Most think AGI's 'human-like reasoning and performance' remains elusive
New Paradigms Needed: Breakthroughs likely require going beyond purely data-driven approaches, incorporating insights from across multiple disciplines.
Hybrid Potential: Combining the strengths of different AI techniques (neural networks, symbolic reasoning, etc.) offers a promising avenue for achieving more robust and adaptable AI.
Hinton & Bengio: The Challenge of Abstract Reasoning
Abstract Reasoning is Key: Humans are exceptional at making connections, applying logic, and forming abstract concepts to solve problems. Traditional AI often struggles with this higher-order thinking.
Developmental Trajectory: Children learn concrete skills first, and abstract thinking later. This suggests AI may need a similar progression to achieve true intelligence.
Alternative Paths: They posit that entirely new approaches might be needed, potentially inspired by how our brains function.
Potential for Logic-Based Systems: While embracing neural networks, they don't rule out a role for symbolic AI and theorem-proving in achieving abstract reasoning capabilities.
LeCun: Gaps in Perception and Reasoning
Beyond Data: Even with massive datasets, current AI lacks true understanding and common sense reasoning skills.
Visual Reasoning: Humans effortlessly grasp how objects relate in space and extrapolate from limited visual data. AI still needs significant improvements in this area.
Bengio: Abstraction in the Real World
Messy Reality: Simulators can't fully capture the nuances of human behaviour and the complexity of real-world environments.
Limitations of Deep Learning: While powerful, current deep learning on its own doesn't generate the high-level abstractions humans use to understand the world.
Marcus: Towards a Hybrid Approach
Value in Symbolic AI: The knowledge representation power of rule-based systems should not be discarded entirely.
Combining the Best: He envisions a hybrid model integrating the strengths of symbolic systems (logic, rules) with the pattern recognition and learning abilities of deep learning.
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