Content Marketing World Day 1 Recap

It's day two of Content Marketing World and some reflections on yesterday's um workshop on artificial intelligence. One of the questions that came up a lot in the workshop was where do we get this technology? Where do we get this the use of AI and the some of the techniques demonstrated? And the answer is always two parts, right? It's either build or buy.

But buying a lot of the specific techniques that work the best is very difficult these days because of the nature of computational costs. Most AI is being worked into part into software, which makes sense. But the catch is software providers, particularly SaaS software providers, um have computational requirements. They have to meet. So, in many cases, if you want to be able to use the cutting edge and to get the best quality, you do have to either build it yourself or um or work with a service provider that sets that expectation that yeah, you're gonna give us the data and you'll get an answer, but not right now, like not if not quickly.

Um in my own work with um with customers of Trust Insights, the they understand up front you give us the pile of data and we'll process it and we'll get back to you with the results. But it's not uh we don't have an app, we don't have software that's meant for the end user specifically because there are so many limitations on um the compute computational costs to to make it work well. That it's it's better for the customer to just hand over the data and then come back later and do you know do something else in the meantime. Um then for those folks who are gonna use this technology for their core competency, you've you must build it. You must build it because it's going to be part of your secret sauce, it's gonna be part of how you do business and how you market to the customer better or make better product for the customer, and that's something you don't want a vendor to own, right?

You you want that to be in-house. The other thing that was interesting was that the workshop was intent originally intended to be um uh an advanced workshop where people would come in and walk away with a a functional piece of code. Uh we're gonna use IBM Watson Studio for that, but a lot of folks had no coding experience, and so we had to table that um idea until maybe a few years down the road when when folks have got some experience with these technologies. You don't need to know how to code, but it sure does help. It's like you don't need to learn how to cook.

You can eat, take out, and at restaurants all the time, and you know, like prepackaged foods. But if you want to know how the food is actually made, you need to know how to cook. If you want to be sure about the ingredients in your food, you need to know how to cook. If you are concerned about portion size or calories or sodium or allergens, you need to learn how to cook. And the same thing is true in machine learning and artificial intelligence.

If you're okay with a vendor just doing it all for you and you don't know how they got their results, use that. If you have regulatory requirements that necessitate that you be able to explain to an auditor, here's how this works, or have a service provider that can provide the code to the auditor saying, here's how this works, you need to build it, or you need to have interpretable code. One of the interesting phenomena right now in machine learning is interpretability versus explainability. Explainability is where you tell an auditor, for example, what happened after the code has been run. Here's what it did.

Interpretability is let's step through the code and show you each individual piece, what it does. Interpretability is how you can very easily find things like bias. Explainability is much harder to do that, but explainability is computationally cheaper and faster. So when you see a lot of companies, especially big tech companies saying explainability is our goal with AI, it means that they're not willing to take the performance penalty for interpretability. And so there are some compliance issues with that as well.

So things to think about. Overall, though the workshop was fantastic, had a lot of fun with folks explaining how things work, and uh at the end of it, I think I hope that people got some value out of it and can now speak to their organizations about ways they could be or should be using artificial intelligence within their marketing processes. Um there's still a long road to go for a lot of companies, they're still stuck in that data fundamentals stage, and that's gonna be the case for a long time. One of the points I made was that the journey to AI is not overnight. It's not it's like you don't earn top SEO rank overnight, it takes a long time to get there.

Um the same is true of doing AI well. You have to take a lot of time and accomplish all the prerequisites in order to be able to do it well. So be patient if you're on the journey, be thorough because the more thorough you are, the better the at outcome will be. And uh and learn as much as you can about how the individual pieces are changing in the journey as you embark on it. So, as always, uh please leave your comments below, subscribe to the YouTube channel, to the newsletter.

I'll talk to you soon. Take care. Want help solving your company's data analytics and digital marketing problems? Visit trustinsights.ai today and let us know how we can help you.