Revert "Formatting (Artificial Intelligence)"

artificial_intelligence/README.md

@@ -1,15 +1,15 @@
 ## Artificial Intelligence
 
-* 📜 [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: [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
 
-* 📜 [Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search](efficient-selectivity-and-backup-operators-in-monte-carlo-tree-search.pdf)
+* :scroll: [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. 

artificial_intelligence/judea_pearl/README.md

@@ -1,4 +1,4 @@
-📜[Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) 
+[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) -
 > The paper that began the probabilistic revolution in AI
 > by showing how several desirable properties of reasoning systems
 > can be obtained through sound probabilistic inference.
@@ -7,17 +7,17 @@
 > relationships as the key organizing principle for uncertain knowledge,
 > and described an efficient, distributed, exact inference algorithm.
 
-📜[A Theory of Inferred Causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma.
+[A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma.
 > Introduces minimal-model semantics as a basis for causal discovery,
 > and shows that causal directionality can be inferred from patterns
 > of correlations without resorting to temporal information.
 
-📜[Causal Diagrams for Empirical Research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - Extended Version linked.
+[Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked.
 > Introduces the theory of causal diagrams and its associated do-calculus;
 > the first (and still the only) mathematical method to enable a
 > systematic removal of confounding bias in observations.
 
-📜[The Algorithmization of Counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) 
+[The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) -
 > Describes a computational model that explains how humans generate,
 > evaluate and distinguish counterfactual statements so swiftly and
 > consistently.