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AI is a broad term with many subsets. It can refer to any machine that mimics cognitive functions such as learning and problem-solving. Kurt Rahner, vice president of technology for Kendal, explained some of the subsets of AI.Chatbots are an AI use case that relies on natural language processing to understand and generate responses.Machine learning is a subset of AI that uses machines to analyze data and learn from patterns. Deep learning, a subset of ML, uses deep neural networks to learn from vast amounts of data. Types of neural networks include convolutional neural networks (CNNs), which are often used for images; recurrent neural networks (RNNs); and transformers. The GPT in ChatGPT stands for “generative pretrained transformers.” Rahner emphasized that transformers provide the magic.
elderman explained that technologists need to understand organizational processes at a granular level to create transformational change. They also need to know the business goals and desired ROI before they begin to automate processes with AI tools.In addition, the organization needs solid data governance in place as “AI is only as good as the information provided to it,” he said. Cypress Living built a data lake with raw data from enterprise apps and layered that with AI to create contextual answers (rather than generic answers) based on its operations. He added that the ability to engineer effective prompts is also an important part of AI success.
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