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Trustworthiness in AI: on SciCompBot-the scientific computing chatbot
Anders Christian Hansen1, Fabian Circelli1
1Department of Applied Mathematics and Theoretical Physics, University of Cambridge , Cambridge, UK.
Summary
Developing a trustworthy scientific computing chatbot (SciCompBot) requires addressing AI's tendency for confident incorrectness. Mathematical tools like the solvability complexity index (SCI) hierarchy are key to building reliable AI for complex computations.
Area of Science:
- Computational Mathematics
- Artificial Intelligence
- Scientific Computing
Background:
- Current AI chatbots, while capable of accepting complex scientific problems, lack trustworthiness due to frequent incorrect answers.
- The development of advanced AI systems necessitates a focus on safety, security, and robustness, particularly for critical applications.
Purpose of the Study:
- To explore the development of a trustworthy scientific computing chatbot (SciCompBot).
- To investigate the mathematical challenges in creating reliable AI for advanced computational tasks.
- To propose strategies for overcoming obstacles like non-computability and generalized hardness of approximation (GHA) in AI.
Main Methods:
- Investigating mathematical foundations of computational mathematics, specifically the solvability complexity index (SCI) hierarchy.
- Analyzing the challenges of non-computability and generalized hardness of approximation (GHA) in AI.
- Identifying essential features for trustworthy AI in scientific computing, such as interactive dialogue.
Main Results:
- Demonstrated the necessity of the solvability complexity index (SCI) hierarchy for building trustworthy SciCompBots.
- Presented new strategies to overcome non-computability and generalized hardness of approximation (GHA).
- Identified 'chatty' AI, which initiates dialogue, as a crucial feature for trustworthiness.
Conclusions:
- A trustworthy SciCompBot must incorporate mathematical rigor and interactive dialogue to ensure reliable solutions.
- Overcoming issues of non-computability and GHA is essential for advancing AI in scientific computing.
- This research contributes to the broader goal of safe and robust AI systems.
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