Summary of "20 AI Concepts Explained in 40 Minutes"

This video by GKCS provides a clear, concise overview of 20 fundamental AI concepts, particularly focusing on large language models (LLMs) and their related technologies. It is aimed at engineers building AI applications and anyone interested in understanding AI terminology and methodologies. The video explains each concept with examples and practical insights, helping viewers communicate effectively within teams and grasp deeper AI topics.

Main Ideas and Concepts Explained

Large Language Model (LLM)
- A neural network trained to predict the next token in a sequence.
- Example: Given "all that glitters," it predicts "is not gold."
Tokenization
- Breaking input text into discrete tokens (words, suffixes like "ing," punctuation).
- Essential for processing natural language accurately.
Vectors
- Numerical representations of tokens in an n-dimensional space.
- Similar meanings cluster close; opposite meanings are far apart.
- Enables semantic understanding by LLMs.
Attention Mechanism
- Allows LLMs to understand context by focusing on nearby words.
- Resolves ambiguity (e.g., "apple" as fruit, company, or metaphor).
- Introduced in 2017, popularized by GPT-2 in 2022.
Self-Supervised Learning
- Training without explicit labels; model predicts missing parts of data.
- Uses inherent structure in data (e.g., predicting next word or masked tokens).
- Scalable and reduces need for human-labeled data.
Transformer Architecture
- Specific algorithm using attention blocks and feedforward neural networks.
- Layers of attention capture complex relationships (e.g., sarcasm, implications).
- Can be stacked in many layers (dozens to hundreds).
Fine-Tuning
- Adapting a base LLM to specific domains or tasks via additional training.
- Penalizes undesired but plausible responses to improve accuracy.
- Enables domain-specific models (e.g., medical, financial).
Few-Shot Prompting
- Providing example inputs and outputs at inference time to guide model responses.
- Improves quality without retraining the model.
Retrieval-Augmented Generation (RAG)
- Augments LLM input with relevant external documents fetched dynamically.
- Combines user query, examples, and contextual documents for better responses.
Vector Database
- Stores documents as vectors to enable semantic similarity search.
- Retrieves documents related to user queries even if exact keywords don't match.
- Uses algorithms like hierarchical navigable small world graphs for efficiency.
Model Context Protocol (MCP)
- Protocol to connect LLMs with external data sources or APIs in real-time.
- Enables LLMs to fetch live data and perform actions (e.g., booking flights).
- Makes LLMs interactive and capable of executing tasks.
Context Engineering
- Combining few-shot prompting, RAG, and MCP to manage user context effectively.
- Includes challenges like user preference management and prompt summarization.
- Context summarization techniques (e.g., sliding windows, keyword focus).
Agents
- Long-running AI processes capable of multi-step tasks and interacting with multiple systems.
- Example: Travel agent booking flights, hotels, managing emails autonomously.
Reinforcement Learning with Human Feedback (RLHF)
- Training models by rewarding good responses (+1) and penalizing bad (-1).
- Models learn optimal paths in vector space to maximize positive outcomes.
- Analogous to Pavlovian conditioning but limited compared to human mental models.
Chain of Thought Reasoning
- Training models to explain their reasoning step-by-step.
- Improves problem-solving and response quality.
- Related to reasoning models, including tree of thought and graph of thought approaches.
Multimodal Models
- Models that handle multiple data types (text, images, video).
- Enable richer understanding and generation beyond text.
- Applications include image recognition, video generation, and content creation.
Foundation Models and Smaller Models
- Trend towards smaller, domain-specific models with fewer parameters (3M–300M vs. billions).
- Provide better control, privacy, and efficiency for companies.
- Useful for specialized tasks where large general models are unnecessary.
Distillation
- Process of training smaller models (students) to mimic larger models (teachers).
- Reduces model size and inference cost while maintaining reasonable performance.
Quantization
- Compressing model weights from high precision (32-bit) to lower precision (8-bit).
- Saves memory and reduces inference cost.
- Applied post-training; does not reduce training cost.
Summary and Importance
- Understanding these terms empowers engineers to