Voice of the DBA

Tag: AI

  • 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.

  • AI Helpers or Replacements

    It’s interesting to look at the data business and how companies view DBAs, database developers, BI developers, data scientists, and more. There are some companies that really see value in our services, and I’m grateful for that. I’ve been gainfully employed for well over two decades to work with data. There isn’t much standardization in our jobs or what we’re expected to do, but I’ve grown comfortable with that. That’s one of the reasons the people working with me, my coworkers, are more important than the work. The work is the work.

    As our systems become more advanced, there is concern over how much less some of our skills might be needed with new AI type systems. Will the move to smarter systems mean there will be more opportunity for us? Or less? Certainly in the long term there might be less jobs if systems become really capable, but in the short term I don’t think so. As much as Microsoft has improved SQL Server, and they’ve done great things with easier HA configs, the Query Store, Adaptive Query Processing (coming) and more, they aren’t replacing many of us. Maybe a few, but I think there’s still lots of technical work.

    Darmesh Shah wrote a nice piece on AI and how it will help many of us in our jobs, providing the easy information and guidance for us to focus our skills. He sees bots as helpers, which to me presents new opportunities to interact with and work with customers and data. We will find ourselves more capable with help from AI, not replaced. As we get better machine learning or other adaptive algorithms, we’ll actually find new ways to work with data. This should provide us with new opportunities and new types of jobs that we might grow into. Plenty of people want to dismiss the data science jobs as a popular area where anyone can claim those skills if they know a statistical function and can query data in R, but there are real jobs in those areas, and there are opportunities in new companies that might not have existed in years past.

    There are going to be some amazing new ways that data and more intelligent algorithms will help us see our world in the future. There will also be scary ones, and many we don’t know if we can trust. Somewhere in there, the world will become very, very interesting for those of us that work with data on a daily basis, looking for new ways to extract information from all the bits and bytes that we store. Once again, this is something I do look forward to as a data professional.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.6MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Human and Machine Learning

    I was reading about the Microsoft Cognitive Services and their wider release in preview to more developers. There are a few of the many machine learning services that anyone can use to build more intelligence into their applications. The entire Cognitive Services include some interesting sets of APIs that allow us to build new features and even new capabilities that we might never have considered without the power of machine learning to change the behavior of systems over time.

    With machine learning, we have more and more ways to analyze data, allowing our systems to actually react to the data and become better at their particular task. I think there are certainly some dangers in how these systems might be hacked, but for many uses, I think ways that these platforms can work with speech, images and more might really alter the way in which we decide to build new applications in the future. The goal, at least according to Microsoft, is to have the AI services help humans accomplish tasks, not replace them. That’s something I hope actually comes true.

    I do think that we don’t quite know how these machine learning systems will grow and interact with people over time. They work by analyzing data, and altering behavior based on data, which means that we need to better understand the implications and effects of various algorithms and systems. I hope that more companies and developers spend time experimenting and working with various APIs and services to learn more about them. I’d like to see more projects and proof-of-concept systems.

    Some of us will just build things that aren’t useful, or that don’t even work well. That’s OK. We need to experiment, and understand these are experiments. These aren’t guaranteed ways of producing information from data. As long as we get that, or at least understand some of the work we may do for clients will need to be thrown away, that’s fine. I know there’s pressure in many companies to be efficient and just work on things that help the organization move forward. I’m sure some companies will make bad decisions, or even abuse their AI systems. I’m also hopeful that more will realize that some experimentation is necessary if you want to find new ways to create richer, more reactive and customized systems based on the massive amounts of data we collect every day.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.0MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Intelligence from Data

    There is an incredible amount of data in the world, and all that data is changing the way industries work. That’s the opening to a keynote talk from Jim McHugh at the O’Reilly Artificial Intelligence conference. The talk is short, 12 minutes, and interesting to listen to as Mr. McHugh looks at autonomous cars and healthcare, talking about the impact of artificial intelligence on advancing these industries. There are examples showing how data and AI systems are already being used to change the way the transportation and medical fields can work.

    Whether you want to see more robot help in our world or not, I suspect some level of this is coming, and it’s being driven by data. We have more and more data, and as companies have success in analyzing this data with various types of AI and machine learning systems, there is pressure for other companies to join the trend and build their own systems. We certainly see that with the push from Microsoft that emphasizes the R Services in SQL Server. At the recent Data Science Summit, there was a demo in the keynote (around 17:00) of over 1 million classification queries per second running inside SQL Server. You can even try this yourself on SQL Server 2016 Developer Edition (for free).

    I’m sure that a few of you will start to get more complex analysis projects inside of your organization. Maybe you’ll help develop some sort of prototype, or maybe you’ll just be responsible for helping get the data to the data scientists. I’m also sure that some of you won’t be thrilled with the results. After all, throwing a bunch of data at a few algorithms and expecting some rapid development isn’t likely to work great.

    At least not the first time.

    One of the thing I’ve seen from many people as I study data science, machine learning, and related topics is that this isn’t a simple process. Building a useful and successful machine learning system requires experimentation, and really, ongoing experimentation, as you examine, clean, discard, and make decisions on your data. In fact, the data preparation might be the most difficult and time consuming part of the process. That’s great, since many of us are the people that will work with the data, but it’s bad in that our management might not want to have the patience to experiment, evaluate, and re-tune their systems, much less wait for data to be well prepared.

    I do have high hopes for many complex problems to be assisted with machine learning and artificial intelligence in the future. I’m glad that companies are experimenting, and I think it’s great that so many data professionals are getting excited by the possibilities. Remember that this field is hard, and requires lots of work. Keep learning and growing your skills, and above all, remember that the scoring against your data is more likely to be closer to a baseball game than a bowling match. A 30% success rate might be amazing and those perfect games are likely very close to impossible.

    Steve Jones