For regulated industries, the neurosymbolic AWS AWS promises a secure and explainable agent automation
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AWS implemented on the fact that by providing its automated reasoning verification function on the basis of general availability, it will give more companies and regulated industries the confidence of using and deploying more AI applications and agents.
He also hopes that the introduction of methods such as automated reasoning, which uses validation based on mathematics to determine the truth on the ground, will facilitate companies in the world of neurosymbolic AIA, a stage which, according to the company, will be the next major advance – and its greatest differentiation – in the AI world.
Automated reasoning controls allow users of the company to check the accuracy of the responses and detect the hallucination of the model. AWS has unveiled automated reasoning checks on the basis during its annual re: Invent conference in December, saying that it can capture almost 100% of all hallucinations. A limited number of users could access the functionality via Amazon Bedrock Guard-Railles, where organizations can define responsible AI policies.
Byron Cook, distinguished scientist and vice-president of the AWS automated reasoning group, told Venturebeat in an interview that the deployment of preview has proven that systems like this work in a business framework, and this helps organizations to understand the value of AI which can mix symbolic or structured reflection with the nature of the neural network of genetive.
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“There is this notion of neurosymbolic, this is the kind of nickname under which you could call automated reasoning,” said Cook. “The rise of neurosymbolic AI caused people, while they used the tool, to realize how important this work was.”
Cook said that some customers allowed AWS to examine their data and the documents used to annotate the answers as good or bad, and found that the work generated by the tool was carried out in a similar way to humans with a copy of the Book of rules before them. He added that the concept of truth or correct can often be subject to interpretation. Automated reasoning does not quite have the same problem.
“It was really incredible! It was incredible to have people with logical history in an internal communication channel that was arguing what is true or not, and in five or six messages point to the tool and make Oh, that’s true,” he said.
AWS has added new features to automated reasoning checks for the general version. These include:
- Support to add large tokens documents up to 80k or up to 100 pages
- Simpler policy validation by recording validation tests for repeated races
- Automated generation of scenarios from pre-saved definitions
- Natural language suggestions for the feedback of policies
- Customizable validation parameters
Cook said that automated reasoning checks validate the truth or accuracy in an AI system by proving that a model has not hallucinated a solution or a response. This means that it could offer regulators and worried regulated companies that the non -deterministic nature of the generative AI no longer makes confidence in incorrect responses.
Neurosymbolic and proving the truth
Cook spoke of the idea that automated reasoning checks help prove that many concepts from neurosymbolic AI.
Neurosymbolic AI refers to the combination of neural networks used by language models, with structured thought and the logic of symbolic AI. When neural networks recognize models from data, Symbolic IA uses explicit rules and logical problems. Foundation models are often based on neural networks or in -depth learning, but because the models base their answers on the models, they are subject to hallucinations, a concern that continues to concern companies. But symbolic AI is not very flexible without manual instructions.
Important voices in AI, like Gary Marcus, have said that neurosymbolic AI is critical for artificial general intelligence.
Cook and AWS have been delighted to bring ideas for neurosymbolic to the company. Matt Marshall of Venturebeat spoke of the concentration of AWS on methods such as automated reasoning checks and the combination of mathematics and logic to a generative AI to reduce hallucinations in a podcast.
Currently, few companies offer a productive neurosymbolic AI. These include Kognitos, Franz Inc. and Umnai.
Bring mathematics to validation
Automated reasoning works by applying mathematical evidence to models in response to a request.
It uses a method called the theories of the Modulo of satisfaction, where symbols have predefined meanings, and it solves problems which involve both logic (if, then and, or) and mathematics. The automated reasoning takes this method and applies it to the answers by a model and checks it in relation to a set of policy or truth data on the soil without having to test the answer several times.
For example, in a business parameter, they want to prove that a financial audit is correct. The model replies that a report contains non -approved payments. Automated reasoning checks decompose this into a logical chain:
(FORALL ((Rport))
(=> (Contains aendvendorpayments r)
(must fail r)))
It then enters the definitions, the variables and types defined by the user on the railing of the rocky substratum and solves the equation to prove that the model has responded correctly and based on the truth.
Make the agents proven correct
Cook said that agent uses could benefit from automated reasoning checks, and grant more access to the basic functionality can demonstrate its usefulness. But he warned that automated reasoning and other neurosymbolic AIA techniques are still in his early stages.
“I think that will have an impact on agentic AI, however, of course, the agent work is so speculative at the moment,” said Cook. “There are several techniques like this to discover the ambiguity in the declaration, then to find the kind of key deltas between possible translations, then to return to you and to obtain refinement on this, which, I think, will be the key in terms of emotional journey that I saw the customers cross, they began to play with the generator AI a few years ago.”
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