Amazon is working on a mathematical approach to solve AI’s hallucination problem, where chatbots generate incorrect but confident-sounding responses. This issue has caused businesses to hesitate to fully trust AI models, including those from OpenAI, Meta, and DeepSeek.
To address this, Amazon Web Services (AWS) is leveraging automated reasoning, a branch of symbolic AI that provides mathematical proof to verify the accuracy of AI-generated answers. AWS believes this could help secure valuable AI deals with enterprises that require reliable responses.
Automated reasoning, rooted in ancient mathematical logic, differs from machine learning in that it uses structured rules to ensure AI behaves as expected. AWS Vice President Byron Cook, a leader in the field, noted that Amazon has heavily invested in hiring experts, including 97 PhD interns last year alone.
The company first applied this method to cybersecurity, proving the correctness of cryptographic systems. Now, AWS has introduced Automated Reasoning Checks, a tool designed to verify AI outputs in critical scenarios where accuracy is essential.
Despite its potential, automated reasoning is not a complete solution for eliminating hallucinations. Experts suggest it should be combined with techniques like retrieval-augmented generation (RAG), which integrates external data, and fine-tuning AI models with specific company data.
AWS plans to enhance its hallucination-reduction tools by incorporating multiple strategies, similar to approaches taken by competitors like Microsoft and Google.
While Google does not currently use automated reasoning, its AI team emphasizes the importance of accuracy in AI models. AI experts agree that reducing hallucinations is crucial for businesses to gain confidence in AI-driven decision-making. Amazon’s continued investment in this field highlights the growing demand for reliable AI systems that can operate with greater certainty, ultimately making AI more practical for businesses worldwide.
