The transition from Narrow AI, which is designed for specific tasks, to Artificial General Intelligence (AGI), capable of performing a wide array of human-like cognitive functions, represents one of the most significant technological leaps in modern history. This session will explore the key challenges and breakthroughs in the journey from Narrow AI to AGI, addressing both the scientific and practical barriers that need to be overcome for AGI to reach its full potential. Attendees will gain insights into the technical, philosophical, and ethical questions surrounding the development of AGI.

Key Topics Covered:

• Defining Narrow AI vs. AGI: A comprehensive look at the differences between Narrow AI and AGI, highlighting the capabilities of current AI technologies and the broad, human-like intelligence AGI aspires to achieve.
• Key Technical Challenges in AGI Development: Analyzing the core scientific and engineering challenges that must be solved to transition from Narrow AI to AGI, including advancements in machine learning algorithms, cognitive architectures, and neural networks.
• Cognitive and Neural Models for AGI: Investigating the theoretical and practical models that draw inspiration from human cognition, including approaches based on the brain’s structure and function, and how these models could contribute to AGI.
• Learning Generalization and Transfer Learning: Exploring how AGI systems can be designed to generalize knowledge across diverse tasks and domains, a key capability that distinguishes AGI from narrow AI, which excels in highly specialized tasks but struggles with adaptability.
• Overcoming Data Limitations: Narrow AI systems often require vast amounts of domain-specific data to function optimally, whereas AGI systems need the ability to learn with far less data, much like humans. Discussing the strategies for improving data efficiency and learning in AGI.
• The Role of Reinforcement Learning in AGI: How reinforcement learning, a technique used in many Narrow AI systems, can be scaled and refined to help AGI systems learn and adapt in more complex, real-world environments.
• Symbolic AI vs. Connectionist AI: A debate about the two major approaches to AI: symbolic, which focuses on rule-based systems and logical reasoning, and connectionist, which involves neural networks and pattern recognition. How can combining these approaches bring us closer to AGI?
• Philosophical and Cognitive Foundations of AGI: A discussion of the philosophical challenges in creating AGI, including questions about consciousness, self-awareness, and the nature of intelligence itself. How can these concepts be integrated into AGI models?
• The Path to Cognitive Flexibility and Common Sense Reasoning: Narrow AI excels at specific tasks, but lacks the cognitive flexibility and common sense reasoning inherent in human intelligence. Exploring how AGI can develop these key traits to tackle a wide range of unpredictable and complex tasks.
• Ethical Considerations in Breaking Barriers to AGI: As we move toward AGI, the ethical landscape becomes more complex. What new ethical dilemmas emerge as we push AI systems closer to general intelligence, and how can we ensure that AGI is developed in a way that is aligned with human values?
• Safety and Control Mechanisms for AGI Systems: Ensuring that AGI systems are safe, transparent, and aligned with human goals is critical as they become more autonomous. Exploring the methods being developed to maintain control over AGI systems as they increase in capability.
• AGI and the Future of Human-AI Interaction: How the development of AGI will change the way humans interact with machines. Discussing the implications for human-AI collaboration, including potential changes to work, creativity, and decision-making.
• The Role of AGI in Future Technologies: How AGI will likely serve as a foundational technology for future innovations across diverse fields, such as quantum computing, autonomous vehicles, space exploration, and personalized medicine.
• Milestones on the Path to AGI: Reviewing current progress, key milestones, and major breakthroughs in the journey toward AGI, as well as setting realistic expectations for the timeline and challenges of achieving AGI.
• Collaborative Efforts to Achieve AGI: The importance of international cooperation, public-private partnerships, and cross-disciplinary collaboration in accelerating the development of AGI. How can diverse global efforts come together to address the complexity of AGI?

This session is crucial for AI researchers, engineers, ethicists, policymakers, and anyone interested in understanding the frontier of AGI development. Join us to discuss the journey from Narrow AI to AGI, uncovering the barriers to be broken, and the potential that lies beyond them in shaping the future of intelligence.