Angie D.'s Linkedin Analytics

Where did AI go wrong? The “forgotten” role of Human Experts in Cognitive and Conversation AI (Augmented Intelligence) How much could your company save by leveraging the mutual strengths between machine learning and human expertise (e.g. customer service, financial planning advisors, medical experts, engineers)? The idea of "replacing humans" has presented a consistent point of controversy surrounding the field of AI and statistical computing. Many platforms like IBM Watson Cognition ML are now embracing the beauty of human intuition in ML process development as Augmented AI (e.g., Butner, K. and Ho, G., 2019). The idea is that humans and machines work together, in harmony, towards the mitigation of mutual weaknesses and combined strengths. In this way, the focus is on the consideration of those mutual strengths and weaknesses and the extension of human cognitive intelligence towards the development of Conversation AI/ML models, algorithms, and computing technologies. Educating business experts and stakeholders on the need for human-oriented Cognitive AI training techniques may present a common challenge.; Such techniques can be effectively applied to containment augmentation and long term customer engagement goals. As a Data Scientist, Conversation AI ML my job is to leverage human conversational multi-turn data and extract actionable insights towards the reduction of human to human interactions over time. And the improvement of more meaningful human to human interactions in the short to intermediate term. The problems vary but the foundational concepts are the same. Humans need each other for certain tasks (i.e. high level meta reasoning, financial planning/decision making, specific educational tasks, medical procedures with higher level of associated risk, mechanical and engineering implementation with human esoteric expertise). But humans do not need each other for other lower level cognitive tasks (i.e., retrieval of customer financial information, payment options). As a process engineer I've specialized in helping companies take thousands and thousands of phone conversations and leverage an end to end process towards automating customer interactions with NLU. This data pipeline includes but is not limited to an ML workflow, BI requirements and delivery stages, Quality Control (Intelligence Optimization), Engineering, Deployment/Analytics. Contact me today to learn about how these skills and experiences can be leveraged towards improving your team or companies customer engagement without eliminating the art of human decision making.