Artificial Intelligence and Machine Learning Overview

These flashcards cover key concepts about artificial intelligence and machine learning introduced in the lecture.

Artificial Intelligence (AI)

The ability of computers to perform tasks that typically require human intelligence such as learning, reasoning, and decision-making.

Machine Learning (ML)

A type of AI where systems learn from data without being explicitly programmed, improving performance based on experience.

Difference between AI and ML

AI is a broader concept of machines performing intelligent tasks, while ML is a subset of AI focusing specifically on learning from data.

Structured Data

Data organized in a clear format like rows and columns, making it easy to analyze with tools like pandas.

Unstructured Data

Data that does not follow a fixed format, including text, images, and videos, such as social media posts.

Neural Network

A system modeled after the human brain that processes data in layers to recognize patterns and make predictions.

Deep Learning

A type of machine learning that utilizes complex neural networks to process large amounts of data and learn patterns.

Natural Language Processing (NLP)

A field allowing computers to understand human language, used for tasks like translation and chatbots.

Image Recognition

The ability of computers to identify objects in images using machine learning and neural networks.

Features in Machine Learning

Input variables used to train a model that help the system learn patterns in data.

Training Data

The dataset used to teach a machine learning model, helping it learn patterns and relationships.

Model

A system trained to make predictions or decisions based on patterns learned from data.

Prediction in Machine Learning

The output generated by a trained model based on patterns learned from the data.

Limitations of AI

AI's constraints, including lack of understanding and reliance on the quality of data, which can lead to errors or bias.

Bias in AI

Occurs when a model produces unfair or inaccurate results, often stemming from biased training data.

Ethical Concerns of AI

Issues related to AI such as privacy violations, bias, and job displacement affecting society.

Real-world Uses of AI

Applications of AI in different industries like healthcare and finance, improving task automation and decision-making.

Automation

Using technology to perform tasks without human intervention, enhancing efficiency and reducing manual work.

Impact of AI on Jobs

AI can replace repetitive tasks while creating new job opportunities, changing the nature of work.

Importance of AI Today

AI's significance stems from its ability to enhance efficiency and decision-making, solving complex problems across industries.