What is Generative AI?

#Generative AI

#What is generative AI?

Generative AI is artificial intelligence capable of creating new, original content (text, images, music, code, etc.) rather than just classifying existing data.

Tools like ChatGPT, Claude are examples. It’s like having access to a “cloud Einstein”: a super-intelligent assistant you can interact with via natural language, always available and with access to global knowledge.

Types of Generative AI Models:

Text-to-text: Generate articles, stories, or code snippets.
Text-to-image: Create images from text descriptions.
Image-to-image: Modify or combine images.
Image-to-text: Generate captions or explanations for images.
Speech-to-text: Transcribe audio to text (useful for meetings).
Text-to-audio: Generate music or sounds from prompts.
Text-to-video: Generate videos from prompts (in early stages).

#How are machine learning and AI related?

Artificial intelligence (AI) is the broad field that aims to make machines perform tasks that normally require human intelligence.
Machine learning (ML) is a subset of AI that focuses on algorithms that learn patterns from data, allowing systems to improve performance without explicit programming.
ML trains a model on data; the trained model captures patterns and can make predictions or generate new content when given new inputs.

Common ML types:

Supervised learning — trained on labeled data (input → target).
Unsupervised learning — finds structure in unlabeled data.
Semi-supervised learning — uses a mix of labeled and unlabeled data.
Reinforcement learning — learns by trial and error using rewards.

#What is a model?

A model is the result of training: a mathematical representation (weights, parameters, structure) that encodes what the system learned from data. Once trained, the model can make predictions or generate content for new inputs.

#What is deep learning?

Deep learning is a branch of machine learning that uses artificial neural networks with multiple layers. These deep networks can learn complex, hierarchical patterns from large amounts of data and are the foundation for many modern generative systems (e.g., image generators and LLMs).

Key points:

Neural networks are built from layers of interconnected neurons (units) that transform inputs into outputs.
Deeper networks (more layers) can represent more complex functions.
Deep learning typically requires large datasets and significant compute.

#Generative vs. discriminative models

Generative models aim to produce new data similar to the training examples (e.g., generate images, text, or audio). Examples: GANs, VAEs, diffusion models, autoregressive language models.
Discriminative models focus on classification or prediction (e.g., spam detection, image classification).

Examples:

Generative: create a new image of a cat given a prompt.
Discriminative: decide whether an email is spam or not.

#What are LLMs (Large Language Models)?

Large language models (LLMs), a type of artificial neural network inspired by the human brain. LLMs are trained not by programming but by being exposed to massive datasets (primarily language data from the internet, books, and more). The model learns to predict the next word in a sequence, refining itself through countless iterations.

Capabilities:

Generate natural-sounding text from prompts
Summarize, translate, or answer questions
Produce code snippets or drafts for writing

Limitations and considerations:

They can produce plausible-sounding but incorrect information (hallucinations).
They reflect biases present in their training data.
Large models often require careful prompting and human review for critical tasks.

#Quick takeaways

Generative AI creates new content by learning patterns from data.
Machine learning (including deep learning) is the technique that enables these models.
LLMs are powerful language-focused generative models, but they need guardrails and review.