.svg)
Interoperability in AI
Interoperability is becoming an increasingly important topic in the generative AI space as more model producers toss their hats in the ring and existing producers make their models bigger and better. But what does interoperability (also known as multi-model [which is different from multimodal]) mean? More specifically, what does interoperability mean in common language for a non-technical, marketing-focused human like me, or a marketing leader, for example?
I asked an expert on the matter — Guhan Venguswamy, Jasper’s head of platform engineering — to define it for me.
I took a swing at mirroring his words back to test my understanding. Since I’m from Louisiana and understand life through metaphor, I broke it down using the food of my people:
Interoperability in an AI platform is like gumbo. It’s a stew (the platform) rooted in a wide variety of ingredients (models) that are great on their own, but when combined in a particular way make something delicious. You can remove one or two ingredients or make some substitutions but it’s still gumbo. And Jasper, being a multi-model platform, is gumbo. The Jasper team is the chef hand-picking all the ingredients and their portions for voracious marketers to enjoy.
After humoring me, Venguswamy said, “Yes, it’s pretty similar. The other big thing to think about that is we want to be able to tailor the recipe for our specific customers too. So if some of our customers like certain ingredients over others. We can help them by tailoring a recipe more to their tastes that actually helps them be successful.”
Jasper or any other AI gumbo would not be possible without interoperability allowing for many different models to come together.
I continued to pick Venguswamy’s brain about some of the basics of how interoperability works within enterprise marketing operations. We also discussed data privacy in this realm, why Jasper was built based on that idea, and what interoperability may mean for the future of AI.
Read on, it’s tasty stuff.
{{rule}}
What is interoperability in AI and why is it important for businesses?
Interoperability is the ability to operate among multiple systems. Users want to be able to keep their functionality and operations alive and thriving, regardless of what a provider’s specific function is.
In Jasper’s case, we utilize a number of large language models and foundational models to help us build generative functionality. The important thing for us to consider with interoperability is that we don't want to be too reliant on a particular model for writing a blog, working on social media posts, or any use case. We need to use multiple models to provide functionality in case one model performs certain functions better. Or in case one model is no longer available, or its security or privacy policies change and don't align with an enterprise-level product focus.
How does AI interoperability impact the efficiency of enterprise marketing operations?
One huge benefit is consistent uptime — the period a system remains operational and accessible without any interruptions or downtime. Many existing foundational model providers have lower uptimes than what we are able to provide. The biggest reason for that is our ability to have interoperability and multi-model providers for our key functionality.
Since we're able to decouple our functionality from individual model providers, if a model provider goes down, we're able to move to another provider and allow functionality to continue without any downtime for a marketing team. This allows teams to continue simply doing the task at hand without feeling like they need to worry about whether downstream providers are actually allowing them to do their work.
What about a lack of interoperability?
Let’s say a model provider allows you to do a specific type of generation, for social media posts for example, without any interoperability. Then they decide to tweak their privacy position and your data is no longer as secure as it was. You're now no longer able to use that functionality and you lose the ability to do a key portion of your job.
How does AI interoperability affect data privacy and security?
Data and privacy in the AI space are always growing, always changing. But it doesn't mean that the model providers are always thinking about them the same way. Certain model providers may choose to move away from enterprise-grade solutions and focus on the consumer market. Others may choose to focus on the up-market but lose functionality as a result. Interoperability allows us to keep our functionality while also keeping our focus on data and privacy security.
[.blog-quote]"If a model provider goes down, we're able to move to another provider and allow functionality to continue without any downtime for a marketing team."[.blog-quote]
What can enterprises do to ensure they're not compromising data privacy and security with interoperability?
A really big thing is determining whether an AI platform can decouple its functionality from its providers. This allows companies to provide higher levels of privacy and security, regardless of the models that we use.
Jasper can do that because we have a layer on top of the models we use that allows us to separate customer data from model providers. We only utilize a customer’s data to talk to providers when it’s necessary. And the data is used within the restrictions agreed upon with our customers.
So if I’m an enterprise user, I want to look for a platform that's able to uphold the highest level of data privacy. I want to ensure that they're not just beholden to the terms and licenses set out by the model providers themselves.
Can you tell us more about the choice for Jasper to be rooted in multiple models to power our AI engine?
One of the biggest reasons we chose to go multi-model is because the landscape of gen AI and foundational model providers is moving at lightspeed, with many new providers entering the space. Our ability to select across multiple models means that regardless of how the landscape moves, we can adapt quickly and build on top of the newest and greatest features, including models that we're building ourselves.
We want to make sure we’re contributing to that multi-model ecosystem by providing our own vertically oriented AI for marketing use cases and best practices — one that isn’t reliant on many of the larger foundational model providers.
So our choice to go multi-model is about flexibility, data security, and the ability to contribute to this ecosystem and to our target customers by providing them the best in class in every possible scenario.
Learn more with A Marketing Team's Guide to Generative AI!
What are some common misconceptions about AI interoperability?
One of the biggest misconceptions I hear about foundational models is the larger they are, the better they are at doing everything. We're starting to see that that is not actually the case for a lot of our use cases. Large generalistic models are not always the best-case scenario because they are wide but not very deep in a specific area. It speaks to why we need interoperability because, while working with our customers, we can build a model that is less wide but much deeper in the marketing vertical and allows them to get a lot more value.
We're actually trying to combine the best of both worlds: give users the generalistic use cases of some larger models when they need it and the deep, vertically oriented marketing-specific Jasper model.
For example, maybe a marketer needs research to start building their content. So we’ll use a larger foundational model like OpenAI or Anthropic to help them. Then they need the content to be SEO-optimized as they start refining it. So they need an LLM honed for that marketing-specific task versus something generalistic.
As we look to the future, how do you see AI interoperability evolving and what implications could this have for businesses?
I see AI models evolving to be smaller and more focused. Larger models are great and there will continue to be big players in the space that shape the landscape. But as with any growing industry, it always starts super wide but not very deep. Eventually, people want to see more depth really hone in on specific uses. And I think as we continue to evolve the AI that we're utilizing, we're going to see smaller, more use case-driven models that provide strong user functionality targeted towards a specific task.
When enterprises start building these copilots out, I see the future evolving to where very specific AI copilots have interoperability built in under the hood. The copilots can change how they perform those tasks under the hood without the user or the company really needing to know how that happened. So we can train and build these copilots on top of multiple models to allow companies to get the best of every possible option.
What should marketing leaders consider when evaluating AI platforms in terms of interoperability?
One really good thing to evaluate is how well they're able to experiment with new technologies and new models types. The faster a company can experiment and iterate on utilizing some of the newer technologies and models, the faster they'll be able to incorporate that functionality into their interoperable platform.
We focus a lot on rapidly experimenting with these models. We ask questions like, “Do these models have additional bias? What are they good at? What are they bad at? Are they usable and if so, how can we utilize them for customer use cases?”
So I think companies should be asking AI platforms, “What kind of experimentation do you do on the new models that you're bringing in into your platform?”
