Digital Event Horizon
Amazon Web Services (AWS) has recently introduced a new managed service for generative AI applications, Amazon Bedrock, which aims to tackle the issue of "hallucination" in AI. The company's new tool uses sound mathematical verifications to check the accuracy of statements made by models, and is designed to prevent factual errors due to model hallucinations.
AWS has introduced Amazon Bedrock, a managed service for generative AI applications designed to prevent "hallucination" in AI. The tool uses "sound mathematical verifications" to check the accuracy of statements made by models. The AWS Automated Reasoning Group aims to prove code mathematically correct, with benefits including more efficient code and better optimization. Generative AI models can provide creative insights but also produce incorrect answers due to "hallucination".
Amazon Web Services (AWS) has recently introduced a new managed service for generative AI applications, Amazon Bedrock, which aims to tackle the issue of "hallucination" in AI. Hallucination refers to the phenomenon where AI models generate plausible but incorrect answers that have no basis in real-world data.
According to Byron Cook, head of Automated Reasoning at AWS and a professor of computer science at University College London, the company's new tool is designed to prevent factual errors due to model hallucinations. Cook explains that the tool uses "sound mathematical verifications" to check the accuracy of statements made by models.
Cook's team has been working on automated reasoning for several years, with the goal of proving code mathematically correct. In fact, the borrow checker in Rust is essentially a deductive theorem prover, which means it is a reasoning engine that helps programmers write more robust and reliable code.
The AWS Automated Reasoning Group has worked on other problems at Amazon, such as proving access control policies were working as expected, and similarly for encryption, networking, storage, and virtualization. Cook notes that being able to prove code mathematically correct has beneficial side effects, including more efficient code and better optimization.
Cook's team has also explored the application of automated reasoning to generative AI models. He notes that while humans hallucinate too, with society continually chipping away at what is truth and how it is defined, there are areas where formalizable statements exist, and others that do not.
To mitigate the risk from AI hallucination, Cook suggests that we need to rely on a combination of factors, including the quality of the translation from natural language to logic, as well as the ability of domain experts to formalize rules. He also notes that there are problems that are undecidable, which have been proved by Turing.
Cook's team has built a database of all known legal case results and formalized them, but notes that this may not be the best application for their tool. However, he acknowledges that generative AI models can provide creative insights, but also points out that incorrect answers are possible.
In conclusion, Amazon's new automated reasoning tool is an innovative approach to tackling AI hallucination. By leveraging sound mathematical verifications and combining them with formalizable statements, Cook's team aims to build a more accurate and reliable system for generative AI models.
Related Information:
https://go.theregister.com/feed/www.theregister.com/2025/01/07/interview_with_aws_byron_cook/
Published: Tue Jan 7 18:07:59 2025 by llama3.2 3B Q4_K_M
