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You're joking, right?

Wide-adoption of this would near automate a few people I work with on a daily basis

I predict that soon VCs will start investing in AI, and that press will start covering the developments as well.

He posed the question as if there was doubt as to whether major business escorts would be going all in on AI. I'm responding to that doubt. One of the most conservative moving tech companies, Microsoft, is already 10billion plus in on OpenAI.

An investment arms race is already underway.

Not to mention the need for unicorns to bouy the casino we call Wall St. Get ready for perhaps the biggest bubble we've ever seen.

Seems like this would make a company’s customer service bots massively better. So many of them are terrible.

I’m curious how the field of conversation designing bots moves forward.

No. My company is already looking into it and they are serious.

same here

Yeah it's obvious every big company is looking at AI, most experimenting with it or more.

Yes. That's obvious. I was wondering if the guy above was even asking a real question. It's so obvious.

This sub struggles with tone and context.

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Massive security headache . Not unless I can run my own corporate instance

If the cost is per query answered then we might even see a day where you are not waiting on hold ever again. Companies boasting about "No queue customer support" will catch on quick.

I wonder if it wouldn't make searching for answers a 100 times faster. Most of the call center type jobs I've worked for wanted you to give only answers that were in some knowledge base. Well you could train an AI to learn everything in that knowledge base and recall it instantly to help with any customer problem. Connect it with all the other systems that are used for servicing accounts and I'm pretty confident an AI could be much more efficient than even the best customer service agents.

He wasn't really making a question, it was more of statement or well, just a comment.

Maybe Google can get an idea from you, they have zero customer support, even for app developers on Android. Got your account blocked? - good luck getting any person to help you. People are legitimately terrified of this scenario to the point of giving up on Gmail. Losing all online identities in one go is not fun.

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Customer support for sure. It's already automated in a lot of places, often with very basic chatbots. I just hope it also makes it better for tech savvy people. I really hate dealing with customer support if they go trough all the basic steps first. "Yes I've turned if off and on already 100 times before I called you.'

It's not that bad, the AI only knows the structure of your data and not the data itself.

I think it's Goldman Sachs (I can't recall which mega bank, they are all the same evil blob) has been working on a fine tuned legal aid for internal legal issues and will soon be releasing 90% of their lawyers.

This was discussed over on r/datascience too. We’d love it if it worked out of the box, but the knowledge requirements needed to tell the tool what tables do and what each of their columns mean requires a level of documentation that most companies don’t have reliably, nor would it be standardized enough to allow a model such as GPT to generalize. In a perfect world, metadata is available, and data governance is a significant focus. Often, companies don’t have time to focus on these tasks as they require considerable work. Additionally, even though there are a lot of efforts to standardize, sometimes the underlying concepts need a lot of human intervention prior to being pushed into models.

With this in mind, the title should read, “GPT tool that lets you connect to unrealistically well documented databases, and ask questions in text.”

This May be a factor in convincing a company’s CTO that they need to let us focus on documentation, but right now, governance and metadata are far from priorities for analytics teams.

> the knowledge requirements needed to tell the tool what tables do and what each of their columns mean requires a level of documentation that most companies don’t have reliably

See the potential. This is where this tool it's now. Where is it gonna be in a year? .)

Technical citation ?

You can look at an example here: https://platform.openai.com/examples/default-sql-translate

That makes much more sense . Schemas are a dime a dozen

>This was discussed over on
>
>r/datascience
>
>too. We’d love it if it worked out of the box, but the knowledge requirements needed to tell the tool what tables do and what each of their columns mean requires a level of documentation that most companies don’t have reliably

If your tables and columns are named explicitely you can get away with just feeding it your database schema and the AI will figure out what you are talking about.

If not, you can create views to make it more clear what each table and column means and feed it that instead.

You can also give it special rules to keep in mind. For example, if in your DB a "man" is identified as "1" and a woman as "2", you can add this instruction to your prompt and the AI will understand that whenever you are looking for a man it needs to check for the value "1".

I expect text-to-SQL will become a standard pretty soon. It's just way too strong.

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A very scary and recent story: "Google flags man as sex abuser after he sends photos of child to doctor"

Even after getting everything sorted out with the police, he still got locked out of his Google account forever.

Not sure this is actually a huge bottle neck. You will just dedicate a few people to always ensuring that the metadata and documentation is updated. That is then how data roles start to shift in an AI world. You then might not need a team of 20 data people, but can get away with 10.

You are welcome to provide more context and tone to your previous comment.

That’s the thing though, you need to know what you’re looking for in the database in order for the database to be able to provide you with data. AI can guess, sure, but you won’t be able to trust the results unless you’re familiar with the database, and ensure the AI is as well. I agree it’s not a breaking case, it’s just a case of considerable resource reallocation.

In your example as well, even though it is an implicit assumption that gender is an easy construct to define, that May not be the case. Are we talking sex at birth? Sex at point of observation? Identifying gender? Constructs require a lot of data understanding and finessing in a manner that end users won’t be able to clearly be able to pull without a human directing the AI somehow by providing data availability and documentation. Once you have those human data prep processes done, yes, you want your end users to be able to ask questions of the data readily. But this requires a fair bit of human anticipation as to what should be available to the AI given end-user business needs.

There's definitely a learning curve for the user to learn to properly express themselves. But there's also different strategies you can use to help them.

A library of common prompt examples is a first one.

A UI with predefined chunks of query that you assemble is another.

You could also use embeddings to detect ambiguity and ask your user for precision.

You also don't need to expose your whole schema right away, this can be done gradually. You start with the most common requests and build from there. This way you don't need to invest a huge amount of resources from the beginning.

We are barely scratching the surface on how to use it. This will be common practice pretty soon.

If you are in a position that you have access to a database at work, I strongly suggest that you give it a try. It's surprisingly good.

Totally agree. The company's CTO or business users need to buy into this in order to allow resource allocation. It's promising, it just requires a hell of a lot of "human in the loop" at the moment in order to finess the data to a point that the AI could produce reliable results from hidden concepts and constructs in raw tables. I think the assumption currently is that your data is finessed already for GPT to take over and produce reliable and clean results. Those 10 data people will certainly be supported by data cleaning staff. That's where it should be anyhow. No data scientist likes spending the 80% of their time cleaning and prepping the data, but that's where we are now.

I do too! Just need the buy in from stakeholders, and support, as stated in my other comments.

>If you are in a position that you have access to a database at work, I strongly suggest that you give it a try. It's surprisingly good.

I'll give it a try with synthetic data! Maybe I'll be surprised at the amount of finessing it doesn't take. I assume it's gonna take quite a bit to make it work, but I'll give it a shot!

And yet there are still a lot of people with "if you did nothing wrong you have nothing to hide" mentality, justifying and defending invasions into privacy from governments and corporations.

Fun starts here: https://platform.openai.com/examples/default-sql-translate

Then "all you need" is to create an API call with python to get the query from OpenAI and send that query to your database through another API call.

Start small, there's a lot of little quirks but the potential is definitely there.

I expect that in the coming years you will start to see a bunch of articles about the best practice on how to integrate an AI with a database.

Good luck.

then, by the end of the 5-10 minute checklist, did you restart your computer, did you update the app, did you try a different browser, did you try in 'private' mode to ensure no cookie conflicts, etc the non-english as their first language support will be out of ideas and pass you off to another support person only to repeat the process.

- my experience with comcast/xfinity customer support

I honestly can't wait for this to be replaced by a gpt model.