Voice of the DBA

Tag: AI

  • AI Is Great and Tech is Failing

    On a recent weekend, I got a text from my bank that they had declined a charge to one of my business accounts. I called them back and they let me know there had been a couple of weird charges on the account that their AI system detected. This seems to happen every year or two so I wasn’t overly worried. I cancelled the card and ordered a new one.

    A day later, my wife got a call about our credit card with the same issue. She cancelled the card and got new ones ordered. However, I use that card to travel and I had a trip booked. Suddenly I was without a credit. Luckily, we have another card for my wife’s business that I could use. I called the bank and had a card expedited, but the situation created some stress. In fact, I panic-bought an RFID-shielded wallet. I’ve resisted for years, using an older, large wallet me daughter bought for me one Father’s Day that always reminds me of her. The timing across a few cards was weird, and I suspect my wallet got scanned somewhere and both card numbers were stolen.

    A few things. First, be careful with the new tap cards, as they can be scanned and read from a distance, albeit a short one. Second, having a spare payment method might be nice in this age of non-cash transactions. Third, why is technology failing with new cards?

    I lost a card last year and knew it was gone. There were no charges, but I couldn’t find it and needed a replacement, so I cancelled it and ordered a new one. In minutes the digital cards on my phone (and watch) had been replaced. I had new numbers and could transact business.

    Why would this be different? The banks arguably have better knowledge of my digital wallets, and replacing those is much easier than relying on a snail mail server and the time it takes to deliver cards. In my rural area, we regularly have reports of stolen mail, with thieves targeting credit cards and physical checks sent by snail mail.

    This was a minor issue in my life, and I am fortunate I have other ways to manage payments in this minor crisis. I was (and am) happy that AI systems are often detecting fraud. I haven’t had any fake charges go through in a decade and almost every real charge is approved, even with my crazy travel schedule. However, I’m disappointed in technology in this case.

    Many organizations are engaged in a digital transformation. They’re hiring software developers and trying to take advantage of all the data they have to improve services and efficiency. Security, service, and spending would be served better with a little technology improvement here.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.

  • AI Sings the Blues

    Quite a few of you aren’t thrilled or enamored by GenAI (Generative Artificial Intelligence) with things like ChatGPT or CoPilot. Some of you love it or find it fascinating. My guess is many of you are like me and you’re not quite sure how useful this is with a healthy dose of skepticism.

    No matter how you feel, I find this piece on AI Blues fascinating to read. It talks about some of the problems with AI and how people are becoming less tolerant of the small errors that AIs make. I think people might be upset with some of the issues, but I also think these are types of things we might see in a colleague as well, and we might tolerate them for much longer than we think.

    The thing in the piece that really caught my eye is that training models to make less errors might make them less creative. I don’t think they are very creative, but I do think part of the idea is that they might do something better than we could do, at least for those of us working in a space where we’re a beginner to intermediate (or advanced beginner). In many cases, we’d expect better output from a model.

    The note about training not being often or in-depth enough as well is interesting. Perhaps some of the models ought to be trained separately, with some having a more specific focus, like learning Python and producing very few errors, or learning Python to produce interesting solutions to large scale problems even if there are some errors in the code.

    I could see similarly more tightly trained models for other areas, such as marketing email or legal issues. Maybe we need a lot of different models tailored for different jobs. Of course, if companies are struggling to generate revenue on AI already (and I think they are), then can they afford to train and retrain specific models focused for smaller audiences?

    Maybe, and maybe Moore’s law will help bail out some AI experiments, but I’m not sure. For now, I continue to experiment, and I think skilled tech people ought to do the same. I don’t think it will replace us, but I do think it can be a useful tool in many ways to help us become more productive.

    Steve Jones

  • Expanding Northwind with Prompt+

    I was trying to do a little testing of our subsetter and needed a larger database. So I decided to ask Prompt + to help me with a little AI assist. This post looks at what happened.

    This is part of a series of experiments with AI systems.

    Getting More Data

    I had a Northwind_FullRestore from my experiments with Alex Yate’s quick PoC tool. I selected that database and asked for a little code. I asked the AI to give me 1000 more rows of data in the Customers table. I got this code.

    -- This script inserts 1000 rows data entry into the dbo.Customers table
WITH numbers
AS ( SELECT TOP (1000)
             ROW_NUMBER () OVER (ORDER BY
                                   (SELECT NULL)) AS num
      FROM sys.all_objects)
--INSERT INTO dbo.Customers
--  (CustomerID, CompanyName, ContactName, ContactTitle, Address, City, Region, PostalCode, Country, Phone, Fax)
SELECT
   'CUST' + RIGHT('0000' + CAST (num AS nchar(4)), 5)
, 'Customer' + CAST (num AS nvarchar)
, 'Contact' + CAST (num AS nvarchar)
, 'Title' + CAST (num AS nvarchar)
, 'Address' + CAST (num AS nvarchar)
, 'City' + CAST (num AS nvarchar)
, 'Region' + CAST (num AS nvarchar)
, 'PostalCode' + CAST (num AS nvarchar)
, 'Country' + CAST (num AS nvarchar)
, 'Phone' + CAST (num AS nvarchar)
, 'Fax' + CAST (num AS nvarchar)
FROM numbers;

    Then I decided to go big. I changed my prompt, as you see below, and got more data. My new prompt: expand this to add 10GB of data to this table

    I got this code:

    -- This script adds approximately 10GB of data to the dbo.Customers table
-- Firstly, we need to calculate the approximate size of each row. 
-- For simplicity, we'll assume each row is ~1KB in size after considering nvarchar data expansion.
-- Therefore, to add 10GB of data, we need approximately 10,000,000 rows.
WITH numbers
AS ( SELECT TOP (10000) -- This will generate 10,000 rows in the CTE
             ROW_NUMBER () OVER (ORDER BY
                                   (SELECT NULL)) AS num
      FROM
        sys.all_objects a
      , sys.all_objects b)
INSERT INTO dbo.Customers
   (CustomerID, CompanyName, ContactName, ContactTitle, Address, City, Region, PostalCode, Country, Phone, Fax)
SELECT
   'CUST' + RIGHT('00000000' + CAST (numbers.num + seqnum AS nvarchar), 8) -- 8 character CustomerID to accommodate large number of rows
, 'Customer' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Contact' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Title' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Address' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'City' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Region' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'PostalCode' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Country' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Phone' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
, 'Fax' + CAST (numbers.num AS nvarchar) + CAST (seqnum AS nvarchar)
FROM
   numbers
   CROSS JOIN
     ( SELECT TOP (1000)
              ROW_NUMBER () OVER (ORDER BY
                                    (SELECT NULL)) AS seqnum
       FROM sys.all_objects) seq;

    The Results

    The results were a bit underwhelming. I ran it and got this:

    8-2-2024-0005

    The problem here is that the CustomerID is a 5 character field. The comment in the code notes that this is now an 8 character field, but that’s not great as I might not want to change the size of the field.

    I then asked this: change this script to ensure the customerID is less than 5 characters and alphanumeric

    I got the same script, but the first line of the select was:

    'CUST' + RIGHT('00000000' + CAST (numbers.num + seqnum AS nvarchar), 8) 
-- 8 character CustomerID to accommodate large number of rows

    Now when I run it, I get a problem with postal code, which now has the truncation error.

    My Thoughts

    It’s easy to say this was a failed experiment. I didn’t get working code. But I got a bunch of code that was close to what I need, in a fraction of the time that it would take me to write this, even with SQL Prompt. Then add in the fact that I can edit this code to what I need, which works, and saves me times.

    I think this has potential for shortcutting some work and getting me closer to what I need quickly, even if it’s not perfect. If I’d have asked a junior dev to help me with this, I might still have to edit their code. Just as I do with my AI assistant.

  • Using AI for the First Draft

    At the Redgate Summit in London, I ran a panel talking about Platform Engineering and how we can make developers more productive. One of the questions from our audience revolved around AI (Artificial Intelligence) technologies and how they might assist. As a note, AI tech includes a lot of different things, like machine learning (ML) among other things, but a lot of people seeing the media and hype around LLMs (large language models)  think those are AI. They see AI as what is implemented in ChatGPT and Copilot, which is correct, but incomplete.

    One of the panelists, Jeff Smith of Redgate, said that he views the output from AI as a first draft, something that bootstraps further work by a human. This can save time and can help someone be more productive, but it’s a starting point and a boost, not a final product.

    I had not thought of AI this way, even though I expect to have to test, refine, and edit the results I’ve gotten from various assistants. While it might be possible for me to keep refining my prompt and perhaps generate better results (code and/or text), I don’t know that it’s often worth the time to do much of this. I might do a little, but it’s much easier to take the result of AI as a first draft and then make it fit my requirements.

    In much the same way I use Stack Overflow or SQL Server Central for code snippets. They often give me a starting point and something I can then adapt to my needs without all of the time and effort spent on reading an API or SDK, experimenting with the syntax, and then actually using this in some code I’m writing. Similarly, I can get a head start on writing a summary or a pitch, though I’ll admit I’ve found it less useful there. In fact, for many of my editorials, I’ve tried to use it to summarize an article or web link, and it does a very poor job (IMHO).

    Some of you might be concerned or worried about this, and some might not. To me, the average-to-poor code produced isn’t any different than the legions of Stack Overflow developers or SQL Server Central DBAs who take code from an online forum and fail to test it. Anyone using any code (or prose) should test (proof) it and verify it works appropriately in their situation. We have a lot of people who don’t do that now and AI might exacerbate that with more code, but I don’t know if we are in a worse situation. We certainly seem to be able to produce lots of average tech professionals now who turn out plenty of average code.

    Ultimately AI is a tool that does exist in the world and will continue to exist. We have to learn to work with it and use it to our advantage. Good developers and DBAs will do that. I’d like to think that many organizations will look to hire and use those people as their staff and use fewer of the people who don’t bother to test and improve the results from their tools.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.