strongly considering becoming one of those people who insists on never referring to a particular thing by its common name for political reasons because framing big probability models as "artificial intelligence" and pretending they're anything like HAL 9000 or Data is doing so so much damage to laypeople's understanding of a technology that primarily exists to make their lives worse
- He/him
CoHost has made the mistake of activating my account
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in reply to @nex3's post:
Blaming the matrices is a category error, the problem is the organizations that create them and how they are used.
I started to say the matrices are not fundamentally cursed but on reflection some of them do embed racism, copyright infringement, etc. But the problem is not the matrices, it's the systems and incentives that cause them to be created with disregard for such issues.
Statistical models is the phrase I use. It's more accurate and more demystifying.
Bias laundering machines? Wait, that abbreviates to BLM. Bias laundering boxes.
To be fair to the people working on those models, I think most are pretty hesitant to compare their models to true intelligence. I recall reading a Yann LeCun post where he pretty plainly states that ML models do not have the learning capacity of a baby, and that they are not even close to having it. The people who seem to brag the most about ML models are adjacent people like journalists and marketing folks, or people in other, cursorily related fields. As usual, people doing the actual work are shouted over by less informed people with the most potential to profit.
I think a lot about how on twitter I got out of the habit of referring to particular topics by their real names just to avoid dealing with namesearching engagement drivers so like yeah, this is good
I recently referred to ML as "typewriter monkeys at scale" and I stand by this as more accurate terminology.
What did that one twitter screencap name? "Torment Nexus"? I propose we call it torment nexus, or pain noci for plural. Considering it's been discovered you can "traumatize" a neural network it's probably accurate. That, or, neural networks.