networks.

Narrow AI

A type of AI that focuses on single tasks, such as predicting purchases, planning schedules, or acting as virtual assistants.

Broad AI

A midpoint between Narrow and General AI that is more versatile and capable of handling a wider range of related tasks, often used in business processes.

General AI

A level of AI capable of performing any intellectual task a human can, including abstract thinking, strategizing, and creativity.

Artificial Superintelligence (ASI)

A potential future evolution of AI that may lead to self-aware machines capable of surpassing human intelligence.

Structured Data

Organized data arranged in rows and columns, such as names, dates, addresses, and stock prices, making it easy to analyze.

Unstructured Data

Data that lacks specific organization, such as images, text documents, customer comments, and song lyrics, requiring specialized tools to analyze.

Semi-structured Data

A data format that uses metadata to identify characteristics and organize data into fields, such as a social media video with hashtags.

Natural Language Processing (NLP)

A broad domain of AI focusing on how computers interact with human language through understanding, interpretation, and generation.

Natural Language Understanding (NLU)

A subfield of NLP focused on extracting meaning, intent, and sentiment from human language to help computers understand it.

Natural Language Generation (NLG)

A subfield of NLP that takes structured data as input and transforms it into coherent and readable human text or speech.

Computer Vision

A domain of AI that enables computers to interpret and understand visual information from images and videos, performing tasks like object detection.

Pixel

A tiny colored dot within a digital image grid that contains information about color and intensity.

Resolution

The total number of pixels along the width and height of an image, such as $1920 \times 1080$.

Machine Learning (ML)

A subset of AI that develops algorithms and models enabling computers to learn from data and make decisions without explicit programming.

Deep Learning (DL)

An AI function inspired by the human brain's neural structure that uses multiple levels of calculations to process data and create patterns for decision-making.

Neural Networks (ANNs)

A subset of Machine Learning comprising input, hidden, and output node layers that activate when outputs exceed a specified threshold.

Deep Neural Network

A neural network that contains more than three total layers, including the input and output layers.

Supervised Learning

A machine learning type where the model learns from labeled data, mapping input examples to the correct output labels.

Unsupervised Learning

A machine learning type where the model learns from unlabeled data to find hidden patterns, clusters, or structures without explicit guidance.

Reinforcement Learning

A machine learning type where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards through trial and error.

Feature Extraction

A process built into deep learning that allows models to learn representations of raw data on their own without human input defining characteristics.

Machine Learning Engineer

A professional who bridges software engineering and data science, utilizing programming frameworks like Java or Python to develop scalable data models.

Data Scientist

A professional who uses predictive analytics and machine learning to extract insights from large datasets for business decision-making.

Robotics Engineer

A professional who designs and maintains AI-powered robots and mechanical devices that perform tasks based on human commands.

AI Ethicist

A specialist who addresses bias, potential misuse, and regulatory frameworks to ensure AI technologies are used responsibly and transparently.

TensorFlow

An open-source machine learning platform providing tools and libraries for developing sophisticated AI applications.

SciPy and NumPy

Python libraries used for scientific computing, mathematical operations, and manipulating or visualizing data.