Voice of the DBA

Tag: AI

  • The Downsides of AI

    I remember reading about AI as a college student, and I always wondered if we’d get computing algorithms that might actually make better decisions than informed humans. As my tech career grew, I saw the advancement in systems, like Deep Blue, that showed we could build better systems that might beat humans at their own games. The recent success of AlphaGo makes me wonder what the next challenge will be?

    Certainly machine learning (ML) and AI-like systems are becoming quite popular in business these days. More and more companies are trying to find ways to use these computer science tools to improve their organizations capabilities. Whether these techniques will continue to be popular remains to be seen, but in the short term, all the efforts of companies like Microsoft and Amazon will get many companies to try to build intelligent systems.

    There is a lot of data being used to fuel these AI and ML applications, so it’s a natural fit that many data professionals will want to be involved. It’s a complex area, where lots of data is used to conduct lots and lots of experiments. That type of work my not suit many of you, but give it a try on one of these platforms and see what you think. Perhaps this is work you will enjoy. However, even if you enjoy the work and become good at it, there’s potentially some issue that may impact the use of these systems.

    We don’t know how most of the applications actually work. That’s because we haven’t programmed them. In some sense, many of these systems learn from data, and with a little guidance, but there isn’t necessarily a debug log that might explain all the actions. That is perhaps the dark secret of AI. We don’t really understand what’s going on.

    The article linked above looks at autonomous cars, which a number of companies are researching and building. One of the issues with the complex system(s) that runs the car is that we don’t necessarily know how it works. This might not bother technical people, but it certainly does bother many people. When there is a problem, and there will be a problem since there are always failures, how do we determine what went wrong and make it better? Add more data and retrain the system? I’m not sure that is a solution that most people will accept.

    Ultimately I think some of the research and work on AI needs to focus on allowing the model to output a set of data on what data is being weighed and the impact on the flow of information that leads to a decision. I know this is a tremendous amount of data, but it is probably the type of analysis that is needed for accountability in these systems. After all, it’s not just cars where this matters. Imagine that your have an AI system that cuts off all AC in the summer, or decides not to order enough resources for a busy time period. How do we explain to management that “the system just decided” on that course of action? I really think we need better tools for analyzing these models.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.1MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Who’s Smarter? Humans or AI Systems?

    I used to watch science fiction movies and imagine we’d have these incredible machines that could interact with humans in amazing ways. I always believed more in the helpful and useful AI in Star Wars than the dangerous systems of movies like The Terminator or The Matrix. I could even imagine a time when we have sentient systems, though I’m not sure they are coming anytime soon.

    I used to assume the Asimov’s Laws of Robotics would be implemented without any issue. After all, doesn’t everyone worry about security? These days I’m more cynical, and I wonder if anyone would actually bother to program limitations in a system. I expect more people would assume we’ll add in the laws later, once we know the other features are working.

    I do think we will get more and more systems like IBM’s Watson, that are designed to handle many tasks. When I read pieces like this one from HBR, I actually get a little worried. Not because machines will destroy humans, but they will greatly impact and change our society, potentially in disruptive ways for society. While we’ve seen issues with manufacturing over the years, there are still humans involved, and we have learned to find other jobs over time.

    The AI revolution, if that’s what’s happening, will occur quicker, and if the HBR piece is correct, will impact many more people, across a shorter period of time. Our economies and workforce may not adapt as quickly, which has a variety of economic implications for all of us, even those of us that might be secure in our positions. If we can teach systems to accomplish tasks at a level even close to what the average human can do, we might find ourselves either out of a job or working with some sort of robot.

    I don’t know how quickly we’ll advance, or even how far we will go, but I do find that the leverage offered by implementing advanced technology is certainly increasing in a way that worries me.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.3MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • The Digital Twin

    The world is changing quickly, and it’s becoming incredibly personalized thanks to digital technologies. I saw a fascinating video from GE on digital twins. These are digital representations of their products, using specific data (sensor, visual, weather, settings, etc.) to build a model of a specific piece of equipment. This model runs on a platform and constantly analyzes new data to evaluate and predict the performance of the system. With equipment like jet engines, power plants, and more, a tiny increase in efficiency can translate into incredible cost savings or revenue increases.

    The idea of using digital twins is taking hold in other fields, and I expect that we’ll continue to see all sorts of digital representations and models of real world items. We are even starting to see this in medicine, with personalized treatments for various diseases, including cancer. By using more data, and powerful computing capabilities, we can tailor our treatment to the individual and their particular ailment.

    This personalization has an annoying side as well, after all many of us have experienced more targeted advertisements and annoying uses of our personal information, however much of that is crude, and lacking in sophistication. Perhaps if the models using data about me would be less annoying if companies didn’t try to sell me a laptop a week after I’ve purchased one, or show me sales on products when I’m looking at SQL Server articles.

    I’m amazed and hopeful that our computing systems will evolve, with more talented data scientists blending their knowledge in some problem domain with powerful computing capabilities and lots of data. I expect that some of the challenges we face with our aging infrastructures and physical systems will be helped by extensive data and powerful machine learning models specific to an instance.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.2MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • The March of AI

    Machine learning and AI systems are becoming more and more popular all the time. The constant onslaught of media articles and hype is pressuring more and more companies to experiment with AI systems. Whether these techniques work or not, no manager wants to be the one that has ignored the trend. A failed machine learning venture might be preferable to the lack of any project in the eyes of many in upper management.

    Our vendors press as well, after all, sales are on the line. I wasn’t sure how useful the R language would be inside SQL Server, but I am amazed at the effort and popularity of R among SQL Server professionals. R, and possibly Python soon, are becoming integrated into every product and tool from Microsoft, as well as other vendors. Every month I see more and more people experimenting and learning how to use R for a wider variety of tasks, from analyzing disk space and database performance to extrapolating business measures.

    Most of us aren’t skilled enough in math and statistics to really develop and build intelligent systems. We don’t have the background, or we haven’t worked in those areas for a long time. However, many of us can learn to work with ML and AI systems, managing the models, integrating the code others write into our applications. I do think there is value in learning a bit about these technologies, though I don’t think you need to become an expert. Some of you might want to be experts, and I wish you the best of luck on your journey.

    I do believe that AI will change the world. It’s going to become a larger and larger part of many systems and processes throughout the world. These intelligent systems will press and push humans in ways that we don’t expect and that we might not like. For us data professionals, we will be at the heart of many of these changes, with opportunities to grow our careers we might never had considered.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.0MB) podcast or subscribe to the feed at iTunes and Libsyn.