context

It just seems like a functioning organization would have this planned out with a team

the purpose of a system is what it does. maybe it's not so much that the organization is dysfunctional, nor that it's functional in a way that would need this planned out with a team but didn't do that for some reason. maybe it's functioning correctly to leave this unplanned and without a team.

he didn’t tell me I’d be developing the framework for system interfacing on the front and back end for 60K

just use chatgpt for that

bump

systems infrastructure is strictly an expense that doesn't contribute in any way to revenue. i am very intelligent and make good financial decisions.

exactly! our plan to forcibly relocate them until they simply cease to exist is much more humane!

~~uncritical~~ critical support to the democratic people's republic of korea and their fight against the genocidal imperialist american occupiers

i mean, this dude is just spouting bullshit, but yes, in a way. it'd be better to say that the gnostics were describing the proto-capitalist roman imperial slave-based system that eventually evolved into modern capitalism. but the early ecumenical conflicts and subsequent violent repression of so-called gnostic heresies were, imo, mostly about control of the early christian anti-imperialist movement and figuring out how to synthesize it with the existing roman power structures, a process that took 300 years.

in modern terms, the demiurge is the paperclip-making super ai, a blind, unthinking, extremely powerful entity of human creation but no longer under human control. and capitalism is certainly that.

kamaleficent

weaponized smugness

this one?

https://arxiv.org/abs/2404.04125#

Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets? We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the "Let it Wag!" benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found.

where zero-shot learning means:

Zero-shot learning (ZSL) is a machine learning scenario in which an AI model is trained to recognize and categorize objects or concepts without having seen any examples of those categories or concepts beforehand.

Most state-of-the-art deep learning models for classification or regression are trained through supervised learning, which requires many labeled examples of relevant data classes. Models “learn” by making predictions on a labeled training dataset; data labels provide both the range of possible answers and the correct answers (or ground truth) for each training example.

While powerful, supervised learning is impractical in some real-world scenarios. Annotating large amounts of data samples is costly and time-consuming, and in cases like rare diseases and newly discovered species, examples may be scarce or non-existent.

https://www.ibm.com/topics/zero-shot-learning

so yeah, i agree, the paper is saying these models aren't capable of creating/using human-understandable concepts without gobs and gobs of training data, and if you try to take human supervision of those categories out of the process, then you need even more gobs and gobs of training data. edge cases and novel categories tend to spin off useless bullshit from these things.

because actual knowledge generation is a social process that these machines aren't really participants in.

but there's some speculation that the recent stock market downturn affecting tech stocks especially may be related to the capitalist class figuring out that these things aren't actually magical knowledge-worker replacement devices and won't let them make the line go up forever and ever amen. so even if the suits don't really digest the contents of this paper, they'll figure out the relevant parts reventually.