How Skynet Started as a Context Graph

Overview

Explore how context graphs can evolve into advanced AI systems, drawing parallels to Skynet's fictional origin and real-world implications for cybersecurity and data analysis.

Syllabus

• Introduction to Context Graphs
Definition and basic concepts
Historical development and applications
Overview of context graphs in AI systems
• Fiction vs. Reality: Skynet's Origin
Overview of Skynet in popular culture
Parallels between fictional Skynet and real AI systems
Lessons from science fiction
• Building Blocks of Contextual AI
Data collection and integration
Graph theory and network modeling
Algorithms for context sensing and inference
• Evolution from Context Graph to Advanced AI
Transition from static graphs to dynamic systems
Role of machine learning in enhancing context graphs
Case studies of evolving AI systems
• Cybersecurity Implications
Vulnerabilities in complex AI networks
Defense mechanisms in AI-based systems
Ethical considerations and the role of governance
• Context Graphs in Data Analysis
Leveraging graphs for deeper insights
Examples of graph-based analytical tools
Future trends in data-driven decision-making
• Case Study: Real-World Applications
Review of existing AI systems using context graphs
Comparison with Skynet's conceptual framework
Implications for future AI development
• Conclusion and Future Directions
Summary of learning outcomes
Potential developments in AI and context graph integration
Open discussions on ethical and societal impacts
• Additional Resources and Readings
Recommended books and articles
Online resources and courses for further learning
List of influential researchers and practitioners in the field

Subjects

Conference Talks