Voice of the DBA

Tag: AI

  • 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

     

  • The Black Boxes

    Machine learning and artificial intelligence seem to be the hot topics these days. From bots that can interact with people to systems that learn and grow as they process more data, it seems that science fiction is becoming reality. At least, in limited ways. Autonomous cars, perhaps the highest profile example of these topics, are advancing and being tested in a few locations around the world, but I think we are a long way from having human controlled and autonomous cars interacting freely at any scale. There are still plenty of issues to work out, and the consequences from mistakes require serious consideration.

    I was thinking of this as I read an interesting question: Whose black box do you trust? It’s a look at algorithms and machine learning, and the impact they have on the world around us, despite many of us not understanding how they work. The main examples in the piece are in the area of journalism as it relates to social media (primarily Google and Facebook), but also touches on autonomous vehicles, both autos and planes. The latter was a bit of a shock to me as I assumed humans always handled takeoff and landing, something the author says doesn’t happen at SFO. Some searches around pilot sites seem to note that automated landing is done regularly to test systems, but is used in a minority of cases.

    The question is, do we trust the black boxes that run our systems, and really, does it matter? In the piece, Tim O’Reilly says he has four tests for trusting an algorithm:

    • the outcome is clear
    • success is measurable
    • the goals of the creators align with the goals of the consumer
    • does the algorithm lead creators and users to better long term decisions.

    Those are interesting ways to evaluate a system, though I think the problem is that the last two are a bit nebulous. One of the things that I see more and more as I get older is that the same data or the same facts can lead two different people (or groups) to two different results. Our goals, our interpretation of events, even the weights we place on the various factors in a complex system vary dramatically from person to person. In such a world, can we truly evaluate what the goals of a creator are? Forget about consumers, assume one person building a software system. They will have multiple goals, and do we really think their goals can be easily listed? Or weighted/ranked appropriately? What about when the goals change?

    I really think that the black boxes need more disclosure, though I freely admit there isn’t a good way I know of to do this. However, I do know one thing that can be better disclosed: data. We can have more openness and analysis of data from software systems along with some accountability by creators for the impacts of their software. Again, I don’t know how to enforce accountability, especially at scales that encompass millions of consumers and easily cross country borders. That is a problem I think we need to find ways to tackle, at least at some manageable level. Maybe using the 80/20 rule where 80% of consumers and creators find the outcome to be a good one.

    The world of technology and software are advancing and growing extremely quickly. Certainly hardware advances, but it seems the last 5-10 years have been more about new and different software applications that fundamentally alter the way humans can interact in social, business, and government situations. Underpinning all the changes is data. New data, more data, and novel ways of working with this data in ways that were unheard of 20 years ago. It’s an amazing time to be a data professional.

    Steve Jones

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