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AWS’s Commitment to Enterprise AI
AWS is capitalizing on the launch of its Automated Reasoning Checks feature on Bedrock, now available to the general public. This initiative aims to instill greater confidence in enterprises and regulated industries, encouraging them to adopt and deploy a wider array of AI applications and agents. By introducing automated reasoning—an approach that employs mathematical validation to ascertain ground truth—AWS hopes to facilitate a smoother transition for businesses into the realm of neurosymbolic AI, which the company identifies as the next significant advancement and a key differentiator in AI technology.
Features of Automated Reasoning Checks
Automated Reasoning Checks empower enterprise users to verify the accuracy of AI responses and identify instances of model hallucination. AWS first introduced this feature during its annual re:Invent conference in December, asserting that it can detect nearly 100% of all hallucinations. Initially, a select group of users accessed this feature through Amazon Bedrock Guardrails, allowing organizations to establish responsible AI policies. Byron Cook, a distinguished scientist and vice president at AWS’s Automated Reasoning Group, shared with VentureBeat that the preview rollout demonstrated the effectiveness of such systems in enterprise settings, helping organizations appreciate the value of AI that integrates symbolic or structured reasoning with the neural network capabilities of generative AI.
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The Importance of Neurosymbolic AI
Cook elaborated on the concept of neurosymbolic AI, which encompasses automated reasoning. He noted that the rising interest in this field prompted users to recognize the significance of their work. Some clients permitted AWS to review their data and the documents used to validate answers, revealing that the tool’s output was comparable to that of humans equipped with a rulebook. Cook emphasized that the interpretation of truth or correctness can often be subjective, whereas automated reasoning mitigates this issue.
“It was truly remarkable! It was fascinating to see individuals with logical backgrounds engage in discussions about what is true or not in an internal communication channel. After just a few messages, they would point to the tool and realize, ‘Oh, it is correct,’” he remarked.
New Features in Automated Reasoning Checks
AWS has introduced several new features to Automated Reasoning Checks for general release, including:
– Support for large documents of up to 80,000 tokens or 100 pages
– Simplified policy validation through saved validation tests for repeated use
– Automated scenario generation from pre-saved definitions
– Natural language suggestions for policy feedback
– Customizable validation settings
Cook explained that Automated Reasoning Checks validate the truthfulness or correctness of an AI system by ensuring that a model did not hallucinate a response. This capability can provide regulators and enterprises with greater assurance that the unpredictable nature of generative AI will not yield incorrect outputs.
The Future of Neurosymbolic AI
Cook highlighted that Automated Reasoning Checks help substantiate many principles of neurosymbolic AI. This field combines the pattern recognition abilities of neural networks with the structured reasoning and logic of symbolic AI. While foundation models often depend on deep learning, they can be susceptible to hallucinations, which remain a significant concern for enterprises. Conversely, symbolic AI lacks flexibility without manual input. Influential figures in AI, such as Gary Marcus, have stated that neurosymbolic AI is essential for achieving artificial general intelligence.
Cook and AWS are enthusiastic about introducing the concepts of neurosymbolic AI to the enterprise sector. In a podcast, VentureBeat’s Matt Marshall discussed AWS’s emphasis on methods like automated reasoning checks and the integration of mathematics and logic into generative AI to reduce hallucinations.
Currently, few companies offer productized neurosymbolic AI solutions, including Kognitos, Franz Inc., and UMNAI. Automated reasoning functions by applying mathematical proofs to models in response to queries, utilizing a method known as satisfiability modulo theories, where symbols have predefined meanings, solving problems that involve both logic (if, then, and, or) and mathematics.