Solving AI’s Causal Reasoning Failures

In this video, the host of ‘code_your_own_AI’ addresses the challenges and failures of AI in causal reasoning, particularly referencing a previous video on sending a seventh child to Stanford. The video highlights how major language models like GPT-4 Omni, Claude 3 Sonnet, and Gemini Pro failed to solve a simple four-line prompt about this scenario. The host explains the importance of causal reasoning in AI and how current models, despite extensive pre-training, often fail in real-world applications due to the complexity of human language.

The video delves into the reasons behind these failures, emphasizing that AI models are trained on specific datasets and may not generalize well to new, unseen prompts. The host discusses various strategies to improve AI’s causal reasoning, such as providing step-by-step reasoning paths, breaking down complex queries into simpler ones, and using system prompts to guide the models. They also mention advanced methodologies like DSP (Dynamic System Prompting) and TX Graph from Stanford University, which offer more sophisticated ways to enhance AI reasoning capabilities.

The host suggests that AI’s failures present learning opportunities, both for improving AI models and for better understanding how to communicate with them. They promise to explore three new frameworks in the next video to assist proprietary language models in reasoning processes, integrating recent research and advanced reasoning structures. The video concludes with a call for community engagement and feedback to refine these approaches further.