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artificial_intelligence/README.md

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 ## Artificial Intelligence
 
-* :scroll: [Analysis of Three Bayesian Network Inference Algorithms: Variable Elimination, Likelihood Weighting, and Gibbs Sampling](3-bayesian-network-inference-algorithm.pdf) by Rose F. Liu, Rusmin Soetjipto
+* 📜 [Analysis of Three Bayesian Network Inference Algorithms: Variable Elimination, Likelihood Weighting, and Gibbs Sampling](3-bayesian-network-inference-algorithm.pdf) by Rose F. Liu, Rusmin Soetjipto
 
-* :scroll: [Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search](efficient-selectivity-and-backup-operators-in-monte-carlo-tree-search.pdf)
+* 📜 [Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search](efficient-selectivity-and-backup-operators-in-monte-carlo-tree-search.pdf)
 
-* [Computing Machinery and Intelligence](http://www.csee.umbc.edu/courses/471/papers/turing.pdf) by A.M. Turing
+* 📜 [Computing Machinery and Intelligence](http://www.csee.umbc.edu/courses/471/papers/turing.pdf) by A.M. Turing
 
-* [Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) folder - Papers by Judea Pearl, 2011 winner of the ACM Turing Award.
+* 📜 [Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) folder - Papers by Judea Pearl, 2011 winner of the ACM Turing Award.
    * [:open_file_folder: Summary of Papers](judea_pearl/)
 
-* [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf) by Silver et al.
+* 📜 [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf) by Silver et al.
 
-* [A Universal Music Translation Network (2018)](https://arxiv.org/pdf/1805.07848.pdf) by Noam Mor, Lior Wolf, Adam Polyak & Yaniv Taigman
-    > This paper proposes a method for translating music across musical instruments, genres, and styles. It is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-independent encoder enables translation even from musical domains that were not seen during training. The method is unsupervised and does not rely on supervision in the form of matched samples between domains or musical transcriptions. This method is evaluated on NSynth, as well as on a dataset collected from professional musicians, and achieve convincing translations, even when translating from whistling, potentially enabling the creation of instrumental music by untrained humans. 
+* 📜 [A Universal Music Translation Network (2018)](https://arxiv.org/pdf/1805.07848.pdf) by Noam Mor, Lior Wolf, Adam Polyak & Yaniv Taigman
+    > This paper proposes a method for translating music across musical instruments, genres, and styles. It is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-independent encoder enables translation even from musical domains that were not seen during training. The method is unsupervised and does not rely on supervision in the form of matched samples between domains or musical transcriptions. This method is evaluated on NSynth, as well as on a dataset collected from professional musicians, and achieve convincing translations, even when translating from whistling, potentially enabling the creation of instrumental music by untrained humans.