You Ask, I Answer: Managing Rectangular Data with Generative AI?

Today's episode of You Ask I Answer was recorded in front of a live studio audience at the Digital Now conference in Denver, Colorado, in November 2023. The session title was appropriately, You Ask, I answer live. Generative AI QA. Enjoy. And the other follow-up is is there anything that we as someone that are trying to be advocates for proper use of it?

Is that identifying every article that has any inspiration that was from AI? Is that only creating exclusive contents and you're using AI for idea generation? Do you have any philosophy on how to prevent this? Well, so I think the transparency is important. Particularly for associations, uh transparency is important to say here's what we are and are not doing with this software.

Here's what we've published, you know, here's how it was made. It's kind of like you know, you look at this thing here. Right? On here is a nutrition label. It says, here's what's in this bag.

Now it may or may not be true, like it says uh vegetable oil, that's a bit vague. Um there's a lot of vegetables. But at least you know what's in the bag, more or less, and you know what's harmful. And we're starting to see some of that in AI with people labeling data sets, like, hey, here's what's in this data set. When you publish information, you might want to think about like what is the nutrition label for this document even look like?

Can we prove where it came from? Can we show an ingredients list? And if AI is part of that, that's fine, right? Like no one dings a company for saying, hey, you used a you used a blender for making this instead of you know mixing it by hand. But we understand that there's there's these tools in supply chain.

So I would say that's an important part. And then what we were talking about earlier about certification, saying, as an association, you are in a unique position to say, when we publish this, we've certified that it's true. If there's research or data coming from members, we are putting our stamp of approval saying we we have vetted this, we have peer-reviewed it, and this is true. And other things you may see out there on the interwebs that may contradict that, we haven't vetted it. We haven't processed it, so we can't say that this is true.

Especially around stuff like health. There's so much misinformation about health, and like in food and nutrition, that if you are an association in that space, you have the unique opportunity to say, like, we will tell you what is and is not true. In the back there. Sorry. That's an opportunity for us.

Yes. Are members going to come to us knowing that we're a reliable student? And we're going to be able to be trusting. Exactly. And that's why that disclosure and transparency about AI is so important so that your members continue to trust you.

When you publish AI generated content, you say this was generated by AI, but it's been reviewed by one of our team, and we can certify that this, even though it machine generated, it's still true. Other questions? Dad jokes. And I think mostly that's like language, you know, like text extracting key points, action items from text. We're an organization that's lots of data, like structured data.

It seems like AI isn't really the tool to use to understand like data that's in like tabular format, or but there's I don't know, are there tools that are developing that are more geared towards, you know, we're interested in say extracting data from like graph images, you know, things like that. Um, and I just don't know what the state of the art is in terms of those kinds of tools. So for tabular data, you're if you want to work with that data, your best bet, if you uh if assuming you don't already have the tooling, is actually working with um GPT4, particularly the advanced data analysis module, because what the tools do is they can write you code, right? So they can write you Python code that can process data for specific things. So if I if I go in here, let's go here and let's start ourselves a new prompt.

Um you are a Python programming expert. You know uh NumPy, pandas, data science, data extraction, um, data cleansing. Your first task is to ingest data from a SQLite database named Bob. And the table name is members. Write the appropriate code to extract the data, identify numeric columns, and produce a lasso regression of the churn column, uh based on the churn column.

Now, this is completely fictitious, but what it's going to start doing is essentially come uh start writing you the code that you need to programmatically access that using using Python in this case. So if you have rectangular data, tabular data, and you want to extract insights from it, you may not necessarily be able to load it into one of these tools, but you can have them write you the tooling you need to then do those things. Particularly if you if you know what you want, but you don't know how to do it. Like lasso regression and ridge regression, for example, are two classical methods for figuring out, hey, I've got a bunch of numbers and an outcome. Of all these numbers I have, which one relates to the outcome best and gets rid of noise that we don't need.

Lass of regression is one of those techniques. So you might say, I've got a lot of data and I've got an outcome I care, but I don't know how to figure out what's real and what's not. The tool will eventually, when you chat with it, say, you know, these are some choices for regression with it that you can then take and and try out on your data. That's how I tackle structured data. For vision data, uh right now it they all kind of suck.

Um they're okay, but they have a very hard time, particularly with poorly made graphs, of extracting data out of those graphs because it it's the same problem you and I have. You look at a graph that's badly done, you're like, I don't know what that says, other than there's a line that's going up and to the right. Um if you can't, if you look at the graph and you can't figure out what the data is, there's a good chance the machine can't either. Wow, it's really slow. Other questions.

I really like the if you have people who can write code that can inspect the work and help get running, this is a phenomenal way to build tooling within your organization for those efficiencies because there's things you do every month or every week or every day that are just repetitive. Like you get a spreadsheet full of data, and you're like, oh, I gotta copy and paste out this and this and this to make this this PowerPoint. You give that to the machine, you say, here's what I need to get out. Write me the code to access the spreadsheet and pull out these relevant data points, and it will do that. And then if your computer has Python installed on it, or you've got a server somewhere in your organization that has it on it, then you run that code against its spreadsheet every month, and now you're not spending an hour and a half copying and pasting anymore.

Now you just run the code and you get on with your day. There's lots and lots of those little wins throughout everyone's workday that the the challenge is not the technology, the challenge is knowing to even ask the question hey, can I get a machine to do this? Like this seems like an easy thing. Can I get a machine to do this? And the answer is usually is yes.

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