Leading the Way in AI: Transforming Reasoning Accuracy from a CEO's Question

An executive in charge of risk management challenged me earlier this year regarding the reliability of AI when interpreting financial statements. His inquiry, however straightforward, was profound: after fine-tuning, RAG, or any of these techniques, what kind of error rate can we expect when we deploy a model? 'Optimizing AI Reasoning: A Hamiltonian Dynamics Approach to Multi-Hop Question Answering', my most recent publication on arXiv, is based on this practical issue. This work presents an innovative paradigm for studying and improving AI reasoning, drawing on ideas from physics. Reasons for its relevance:

1. With this newfound precision, we can measure the reliability of reasoning to a level never before seen.
2. This framework offers new techniques to enhance AI reasoning capabilities.
3. Explainability: It sheds light on the reasoning process behind AI system recommendations.

This is a small step to potentially disrupt sectors such as healthcare, legal services, and finance that rely on precision. The goal should not be merely improved AI, but rather the development of AI systems that are trustworthy and simple to understand. While the research dives into the technical details, the consequences are readily apparent: we're getting closer to AI systems that can reason reliably like humans and more. Newly released models may provide 'advanced thinking capabilities'. But how can we know that logic will lead us to our goals? Is it possible to give a number based on the likelihood of providing inadequate or incorrect information to, say, our clients?? If you would like to proceed further, you can find the complete document here. View the article at arXiv. Even non-scientific individuals should have the opportunity to read it—it's somehow advanced.

Where do you see physics and AI interacting?

Is it necessary to quantify your model's reliability?

In order to reflect the unique aspects of your company, do you believe it will be necessary to modify the model's reasoning capabilities?

Could this have an effect on your field?