AI and Its Future - Artificial General Intelligence

Overview

Explore AI's current limitations, future potential, and research directions towards Artificial General Intelligence in this insightful presentation by Eric Steinberger.

Syllabus

• Introduction to Artificial Intelligence
Definition and types of AI (Narrow AI vs. Artificial General Intelligence)
Overview of AI history and milestones
Current state of AI technologies
• Limitations of Current AI Systems
Task-specific nature of Narrow AI
Cognitive and creative limitations
Data dependence and training requirements
Ethical and societal implications
• Concepts and Characteristics of Artificial General Intelligence (AGI)
Defining AGI and its capabilities
Comparison of AGI and human intelligence
AGI in popular culture vs. scientific literature
• Approaches and Research Directions Toward AGI
Symbolic AI vs. Connectionist approaches
Hybrid models and transfer learning
Reinforcement learning and unsupervised learning
Brain-inspired computing and neuromorphic engineering
• Challenges in Achieving AGI
Technical challenges in cognition, reasoning, and emotion
Scalability, adaptability, and robustness issues
Risks and safety concerns
• Future Potential of AGI
Potential applications and impact on industries
Long-term societal and economic implications
Philosophical and ethical considerations
• Case Studies and Current Research
Examination of leading AGI research entities and projects
Breakthroughs and significant research papers
• Concluding Thoughts on the Path to AGI
Summary of key insights
Future outlook and emerging trends in AGI research
Discussion on responsible AI development and governance

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Subjects

Conference Talks