Voice of the DBA

Tag: AI

  • AI Ethics

    I found this article to be an interesting look at how we might add ethics to AI systems in one area. As the article points out, “… today there is no broadly accepted AI ethics framework, or means to enforce it. Clearly, ethical AI is a broad topic …”.

    Glad someone is thinking, or many people are, but sad that we aren’t really moving in a direction that creates a better system for us humans to work under, or be bound by. To be fair, I do think this is a very difficult topic and hard for any large group of people to agree on what should be done.

    Network monitoring is a fairly narrow problem domain, at least compared to many others. The article notes there are places where AI can, and does help humans that work in computer networking. That being said, how does the AI handle ethical considerations. For example, can we ensure the AI handles data privacy appropriately. This could be in compliance with some regulation like GDPR. It could also be in a manner that doesn’t disclose data inside a company to other systems or humans who shouldn’t see the specifics of network traffic (like passwords, credit cards, or any sensitive information).

    There are also other considerations. While we see bias in AI systems trained on previous human behaviors, because humans are biased, will network AIs similarly have bias? Will they be less helpful for power users, who have a wide variety of traffic? Those are often privileged users, who might benefit the most from helpful monitoring. Will AIs discriminate against a user when another humans trains or influences it against them? A crude example might be a network admin that doesn’t like women. They enforce more strict rules against women, and influence the AI to do the same. How will the AI, or others, detect this type of issue?

    Maybe the most difficult thing with AI is with corner cases. The ethical dilemmas that might not be easy to solve can confound an AI. Maybe the most ethical choice here is to seek other counsel or let other people help make a decision. To be fair, this is hard for humans to do as well, but for some reason we seem to trust computers less. At least some of us do.

    Ethics is a challenging issue. I find it to be difficult as a human, and my own inconsistency means I might react differently at different times or in different places. How we translate that to AI systems, which are increasingly a part of our world, is going to be hard. I don’t have answers, but I lean towards transparency, accountability from the humans in charge, and the ability to reverse (and apologize for), poor decisions.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • A Strange AI Achievement

    We are seeing AI and ML becoming used in more and more industries, but one that seems to be a place where it is embraced with some success. From speech recognition and transcription to analyzing imaging, computers have helped medical professionals improve the care they give to patients.

    Just as CAD has helped manufacturers, AI systems are being used in medical research, trying to model and screen medications to try and determine which ones are potentially useful in treating various diseases. We also have used them to better tailor treatments for certain diseases, like some cancers. It seems that applying computing against vast troves of data is proving itself beneficial.

    What if an AI were able to develop something new and it were awarded the Nobel Prize? That’s the premise in a scenario published in the Economist. The article opens with the controversy of how the guidelines for the prize are interpreted, paving the way to award the prize to the AI. Perhaps even more interesting in the scenario is that the effort is the result of a poor software upgrade that allowed the AI to read more medical papers that it was previous given access to examine.

    It’s an interesting idea. Who gets the credit? Certainly, the humans that help train the model and put it to work deserve some credit, but they are really the assistants. If they were to use the prize for more research, do we think they could replicate the innovation? Maybe it doesn’t matter. Not many people win the Nobel prize twice. Perhaps using the prize to continue allowing the AI to conduct research would make the most sense, though I don’t know if the humans around the system would accept that.

    I don’t think AI systems are more intelligent than humans, but they can consider and try more possibilities than humans, given enough data. They might notice something that we’d miss, and they can remain more focused on a problem than we can. After all, we need to rest and care for our bodies.

    I don’t know if this will happen in the 2030s, as the scenario imagines, but I do think this is a possibility as we start to use computing to search for new innovations in research. I just hope that as these discoveries take place, they are used to better the entire world, and not just enrich a few humans.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • Finding Legal Data

    Many of us have likely been asked about data science or machine learning in the last few years by someone in our organization. This has become a hot field, with many companies looking to try and find ways to use the technology to improve their work in some way. While I don’t know how successful these projects have been in organizations, I know that some areas seem to be finding practical applications. Image recognition, translation, and even some application scoring systems have benefited from machine learning algorithms.

    To build a successful model that works well, we need training data. Often lots of training data, and then have some metadata about how our training data might be applicable to a particular question. For example, if we have lots of pictures of dogs, we might need to tag the different breeds in order for a system to differentiate among them. If we want loan applications scored, we should have a corpus of documents that are already scored. This metadata allows the system to learn.

    There is a company, Clearview AI, that markets itself as a facial recognition system. To build their model, they scraped images from YouTube and other Internet sources, without consent from Google or the subjects of the videos. This is interesting, as the data itself is publicly available, but gathering it into a database and using it for other purposes might run afoul of data privacy laws, like the GDPR.

    I don’t quite know how to feel about this use of public data. While I don’t mind people viewing my pictures, I’m not sure that I like the idea of them being copied into a database for some other purpose. That might seem silly, or even strange, but I do think there is power in data and more power in more data. Allowing others to put my images in a database and use them, perhaps to train a model to recognize me, feels like overreach.

    If you need data for your company, or your idea, what can you do? Many people just scrape Google, Facebook, etc., and get data. That might cause you some legal issues in some places, and you ought to be aware of the implications if you choose to do this, or you are asked to do this. I don’t think this is how we want data to be gathered. I know there are some guidelines for responsible AI at Microsoft, but not necessarily rules in place for many companies. Hopefully that will change over time.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • 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.