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I worked at a place that was selling ML powered science instrument output analysis. It did not work at all (fake it till you make it is normal, was told). So
by Tistel 7y ago
I worked at a place that was selling ML powered science instrument output analysis. It did not work at all (fake it till you make it is normal, was told). So there was a person in the loop (machine output -> internet -> person doing it manually pretending to be machine -> internet -> report app). The joke was “organic neural net.” Theranos of the North! ML is a great and powerful pattern matcher (talking about NN not first order logic systems) right now, but, I fear we are going into another AI winter with all the over promising.
- wlesieutre 7y agoThere's a recent xkcd about your company: https://xkcd.com/2173/ https://xkcd.com/2173/ "We trained a neural network to oversee the machine output"
- rm_-rf_slash 7y agoWe won’t ever have an AI winter like in the 70s again. A lot of ML is already very useful across many domains (computer vision, NLP, advertising, etc). Back then, there was almost no personal computing, almost no internet, smol data, and so on. Stuff you need for ML to be useful and used. So what if some corporate hack calls linear regression “AI”? The results speak for themselves. The ML genie is too profitable to go back in the bottle.
- Tistel 7y agoall fair points. I have have used it to do amazing things. It’s not going away. Just that AGI seems very far away. I think CS is like biology before the discovery of the microscope (why are we getting sick? Microorganisms etc). Or DNA. Once that big breakthrough happens we will quickly transition to a new local maximum.
- BlueTemplar 7y agoDidn't linear regression used to be called "AI" as recently as a decade ago?
- claytonjy 7y agoit still is, but the people who mean "regression" when they say "AI" will generally not admit this
- TeMPOraL 7y agoIt's still better in many cases than modern ML (especially if you incorporate explainability and efficiency as metrics of "better" next to the predictive power), so I wouldn't object much if a company called it "AI". In fact, if I learned that an "AI" behind some product was just linear regression, I'd trust them more.
- plaidfuji 7y agoI personally don’t see a problem with this. Where do you draw the line at model simplicity? Are decision trees too simple to be AI? What about random forest? Are deep neural nets the only model sophisticated enough to be “AI”? It’s not the model, it’s how you use it.
- sanxiyn 7y agoThat sounds at least achievable, unlike examples in OP.