To make artificial intelligence smarter, safer and more aligned with human reasoning, neuro-symbolic AI blends data-driven learning with symbolic logic. This hybrid approach combines the pattern recognition power of neural networks with the structured reasoning of symbolic systems. Recognizing its potential, AWS is investing in neuro-symbolic AI by providing the infrastructure, tools and research to scale hybrid systems. The tech giant is combining deep learning advances with formal logic to unlock new frontiers in AI reasoning, according to Byron Cook, vice president and distinguished scientist at AWS. Neural networks excel at learning from raw data, while symbolic AI thrives at logic and reasoning but falters with unstructured inputs. By combining the strengths of both, neuro-symbolic AI enables systems that can learn and reason, paving the way for more intelligent, adaptable AI, Cook pointed out. “For example, you can use it to synthesize more data to train over, you can combine it with reinforcement learning and then you can also sidecar tools out,” he said. “At our inference time or after inference, you can move the statements coming out of a language model, put them into logic and then prove or disprove the correctness of them. Automated reasoning is the symbolic manipulation of, like you move symbols around and you deduce things that are true about the semantics that those formally represent. You can do all kinds of things in combination with machine learning. The tools are practical now, and you can put them together. Customers really needed these tools themselves before doing deployments, and so that led to IAM Access Analyzer and VPC Reachability Analyzer. Now with automated reasoning checks and bedrock guardrails, we’re using the very same tools we’re using now to address incorrectness due to hallucinations.”