🧠 AI Flashcard Study Tool

Pixels. A computer sees an image as an array holding number values for color and intensity.

Ensuring the fair, responsible, and transparent use of AI technologies, particularly around issues like bias, privacy, and accountability.

Lots of data and lots of computing power.

The process of analyzing large datasets to find useful patterns, trends, or relationships that can be used for decision-making.

Data Quality and Bias, Ambiguity in User Input, Limitations in Training, Overgeneralization, Misalignment with User Intent, and Limitations in Reasoning.

A type of machine learning where the model is trained on labeled data, meaning the input-output pairs are provided during training.

A branch of AI focused on enabling computers to understand, interpret, and generate human language.

CPU: General-purpose, few powerful cores for sequential tasks. GPU: High parallelism (ability to perform thousands of computations simultaneously), executed by thousands of cores for tasks like AI training. TPU: Specialized hardware by Google *specifically* for AI workloads (matrix operations).

The development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation.

An AI-driven program designed to simulate conversation with human users, typically through text or voice.

An advanced machine learning technique in which individual pixels in an image are classified according to the object to which they belong.

At Dartmouth College in 1956.

Traditional AI is often rule-based or used for prediction (like spam filtering), while Generative AI learns from data to create new, original content.

A technique used in computer vision for extracting text from an image.

Input layer (receives data), Hidden layer(s) (processes data), and Output layer (produces the final result).

High electricity consumption for training and queries, expansion of energy-intensive data centers, and vast water usage for cooling.

Algorithmic trading, fraud detection, risk management, credit scoring, and personalized financial services.

Reasoning, Learning, Problem Solving, Perception, and Linguistic Intelligence.

John McCarthy. He defined AI as 'The science and engineering of making intelligent machines, especially intelligent computer programs'.

A subset of AI focused on creating new, original content (e.g., images, text, music) based on learned patterns from large volumes of data.

Language, Speech, Web Search, Vision, and Decision.

Algorithms modeled after the human brain, consisting of layers of interconnected nodes (neurons) that process data to identify patterns and make decisions.

Anything that can perceive its environment through sensors and act upon that environment through effectors.

Intent classification (understanding what a user is trying to accomplish) and entity extraction (recognizing key information like time, place, or name).

A subset of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed.

Sensing and Perception, Decision-Making and Control, Mapping and Localization, and Safety and Redundancy.

Powerful software systems designed to understand, generate, and manipulate human language, text, and other media using deep learning on a massive scale.

The Input Layer receives data; weights are assigned to inputs; inputs are multiplied by weights and summed; an Activation Function determines if the node 'fires'; and data passes to the next layer.

The process of extracting information from large volumes of often unstructured data to create a searchable knowledge store.

GPU (Graphics Processing Unit).

Disease diagnosis, personalized treatment plans, drug discovery, and robotic surgery.

AI is the broad concept of enabling computers to mimic human behavior, while ML is a specific subset of AI that allows machines to learn from data.

A set of technologies (like chatbots and speech-based assistants) that enable computers to simulate conversations and automate communication.

To create systems that can learn, reason, perceive, plan, and solve problems.

A set of pre-defined rules and instructions, designed to solve a specific problem, in both a correct and efficient manner.

An AI field that enables machines to interpret and process visual data from the world, such as still images and videos, and extract information from them.

A branch of NLP that converts the natural language spoken by humans into structured data.