“Within the next ten years, the first thing to read any piece of text in your business won’t be human. The first eyes that will read that text will be AI.” Meryem Arik, Co-founder and CEO at TitanML, shared her bold vision on the future of AI at the 2024 Everyday AI New York conference. This blog will recap the top insights from the session and go through how the evolution of AI will fundamentally change how we handle unstructured data and redefine productivity and creativity in the workplace.
The Comeback of Unstructured Data With AI
Meryem’s assertion is bold, but it underscores the transformative potential of AI. We are on the brink of an era where AI will be ubiquitous in business operations. This change is driven by the need to process and analyze vast amounts of unstructured data that organizations generate.
Unstructured data, which makes up 80%-90% of all organizational data, includes text, images, emails, reports, social media feeds, and more. Unlike structured data, which fits neatly into databases and spreadsheets, unstructured data is challenging to analyze. Currently, this analysis is mostly manual, slow, and prone to oversight.
Fortunately, AI excels at processing unstructured data, offering the potential for faster, more accurate insights. By deploying AI to review and analyze data, businesses can free human workers to focus on tasks that require creativity and critical thinking. AI will not replace human effort; it will augment it, allowing us to focus on the most interesting and value-driven activities.
Building a Differentiated AI Ecosystem
To make AI omnipresent in business, we must build a differentiated AI ecosystem. This involves understanding where and how to integrate AI across business operations. According to Meryem, there are two main ways to access AI capabilities: API-hosted models and self-hosted models.
1. API-Hosted Models
API-hosted models are popular for proof of concepts (POCs) because they are easy to implement and offer access to proprietary models that may outperform open-source alternatives for specific tasks. They are also cost-effective at a small scale since you pay per use, which is ideal for experimentation.
2. Self-Hosted Models
Self-hosted models, on the other hand, offer greater control and scalability. They are ideal for handling sensitive data and deploying models that require customization or are used at a large scale. Self-hosting allows organizations to fine-tune models with their own data, enhancing performance and ensuring privacy and compliance.
Many enterprises are investing in self-hosted AI capabilities to maintain control and security over their data, avoid third-party dependencies, and leverage the customizability of open-source models.
Choosing the Right Tools to Implement AI Everywhere
As organizations plan to deploy AI across various applications, it's crucial to choose the right tools for each task. The decision to use API-hosted models, self-hosted models, or a hybrid approach depends on factors like data sensitivity, scale, performance requirements, and strategic importance.
For applications where control and security are paramount, such as M&A data, self-hosting is the best option. For highly specialized applications like molecule discovery, the ability to customize and fine-tune models makes self-hosting advantageous. Meanwhile, a hybrid approach can offer the best of both worlds, with certain models self-hosted and others using API-based services for less sensitive data or less critical applications.
Getting Started With AI Deployment: TitanML x Dataiku
TitanML provides a platform to help businesses self-host language models securely and efficiently. By deploying models in a controlled environment, TitanML enables organizations to build AI capabilities tailored to their unique needs. This platform supports various model types, including language models, vision models, and more, all accessible through a simple API that integrates seamlessly with existing tools.
Recently, TitanML announced their integration with the Dataiku LLM Mesh, enabling organizations to leverage Dataiku's robust platform alongside TitanML's self-hosted AI solutions. This collaboration allows businesses to decide whether to self-host or use API models based on their specific requirements.
By strategically deploying AI to analyze and interpret unstructured data, organizations can unlock new levels of efficiency and innovation. Meryem’s vision of AI as the first reader of text in our businesses is not just a futuristic dream; it's a roadmap to a more intelligent and agile workplace.