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Industries like insurance, finance, and media have all found impactful ways to apply symbolic AI and deep/machine learning to common pain points when taking unstructured language data from documents, social content, and emails. By extracting sentiment and the deeper meanings of words that only humans can do, language can be turned into data, and actionable insights for a wide range of use cases can be drawn from it. Walt Mayo, CEO of expert.ai, the natural language processing AI company trusted by The Associated Press, Dow Jones, Gannett, and other top brands, joins me on the Tech Talks Daily podcast. We discuss how combining a symbolic approach to AI with machine learning can form an innovative hybrid model.
Walt talks about the difference between symbolic AI and deep learning/machine learning and what kinds of use cases are typically applied together. We also discuss the strengths of each and what organizations can gain by combining them. Finally, we discuss how a hybrid approach addresses the "black box" problem in AI; and scaling existing human biases.
By Neil C. Hughes5
198198 ratings
Industries like insurance, finance, and media have all found impactful ways to apply symbolic AI and deep/machine learning to common pain points when taking unstructured language data from documents, social content, and emails. By extracting sentiment and the deeper meanings of words that only humans can do, language can be turned into data, and actionable insights for a wide range of use cases can be drawn from it. Walt Mayo, CEO of expert.ai, the natural language processing AI company trusted by The Associated Press, Dow Jones, Gannett, and other top brands, joins me on the Tech Talks Daily podcast. We discuss how combining a symbolic approach to AI with machine learning can form an innovative hybrid model.
Walt talks about the difference between symbolic AI and deep learning/machine learning and what kinds of use cases are typically applied together. We also discuss the strengths of each and what organizations can gain by combining them. Finally, we discuss how a hybrid approach addresses the "black box" problem in AI; and scaling existing human biases.

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