Generation AI

AI Reasoning: How Machines Think and Learn

Episode Summary

In this episode of Generation AI, hosts JC Bonilla and Ardis Kadiu break down the complex world of AI reasoning in anticipation of the OpenAI's o1 (aka strawberry) model. They explain how AI systems make decisions, from neural networks to symbolic logic, and discuss the growing importance of explainable AI.

Episode Notes

In this episode of Generation AI, hosts JC Bonilla and Ardis Kadiu break down the complex world of AI reasoning in anticipation of the OpenAI's o1 (aka strawberry) model. They explain how AI systems make decisions, from neural networks to symbolic logic, and discuss the growing importance of explainable AI. The conversation covers key trends in AI reasoning, including neurosymbiolic systems and human-AI collaboration, with practical examples of how these concepts are being applied to build AI software in support of student recruitment and engagement.

Introduction to AI Reasoning (00:00:06)

• JC Bonilla and Ardis Kadiu discuss the importance of AI reasoning
• Overview of knowledge representation, reasoning algorithms, and learning algorithms in AI

Types of Reasoning in AI (00:07:03)

• Explanation of deductive, inductive, abductive, and common sense reasoning
• How these reasoning types are applied in AI systems

Explainable AI (XAI) (00:15:54)

• Definition and importance of explainable AI
• Example of XAI in loan application processes
• Discussion on transparency, fairness, and model improvement

Neurosymbolic AI (00:24:26)

• Integration of neural networks and symbolic reasoning
• Example of neurosymbolic reasoning in medical diagnosis
• Benefits of combining deep learning with domain-specific expertise

Human-AI Collaboration (00:29:22)

• Concept of "human in the loop" in AI systems
• Applications in various industries including higher education
• Balancing AI autonomy with human input and oversight

AI Reasoning in Higher Education (00:30:29)

• Ardis Kadiu explains Element451's approach to AI reasoning
• Discussion on building AI playbooks for student recruitment and engagement
• Challenges in encoding expert knowledge into AI systems

LLMs and Reasoning Capabilities (00:32:36)

• Limitations of current Large Language Models (LLMs) in true reasoning
• Explanation of how LLMs currently predict based on patterns rather than reason
• The need for step-by-step reasoning data to improve LLM capabilities

OpenAI's O1 (Strawberry) Model (00:35:58)

• Introduction to OpenAI's anticipated reasoning breakthrough model, O1 (aka Strawberry)
• Explanation of the "Strawberry problem" in AI reasoning
• Discussion on the potential impact of true reasoning capabilities in AI systems

The Future of AI Reasoning in Education (00:37:22)

• Implications of improved AI reasoning for personalized student engagement
• Potential for AI to make more informed decisions in educational contexts
• The importance of developing AI systems that can truly reason, not just predict

Conclusion and Implications (00:37:59)

• Recap of key AI reasoning concepts and their importance in higher education
• Discussion on the ethical considerations and regulatory aspects of AI in education
• Final thoughts on the future of AI reasoning and its potential to transform higher education