Voice of the DBA

Tag: AI

  • 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

    Listen to the MP3 Audio ( 5.6MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • The Creepiness of AI

    Last year, I watched a keynote talk from Matthew Renze about AI. In his talk, there were examples of the amazing things that Artificial Intelligence can do, as well as some of the creepier things have have been developed. It was an interesting talk, one that gives me inspiration and hope for the benefits of better computer algorithms as well as the concerns for various issues that we may be unprepared to deal with as a society.

    One of the more controversial items that occurred recently with AI was the Google phone call, where a computer answers a call and interacts with a human. What’s disconcerting here is that the person doesn’t know this is a computer, and there are speech patterns the computer uses, like um interspersed in the answers, that deceive someone. While this certainly might be helpful in scheduling situations shown in the call, there is a downside. Could you imagine artificial personas used in telephone scams or phishing situations? A help desk knowing some information and then asking for verification of other data?

    There are perhaps greater concerns, such as the work done with imitations of President Obama. There are fake speeches, generated by computer. While movie studios might want fake actors used to reduce labor costs, do we worry about the implications of a computer actually being able to imitate one of us in a video call?

    The use of AI and ML, with lots of data an organization might have gathered could be good and bad, but certainly opens the world to more problems than benefits if there isn’t mandatory disclosure of the cases where this is used. Since there are always going to be criminal elements that don’t obey rules, this might be very scary.

    There are certainly other issues, such as Target predicting a pregnancy, which was the first really, creepy data analysis thing I saw. That one is a few years old, and still bothers me as it was accurate, but an unrefined use of the data. A good example of where marketing groups are a bit too excited to use AI/ML technologies and don’t think through the implications. Fortunately this case seems to have dampened some of enthusiasm for prediction in retailing.

    Perhaps this item that is a bit funny, but it is also very worrisome for me. It’s the case of an AI system playing video games. The AI system decided the best way to get the best score was to pause the game. Rather than compete and try to do better, the computer decided to just stop. A completely unexpected outcome, probably because the feedback and expectations weren’t explicit. Since it seems quite often humans don’t specify their requirements or expectations very well, I could imagine this being a very large issue in AI systems as they are used more often. It could even be deadly or problematic when a system does something we didn’t anticipate, and impacts human health.

    Most of us won’t work with AI much as a technician, other than providing or managing some of the data. I do expect AI and ML systems to touch more and more of our lives, perhaps using our data for good, perhaps not. Hopefully we can help steer applications into the former more than the latter situation.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.7MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Badly Trained AI

    Most of us know that data is being used to make more and more decisions inside of all kinds of organizations from retail giants to banks to sports teams. We are constantly asked, or see reports, of data driven decisions. We often need to show some data that supports and explains the rationale for making some choice. As our populace becomes more data savvy, I expect this trend to continue.

    AI (Artificial Intelligence), and the related Machine Learning (ML), are becoming more and more widely used. From mobile phones to autos to trading systems, we regularly see new “AI capabilities” being added to products and services. No business or industry seems immune, and I’m sure many of you are seeing AI being incorporated or feeling pressure to start using some AI in your work. As you work with AI, or start to, you’ll quickly realize the importance of data in your efforts.

    This is true for the cleanliness of data, but perhaps even more important in the tagging of data sets. As Amazon learned, building an AI or ML system, is hard. They scrapped one system that was being used to rate resumes and help their recruiters sort through the volume of applications they received. Why? Because of bias.

    Apparently the system would downgrade women’s resumes for various reasons. To me, this is a perfect example of a principle I’ve had throughout my career: garbage in garbage out. In this case it’s not necessarily bad data that was the problem, but bad tagging of what was a good and bad resume, probably from the internal prejudices of a few people.

    There will be more dangers as we use ML and AI technologies in our work. It won’t be enough that we clean the raw data for training, but also that we clean and properly manage the tagging of what data sets represent the results we are looking for. Like in much of our software, it’s easy for us to only consider the happy path, to only tag those items we think are good results. That is useful, but we might also be unconsciously tagging other results as bad, which appears to have happened to Amazon.

    We can build systems that do a better, more rational job than most humans, but we need extraordinary care to ensure our training data lacks bias. Unfortunately, most people both think they’re not biased and are unwilling to spend extra resources to deeply examine the data. Two things that worry me about the future of our AI/ML systems that will inform us.

    Steve Jones

  • Self-Healing ETL

    The process of building ETL flows is cumbersome and time consuming. Many consultants have made a lot of money by building ETL processes that manage the movement of data in and out of databases, including to data warehouses. There is a lot of importance placed on consistent, quick, and accurate data loading. In fact, one of the most popular job needs these days is still for ETL developers. Even as data science grows and becomes popular, quite a bit of the work in data science is data preparation, which often involves ETL, or ELT.

    One of the struggles with an ETL process is the tedious nature of building lots of flows that are very similar. There may be a wide variety of ways to get data to move, especially as we have a crazy set of inconsistent formats in which data appears, but once we have the flow, we often repeat that for many different files or tables. I think Biml makes this better, but it’s still not simple, and there can be plenty of issues that still arise as the source inputs change.

    There was an interesting article that describes the use of AI to assist in ETL flows. While there is some basic matching up of source and target data right now, what if AI would add a few things. The idea of looking at the data you’ve matched and suggesting alterations that can potentially help with data quality is interesting. The system would have to learn what issues occur over time and what good data looks like, but perhaps this could even extend when the source data gets slightly mangled in formatting. Perhaps detecting fixed width fields that have grown and shifted all columns would be possible, and even adjust the data flow.

    I am actually more interested in detecting PII data and alerting developers, or even operations people that the ETL process needs to be changed or secured in some way. AI should be able as well to help Operations people with scheduling, even pausing or moving ETL work to other machines to better scale the flow. My view would be that any AI system ought to be looking for anomalies and detecting those, as pattern recognition is one thing AI is good at.

    I’d think this would be more of an alert capability as I’m not sure I trust an AI system to actually adjust data. ETL flows are often too important to have data mangled in an unexpected way. Perhaps that’s too cautious, as that’s what developers do all the time. They mandle data, and we find ways to recover. Ultimately, I think that having lots of logging on what a developer or AI system does is more important. This might be especially true if voice commands are used to build the flows. I could see lots of ambiguities and mistakes from business analysts that are trying to describe how to build an ETL flow.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 4.2MB) podcast or subscribe to the feed at iTunes and Libsyn.