What is neuro-symbolic AI? This powerful form of AI mixes awareness of sequences with logic Neuro-symbolic AI combines the pattern recognition capabilities of deep learning neural networks with the logical reasoning of symbolic AI. The result is a smarter system of artificial intelligence that can draw insights from data while applying rules, facts and reasoning to reach reliable decisions. As organizations search for AI systems that are more transparent, explainable and capable of complex decision-making, neuro-symbolic AI is emerging as one of the field’s most promising developments. Its impressive abilities could dramatically influence how businesses, researchers and governments use artificial intelligence in the years ahead. Ohad Elhelo, co-founder and CEO of AUI, spoke about the current state and future of neuro-symbolic AI during the University of Cincinnati’s Future of Commerce: AI+Robotics Summit 2026. The signature event, hosted at the UC 1819 Innovation Hub and Digital Futures complex, brought together national leaders on automation-related topics. Elhelo, a leading expert on neuro-symbolic AI, explained how this form of AI works, its top use cases and where the emerging technology is headed. How does neuro-symbolic AI work? Neuro-symbolic AI works by learning from data and applying logic to it to reach conclusions. The name “neuro-symbolic” refers to the two unique AI approaches the model combines: deep learning neural networks and symbolic AI. Put simply, “neuro” learns as it goes while “symbolic” deduces from what it knows. Deep learning neural networks are AI systems trained on massive amounts of data that identify relationships across it. These systems improve their performance by recognizing trends in large datasets rather than relying on predefined rules. Large language models (LLMs) such as ChatGPT, Claude and Google Gemini mainly run on pattern recognition through a neural system. “Neural approaches – deep learning – has driven all the great breakthroughs