(1) Fundamentals of Generative AI

Building machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Artificial intelligence (AI)

A subfield of AI where machines learn from data to perform specific tasks.

Machine Learning (ML)

An application of ML that focuses on creating new content. Can be multimodal, enabling them to process and generate different types of data like text, images, and code simultaneously

Generative AI

A subset of ML that uses artificial neural networks with many layers to extract complex patterns from data.

Deep Learning

Powerful ML models trained on massive amounts of unlabeled data, allowing them to develop a broad understanding of the world.

Foundation Models

A type of foundation model that is designed to understand and generate human language.

Large Language Models (LLMs)

Name 2/4 things Gen AI can do.

1. Create (Generate new content)

2. Summarize (Condense Information into concise summaries)

3. Discover (Find information at the right time)

4. Automate (Automate previously manual tasks)

Data that has associated tags, such as a name, type, or number.

Labeled data

Raw, unprocessed information that hasn’t been tagged and lacks meaning by itself such as unorganized photos or streams of audio recordings.

Unlabeled Data

Name the 3 ML primary learning approaches.

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Leanring

This ML learning approch trains models on labeled data to predict outputs for new inputs.

Supervised Learning

This ML learning approch uses unlabeled data to find natural groupings and patterns.

Unsupervised Learning

This ML learning approchlearns through interaction and feedback to maximize rewards and minimize penalties.

Reinforcement Leaarning

What are the 3 key features of foundation models.

1. Trained on diverse data. 2. Flexible to wide range of use cases. 3. Adaptable to specialized domains through additional, targeted training.

The method of interacting with foundation models and guiding them. It involves providing them with instructions or inputs to generate desired outputs.

Prompting

The art and science of creating effective inputs, known as prompts, for generative AI models to maximize their value and tailor responses to specific needs.

Prompt engineering