Articles

  • Aug 9, 2024 | ibm.com | Rina Caballar |IBM Data

    Many generative AI tools seem to possess the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next verse in a song or poem. Software like DALL-E or Midjourney can create original art or realistic images from natural language descriptions. Code completion tools like GitHub Copilot can recommend the next few lines of code. But generative AI is not predictive AI.

  • May 9, 2024 | ibm.com | IBM Data

    The healthcare industry faces arguably the highest stakes when it comes to data governance. For starters, healthcare organizations constantly encounter vast (and ever-increasing) amounts of highly regulated personal data. The impact of healthcare data usage on people’s lives lies at the heart of why data governance in healthcare is so crucial.In healthcare, managing the accuracy, quality and integrity of data is the focus of data governance.

  • Sep 27, 2023 | ibm.com | IBM Data

    Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content. There are two types of these generative AI models: proprietary large language models and open source large language models. Proprietary LLMs are owned by a company and can only be used by customers that purchase a license.

  • Aug 15, 2023 | ibm.com | IBM Data

    Artificial intelligence (AI) adoption is here. Organizations are no longer asking whether to add AI capabilities, but how they plan to use this quickly emerging technology. In fact, the use of artificial intelligence in business is developing beyond small, use-case specific applications into a paradigm that places AI at the strategic core of business operations.

  • Aug 11, 2023 | zephyrnet.com | IBM Data

    The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. The information can deepen our understanding of how our world works—and help create better and “smarter” products.

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