This is a good example of how AI can be used well.
Current AIs are effectively fuzzy pattern detection and matching engines. This one can sift all the data coming in, and spot patterns that previously corresponded to problems. It then flags them for human interpretation.
The AI chunks the vast sea of data. A human is then involved to sanity check what it has found, and react accordingly. E.g. a pattern appears that often precedes a broken rail within a month. A human can check the subset of the data, and schedule a maintenance team a week later. Conversely, a pattern that leads by hours would require an immediate response.
Exactly this. The number of fields machine learning breakthroughs can be applied in, just for processing huge collections of unstructured data, is truly mindboggling. We’re in for an interesting ride!
Fun fact: Salesforce has been selling this exact service for ~8 years now. Its old tech.
It’s their Wave Analytics package.
Also some application of similar tech has worked itself into industrial machines and factories over the last 10 years or so, it’s downright ubiquitous for anything that’s expensive and requires maintenance/ upkeep. Also it’s well intertwined with the ML tech we see consumer facing nowadays, the image recognition of 4+ years ago was made to recognize issues with materials, unexpected growing patterns, anomalies, as well as recognition and counting etc… before we got just point your camera and it’ll tell you what you’re looking at.
But how did China do this when we banned the sale of nvidia gpus to China?
Nvidia GPUs aren’t the only way to run a machine learning type system. They are just the easiest to use, currently. China has also been developing their own AI optimised chips. Though I don’t know much/anything about them.
Pretty sure the other commentor was being sarcastic.
I still want to know
Well it’s like the other person said Nvidia aren’t really doing anything magic, Cuda is great but it’s not the only option.
Interesting. They are using AI to identify problems before they happen.
Pretty reasonable. The title makes it seem like they have a hairbrained scheme…
Many problems have early warning signs. Just train the model to notice those signs.
At least in many industrial applications, the vibration of an electric motor or an axle is a good measurement. Also, the temperature of a ball bearing can tell you a lot. That’s just the basics though, because you can also train the model to look at fancier details.
This is a perfect use for AI to automate systems. The job of maintaining a train isn’t dynamic, it’s very predictable, AI shouldn’t haven’t a problem doing this task well.
AI is not needed for predictable tasks, it excels in finding predictions in a mass of data.
For example, one of the most dynamic systems we have is global weather and GraphCast is incredible at it.
Removed by mod
Ahh yes, c(ai)de.
