We Are A Sentiment Thermometer: Difference between revisions
From Algolit
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| + | | Type: || Algoliterary exploration | ||
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| + | | Dataset(s): || Glove, 1984 by George Orwell, Frankenstein by Mary Shelly | ||
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| + | | Technique: || [[word embeddings]], Scikit Learn supervised machine learning | ||
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| + | | Developed by: || Common Crawl/GloVe, Rob Speer/ConceptNet, Algolit | ||
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A language model recounts its story in a metaphorical way. You are guided through a multidimensional world in which artificial intelligences lead explorations, explore landscapes and create maps that allow them to go along paths of predictions. | A language model recounts its story in a metaphorical way. You are guided through a multidimensional world in which artificial intelligences lead explorations, explore landscapes and create maps that allow them to go along paths of predictions. | ||
| − | ''We are a Sentiment Thermometer'' is a collective being based on classic supervised machine learning and unsupervised Neural Networks pretrained GloVe word embeddings. They can either judge a sentence on its positive or negative sentiment, or it can guide you through its components and show how they are made, which choices led to their functioning, who developed each of the elements, how each part can be replaced. Using the collective intelligence of the internet as training data, they show how their scores and judgements are influenced by the data they have been trained with. Our human prejudices and cliches are passed on to machines and induce them with racist and other biases. | + | ''We are a Sentiment Thermometer'' is a collective being based on classic supervised machine learning and unsupervised Neural Networks pretrained GloVe word embeddings. They can either judge a sentence on its positive or negative sentiment, or it can guide you through its components and show how they are made, which choices led to their functioning, who developed each of the elements, how each part can be replaced. |
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| + | Using the collective intelligence of the internet as training data, they show how their scores and judgements are influenced by the data they have been trained with. Our human prejudices and cliches are passed on to machines and induce them with racist and other biases. | ||
Based on a script by Rob Speer: https://blog.conceptnet.io/2017/07/13/how-to-make-a-racist-ai-without-really-trying/ | Based on a script by Rob Speer: https://blog.conceptnet.io/2017/07/13/how-to-make-a-racist-ai-without-really-trying/ | ||
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| + | [[Category:Algoliterary-Encounters]] | ||
Latest revision as of 14:12, 2 November 2017
| Type: | Algoliterary exploration |
| Dataset(s): | Glove, 1984 by George Orwell, Frankenstein by Mary Shelly |
| Technique: | word embeddings, Scikit Learn supervised machine learning |
| Developed by: | Common Crawl/GloVe, Rob Speer/ConceptNet, Algolit |
A language model recounts its story in a metaphorical way. You are guided through a multidimensional world in which artificial intelligences lead explorations, explore landscapes and create maps that allow them to go along paths of predictions.
We are a Sentiment Thermometer is a collective being based on classic supervised machine learning and unsupervised Neural Networks pretrained GloVe word embeddings. They can either judge a sentence on its positive or negative sentiment, or it can guide you through its components and show how they are made, which choices led to their functioning, who developed each of the elements, how each part can be replaced.
Using the collective intelligence of the internet as training data, they show how their scores and judgements are influenced by the data they have been trained with. Our human prejudices and cliches are passed on to machines and induce them with racist and other biases.
Based on a script by Rob Speer: https://blog.conceptnet.io/2017/07/13/how-to-make-a-racist-ai-without-really-trying/