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#blastfromthepast

4 posts4 participants1 post today
Public
Chinicuil

Brutal video sobre como básicamente tenemos que buscar alternativas más humanas y colectivas para las apps de citas, asi como un análisis super interesante de su modelo de negocio, lo que hemos perdido, nuestras interacciones en el modelo "Barbie"...de esto último no voy a hacer spoiler porque es un conceptazo
youtube.com/watch?v=0r8O9WxIrb

YouTubeSwiped Out: Exposing the Dating App GriftBy EpistemOphelia
#chinirecomienda#blastfromthepast#tootrecovery
Public
Chinicuil

Super interesante análisis de cómo es probable que los avances en AI “generativa” estén alcanzando su límite. Aqui está el paper arxiv.org/abs/2404.04125 y aquí el video que lo explica bastante bien: m.youtube.com/watch?v=dDUC-LqV

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arXiv.orgNo "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model PerformanceWeb-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.
#ai#generativeAI#ia
Public
Chinicuil

Descartes era medio pendejo: Hay una frase zulú que dice: "Una persona es persona a través de otras personas". Esta es una explicación más rica y mejor que "pienso, luego existo".
Lindo artículo de Abeba Birhane con audio pa escucharlo (ta en inglés)
aeon.co/ideas/descartes-was-wr

<p>Detail from <em>Young Moe</em> (1938) by Paul Klee. <em>Courtesy Phillips collection/Wikipedia</em></p>
AeonDescartes was wrong: ‘a person is a person through other persons’ | Aeon IdeasWhat’s a better way to understand human psychology – ‘I think, therefore I am’ or ‘A person is a person through other persons’?
#descartes#ubuntu#blastfromthepast
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