GPT-2 (nonfiction): Difference between revisions
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'''GPT-2''' is | '''GPT-2''' ('''Generative Pretrained Transformer 2''') is a language model that was trained on 40GB of text scraped from websites that Reddit linked to and that had a Karma score of at least two. | ||
The developers at OpenAI describe GPT-2 as "a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training." | |||
Because the model is probabilistic, it returns a different response every time you enter the same input. | |||
== In the News == | |||
<gallery> | |||
</gallery> | |||
== Fiction cross-reference == | |||
* [[Forbidden Ratio]] | |||
* [[Gnomon algorithm]] | |||
* [[Gnomon Chronicles]] | |||
== Nonfiction cross-reference == | |||
* [[Artificial intelligence (nonfiction)]] | |||
* [[Computation (nonfiction)]] | |||
* [[Machine learning (nonfiction)]] | |||
External links: | |||
* [https://www.youtube.com/watch?v=89A4jGvaaKk Unicorn AI - Computerphile] @ YouTube | |||
* [https://boingboing.net/2019/07/05/computerphile-explains-the-fas.html Computerphile explains the fascinating AI storyteller, GPT-2] @ Boing Boing | |||
* [https://blog.floydhub.com/gpt2/ How to Build OpenAI's GPT-2: "The AI That's Too Dangerous to Release"] | * [https://blog.floydhub.com/gpt2/ How to Build OpenAI's GPT-2: "The AI That's Too Dangerous to Release"] | ||
[[Category:Nonfiction (nonfiction)]] | |||
[[Category:Artificial intelligence (nonfiction)]] | |||
[[Category:Computing (nonfiction)]] |
Latest revision as of 07:43, 6 July 2019
GPT-2 (Generative Pretrained Transformer 2) is a language model that was trained on 40GB of text scraped from websites that Reddit linked to and that had a Karma score of at least two.
The developers at OpenAI describe GPT-2 as "a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training."
Because the model is probabilistic, it returns a different response every time you enter the same input.
In the News
Fiction cross-reference
Nonfiction cross-reference
External links: