Can AI Solve MENSA-Level Abstract Reasoning Puzzles? | USC Study on AI’s Cognitive Limits

AI vs Human Cognition: Researchers at USC have tested artificial intelligence against complex visual puzzles that challenge even human brains. In this video, we dive deep into their findings on AI’s ability to solve Raven's Progressive Matrices, a gold standard for abstract reasoning. Multi-modal large language models (MLLMs) like GPT-4V were pushed to their limits to understand how AI handles tasks that require both visual processing and logical deduction.

Surprising results emerged as closed-source AI models outperformed open-source models, but even the best models still struggled with crucial reasoning tasks. The study highlights the current limitations of AI in understanding complex visual patterns and deductive reasoning, an area where human cognition excels. However, the use of Chain of Thought prompting showed promise in improving AI’s problem-solving abilities.

Discover how AI is progressing toward human-level intelligence, why it still struggles with abstract reasoning, and the future of AI and cognitive science. Watch till the end for the latest breakthroughs in AI research!

Key Points Covered:
USC Research on AI's cognitive limits
AI and abstract reasoning challenges
GPT-4V vs open-source AI models
The importance of Chain of Thought prompting in improving AI reasoning
Visual puzzles like Raven’s Progressive Matrices
The future of AI cognition and human-level reasoning
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