Voice of the DBA

Tag: AI

  • Will ChatGPT Write Our Queries?

    The short answer is no.

    The longer answer is it depends, and perhaps to be more complete, ChatGPT likely will help us produce the simple, tedious queries with much less effort.

    There has been a lot of news about AI and ChatGPT and how well it performs for a computer. That last phrase is important because while it’s impressive, I don’t know that any of us would consider ChatGPT to be in our list of the Top 5 Dream Dinner Guests. The tool isn’t that impressive compared to most people, especially our friends.

    I look at Ayende Rahien’s blog often, and he recently had some thoughts on this topic, experimenting with ChatGPT and coding. Some things worked; some didn’t. In general, to get good code, you still need a subject matter expert, which is also the case with humans. We have plenty of people writing code that aren’t great at their jobs. We do see a lot of bad code, but we also have some great coders that help others to learn or just refactor their code later.

    So is ChatGPT better than a below-average developer? I don’t know. While the tool will get better, one advantage with people is we can complain to them, or send them problem queries, and they’ll learn to paste better code in from the SQL Server Central forums or StackOverflow or somewhere else. Perhaps ChatGPT will learn, but will it learn to improve and refactor code? I’m not sure how easy it will be to teach the system to edit something rather than just produce new code.

    I do think that the assistance features of AI, suggesting ways to complete sentences or lines of code, can be very valuable. They are a great productivity tool that can enhance your ability to get work done. If they learn to work in your style, suggesting the things you’ve done in the past, then these tools will help. As with most tools, they aren’t a replacement for your knowledge, but merely a lever to make you more efficient.

    Steve Jones

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

  • AI Medical Record Scanning

    Artificial Intelligence (AI) systems continue to pervade many industries, usually where there is a lot of data and human developers struggle to build solutions that handle the complexities of the problem. Often the experts in these subject areas can’t fully articulate the reasoning behind how and why they use data to arrive at some conclusion. We developers often struggle to get clear specifications from clients in simple cases, so I certainly understand why AI might be attractive in complex problem spaces.

    I think AI is a promising way to try to tackle some of these issues, some of which are important to humans. These systems can achieve a focus and analysis of complex data in a way that very few people, if any, can. The sheer volume of data and myriad of relationships among the various different metrics captured eludes the ability of most people to properly analyze.

    I found an article that looks a neat non-intrusive way of analyzing medical records from patients to detect blood issues. In this case, an AI looks at the records of treatment and test results, looking behind the doctors and nurses to catch patterns that can indicate blood poisoning. The signs are subtle, and in today’s world where the humans are often overloaded, hand-offs between people can be incomplete. This means that doctors and nurses sometimes miss things. The AI doesn’t order treatment or prescribe anything, but raises a flag to alert humans. The medical staff can then review things, examine the patient, and decide on the treatment.

    Early results seem promising, and more importantly, this is the type of lever that computing can bring to leverage human expertise and help humans. Not only can they better treat patients, but potentially this can help enhance the understanding of what signs a doctor or nurse should look for in future patients. When a flag is raised and an issue detected, the staff can go over notes or discuss how they might have caught this themselves. A little continuous learning applied between humans and machines, just what we’d want in a DevOps software pipeline in technology.

    Steve Jones

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

  • Digging into an AI Algorithm

    One of the more successful uses of AI (artificial intelligence) has been in the medical field. It seems that there is a tremendous amount of data, high variability in some aspects of the target of the data (the patient), and a need to constantly review very tedious volumes of data. A place where AI might, and has, made a difference to help humans, not replace them.

    There’s a neat article that dives into a bit of how a medical algorithm works for detecting sepsis. As you scroll, the article tries to explain in layman’s terms what the AI system(s) are doing. It goes through the necessity of regular data, which I think is a good design to push humans to gather data. It then talks about a variety of neural networks being used to analyze data, based on previous training from humans and past results.

    It’s interesting to think of multiple nodes coming to their own conclusions are different times of the day and then later nodes looking at the results of previous nodes. That’s what a human would often do, looking back at recent history and giving that some weight. The system comes up with a number that a human can use to consider in their diagnosis.

    It is interesting that the article talks about the need for humans to better communicate to use this system. I think that’s often a key with any computer assistance. The humans still need to use the computer as a tool and not as the final decision maker. However, at least in this instance, the tool seems to be helping reduce deaths in the hospital. I would hope that this is because the algorithm is detecting some patterns that are easy for a doctor to miss. It could be because the new system is focusing people’s attention better, and possibly this is a combination of both of these possibilities.

    I do think that AI systems can be very helpful in assisting humans in many tasks, and medicine might be one of the places where AI will be most useful. The sheer volumes of data, the complexity of the cases, and the load placed on medical workers to monitor many people can overwhelm any one doctor or nurse. Having a system that might remind them of a small detail will hopefully save lives.

    Steve Jones

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

  • AI Ethics

    I found this article to be an interesting look at how we might add ethics to AI systems in one area. As the article points out, “… today there is no broadly accepted AI ethics framework, or means to enforce it. Clearly, ethical AI is a broad topic …”.

    Glad someone is thinking, or many people are, but sad that we aren’t really moving in a direction that creates a better system for us humans to work under, or be bound by. To be fair, I do think this is a very difficult topic and hard for any large group of people to agree on what should be done.

    Network monitoring is a fairly narrow problem domain, at least compared to many others. The article notes there are places where AI can, and does help humans that work in computer networking. That being said, how does the AI handle ethical considerations. For example, can we ensure the AI handles data privacy appropriately. This could be in compliance with some regulation like GDPR. It could also be in a manner that doesn’t disclose data inside a company to other systems or humans who shouldn’t see the specifics of network traffic (like passwords, credit cards, or any sensitive information).

    There are also other considerations. While we see bias in AI systems trained on previous human behaviors, because humans are biased, will network AIs similarly have bias? Will they be less helpful for power users, who have a wide variety of traffic? Those are often privileged users, who might benefit the most from helpful monitoring. Will AIs discriminate against a user when another humans trains or influences it against them? A crude example might be a network admin that doesn’t like women. They enforce more strict rules against women, and influence the AI to do the same. How will the AI, or others, detect this type of issue?

    Maybe the most difficult thing with AI is with corner cases. The ethical dilemmas that might not be easy to solve can confound an AI. Maybe the most ethical choice here is to seek other counsel or let other people help make a decision. To be fair, this is hard for humans to do as well, but for some reason we seem to trust computers less. At least some of us do.

    Ethics is a challenging issue. I find it to be difficult as a human, and my own inconsistency means I might react differently at different times or in different places. How we translate that to AI systems, which are increasingly a part of our world, is going to be hard. I don’t have answers, but I lean towards transparency, accountability from the humans in charge, and the ability to reverse (and apologize for), poor decisions.

    Steve Jones

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