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.