Earley Information Science’s Post

Happy to share my thoughts on the issues with unstructured data in delivering the quest of more output with AI. Thanks AI Magazine for sharing my thoughts! Key points I talked about - ⚫ Data management capacity requirements to scale with AI is exponentially larger which should be taken into account by enterprises ⚫ There should be a balancing act between quality and quantity of data. Without this balance, there is no AI! ⚫ Responsible data management practices implemented at the human or organizational level will become increasingly important as AI advances. Read the interview here - https://lnkd.in/epPhZ-ZC #ResponsibleAI #datamanagement #dataquality

It's NO question AI is helpful. You SHOULD question what data it's using...and the responses based on that data. There's a better (for your business) way to work.

Should AI models be designed with the ability to "unlearn" or "forget"? What if a company has trained a model on sensitive and private data and has no customer consent? What if regularion kicks in and requires that companies / owners of AI models have them "unlearn" certain specific data? Probably smart to start thinking of how to design our AI models with this capability. What do you think and why? Great Harvard Business School article on the topic. #artificialintelligence #genai #dataprivacy

Unlocking the power of generative AI requires stepping up the data game! 💪 Learn the 3 stages of data maturity that are crucial for #AI application competence. #DataQuality

🧠 Embarking on a data project? When prepping for #GenerativeAI, these three pivotal factors can't be ignored: Data accessibility, quality, and quantity. Ensure your #DataStrategy is robust and aligned with success. 📘 Dive into understand these crucial elements and elevate your AI initiatives! #DataQuality

🧠 Embarking on a data project? When prepping for #GenerativeAI, these three pivotal factors can't be ignored: Data accessibility, quality, and quantity. Ensure your #DataStrategy is robust and aligned with success. 📘 Dive into understand these crucial elements and elevate your AI initiatives! #DataQuality

Is your data ready for the power of #GenAI? Generative AI can revolutionize your business, but only if your data is up to the task. Without strong #dataquality, even the best #AI initiatives will fail to deliver meaningful results. In this article, we explore the challenges organizations face when implementing Gen-AI and how to overcome data roadblocks for long-term success. Trust in your #data is the first step to trust in your AI. Learn more ➡️ https://lnkd.in/gDFZWR-5 #DataManagement #DataStrategy #Analytics #CDO #CloudMigration #GraphDatabase #DataPlatform

Loved this post by Ari Bendersky on Salesforce's 360 blog, discussing how RAG, or retrieval augmented generation, can enhance the effectiveness of generative AI tools. RAG helps companies retrieve and use internal data from various sources and requires a vector database to store this data in a format that the AI tool can understand and pull from. The ability to use internal data, both structured and unstructured, improves the relevance and accuracy of the AI output. As we all know, the primary concern for executives around AI is trustworthiness. That's why we're seeing a mass effort to improve data quality and enablement for AI use cases. As companies continue to focus on this, I expect to see more about RAG, vector databases, and domain-specific genAI tools to increase user confidence and enhance the accuracy of AI outputs. #genai #llm #rag #data #dataquality

Data quality: the top hurdle for successful AI? 🤖 🤔 This insideBIGDATA study explores the issue, calling for more human expertise across the #ML Ops lifecycle. Check it out! #AI #DataQuality #MachineLearning