A recap of day 1, workshop day, at Content Marketing World and a few takeaways from the AI workshop I led.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
Each day to content marketing, and some reflections on yesterday's 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 some of the techniques demonstrated? And the answer is always two parts, right? So 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 into software, which makes sense.
But the catch is, software provider, desperately SAS software providers have computational requirements they have to meet, they have to their apps have to be fast, we've gotten so comfortable, as a society with instant everything that the idea of pushing a button and waiting for a result, five or 10, or 15 minutes is uncomfortable it it's offensive to some people.
And so what happens then, is that SAS providers, use the cheapest algorithms possible to make their applications faster, which is fine from a user experience perspective, but from a data integrity, and best result, perspective is not so good.
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, or work with a service provider that sets that expectation that Yeah, you're going to give us the data and you'll get an answer, but not right now.
Like, not quickly, in my own work with with customers are trusted insights that they understand up front, you give us the pile of data and will process and we'll get back to you with the results.
But it's not.
We don't have an app, we don't have software that's meant for the end user specifically, because there's so many limitations on the compute computational costs to make it work well, that is, it's better for the customer, to just hand over the data and then come back later and do something else in the meantime.
And then for those folks who are going to use this technology for their core competency, you, you must build it, you must vote because it's going to be part of your secret sauce, it's going to be part of how you do this 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 want that to be in house.
The other thing that was interesting was that the workshop was originally intended to be an advanced workshop, where people would come in and walk away with a functional piece of code.
We're going to use IBM Watson studio for that.
But a lot of folks had no coding experience.
And so we had to table that idea until maybe a few years down the road 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 need to learn how to cook.
You can eat takeout at restaurants all the time, and pre packaged 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 of 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.
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 auditors and here's how this works.
You need to build it, or you need to have interpreted code.
One of the interesting phenomena right now, in machine learning is interpreter ability versus explain ability.
Explain ability is where you tell an auditor, for example.
What happened after the code has been written, here's what it did interpret ability is let's step through the code and show you each individual piece of what it does.
interpreter ability is how you can very easily find things like bias, explain ability is much harder to do explain ability is is computationally cheaper, and faster.
So when you see a lot of companies, especially big tech companies saying explain ability is our goal with AI means that they're not willing to take the performance penalty for interpret ability.
And so there are some compliance issues with that as well.
So things to think about.
Overall, the workshop was fantastic, had a lot of fun with folks explaining how things work.
And 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.
There's still a long road to go for a lot of companies, they're still stuck in that data fundamental stage.
And that's going to be the case for a long time.
One of the points I made was that journey to AI is not overnight.
It's like you don't earn top, SEO rank or night, it takes a long time to get there.
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 30 because the more thorough you are, the better the outcome will be.
And and learn as much as you can about how the individual pieces are changing and the journey as you embark on.
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