LLMs, Data, Agents, and the Future of AI
Session Overview
- Understanding LLMs and Context
- Memory Systems in AI
- Data Interaction Capabilities
- AI Agents and Agentic Systems
- Future Implications
LLMs and Context
Large Language Models are neural networks trained to predict the next token in a sequence based on previous tokens
Context Windows
- LLMs process text within a fixed “context window”
- Think of it as the model’s working memory
- Common sizes:
- GPT-4o: 128K tokens
- Claude: 200K tokens (~500 pages of text)
How Context Works
- Context is like a conversation buffer
- Earlier information influences later responses
- Models can reference and build upon previous exchanges
- But context is temporary and limited
Memory Systems
Three main types of AI memory:
- Short-term (Context Window)
- Working Memory (Active Processing)
- Long-term (Persistent Storage)
Short-term Memory
- Limited by context window size
- Temporary and volatile
- Cleared between sessions
- Perfect recall within window
Long-term Memory
- Vector Databases
- Knowledge Graphs
- External Databases
- Persistent Storage Systems
Retrieval Augmented Generation
- Combines LLM capabilities with external knowledge retrieval
- Process:
- Query understanding
- Relevant content retrieval
- Context augmentation
- Enhanced response generation
- Enables up-to-date and accurate responses
- Reduces hallucination
Data Interaction
How LLMs can work with data:
- Direct Data Analysis
- Tool Usage
- API Interactions
- Database Queries
Data Capabilities
- Text Processing
- Code Generation
- Data Analysis
- Visualization Creation
- Natural Language Queries
AI Agents
Autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals
Components of AI Agents
- Perception System
- Decision Making
- Action Generation
- Memory Management
- Tool Usage
Agentic Systems
Key characteristics:
- Autonomy
- Goal-oriented behavior
- Continuous learning
- Tool manipulation
- Environmental awareness
Types of AI Agents
- Task-specific Agents
- General-purpose Agents
- Multi-agent Systems
- Collaborative Agents
Agent Architecture
Memory in Agents
How agents maintain state:
- Context Management
- Persistent Storage
- Experience Recording
- Knowledge Updates
Tools and Functions
Agents can:
- Use external APIs
- Execute code
- Manipulate files
- Query databases
- Call other services
Real-world Applications
- Automated Research
- Code Generation
- Data Analysis
- Customer Service
- System Administration
Future Implications
Key considerations:
- Ethical Use
- Safety Measures
- Control Mechanisms
- Scalability
- Human Oversight
Session Review
- LLMs and Context Management
- Memory Systems
- Data Interaction Capabilities
- AI Agents and Architecture
- Future Considerations
Thoughts
What are the implications of agentic AI systems for:
- Scientific Research?
- Data Analysis?
- Software Development?
Earth System Data Science in the Cloud
LLMs, Data, Agents, and the Future of AI