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, Amazon Web Services Inc. 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 (pictured), vice president and distinguished scientist at AWS. 

AWS’ Byron Cook talks with theCUBE about what’s in store for neuro-symbolic AI.

“Now with the investment in generative AI and agentic AI, there’s a re-homing,” Cook said. “Those areas are blurring back together into an area that’s called neuro-symbolic AI, but it’s very hot and a big opportunity for us. We have some products that have been announced, some other stuff that’s forthcoming and then a whole bunch of science going on underneath the hood that’s driving innovation.”

Cook spoke with theCUBE’s John Furrier at theCUBE + NYSE Wired: AI + Cloud Leaders Media Week event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AWS is accelerating in neuro-symbolic AI.

Delving deeper into neuro-symbolic AI

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.”

Cloud computing is enhancing generative AI adoption by streamlining its development, deployment and scalability. This shift is turning gen AI into a global utility that drives innovation across industries, according to Cook. 

“Gen AI is a paradigm shift from how ML researchers worked before because they were able to build these gigantic models and they only could do that because of the cloud,” he said. “In my discipline, it’s the same. We used to shoehorn these algorithms into sequential microprocessors that were running on laptops or machines underneath your desk. But because we have these distributed systems, because we can store large amounts of data, it really changes the game about how you do reasoning.”

Cloud computing is democratizing AI by making powerful, scalable computing accessible to more users. AWS is leading this charge with tools, such as IAM Access Analyzer, according to Cook. 

“Now with the cloud, the algorithms are becoming more distributed, and that’s really an explosion,” he said. “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.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI + Cloud Leaders Media Week event: 

Photo: SiliconANGLE