Voice of the DBA

Tag: artificial intelligence

  • A Great Use for AI

    In the last couple of years there has been a tremendous amount of hype for machine learning and artificial intelligence as a way to improve the world. Plenty of companies have tried to implement ML/AI to generate more revenue or improve their products, often with mixed success. However, I recently saw a place where I think AI might shine.

    I’ve never owned a Roomba or any robot vacuum, and I’ve never encountered a poopocalypse scenario. I do have a cat that is an avid hunter, so I certainly could envision something similar with a carcass in the house, but apparently, some owners of these vacuums have had a very messy experience when they pet has an accident and the robot vacuum attempts to clean the floor.

    The company has implemented a camera and AI to try and avoid this happening, as well as avoiding other obstacles. How this will actually work remains to be seen, but it’s a good place to use AI to try and detect objects that might cause issues, notify the owner, and avoid creating a mess when trying to clean one up.

    This is also a place of low impact if the AI doesn’t work perfectly. If the model can’t determine what an object is, avoid it and flag the situation. Allowing owners to provide feedback and improving the model over time is what I’d want to see, with regular improvements that might help the system tell when an object is something that could cause issues. If Roomba does a good job, they’ll use this as an opportunity to gather data and improve their products.

    AI/ML isn’t often a build it and forget it technology for systems. These technologies use models, which are inherently incomplete and don’t always match the real world well. They need a lot of training, with new data, across time to become something that works really well.

    Are they worth the effort for most systems? I don’t know. I do know that good data science is needed, lots of data for training and testing, and a set of boundaries where the system works well and where it doesn’t. I suspect we’ll see more businesses having success with AI over time, but not in all situations. I suspect older extrapolation and human judgment work just as well for lots of problems.

    Knowing when each might work more efficiently will be a challenge for years to come.

    Steve Jones

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

  • Where We Need Better AI Disclosure and Responsibility

    There are a lot of contract and gig jobs in the world today. It used to be this type of work was widely spread throughout programming and technology, but these days many types of jobs are commonly completed using contract workers. I like the flexibility of contract work, but I also think that these workers need to be better at saving and planning for the future because of less employment stability. Usually they are paid more, but they need to use that to reduce the risk of being unemployed.

    One trend that I’ve seen taking place in some of these positions is the use of software and AI to determine if a worker is doing an acceptable level of work on a regular basis. Amazon might be one of the highest-profile companies doing this, especially as they expand into the delivery business. They are using the power of computers to manage an army of workers rather than traditional human managers. This includes terminating them. There’s an article that talks about some of the experiences of their workers.

    I don’t know how their system works, but I do know the frustration of trying to work with a company that doesn’t use humans for many tasks. If you’ve ever tried to contact Google, you know that it’s incredibly difficult to actually communicate with a human. Google seems to think that its automated systems can handle all situations. They might handle many things, but they don’t do a good job in plenty of situations, and there is little recourse to have a human intervene.

    I do think that AI and ML can help our companies better interact with the world in many cases, but these systems are certainly looking for broad patterns. Maybe these patterns handle the middle 80% of cases, or maybe it’s more like 60%, but there are plenty of situations where humans ought to be involved. Maybe more important, when someone uses these systems to make decisions that impact human life, there should be some explanation and understanding of how the model impacts this specific situation. We want to know why the computer comes to its conclusion in medical care, employment, legal issues, or maybe anything other situation.

    There is work being done to try and explain how these models work. The important thing, however, is to ensure that while we may understand the model, we also need to disclose the reasoning to those affected by the systems. Any appeal process should include this explanation, and likely with a human involved at some point to help evaluate the model for accuracy, fairness, or any other measure that is relevant. To me, we ought to require this of companies using AI models in their business practices.

    Steve Jones

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