Learn from Googlers who are working to ensure that a robust framework for ethical AI principles are in place, and that Google’s products do not amplify or propagate unfair bias, stereotyping, or prejudice. Hear about the research they are doing to evolve artificial intelligence towards positive goals: from accountability in the ethical deployment of AI, to the tools needed to actually build them, and advocating for the inclusion of concepts such as race, gender, and justice to be considered as part of the process.

Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF
TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io

Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1
Get started at → https://www.tensorflow.org/

Speaker(s): Jen Gennai, Margaret Mitchell, Jamila Smith-Loud

TC3A01

Artificial Intelligence in Creative Writing

www.mlprague.com
Slides: http://www.slideshare.net/mlprague/ji-materna-artificial-intelligence-in-creative-writing

Computers just got a lot better at mimicking our language.

Join the Open Sourced Reporting Network: http://www.vox.com/opensourcednetwork

Something big happened in the past year: Researchers created computer programs that can write long passages of coherent, original text.

Language models like GPT-2, Grover, and CTRL create text passages that seem written by someone fluent in the language, but not in the truth. That AI field, Natural Language Processing (NLP), didn’t exactly set out to create a fake news machine. Rather, it’s the byproduct of a line of research into massive pretrained language models: Machine learning programs that store vast statistical maps of how we use our language. So far, the technology’s creative uses seem to outnumber its malicious ones. But it’s not difficult to imagine how these text-fakes could cause harm, especially as these models become widely shared and deployable by anyone with basic know-how. Read more here: https://www.vox.com/recode/2020/3/4/21163743/ai-language-generation-fake-text-gpt2

Open Sourced is a year-long reporting project from Recode by Vox that goes deep into the closed ecosystems of data, privacy, algorithms, and artificial intelligence. Learn more at http://www.vox.com/opensourced

This project is made possible by the Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.

Watch all episodes of Open Sourced right here on YouTube: http://bit.ly/2tIHftD

Try out natural language generation and detection with these tools:
https://demo.allennlp.org/next-token-lm
https://talktotransformer.com/
https://transformer.huggingface.co/
https://grover.allenai.org/
https://www.ai21.com/haim
http://gltr.io/
https://play.aidungeon.io/
https://huggingface.co/openai-detector/

Sources:
https://ruder.io/nlp-imagenet/
https://medium.com/@ageitgey/deepfaking-the-news-with-nlp-and-transformer-models-5e057ebd697d
https://openai.com/blog/better-language-models/
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/
https://veredshwartz.blogspot.com/2019/08/text-generation.html
http://www.mattkenney.me/gpt-2-345/
http://www.mattkenney.me/gpt-2/
https://jalammar.github.io/illustrated-gpt2/
https://mc.ai/introduction-to-language-modelling-and-deep-neural-network-based-text-generation/
https://fortune.com/2020/01/20/natural-language-processing-business/
https://www.vox.com/future-perfect/2019/2/14/18222270/artificial-intelligence-open-ai-natural-language-processing
https://www.newyorker.com/magazine/2019/10/14/can-a-machine-learn-to-write-for-the-new-yorker
https://www.youtube.com/watch?v=GEtbD6pqTTE
https://arxiv.org/pdf/1905.12616.pdf
https://arxiv.org/abs/1911.03343
https://arxiv.org/abs/1904.09751
https://techscience.org/a/2019121801/
https://www.middlebury.edu/institute/sites/www.middlebury.edu.institute/files/2019-11/The%20Industrialization%20of%20Terrorist%20Propaganda%20-%20CTEC.pdf?fv=TzdJnlDw
http://newsyoucantuse.com/
https://aiweirdness.com/post/168051907512/the-first-line-of-a-novel-by-an-improved-neural
https://aiweirdness.com/post/159302925452/the-neural-network-generated-pickup-lines-that-are
https://www.nytimes.com/interactive/2018/10/26/opinion/halloween-spooky-costumes-machine-learning-generator.html
https://aiweirdness.com/post/160985569682/paint-colors-designed-by-neural-network-part-2
https://www.reddit.com/r/SubSimulatorGPT2/
https://twitter.com/dril_gpt2
https://cloud.google.com/text-to-speech/

Vox.com is a news website that helps you cut through the noise and understand what’s really driving the events in the headlines. Check out http://www.vox.com.

Watch our full video catalog: http://goo.gl/IZONyE
Follow Vox on Facebook: http://goo.gl/U2g06o
Or Twitter: http://goo.gl/XFrZ5H

Never thought this day would come where I was writing my own Machine Learning Neural Network Projects… prepare to have SOME FUN!

CODE IS IN PART 4: https://www.youtube.com/watch?v=g-HePO2bcTY

PATREON: https://www.patreon.com/Jabrils

SUBSCRIBE FOR MORE SEFD SCIENCE: http://sefdstuff.com/science

Table Of Contents
—–
0:00 – Intro
0:10 – My AI Story
1:58 – Starting point
2:16 – Introducing Forrest
2:35 – Discovering Forrest’s Problem
3:20 – How the joystick works
3:59 – Exploring our A.I. options
4:47 – Monster Boss Battle Course
4:53 – Recap on whats going on
5:40 – Setting up our inputs
6:30 – Our Neural Network structure & how it works
8:11 – Inputting our Neural Network into Forrest
8:56 – Conclusion

Please follow me on social networks:
twitter: http://sefdstuff.com/twitter
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Music
—–
Coming soon

REMEMBER TO ALWAYS FEED YOUR CURIOSITY

#AI #MachineLearning #gamedev

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