Voice of the DBA

Tag: AI

  • The Degradation of the Turing Test

    The Turing Test from Alan Turing was proposed as a test of an intelligent system. Could a human determine if the other party in a conversation was a machine? This was an interesting way of imagining how powerful a computer might grow and the types of answers it might give to a human. Interestingly enough Turing didn’t argue about the correctness of the answers, just that they appeared to be from a human.

    In some sense, I wonder how many people would have been fooled by the GPT-3 bot on Reddit. It posted comments for a week to a variety of threads. You can look through the posts by the “thegentlemetre” user, but this one caught my eye, and as I read it, I was surprised how much this looks like things I’ve seen posted on the Internet.

    Is this AI bot intelligent? I don’t know, but I do think that the quality of comments and posts on all sorts of threads and articles seems to go down over the years. Maybe it’s humans that are becoming worse at online communications rather than computers are getting smarter. Really, I think both things are happening.

    AI/ML systems are getting better at mimicking what humans do, and I suspect that in many cases, especially in small samples, they can fool many people, perhaps most. That’s disconcerting, especially as I already feel many people are a worse version of themselves online, without feedback and social cues directly available. Having bots add to the volume of poor communications and comments doesn’t seem useful to society in general.

    While I do think that AI systems can dramatically help us with mundane tasks and tedious work, I also think there can be problems if they become rigid in their actions, without allowing for some flexibility. Humans have discretion, and while they might not use it fairly, or even in ways that their organizations approve of, but they are flexible. Seeing these posts, I wonder if the AIs can learn to be flexible as well. I think they can be.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • AI Data

    At the Microsoft Ignite conference recently, I saw a talk that mentioned the Microsoft Garage Project, Trove, which is designed to help people provide data for AI projects in a new way. You can read more about it and get the app for Android mobile devices.

    Trove is built to help AI researchers find images and use them in projects. However, the data they get is provided by users, who make the choice to include their data. This is different than many AI projects, where anyone doing AI work often just gets data from various sources, sometimes without permissions, but often without the individuals who own the data understanding where their data is being used or for what purposes.

    I like the idea here of people specifically giving permission for their data to be used. It’s a good way for volunteers to provide data, and have some control over how the information you provide might be used and where it is used. That doesn’t mean this is necessarily a good model for the future. First, I’m not sure we can easily verify that the images someone submits are their own. I could see that if there are payments made, I’m sure people will try to game this and earn more money by using images they don’t own. We already have problems with people publishing content they didn’t create. I’m sure we’ve have plenty more with something like Trove.

    The other issue, and likely the biggest one I think is a problem, is that trying to understand what data is collected and how it’s used by many companies is a challenge. Even when there is some disclosure, it can be difficult to understand what is being released. Even while reading this document on SQL Server data collection, I’m not sure what might be collected on my system that could be an issue.

    I don’t think this is malicious or deceitful on Microsoft’s part, I’m just not sure I can understand the implications. That is where I feel we, as a society, and certainly with regards to regulations, are woefully immature. We don’t have good controls, but I’m not sure we really know what we’d want.

    This is a thorny problem, and one I know we need to find better solutions to over time. Especially as we use more and more data for large scale research and applications in areas such as Artificial Intelligence and Machine Learning.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • The Computing Revolution

    One of the keynotes at this year’s Build conference was on the Future of Tech. This was a mix of live talk and recorded pieces from Kevin Scott, CTO at Microsoft. This talk looks at the innovation of tech, with lots of AI/ML, but also with the idea that data is fundamental to the future. While he notes that there are different eras in computing as we have breakthroughs, one of the fundamental things that will change the world is the data explosion.

    We constantly push through boundaries and past constraints that limited us just a few years ago. I can still remember a professor in college bragging about the 32MB of RAM in his Solaris workstation, at a time when many of us had 1,2, or maybe 4MB. I think about that each time I get a new device. In a generation of my life, we’ve grown an order of magnitude, and pushed into a new world where I don’t have an exponential level of computing on my desk, but in my pocket.

    We might appreciate or worry about the growth of data, and it’s implications, or we may get excited and embrace it. Either way, big data is coming, and it continues to come to many organizations. While most of us aren’t pushing 10TB+ databases, some of us are, and few of us think in MB anymore. Makes you think that perhaps the default settings in SQL Server for MB in some cases are silly. Surely a MB is a like a penny (or pence in the UK), take one, leave one, but they don’t really count. Not until there are hundreds of them, and even then, maybe only a thousand is worth much mental effort.

    I do agree that the big win with the Internet, and with many pieces of software, isn’t the platform, but the ways in which people take advantage of the platform. I think that the creativity of developers, taking advantage of incredible computing on mobile, is what has created an amazing revolution. However, it’s not really just the creative software, but the understanding and use of data at scale that has really enabled software to change the world. Access to lots of data, whether this is people wanting car rides, sharing their thoughts and images, or just the bits that make up the music of the world are the ways in which software can become incredibly popular. Also, this is how things might get incredibly dangerous.

    I do think that AI has the chance to become an inflection point in computing, helping to create more applications that will work with humans in new ways that harness our knowledge, ethics, and vision with the computing power that continues to grow in incredible ways.

    The keynote is an interesting watch. It’s about 45 minutes, but if you want to dream a bit, it’s worth your time.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.

  • Smarter Design

    I have build a lot of PowerPoint decks over the years. Many of presentations, some for explaining a process, and once in awhile, I use it to create an image that’s simple, but beyond my skills. Even putting some geometric shapes together in a clean, aligned manner, is something I’ve found easier to do in PowerPoint than Paint.Net.

    Some of you may have noticed the PowerPoint Design Ideas that pop up when you create a new slide. At first I didn’t pay much attention, but I’ve started to take advantage of the ideas at times. The way that slides are put together is often much more visually appealing than any I’d think of by myself. Sometimes I take an idea and modify it slightly. At first I thought most of these were just standard templates, but Microsoft is starting to use more ML in the examples that pop up on the side.

    There’s a good AI Show episode at the bottom of this post that explains some of what they do. From image recognition to intelligent cropping to natural language processing, they are finding ways to make better design ideas available. the episode is only minutes and worth watching if you want to know more about how they use technology in their recommendations.

    To me, this is a good example of how technology and humans work together. Someone needs to come up with some good designs, based on certain situations. Users then provide them feedback on if they’re useful or not, and they can modify things. I even learned how to change the icons for the designs if I don’t quite like them. My changes (and yours) get fed back to new models as training for the future.

    Would this make all presentations look alike? I think there is some danger here that we start to coalesce into certain patterns. We might see lots of slides with dates become timelines, but they won’t be exactly the same, and there are different ideas based on content. Even the words in the titles. I made two slides, same content with a different title and I got slightly different recommendations.

    I think AI and ML have good futures, but not as the sole decision makers for how to react to data. Instead, I think humans constantly providing feedback, input into models, changing weights, and yes, helping with more complex things like design, will improve the results that more of us get from our tools.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher or iTunes.