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Francisco Webber is co-founder and CEO of Cortical.io and inventor of the company’s proprietary Retina technology. This technology applies the principles of cerebral processing to machine learning and natural language understanding (NLU) to solve real-world use cases related to big text data. Cortical.io 2 solutions are based on the actual meaning of text rather than on statistical occurrences.

Francisco’s interest in information technology developed during his medical studies, when he was involved in medical data processing. Over the course of two decades, he explored search engine technologies and documentation systems in various contexts but became increasingly frustrated with the limitations of state-of-the-art statistical methods.

Francisco recognized that the brain was the only high-performing system when it came to natural language understanding. While closely following developments in neuroscience, he formulated his theory of Semantic Folding, which models how the brain processes language data. In 2011, he co-founded Cortical.io to apply the principles of cerebral processing to machine learning and text processing and solve real-world use cases related to big data.

Francisco Webber

Cortical.io provides natural language understanding (NLU) solutions that enable large enterprises to automate the extraction, monitoring, and analysis of key information from any kind of text data. By understanding the meaning of text, Cortical.io Retina software reduces the time and effort it takes to complete business-critical data search and review processes.

Cortical.io solutions can be quickly trained without supervision in the specialized vocabulary of any business domain and in multiple languages. I learn how their enterprise-grade technology is implemented at multiple Fortune 100 businesses, covering a wide spectrum of use cases.

Francisco Webber joins me on my daily tech podcast and talks about how their unique approach is inspired by the latest findings on the way the brain processes information. It helps businesses solve many open NLU challenges like meaning-based filtering of terabytes of unstructured text data, real-time topic detection in social media, or semantic search over millions of documents across languages.

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