Unraveling the Mysteries of AI Interoperability for Your Business

In the exciting world of AI, interoperability is a hot topic. As more and more model producers enter the scene and existing ones improve their offerings, it’s important to understand what interoperability means and why it’s relevant to non-technical individuals like marketers. To help shed some light on the subject, I turned to Guhan Venguswamy, Jasper’s head of platform engineering.

Interoperability in AI

To make it easier to grasp, let’s compare interoperability in an AI platform to the flavors of a delicious gumbo. Just like gumbo is a stew made up of various ingredients that come together to create something amazing, an AI platform is a combination of different models that work in harmony. Each model has its own strengths, but when they are combined in the right way, they produce outstanding results. Jasper, being a multi-model platform, is like a perfect gumbo crafted by talented chefs who carefully select and balance the ingredients to satisfy the cravings of voracious marketers.

But why is interoperability so crucial? As Venguswamy explains, it allows us to tailor the recipe to our customers’ preferences. Just like some gumbo lovers prefer certain ingredients, we can customize the functionality of our AI platform to meet the unique needs of each customer, ensuring their success.

Without interoperability, an AI gumbo like Jasper would not be possible. It enables different models to work together seamlessly, creating a powerful and versatile platform for enterprise marketing operations. Not only does it guarantee consistent uptime and efficiency, but it also safeguards data privacy and security.

Intrigued? Keep reading to discover more about the basics of AI interoperability, its impact on marketing operations, and its potential for the future of AI.

How can enterprises protect data privacy and security while ensuring interoperability?

A crucial factor is ensuring that an AI platform can separate its functionality from its providers. This way, companies can maintain high levels of privacy and security regardless of the models they use.

At Jasper, we achieve this by having a layer that separates customer data from model providers. We only access a customer’s data when necessary and within the agreed-upon restrictions.

If you’re an enterprise user, it’s essential to find a platform that prioritizes data privacy. Make sure they’re not solely reliant on the terms and licenses set by model providers.

Can you explain why Jasper is rooted in multiple models for our AI engine?

We decided to go multi-model because the landscape of AI and model providers is constantly evolving. With new providers entering the market at a rapid pace, being able to select across multiple models allows us to adapt quickly and incorporate the latest features, including those we develop ourselves.

Our goal is to contribute to the multi-model ecosystem by providing our own AI for marketing, independent of larger foundational model providers. This choice gives us flexibility, enhances data security, and ensures we deliver the best solutions to our customers in every scenario.

What are some misconceptions about AI interoperability?

A common misconception is that larger models are better at everything. However, for many use cases, this is not the case. Large generalistic models lack depth in specific areas. This is precisely why interoperability is vital. By working with our customers, we can build a model that is more focused and provides greater value in the marketing vertical.

Our approach combines the benefits of larger models for general use cases and our own deep, marketing-oriented Jasper model. This way, marketers can leverage broader models for initial content creation and our specialized model for refining it with SEO optimization.

Looking ahead, how do you envision the evolution of AI interoperability and its implications for businesses?

I believe AI models will become smaller and more focused in the future. While large models will still have a place, the industry is shifting towards providing depth and specialization. As we continue to refine the AI we use, we’ll see more use case-driven models that offer strong functionality for specific tasks.

When enterprises develop their own AI copilots, I see a future where interoperability is seamlessly integrated. Copilots will be capable of adapting their performance without requiring user or company involvement. By training and building copilots on top of multiple models, companies can benefit from the best options available.

What should marketing leaders consider when evaluating AI platforms for interoperability?

An important aspect to evaluate is how well a platform allows for experimentation with new technologies and models. The ability to quickly experiment and iterate with emerging developments enables faster incorporation of new functionality into an interoperable platform.

At Jasper, we prioritize rapid experimentation with models, considering factors such as bias, strengths, limitations, and usability. When evaluating AI platforms, it’s essential to ask about their approach to experimenting with new models.