#wordembeddings — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #wordembeddings, aggregated by home.social.
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How many times do you need to shuffle a card deck to make it truly random?
How much uranium does one need for a nuclear bomb?
How does autocomplete work?
The answer are #MarkovChains
How a feud in Russia led to modern prediction algorithms.
https://www.youtube.com/watch?v=KZeIEiBrT_w
https://piped.video/watch?v=KZeIEiBrT_w
https://inv.nadeko.net/watch?v=KZeIEiBrT_w
#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo
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How many times do you need to shuffle a card deck to make it truly random?
How much uranium does one need for a nuclear bomb?
How does autocomplete work?
The answer are #MarkovChains
How a feud in Russia led to modern prediction algorithms.
https://www.youtube.com/watch?v=KZeIEiBrT_w
https://piped.video/watch?v=KZeIEiBrT_w
https://inv.nadeko.net/watch?v=KZeIEiBrT_w
#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo
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How many times do you need to shuffle a card deck to make it truly random?
How much uranium does one need for a nuclear bomb?
How does autocomplete work?
The answer are #MarkovChains
How a feud in Russia led to modern prediction algorithms.
https://www.youtube.com/watch?v=KZeIEiBrT_w
https://piped.video/watch?v=KZeIEiBrT_w
https://inv.nadeko.net/watch?v=KZeIEiBrT_w
#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo
-
How many times do you need to shuffle a card deck to make it truly random?
How much uranium does one need for a nuclear bomb?
How does autocomplete work?
The answer are #MarkovChains
How a feud in Russia led to modern prediction algorithms.
https://www.youtube.com/watch?v=KZeIEiBrT_w
https://piped.video/watch?v=KZeIEiBrT_w
https://inv.nadeko.net/watch?v=KZeIEiBrT_w
#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo
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How many times do you need to shuffle a card deck to make it truly random?
How much uranium does one need for a nuclear bomb?
How does autocomplete work?
The answer are #MarkovChains
How a feud in Russia led to modern prediction algorithms.
https://www.youtube.com/watch?v=KZeIEiBrT_w
https://piped.video/watch?v=KZeIEiBrT_w
https://inv.nadeko.net/watch?v=KZeIEiBrT_w
#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo
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🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings
https://drive.google.com/drive/folders/11Z3_UGQjGONyHyZbj_kIdgT-LglZH4Ob
#nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI
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Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings
https://drive.google.com/drive/folders/11Z3_UGQjGONyHyZbj_kIdgT-LglZH4Ob
#nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI
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Procesamiento de Lenguaje Natural (#NLP):
#SBWCE: https://mastodon.social/@crscardellino/110140900012839359
Introducción a #NLP: https://mastodon.social/@crscardellino/110141134602644365
#WordEmbeddings: https://mastodon.social/@crscardellino/110141137402593234
Introducción a #HuggingFace: https://mastodon.social/@crscardellino/110254937550244307
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In this [Computerphile] video Robert Miles shows how the latest generation of chatbot AI can be glitched into doing the weirdest things #ChatGPT #AI #GenerativeAI #ArtificialIntelligence #chatbot #WordEmbeddings
https://youtu.be/WO2X3oZEJOA -
In this [Computerphile] video Robert Miles shows how the latest generation of chatbot AI can be glitched into doing the weirdest things #ChatGPT #AI #GenerativeAI #ArtificialIntelligence #chatbot #WordEmbeddings
https://youtu.be/WO2X3oZEJOA -
🚀 Sehr cool! Der Band zum #DFG-Symposium "#Digitale #Literaturwissenschaft", herausgegeben von @fotis_jannidis, ist erschienen! 848 Seiten stark und #OpenAccess! https://link.springer.com/book/10.1007/978-3-476-05886-7 – Es geht um Literatur und Digitalität, Digitale #Edition, #Annotation, Quantitative #Textanalyse und Literaturwissenschaft und #Bibliothek. – Von mir dabei, ein Beitrag zu #WordEmbeddings für die Literaturwissenschaft: https://doi.org/10.1007/978-3-476-05886-7_22 – #CLS #DH
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🚀 Sehr cool! Der Band zum #DFG-Symposium "#Digitale #Literaturwissenschaft", herausgegeben von @fotis_jannidis, ist erschienen! 848 Seiten stark und #OpenAccess! https://link.springer.com/book/10.1007/978-3-476-05886-7 – Es geht um Literatur und Digitalität, Digitale #Edition, #Annotation, Quantitative #Textanalyse und Literaturwissenschaft und #Bibliothek. – Von mir dabei, ein Beitrag zu #WordEmbeddings für die Literaturwissenschaft: https://doi.org/10.1007/978-3-476-05886-7_22 – #CLS #DH
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Leseempfehlung zum Thema #KI: @hannesbajohr im #Merkur. Weshalb #DALLE2 die bedeutsamere Entwicklung darstellt als #GPT3, wieso #WordEmbeddings basierte Systeme (bislang) Korrelationen, aber nicht Intentionen (und Kausalitäten) reproduzieren können und wie #KI menschlichen Nutzenden "dumme Bedeutung" aufzwingt: https://hannesbajohr.de/wp-content/uploads/2022/11/Bajohr_Dumme_Bedeutung.pdf
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Zu Weihnachten gibt es einen neuen #Tuwort-Podcast. In Nummer 8 sprachen wir über die Vermessung des Klassenbegriffs mittels #WordEmbeddings, über die angeblich imperial motivierte Unterdrückung des Österreichischen #Hochdeutsch und über #LeichteSprache in der Wissenschaftskommunikation. Daneben geht es um lachende Roboter, das Wort des Jahres und GPT-3. Viel Spaß beim Reinhören!
Link zur Folge:
https://www.tuwort.com/index.php/2022/12/23/tuwort-8/ -
Zu Weihnachten gibt es einen neuen #Tuwort-Podcast. In Nummer 8 sprachen wir über die Vermessung des Klassenbegriffs mittels #WordEmbeddings, über die angeblich imperial motivierte Unterdrückung des Österreichischen #Hochdeutsch und über #LeichteSprache in der Wissenschaftskommunikation. Daneben geht es um lachende Roboter, das Wort des Jahres und GPT-3. Viel Spaß beim Reinhören!
Link zur Folge:
https://www.tuwort.com/index.php/2022/12/23/tuwort-8/ -
Der #tuwort-Podcast Nr. 8 ist erschienen: Wir sprechen über #GPT3, lachende #Roboter, #Zeitenwende, #WordEmbeddings zur Vermessung von Kultur, Österreichischen #Standard und #LeichteSprache in der Wissenschaftskommunikation. Mit Sandra, @josch und mir. https://www.tuwort.com/index.php/2022/12/23/tuwort-8/ @tuwort
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Der #tuwort-Podcast Nr. 8 ist erschienen: Wir sprechen über #GPT3, lachende #Roboter, #Zeitenwende, #WordEmbeddings zur Vermessung von Kultur, Österreichischen #Standard und #LeichteSprache in der Wissenschaftskommunikation. Mit Sandra, @josch und mir. https://www.tuwort.com/index.php/2022/12/23/tuwort-8/ @tuwort
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Something I have used a lot this year and is excellent https://github.com/RichardScottOZ/geoscience_language_models - a fork of the #NRCAN original with a few updates and also doing some things in #Portuguese #Brazil - it is really well done and has pretty good #OpenSource models available too - with a Canada focus. I would have put a million documents through it roughly. #NLP #DataScience #DataEngineering #Python #Geoscience #gloVe #geology #wordembeddings #MineralExploration #Copper #Nickel #GitHub #OpenSource
