dat_math

it's hard to choose a favorite among the >10k edible legumes but I want to try the ice cream beanis

I must have missed something. Why did they return?

schizandra kinda gives it away

LLM

MLL

I think the whataboutism is one of the more intellectual contests, but the bernie bro-down is a classic

Does your go to 11?

maybe not? I would think the fact that the dialectic over intervention/interference is so thoroughly explored means it can't be inherently expansionist but idk I essentially skipped TOS

expansionism

explain the prime directive...

What am I missing here?

Mostly, the pith of what I wrote, which has little to do with value judgement, quality of diagnosis, or even patient outcomes, and more to do with the similarity between the neurological effects on the practitioners associated with using descriminative models to do object detection or image segmentation in endoscopy and those of using generative models to accomplish other tasks

You claimed they had nothing to do with each other. I disagree and stated one way in which they are similar: both involve the practitioner forfeiting the deliberate guidance of their attention to solve a problem in favor having a machine learning model do it and maybe doing something else with their attention (maybe not, only the practitioner can know what else they might do). It would seem in the "Endoscopist deskilling..." paper, that particular variable was left free (as opposed to being controlled in some way that could be task-relevant or task-irrelevant, to provide a contrast and better understand what's really going on in practitioners' minds).

To elaborate a bit further, when I said,

a human is deliberately forfeiting their opportunity to exercise their attention to solve some problem in favor of pressing a "machine learning will do it" button

I didn't mean that a human is necessarily no longer doing anything with their attention. Specifically, when a human uses a machine learning model to solve some problem (e.g., which region of an image to look at during a colonoscopy), this changes what happens in their mind. They may still do that function themselves, compare their own ideas of where to look in the image with the model's output and evaluate both regions, or everywhere near both regions, or they might do absolutely nothing beyond looking solely in the region(s) output by their model. We don't know and this is totally immaterial to my claim, which is that any outsourcing of the calculation of that function alters what happens in the mind of the practitioner. It's probable that there are methodologies that generally enhance performance and protect practitioners from the so-called deskilling. However, merely changing the function performed by the model in question from generative to discriminative does not necessarily mean it will be used in way that avoids eroding the user's competence.

This guy remembers the fundamental theorem

so it is image recognition and has nothing to do with generative ai.

I'm not sure if I'd go so far as to say it has nothing to do with generative AI

in both cases, a human is deliberately forfeiting their opportunity to exercise their attention to solve some problem in favor of pressing a "machine learning will do it" button

for those with the ~~eyes to see~~ lungs to inoculate