Introduction to Artificial Intelligence and Generative AI

This flashcard set covers the fundamentals of Artificial Intelligence, including Machine Learning, Deep Learning, Generative AI phases, AI agents, ethics, and specific use cases.

What is Artificial Intelligence (AI)?

Technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity, and autonomy.

What is Generative AI (Gen AI)?

A technology that can create text, images, video, and other content.

What are the two core technologies that Gen AI tools are built upon?

Machine learning (ML) and deep learning.

Within the context of machine learning, what is 'inference'?

A conclusion reached on the basis of evidence and reasoning.

What are some examples of machine learning techniques or algorithms listed in the notes?

Linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), K-nearest neighbor (KNN), and clustering.

How are neural networks structured and what are they modeled after?

They are modeled after the human brain's structure and function, consisting of interconnected layers of nodes (analogous to neurons) that process and analyze complex data.

What is Supervised learning?

The use of labeled data sets to train algorithms to classify data or predict outcomes accurately.

What is labeled data?

Raw info—such as images, text, or audio—that has been tagged with meaningful tags or answers.

How is Deep Learning defined?

A subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

How does the structure of deep learning models differ from classic machine learning models?

Deep learning models include an input layer, an output layer, and at least 3 (but usually hundreds) of hidden layers, whereas classic models usually have 1 or 2 hidden layers.

What is Semi-supervised learning?

A method that combines supervised and unsupervised learning by using both labeled and unlabeled data to train models.

What is reinforcement learning?

A learning method that learns by trial and error and reward functions rather than extracting information from hidden patterns.

What is transfer learning?

Knowledge gained through one task or data set is used to improve model performance on another related task or different data set.

What are Variational auto encoders (VAEs)?

Models that enable the generation of multiple variations of content in response to a prompt or instruction.

What are Transformer models?

Models trained on sequence data to generate extended sequences of content such as words in sentences, shapes in an image, or frames in a video.

What are the three general phases in which Generative AI operates?

1. Training (to create a foundation model), 2. Tuning (to adapt the model), 3. Generation, evaluation, and more tuning (to improve accuracy).

What is Reinforcement Learning with Human Feedback (RLHF)?

A process where human users evaluate the accuracy or relevance of model outputs so that the model can improve itself.

What is Retrieval Augmented Generation (RAG)?

A technique for extending the foundation model to use relevant sources outside of the training data to refine parameters for greater accuracy.

What is an AI agent?

An autonomous AI program that can perform tasks and accomplish goals on behalf of a user or system without human intervention.

What is Agentic AI?

A system of multiple AI agents whose efforts are coordinated or orchestrated to accomplish complex tasks.

What are the common benefits of AI mentioned in the notes?

Automation of repetitive tasks, more and faster insight from data, enhanced decision making, precision, 24x7 availability, and reduced physical risks.

What are the four categories of AI challenges and risks?

Data risks (poisoning, bias), Model risks (theft, manipulation), Operational risks (model drift, breakdowns), and Ethics and legal risks (privacy violations, biased outcomes).

What is the difference between Weak AI and Strong AI?

Weak AI (Narrow AI) is designed for specific tasks like Alexa or Siri, whereas Strong AI (Artificial General Intelligence/AGI) is the theoretical ability to apply knowledge across any task at human-level intelligence.