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12 most popular AI use cases in the enterprise today

CIO Business Intelligence

Organizations all around the globe are implementing AI in a variety of ways to streamline processes, optimize costs, prevent human error, assist customers, manage IT systems, and alleviate repetitive tasks, among other uses. And with the rise of generative AI, artificial intelligence use cases in the enterprise will only expand.

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AI Is Expanding Our Video Content Creation Options In Stupendous Ways

Smart Data Collective

At Smart Data Collective, we have talked about a few impressive technological trends that are shaping modern business in the 21st-century. You can use deep learning technology to replicate a voice that your audience will resonate with. Use AI technology to streamline the visual design process.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

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The most valuable AI use cases for business

IBM Big Data Hub

Creative AI use cases Create with generative AI Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. Generative AI can produce high-quality text, images and other content based on the data used for training.

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Responsible AI Relies on Data Literacy

DataRobot

What Is Data Literacy? Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. rule-based AI , machine learning , deep learning , etc.) rule-based AI , machine learning , deep learning , etc.)

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

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MLOps and the evolution of data science

IBM Big Data Hub

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. MLOps and IBM Watsonx.ai