What is multimodal AI?

Welcome to the latest edition of KX Pulse!  

In this edition, our lead article comes from Ryan Siegler who considers the growing enterprise value of multimodal AI. We also feature a blog from Michael Ryaboy exploring the common pitfalls of choosing embedding models for AI applications (don't forget to download his ebook).

We finish with a link to EY’s recent AI Pulse Survey, which contains fascinating insights into the current state of enterprise AI adoption, focusing on talent strategy, investment, and ethical considerations. 

Blog: Multimodal AI – Harnessing diverse data types for superior accuracy and contextual awareness 

KX Data Scientist Ryan Siegler explains how multimodal AI combines various data types, such as text, images, and audio, to enhance accuracy and provide deeper insights. Discover how embracing diverse datasets improves decision-making across industries. 

Key Takeaways: 

• Improved accuracy: By leveraging multiple data sources, multimodal AI can deliver more accurate and context-rich results. 
• Contextual awareness: Understanding relationships between different data types helps in creating more intelligent and responsive systems. 
• Broader applications: Multimodal AI is adaptable to industries such as healthcare, finance, and manufacturing, where diverse data is crucial for informed decision-making. 

Read the full blog

Article: Eight reasons why processes fail 

Our CEO, Ashok Reddy, contributes to this article from Process Excellence Network which outlines the key reasons why processes fail and what organizations can do to avoid these pitfalls. From lack of ownership to outdated technologies, learn how to identify and address common process issues. 

Key Takeaways: 

• Misaligned data, tech, and strategy: This disconnect can limit process adoption and ROI, especially with advanced tech like generative AI. 

• Reactive Decision-Making: Siloed data and lack of real-time insights cause reactive decisions, inefficiencies, and missed optimization opportunities. 

• Inhibitors of success: Address both technical and business barriers before launching new processes to ensure success and better adoption. 

Read the full article

Blog: Introducing KDB.AI 1.3 

Discover KDB.AI 1.3, the latest update bringing enhanced features for AI-driven data analysis. Learn how we're improving data processing, performance, and integration capabilities to streamline AI workflows. 

Key Takeaways: 

• Enhanced data processing: KDB.AI 1.3 features improved data processing capabilities, allowing for faster and more efficient handling of large datasets. 

• Improved performance: Significant performance boosts in query speed and data access ensure smoother operations and better AI-driven insights. 

• Seamless integration: The new version supports enhanced integration with popular AI tools, ensuring compatibility and scalability for modern AI applications. 

Read the full blog

Blog: Why you’re probably using the wrong embedding model 

In this blog, Michael Ryaboy explores the common pitfalls in choosing embedding models for AI applications, explaining why many organizations may be using suboptimal models and how to choose the right one for better results. 

Key Takeaways: 

• Model selection challenges: Many organizations fail to choose the right embedding model for their needs, impacting the accuracy of their AI solutions. 

• Optimizing for use case: Understanding your specific use case is essential in selecting the most effective embedding model for optimal AI performance. 

• Performance and scalability: Choosing the right model ensures better scalability and performance, particularly when handling large datasets. 

Read the full blog

Blog: ARK 7 – Supportability enhancements for improved system performance 

This edition of the ARK series on Data Intellect takes a deep dive into system ‘supportability’ for kdb+ applications and how organizations can maintain high-performance systems with minimal downtime.   

Key Takeaways: 

• Focus on efficiency: Monitor your systems and ensure you are not overusing infrastructure resources or impacting your application’s latency. 

• Logging practices: When investigating an issue, the detail and relevancy of logging files will help you solve problems as quickly as possible. 

• Coding standards: Writing code that is easily readable with plenty of comments ensures that we can understand when something has gone wrong. 

Read the full blog

Article: Five AI adoption strategies for organizations 

EY’s AI Pulse survey outlines five strategies that organizations should adopt for successful AI implementation. From leadership alignment to data governance, this article provides actionable insights for businesses looking to integrate AI effectively. 

Key Takeaways: 

• Leadership alignment: Ensuring that AI initiatives are aligned with business leadership is crucial for driving AI adoption. 

• Data governance: Implementing robust data governance practices is key to ensuring the success and sustainability of AI solutions. 

• Scalable infrastructure: Organizations need to build scalable and flexible infrastructure to support long-term AI growth and innovation. 

Read the full article

Applications are still open for our Developer Advocacy Program, ‘Community KXperts’! This program is ideal for anyone passionate about sharing their knowledge on KX through blogs, articles, or other content. To apply, contact evangelism@kx.com or fill out this form.  

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