Voice of the DBA

Tag: AI

  • How Much AI Do We Need?

    I like to cook and bake. The act of putting something together relaxes me, and I’ve certainly had plenty of time in 2020 to do this. In fact, I’ve been doing so much that my wife and daughter ordered me a new stand mixer while I was making them cookies this past weekend. They thought it would make it easier for me to continue to prepare more meals when life returns to some sort of normalcy.

    I’m not a chef, and I certainly struggle through some recipes, but I enjoy the time working through the act of assembling something. I also struggle with fitting in the time to prepare things around an otherwise busy life. When I saw this article on smart appliances, I stopped to think that some of these might be nice, but perhaps they’re also reduce some of the skills I’ve built and perhaps make my time in the kitchen less enjoyable. Would I become dependent on them, entitled, or even less excited about the task of cooking?

    It’s something I hadn’t considered too often, especially in other areas where AI and “smart” items are becoming more commonplace. I think some of advances in the data platform are good, and certainly some of the cloud services handle tasks that are fairly simple and they do them well. Backups happening in many of the PaaS services are mostly something we don’t need to configure or worry about, though we ought to be sure we know how to actually perform a restore. I have high hopes for some of the index automation and other changes, though there is a ways to go.

    Is the use of more AI in systems better? In some sense I think this can help us cope with busier and more complex lives, and allow us time to focus on the things we want to focus on. At the same time, I suspect these systems aren’t as mature as the marketing suggests, and the options certainly command a premium, which is something that might benefit the vendor more than the consumer.

    Like any advance, this could be helpful or hurtful for any of us. I like to assemble things from scratch more often than not, and I’m not sure if I think the AI gadgetry is something I’d enjoy. For now, I’m taking a small step, with a little mechanical assistance that might let me less time on the physical activity of combining things and more time planning my next steps or chatting with the audience that is often sitting at the counter.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • The Evolution of AI

    I saw a study recently where an AI system was used to analyze code and trying to decide if authors were good. The conclusions were things we’d expect, and quite a few people laughed about this on Twitter. After all, if AI comes to the obvious answer, is it useful? Perhaps, but it’s also a little disappointing.

    I’ve felt that way about AI for some time. Years ago I went through an AI demo for the Titantic data set, coming to the conclusion that the lower you were in the ship, or poorer, the more likely you were to have died. I went through a tutorial with Microsoft on flight data, trying to determine why flights were late. It turned out the later they left, the more likely they were to be late arriving.

    While this might seem silly, and perhaps obvious, I think that’s OK. We don’t expect these early AI systems to be better than humans, at least I don’t. However, what I do expect is that they can do at least as well as humans, and having them return results that we could figure out is a good step. To me, this also gives me confidence that a computer can be used to analyze a complex problem or situation.

    There is more evolution coming in the AI and ML worlds. I have no doubt that these systems will improve, and likely find niches where they are valuable and very helpful in making decisions. The world is gathering more and more data, and it’s becoming hard for humans to analyze it, and maybe more importantly, react quickly. The rate at which data can change is overwhelming, especially for humans. I think that we will need computing systems to help process the vast amounts of data we continue to accrue.

    While I don’t know that I want AI/ML systems making decisions for me, I do think they can help reduce the burden of looking over data and help humans to better focus their analysis in certain areas. A symbiosis of computing software using AI, data, and a human to closely watch it is what I think will really help us become even more efficient and focused in our particular organization.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • The AI Manager

    One of the advantages of a computer is that it will handle repetitive tasks very well. That’s one reason the DevOps world pushes for automation of simple tasks, like compiling code or copying files between machines. We know the software will perform the task the same way every time, giving us a reliable, repeatable process. Even in many AI systems, the structure of the program ensures some level of reliability, though the actual actions or results may vary dramatically based on the inputs.

    For some tasks, this is great. If we have a system watching for a change in code to rebuild the application, that’s certainly a job for a computer rather than a human. If a sensor needs to be checked and an action taken if it exceeds a value, certainly the watching ought to be done by software, though the actions may or may not be something we want a system to do without human input.

    In some sense, I likewise think that the use of AI to manage workers is a bad idea. This was something I read about in Manna, which seemed exxagerated when I first perused the story. Now it seems that some of this is coming true in this story about workers in a call center being manager by an AI system. There is a software on their machines that tracks their activity, the way they interact with customers, and more. If there are potential problems, the software tries to give them messages about how to alter their behavior. Many employees aren’t bothered by this, but I wonder if that will be the case over time, especially if this software starts to affect employment and pay decisions.

    I think this is micromanagement at a level I’d never want to work under. I know that some of these jobs are akin to factory jobs that make widgets, with employees doing the same thing over and over again. However, there also is some creativity and thought required, otherwise I’d assume an AI could just do the whole job. That might be coming, and I wouldn’t be surprised to see it, but as long as humans are working here, there ought to be plenty of human oversight.

    Could you see this in our industry? Maybe AI reminders that you’re slow to close a ticket, or not committing enough, or even that you’re not doing enough testing. It’s crazy to think that there could be software that companies use to manage developers and other technology staff.  My guess is most of you think this would be a poor idea, but I could see this becoming used in help desks and infrastructure at some point. If it works there, who knows what types of AI software might be deployed by companies.

    Perhaps more worrisome for many is that if this becomes a trend, this might affect the middle management levels in companies more than workers. One manager might do the job of 10s,  or even 100s, which is its own societal implications if we use less people as supervisors. Maybe this would make for more efficient companies, and hopefully, less meetings. I don’t know if I think that’s a good evolution of software being used to manage a process.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • Giving Computers Ethics

    I was reading a fascinating paper recently about autonomous cars. I’m actually excited about having a car that can drive itself, though I think this is likely quite a few years away, despite the hype. Ever since I read Red Thunder, I’ve thought that we would first get full time autonomous cars that would either be limited in where they were in use, or part-time autonomous cars that could only be self-driving in certain places. Dense inner cities, or maybe isolated highways might be good places to try this, in my mind.

    While we want to do some programming of these cars, we also have a lot of AI/ML systems in place that run models trained to react in certain ways. They identify things that are moving and stationary, trying to determine how the car should navigate and react. The systems aren’t quite as tightly programmed as many of us expect, with if this then that logic. Instead they have guidelines that are decided upon by the designers and then reactions to data inputs and analysis are a little more fuzzy.

    What are the goals? Well, in most cases they are just moving the car safely down a road. In crisis situations, it’s a little more murky. What happens when collisions are unavoidable? How should the car react? Humans often panic and do strange things, but we don’t want erratic behavior from automated systems, so what should we set as goals? There’s a bit of research that was done to ask humans what they would do when they can consider the situation a little more slowly.

    In short, humans make different decisions in different cultures. There are clusters and tendencies in different parts of the world, which is interesting. While people are people and behave similarly in many cases, we tend to value different things, depending on our views of the world. That can be problematic when we start to expect computer systems to be more consistent or predictable. After all, we should decide how computers react and be able to trust our decisions are followed. It is up to humans to imprint our ethical desires as a society on computer systems.

    This is an area where I feel AI and ML systems are moving faster than our ability to comprehend the implications. I would want to have a framework built for automated systems, certainly cars, and then expect all vendors of systems would implement that framework in their vehicles. However, this goes beyond cars, and in any places where we are using software, AI/ML based or not, we ought to publish a comprehensive outline of the way in which our system works.

    Computers have the capability to improve our world and reduce chaos, but only if we agree on the way in which these systems work, and disclose in a transparent way what data they handle and what decisions they make based on that data. I hope that we start to get better about informing the world the goals and operation of our systems. I’m not sure that will happen anytime soon.

    Steve Jones

    The Voice of the DBA Podcast

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