Grischa Rehmer’s Post

Amazing post by Grischa Rehmer I can add a bit more color. It is all about streamlining the close, consolidate, and reporting cycle. This ties nicely into my work on the close, consolidate, and reporting cycle - Blog – How to streamline the Close Consolidate and Reporting Cycle - https://lnkd.in/gCgD3Bux

Exciting advancements in AI research are paving the way for large multimodal models (LMMs), combining text, images, and more for enhanced capabilities. From assisting the visually impaired to revolutionizing industries like healthcare and e-commerce, the potential of multimodal AI is vast. With the ability to process diverse data types, LMMs offer a more human-like approach to problem-solving and decision-making. As this technology continues to evolve, the possibilities for innovation and transformation are endless. #MultimodalAI #LMMs #AI #Innovation #Technology #ArtificialIntelligence https://lnkd.in/gxUbq2tE

Generative AI is revolutionizing financial planning! 🚀 With IBM Planning Analytics, businesses can automate workflows, run ‘what-if’ scenarios, and make smarter, data-driven decisions. AI-powered insights are helping companies plan faster and more efficiently than ever before. Go visit our website for more information: https://lnkd.in/eBYGaZUR #AI #FinancialPlanning #IBMPlanningAnalytics #TechFix #GenerativeAI

The Importance of Data for 𝗔𝗟𝗟 AI Projects Artificial Intelligence thrives on data. For AI to effectively learn, interpret, understand, make predictions, and act on new inputs, it requires access to trusted, high-quality data. This data must be representative of the dynamic real-world and business scenarios the AI models and applications will encounter.  We've seen that each AI use case has unique data requirements, which are influenced by the specific AI techniques employed. Whether training a model or augmenting it with contextual business information, the data must meet certain quality and availability parameters for the use case. Additionally, data’s lineage, ownership, and purpose should be well-documented to avoid misuse. Data pipelines connect multiple data sources, apply transformations, and deliver refined data to AI systems, as well as data warehouses, lakes, lakehouses or other target systems. To keep up with the exponential growth in the amount of data used by AI systems, automated data pipelines are a necessity. The Dell Data Lakehouse is a great vehicle to centralize transformed data for AI applications and data analytics and can be a destination or source for data pipelines in the enterprise. You can easily orchestrate all our data pipelines from the Dell Data Lakehouse to your data sources and AI use cases, leveraging integrations with best-in-class tools like Data Build Tool (DBT) and Apache Airflow. https://lnkd.in/eNHV8Pxx #lakehouse #starburst #ai #analytics #iwork4dell

Aprovechar la #AI directamente en tu base de datos! -> Oracle Database 23ai adds Oracle AI Vector Search for fast and simple similarity search queries—on both structured and unstructured data. Learn more at DatabaseWorld AI Edition by registering at https://lnkd.in/d_7uHGRX

Chroma enhances the capabilities of AI agents by providing a specialized vector database designed for managing and retrieving embeddings, which are essential for various AI applications. Here are the key ways in which Chroma contributes to the effectiveness of AI agents: Key Enhancements Provided by Chroma 1. Efficient Vector Storage and Retrieval Chroma serves as an AI-native vector database, allowing AI agents to store and manage vector embeddings efficiently. This capability is crucial for tasks that involve similarity searches, enabling agents to quickly find relevant information based on user queries or contextual data. 2. Contextual Awareness By utilizing modern vector storage techniques, Chroma helps AI agents maintain contextual awareness of tasks and interactions. This means that agents can remember previous interactions and use that information to inform future responses, leading to more coherent and personalized user experiences. 3. Simplified Integration Chroma provides an intuitive interface for developers, making it easy to integrate vector search capabilities into AI applications. This simplification allows developers to focus on building features rather than managing complex database operations. 4. Support for Multi-Modal Data The platform can handle embeddings from various types of data (text, images, etc.), enabling AI agents to operate across different modalities. This versatility enhances the agents' ability to process and respond to diverse inputs effectively. 5. Enhanced Performance Chroma is optimized for speed and efficiency, ensuring that AI agents can perform rapid searches and retrievals without significant latency. This performance boost is essential for applications requiring real-time interactions. 6. Scalability The architecture of Chroma allows it to scale horizontally, meaning it can expand its capacity by distributing data across multiple nodes. This scalability ensures that as the volume of data increases, the performance remains consistent. 7. Open Source Flexibility Being an open-source solution, Chroma allows users to customize and extend its functionalities according to their specific needs. This openness fosters community collaboration and innovation in developing advanced AI solutions. In summary, Chroma significantly enhances the capabilities of AI agents by providing efficient storage and retrieval of embeddings, maintaining contextual awareness, simplifying integration processes, supporting multi-modal data, and ensuring high performance and scalability—all critical elements for creating effective and responsive AI applications.

🚀Exciting News from #VentanaResearch! According to Ventana Research’s recent analysis, Oracle has been recognized as the top AI platform in a still very competitive market! 🏆 This evaluation sets a high bar, assessing vendors based on key functional areas essential for AI success, including: • Data Preparation 📊 • AI/ML Modeling 🤖 • Developer and Data Scientist Tooling 🛠️ • Model Deployment 🚀 • Model Tuning and Optimization 🎯 This rigorous, independent study ensures an objective look at the best platforms for AI innovation—irrespective of packaging or pricing structures. Oracle’s #AI platform truly stands out for its comprehensive functionality and support for end-to-end AI initiatives. We’re proud to see Oracle recognized for delivering cutting-edge tools and solutions to empower businesses and developers in shaping the future with AI! 🌐💡 The full story https://lnkd.in/ehz8-54J #Oracle #OracleAI #AIplatform #MachineLearning #DataScience #Innovation

Oracle leads in AI Platforms. This leading platform is used to EMBED AI in Oracle Fusion Applications. AI based insights, recommendations and AI agents .. more and more

https://lnkd.in/eXDeRaKF Considering the mention of increased efficiency, it would be interesting to understand how these AI solutions can optimise existing workflows and potentially identify areas for process re-engineering...

Golf and AI fans. See how the Masters and IBM are enhancing your experience of the tournament with watsonx, hopefully we'll get a full day's play today! #genai #ibm #TheMasters #watsonx