Why is generative AI not so good with mathematics and what can be done about it?

Generative AI models, such as GPT-3 and GPT-4, are designed to understand and generate human-like text based on the patterns observed in large datasets. When it comes to mathematics, there are certain challenges and limitations:

1. Limited Understanding of Deep Mathematical Concepts: These models do not have intrinsic knowledge of mathematical principles. They can perform basic calculations and understand general concepts based on the data they were trained on, but more complex mathematical ideas may be beyond their grasp.

2. Lack of Formal Structure: Mathematics often relies on strict formalisms and symbolic representations. The neural network architecture used in generative models may struggle to capture these intricacies, as it is primarily focused on natural language patterns.

3. Inability to Perform Advanced Calculations: While generative AI models can perform basic arithmetic, they may not be able to handle more complex calculations and proofs that require a deep understanding of mathematical logic and structure.

4. Training Data Limitations: If the training data lacks sufficient examples of complex mathematical reasoning and problem-solving, the model will not learn to handle these tasks effectively

What Can Be Done About It?

1. Specialized Models for Mathematics: Designing models specifically tailored to understand and solve mathematical problems can be a solution. This might involve new architectures that can better capture the formal structure of mathematics.

2. Hybrid Systems: Combining generative AI with symbolic AI systems that can handle mathematical logic and calculations might provide a more robust solution.

3. Enhanced Training Data: Including more advanced mathematical content in the training data, with correct solutions and step-by-step explanations, could help the model learn to handle more complex problems.

4. Collaboration with Mathematical Tools: Integrating generative AI with existing mathematical software and tools could enable it to leverage those tools for complex calculations and reasoning.

5. Human-AI Collaboration: For complex mathematical tasks, AI could act as an assistant to human mathematicians, providing insights, generating hypotheses, and performing routine calculations, while relying on human expertise for deeper understanding and creativity.

In conclusion, while current generative AI models may have limitations in handling advanced mathematical tasks, there are promising avenues for overcoming these challenges through specialized design, integration with other systems, enhanced training, and collaboration with human experts. If the focus is on education, especially within the African continent, these improvements can potentially lead to more effective and engaging mathematical learning tools.