I was honored to contribute a chapter to this book by @hevertonsa Heverton Anunciacao => "The Data Scientist and his Demons" (in Portuguese, "O Cientista de Dados e Seus Demônios") at https://
amzn.to/49KFAT2 My chapter is based on an article, and 2 talks here: 1)
MACHINE LEARNING
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Data Scientist Contributes Chapter to Portuguese AI Ethics Book
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Eigenfaces for AI Interpretability: Understanding Computer Vision
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dunno, just seems pretty cool. i guess, interpretability? i mean, what do people use eigenfaces for?
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Sequence Text Embeddings vs Word Embeddings Explained
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i'm talking about sequence text embeddings not word embeddings
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Sequence-Based Models Capture High-Level Features
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don't think so! these are based on sequences so they should capture much higher-level features
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Eigenfaces for Text: PCA on Domain-Specific Embeddings
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project request: eigenfaces, but for text do PCA on text embeddings from a particular domain and find the "eigentext" embeddings that represent that domain then project back to text with vec2text. what would the eigentexts look like? surprised no one has done this yet
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TheProfessor V1: Abacus Introduces LLM Merging Technology
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Introducing TheProfessor (V1) From Abacus, an LLM cocktail! We’ve recently talked about a fascinating property of LLMs, merging, where you can combine different layers from LLMs without needing pre-training. The merged LLM will then show combined properties of the different
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ODSC East 2024: Comprehensive Data Science and AI Training Experience
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Get a one-of-a-kind training experience at #ODSCEast 2024 — With the virtual pass, you have 12 Tracks + numerous workshops, hands-on training, expert-led talks, & panels to choose from: https://
bit.ly/3SOOAQb @_odsc #ODSC #DataScience #AI #GenAI #MachineLearning #DataScientists -
Simulacra as Conscious Exotica: AI Research Insights
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Simulacra as Conscious Exotica Shanahan et al.: https://
arxiv.org/abs/2402.12422 #ArtificialIntelligence #DeepLearning #MachineLearning -
Gemma’s 256K Token Context: Analyzing Language Distribution Patterns
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Keep in mind Gemma has a 256K tokens, so all text could be quite a bit shorter because there are so many merges. The interesting analysis here is to look at the *distribution* of token counts across different languages, and compare it to that same distribution for previous.
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OlympiadBench: Challenging Benchmark Promoting AGI with Olympiad Problems
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OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems He et al.: https://
arxiv.org/abs/2402.14008 #Artificialintelligence #DepLearning #MachineLearning