AI Basics: Understanding Artificial Intelligence
From simple analogies a 4th grader can understand to the sophisticated technology behind ChatGPT and other AI tools. Let's build your AI knowledge step by step.
Level 1: The Basics Level 2: How AI Learns Level 3: Language Models Level 4: Modern AI Systems
What is AI? (Explained Like You're in 4th Grade)
🧠 Think of AI Like a Really Smart Student
Imagine you have a classmate who:
- Reads incredibly fast - They've read every book in the library
- Has perfect memory - They remember everything they've ever learned
- Never gets tired - They can work 24 hours a day
- Follows instructions exactly - They do exactly what you ask
That's basically what AI is - a computer program that can think, learn, and help solve problems, just like a really smart student!
What Can AI Do?
Simple things AI can help with:
- Reading and Writing: AI can read documents and write letters, emails, or reports
- Answering Questions: Like having a teacher who knows everything
- Finding Patterns: AI can spot things humans might miss
- Making Recommendations: Like Netflix suggesting movies you might like
🔑 Key Point
AI isn't magic - it's just a computer that's really good at finding patterns in information and using those patterns to help you.
AI in Your Daily Life
You probably already use AI without knowing it:
- Your Phone: Siri, voice-to-text, autocorrect
- Email: Spam filters that block junk mail
- Shopping: "People who bought this also bought..."
- Maps: GPS finding the fastest route
- Social Media: Showing you posts you might like
How Does AI Actually Learn?
🎯 Learning Like a Basketball Player
Think about learning to shoot free throws:
- Practice: You shoot thousands of free throws
- Feedback: You see which shots go in and which miss
- Adjustment: You slightly change your technique
- Improvement: Over time, you make more shots
AI learns the same way, but instead of basketball shots, it might practice reading thousands of legal documents to get better at understanding law.
The Three Types of AI Learning
🏫 Supervised Learning
Like school with a teacher:
- Show AI thousands of examples with the "right answer"
- Example: Show 10,000 emails labeled "spam" or "not spam"
- AI learns to identify spam on its own
🎮 Reinforcement Learning
Like learning a video game:
- AI tries different actions
- Gets "rewards" for good choices
- Gets "penalties" for bad choices
- Gradually gets better at the task
🔍 Unsupervised Learning
Like being a detective:
- AI looks at data without being told what to find
- Discovers hidden patterns on its own
- Example: Finding groups of similar customers
🔑 Key Point
AI doesn't actually "understand" like humans do. It's incredibly good at finding mathematical patterns in data, but it doesn't have consciousness or true comprehension.
What Are Large Language Models (LLMs)?
📚 The World's Most Well-Read Person
Imagine someone who has read:
- Every book in every library
- Every article ever written online
- Every legal case in every database
- Every news story from the past 50 years
And they can instantly recall and connect information from all of it. That's essentially what an LLM is - a computer model trained on vast amounts of human text.
How Language Models Work
LLMs work by predicting what word should come next in a sentence, based on all the text they've been trained on.
🔑 The Prediction Game
Example: If I say "The client signed the..." an LLM might predict "contract" because it has seen this pattern millions of times in legal documents.
By chaining these predictions together, LLMs can generate coherent, helpful responses.
What Makes LLMs Special
- Scale: Trained on trillions of words from the internet
- Flexibility: Can switch between legal writing, creative writing, technical explanations
- Context: Can understand and maintain context throughout long conversations
- Zero-shot Learning: Can often perform tasks they weren't specifically trained for
Training Process (Simplified)
- Data Collection: Gather billions of text documents
- Initial Training: Learn to predict next words (takes months, costs millions)
- Fine-tuning: Additional training on specific tasks
- Human Feedback: Humans rate responses to teach the model to be helpful
Understanding Different AI Models
GPT Models (OpenAI)
- GPT-3.5: Powers ChatGPT free version
- GPT-4: More accurate, can analyze images
- GPT-4o: Faster, multimodal capabilities
Best for: General writing, analysis, conversation
Claude (Anthropic)
- Claude 3 Haiku: Fast, cost-effective
- Claude 3 Sonnet: Balanced performance
- Claude 3 Opus: Most capable, highest quality
Best for: Complex analysis, safety-focused tasks
Other Notable Models
- Gemini (Google): Integrated with Google services
- Llama (Meta): Open-source, customizable
- Mistral: European, privacy-focused
Key Technical Concepts
🧠 Transformer Architecture
The breakthrough technology behind modern LLMs. Transformers use "attention mechanisms" to understand which parts of the input are most important for generating the output.
📏 Model Parameters
Think of parameters as the "brain cells" of AI. More parameters generally mean more capability:
- GPT-3: 175 billion parameters
- GPT-4: Estimated 1.7 trillion parameters
- Claude 3 Opus: Undisclosed, but likely similar scale
🎯 Context Windows
How much text an AI can "remember" in a single conversation:
- Early models: ~4,000 tokens (3,000 words)
- Current models: 32,000-128,000 tokens
- Newest models: Up to 1 million tokens (750,000 words)
Choosing the Right Model for Legal Work
For Document Review
Best: Claude 3 Opus, GPT-4
Why: High accuracy, large context windows
For Quick Tasks
Best: GPT-3.5, Claude 3 Haiku
Why: Fast, cost-effective
For Complex Analysis
Best: GPT-4, Claude 3 Opus
Why: Superior reasoning capabilities
Current Limitations
- Hallucinations: AI can confidently state incorrect information
- Training Cutoffs: Knowledge stops at training date
- No True Understanding: Pattern matching, not consciousness
- Inconsistency: May give different answers to identical questions
- Bias: Reflects biases present in training data
Ready to Apply This Knowledge?
Now that you understand the fundamentals of AI, explore how to implement these technologies safely and effectively in your legal practice.