Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.

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http://www.seedpeer.me/details/4848277/TutsPlus---Build-Web-Apps-in-Node-and-Express.html http://www.seedpeer.me/details/5683153/Tutsplus---Catch-Up-with-Ruby-on-Rails-4.html http://www.seedpeer.me/details/4918947/TutsPlus---CodeIgniter-Essentials.html http://www.seedpeer.me/details/5069781/TutsPlus---Connected-to-the-Backbone.html http://www.seedpeer.me/details/5513056/Tutsplus---Designing-Professional-Resumes.html http://www.seedpeer.me/details/5706815/Tutsplus-Easier-JavaScript-Apps-with-AngularJS.html http://www.seedpeer.me/details/6462415/TutsPlus---Easier-JavaScript-with-TypeScript.html http://www.seedpeer.me/details/5868293/TutsPlus---Getting-Started-With-Windows-8-Development-Using-HTML,-CSS-&amp;-JavaScript-V413HAV.html http://www.seedpeer.me/details/6150521/TutsPlus-HTML5-Video-Essentials-PRODEV.html http://www.seedpeer.me/details/4841911/TutsPlus---JavaScript-Testing-With-Jasmine.html http://www.seedpeer.me/details/6593486/TutsPlus---Less-is-More.html http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

What is C#? C# is pronounced "C-Sharp". It is an object-oriented programming language created by Microsoft that runs on the .NET Framework. C# has roots from the C family, and the language is close to other popular languages like C++ and Java. The first version was released in year 2002. The latest version, C# 8, was released in September 2019. C# is a modern object-oriented programming language developed in 2000 by Anders Hejlsberg, the principal designer and lead architect at Microsoft. It is pronounced as "C-Sharp," inspired by the musical notation “♯” which stands for a note with a slightly higher pitch. As it’s considered an incremental compilation of the C++ language, the name C “sharp” seemed most appropriate. The sharp symbol, however, has been replaced by the keyboard friendly “#” as a suffix to “C” for purposes of programming. Although the code is very similar to C++, C# is newer and has grown fast with extensive support from Microsoft. The fact that it’s so similar to Java syntactically helps explain why it has emerged as one of the most popular programming languages today. C# is pronounced "C-Sharp". It is an object-oriented programming language created by Microsoft that runs on the .NET Framework. C# has roots from the C family, and the language is close to other popular languages like C++ and Java. The first version was released in year 2002. The latest version, C# 8, was released in September 2019. C# is used for: Mobile applications Desktop applications Web applications Web services Web sites Games VR Database applications And much, much more! An Introduction to C# Programming C# is a general-purpose, object-oriented programming language that is structured and easy to learn. It runs on Microsoft’s .Net Framework and can be compiled on a variety of computer platforms. As the syntax is simple and easy to learn, developers familiar with C, C++, or Java have found a comfort zone within C#. C# is a boon for developers who want to build a wide range of applications on the .NET Framework—Windows applications, Web applications, and Web services—in addition to building mobile apps, Windows Store apps, and enterprise software. It is thus considered a powerful programming language and features in every developer’s cache of tools. Although first released in 2002, when it was introduced with .NET Framework 1.0, the C# language has evolved a great deal since then. The most recent version is C# 8.0, available in preview as part of Visual Studio. To get access to all of the new language features, you would need to install the latest preview version of .NET Core 3.0. C# is used for: Mobile applications Desktop applications Web applications Web services Web sites Games VR Database applications And much, much more! Why Use C#? It is one of the most popular programming language in the world It is easy to learn and simple to use It has a huge community support C# is an object oriented language which gives a clear structure to programs and allows code to be reused, lowering development costs. As C# is close to C, C++ and Java, it makes it easy for programmers to switch to C# or vice versa. The C# Environment You need the .NET Framework and an IDE (integrated development environment) to work with the C# language. The .NET Framework The .NET Framework platform of the Windows OS is required to write web and desktop-based applications using not only C# but also Visual Basic and Jscript, as the platform provides language interoperability. Besides, the .Net Framework allows C# to communicate with any of the other common languages, such as C++, Jscript, COBOL, and so on. IDEs Microsoft provides various IDEs for C# programming: Visual Studio 2010 (VS) Visual Studio Express Visual Web Developer Visual Studio Code (VSC) The C# source code files can be written using a basic text editor, like Notepad, and compiled using the command-line compiler of the .NET Framework. Alternative open-source versions of the .Net Framework can work on other operating systems as well. For instance, the Mono has a C# compiler and runs on several operating systems, including Linux, Mac, Android, BSD, iOS, Windows, Solaris, and UNIX. This brings enhanced development tools to the developer. As C# is part of the .Net Framework platform, it has access to its enormous library of codes and components, such as Common Language Runtime (CLR), the .Net Framework Class Library, Common Language Specification, Common Type System, Metadata and Assemblies, Windows Forms, ASP.Net and ASP.Net AJAX, Windows Workflow Foundation (WF), Windows Communication Foundation (WCF), and LINQ. C# and Java C# and Java are high-level programming languages that share several similarities (as well as many differences). They are both object-oriented languages much influenced by C++. But while C# is suitable for application development in the Microsoft ecosystem from the front, Java is considered best for client-side web applications. Also, while C# has many tools for programming, Java has a larger arsenal of tools to choose from in IDEs and Text Editors. C# is used for virtual reality projects like games, mobile, and web applications. It is built specifically for Microsoft platforms and several non-Microsoft-based operating systems, like the Mono Project that works with Linux and OS X. Java is used for creating messaging applications and developing web-based and enterprise-based applications in open-source ecosystems. Both C# and Java support arrays. However, each language uses them differently. In C#, arrays are a specialization of the system; in Java, they are a direct specialization of the object. The C# programming language executes on the CLR. The source code is interpreted into bytecode, which is further compiled by the CLR. Java runs on any platform with the assistance of JRE (Java Runtime Environment). The written source code is first compiled into bytecode and then converted into machine code to be executed on a JRE. C# and C++ Although C# and C++ are both C-based languages with similar code, there are some differences. For one, C# is considered a component-oriented programming language, while C++ is a partial object-oriented language. Also, while both languages are compiled languages, C# compiles to CLR and is interpreted by.NET, but C++ compiles to machine code. The size of binaries in C# is much larger than in C++. Other differences between the two include the following: C# gives compiler errors and warnings, but C++ doesn’t support warnings, which may cause damage to the OS. C# runs in a virtual machine for automatic memory management. C++ requires you to manage memory manually. C# can create Windows, .NET, web, desktop, and mobile applications, but not stand-alone apps. C++ can create server-side, stand-alone, and console applications as it can work directly with the hardware. C++ can be used on any platform, while C# is targeted toward Windows OS. Generally, C++ being faster than C#, the former is preferred for applications where performance is essential. Features of C# The C# programming language has many features that make it more useful and unique when compared to other languages, including: Object-oriented language Being object-oriented, C# allows the creation of modular applications and reusable codes, an advantage over C++. As an object-oriented language, C# makes development and maintenance easier when project size grows. It supports all three object-oriented features: data encapsulation, inheritance, interfaces, and polymorphism. Simplicity C# is a simple language with a structured approach to problem-solving. Unsafe operations, like direct memory manipulation, are not allowed. Speed The compilation and execution time in C# is very powerful and fast. A Modern programming language C# programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection, error handling, debugging, and robust security. It has built-in support for a web service to be invoked from any app running on any platform. Type-safe Arrays and objects are zero base indexed and bound checked. There is an automatic checking of the overflow of types. The C# type safety instances support robust programming. Interoperability Language interoperability of C# maximizes code reuse for the efficiency of the development process. C# programs can work upon almost anything as a program can call out any native API. Consistency Its unified type system enables developers to extend the type system simply and easily for consistent behavior. Updateable C# is automatically updateable. Its versioning support enables complex frameworks to be developed and evolved. Component oriented C# supports component-oriented programming through the concepts of properties, methods, events, and attributes for self-contained and self-describing components of functionality for robust and scalable applications. Structured Programming Language The structured design and modularization in C# break a problem into parts, using functions for easy implementation to solve significant problems. Rich Library C# has a standard library with many inbuilt functions for easy and fast development. Prerequisites for Learning C# Basic knowledge of C or C++ or any programming language or programming fundamentals. Additionally, the OOP concept makes for a short learning curve of C#. Advantages of C# There are many advantages to the C# language that makes it a useful programming language compared to other languages like Java, C, or C++. These include: Being an object-oriented language, C# allows you to create modular, maintainable applications and reusable codes Familiar syntax Easy to develop as it has a rich class of libraries for smooth implementation of functions Enhanced integration as an application written in .NET will integrate and interpret better when compared to other NET technologies As C# runs on CLR, it makes it easy to integrate with components written in other languages It’s safe, with no data loss as there is no type-conversion so that you can write secure codes The automatic garbage collection keeps the system clean and doesn’t hang it during execution As your machine has to install the .NET Framework to run C#, it supports cross-platform Strong memory backup prevents memory leakage Programming support of the Microsoft ecosystem makes development easy and seamless Low maintenance cost, as C# can develop iOS, Android, and Windows Phone native apps The syntax is similar to C, C++, and Java, which makes it easier to learn and work with C# Useful as it can develop iOS, Android, and Windows Phone native apps with the Xamarin Framework C# is the most powerful programming language for the .NET Framework Fast development as C# is open source steered by Microsoft with access to open source projects and tools on Github, and many active communities contributing to the improvement What Can C Sharp Do for You? C# can be used to develop a wide range of: Windows client applications Windows libraries and components Windows services Web applications Native iOS and Android mobile apps Azure cloud applications and services Gaming consoles and gaming systems Video and virtual reality games Interoperability software like SharePoint Enterprise software Backend services and database programs AI and ML applications Distributed applications Hardware-level programming Virus and malware software GUI-based applications IoT devices Blockchain and distributed ledger technology C# Programming for Beginners: Introduction, Features and Applications By Simplilearn Last updated on Jan 20, 2020674 C# Programming for Beginners As a programmer, you’re motivated to master the most popular languages that will give you an edge in your career. There’s a vast number of programming languages that you can learn, but how do you know which is the most useful? If you know C and C++, do you need to learn C# as well? How similar is C# to Java? Does it become more comfortable for you to learn C# if you already know Java? Every developer and wannabe programmer asks these types of questions. So let us explore C# programming: how it evolved as an extension of C and why you need to learn it as a part of the Master’s Program in integrated DevOps for server-side execution. Are you a web developer or someone interested to build a website? Enroll for the Javascript Certification Training. Check out the course preview now! What is C#? C# is a modern object-oriented programming language developed in 2000 by Anders Hejlsberg, the principal designer and lead architect at Microsoft. It is pronounced as "C-Sharp," inspired by the musical notation “♯” which stands for a note with a slightly higher pitch. As it’s considered an incremental compilation of the C++ language, the name C “sharp” seemed most appropriate. The sharp symbol, however, has been replaced by the keyboard friendly “#” as a suffix to “C” for purposes of programming. Although the code is very similar to C++, C# is newer and has grown fast with extensive support from Microsoft. The fact that it’s so similar to Java syntactically helps explain why it has emerged as one of the most popular programming languages today. An Introduction to C# Programming C# is a general-purpose, object-oriented programming language that is structured and easy to learn. It runs on Microsoft’s .Net Framework and can be compiled on a variety of computer platforms. As the syntax is simple and easy to learn, developers familiar with C, C++, or Java have found a comfort zone within C#. C# is a boon for developers who want to build a wide range of applications on the .NET Framework—Windows applications, Web applications, and Web services—in addition to building mobile apps, Windows Store apps, and enterprise software. It is thus considered a powerful programming language and features in every developer’s cache of tools. Although first released in 2002, when it was introduced with .NET Framework 1.0, the C# language has evolved a great deal since then. The most recent version is C# 8.0, available in preview as part of Visual Studio. To get access to all of the new language features, you would need to install the latest preview version of .NET Core 3.0. The C# Environment You need the .NET Framework and an IDE (integrated development environment) to work with the C# language. The .NET Framework The .NET Framework platform of the Windows OS is required to write web and desktop-based applications using not only C# but also Visual Basic and Jscript, as the platform provides language interoperability. Besides, the .Net Framework allows C# to communicate with any of the other common languages, such as C++, Jscript, COBOL, and so on. IDEs Microsoft provides various IDEs for C# programming: Visual Studio 2010 (VS) Visual Studio Express Visual Web Developer Visual Studio Code (VSC) The C# source code files can be written using a basic text editor, like Notepad, and compiled using the command-line compiler of the .NET Framework. Alternative open-source versions of the .Net Framework can work on other operating systems as well. For instance, the Mono has a C# compiler and runs on several operating systems, including Linux, Mac, Android, BSD, iOS, Windows, Solaris, and UNIX. This brings enhanced development tools to the developer. As C# is part of the .Net Framework platform, it has access to its enormous library of codes and components, such as Common Language Runtime (CLR), the .Net Framework Class Library, Common Language Specification, Common Type System, Metadata and Assemblies, Windows Forms, ASP.Net and ASP.Net AJAX, Windows Workflow Foundation (WF), Windows Communication Foundation (WCF), and LINQ. C# and Java C# and Java are high-level programming languages that share several similarities (as well as many differences). They are both object-oriented languages much influenced by C++. But while C# is suitable for application development in the Microsoft ecosystem from the front, Java is considered best for client-side web applications. Also, while C# has many tools for programming, Java has a larger arsenal of tools to choose from in IDEs and Text Editors. C# is used for virtual reality projects like games, mobile, and web applications. It is built specifically for Microsoft platforms and several non-Microsoft-based operating systems, like the Mono Project that works with Linux and OS X. Java is used for creating messaging applications and developing web-based and enterprise-based applications in open-source ecosystems. Both C# and Java support arrays. However, each language uses them differently. In C#, arrays are a specialization of the system; in Java, they are a direct specialization of the object. The C# programming language executes on the CLR. The source code is interpreted into bytecode, which is further compiled by the CLR. Java runs on any platform with the assistance of JRE (Java Runtime Environment). The written source code is first compiled into bytecode and then converted into machine code to be executed on a JRE. C# and C++ Although C# and C++ are both C-based languages with similar code, there are some differences. For one, C# is considered a component-oriented programming language, while C++ is a partial object-oriented language. Also, while both languages are compiled languages, C# compiles to CLR and is interpreted by.NET, but C++ compiles to machine code. The size of binaries in C# is much larger than in C++. Other differences between the two include the following: C# gives compiler errors and warnings, but C++ doesn’t support warnings, which may cause damage to the OS. C# runs in a virtual machine for automatic memory management. C++ requires you to manage memory manually. C# can create Windows, .NET, web, desktop, and mobile applications, but not stand-alone apps. C++ can create server-side, stand-alone, and console applications as it can work directly with the hardware. C++ can be used on any platform, while C# is targeted toward Windows OS. Generally, C++ being faster than C#, the former is preferred for applications where performance is essential. Features of C# The C# programming language has many features that make it more useful and unique when compared to other languages, including: Object-oriented language Being object-oriented, C# allows the creation of modular applications and reusable codes, an advantage over C++. As an object-oriented language, C# makes development and maintenance easier when project size grows. It supports all three object-oriented features: data encapsulation, inheritance, interfaces, and polymorphism. Simplicity C# is a simple language with a structured approach to problem-solving. Unsafe operations, like direct memory manipulation, are not allowed. Speed The compilation and execution time in C# is very powerful and fast. A Modern programming language C# programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection, error handling, debugging, and robust security. It has built-in support for a web service to be invoked from any app running on any platform. Type-safe Arrays and objects are zero base indexed and bound checked. There is an automatic checking of the overflow of types. The C# type safety instances support robust programming. Interoperability Language interoperability of C# maximizes code reuse for the efficiency of the development process. C# programs can work upon almost anything as a program can call out any native API. Consistency Its unified type system enables developers to extend the type system simply and easily for consistent behavior. Updateable C# is automatically updateable. Its versioning support enables complex frameworks to be developed and evolved. Component oriented C# supports component-oriented programming through the concepts of properties, methods, events, and attributes for self-contained and self-describing components of functionality for robust and scalable applications. Structured Programming Language The structured design and modularization in C# break a problem into parts, using functions for easy implementation to solve significant problems. Rich Library C# has a standard library with many inbuilt functions for easy and fast development. Full Stack Java Developer Course The Gateway to Master Web DevelopmentEXPLORE COURSEFull Stack Java Developer Course Prerequisites for Learning C# Basic knowledge of C or C++ or any programming language or programming fundamentals. Additionally, the OOP concept makes for a short learning curve of C#. Advantages of C# There are many advantages to the C# language that makes it a useful programming language compared to other languages like Java, C, or C++. These include: Being an object-oriented language, C# allows you to create modular, maintainable applications and reusable codes Familiar syntax Easy to develop as it has a rich class of libraries for smooth implementation of functions Enhanced integration as an application written in .NET will integrate and interpret better when compared to other NET technologies As C# runs on CLR, it makes it easy to integrate with components written in other languages It’s safe, with no data loss as there is no type-conversion so that you can write secure codes The automatic garbage collection keeps the system clean and doesn’t hang it during execution As your machine has to install the .NET Framework to run C#, it supports cross-platform Strong memory backup prevents memory leakage Programming support of the Microsoft ecosystem makes development easy and seamless Low maintenance cost, as C# can develop iOS, Android, and Windows Phone native apps The syntax is similar to C, C++, and Java, which makes it easier to learn and work with C# Useful as it can develop iOS, Android, and Windows Phone native apps with the Xamarin Framework C# is the most powerful programming language for the .NET Framework Fast development as C# is open source steered by Microsoft with access to open source projects and tools on Github, and many active communities contributing to the improvement What Can C Sharp Do for You? C# can be used to develop a wide range of: Windows client applications Windows libraries and components Windows services Web applications Native iOS and Android mobile apps Azure cloud applications and services Gaming consoles and gaming systems Video and virtual reality games Interoperability software like SharePoint Enterprise software Backend services and database programs AI and ML applications Distributed applications Hardware-level programming Virus and malware software GUI-based applications IoT devices Blockchain and distributed ledger technology Who Should Learn the C# Programming Language and Why? C# is one of the most popular programming languages as it can be used for a variety of applications: mobile apps, game development, and enterprise software. What’s more, the C# 8.0 version is packed with several new features and enhancements to the C# language that can change the way developers write their C# code. The most important new features available are ‘null reference types,’ enhanced ‘pattern matching,’ and ‘async streams’ that help you to write more reliable and readable code. As you’re exposed to the fundamental programming concepts of C# in this course, you can work on projects that open the doors for you as a Full Stack Java Developer. So, upskill and master the C# language for a faster career trajectory and salary scope.

What is JavaScript and what does it do? Before you start learning something new, it’s important to understand exactly what it is and what it does. This is especially useful when it comes to mastering a new programming language. In simple terms, JavaScript is a programming language used to make websites interactive. If you think about the basic makeup of a website, you have HTML, which describes and defines the basic content and structure of the website, then you have CSS, which tells the browser how this HTML content should be displayed—determining things like color and font. With just HTML and CSS, you have a website that looks good but doesn’t actually do much. JavaScript brings the website to life by adding functionality. JavaScript is responsible for elements that the user can interact with, such as drop-down menus, modal windows, and contact forms. It is also used to create things like animations, video players, and interactive maps. Nowadays, JavaScript is an all-purpose programming language—meaning it runs across the entire software stack. The most popular application of JavaScript is on the client side (aka frontend), but since Node.js came on the scene, many people run JavaScript on the server side (aka backend) as well. When used on the client side, JavaScript code is read, interpreted, and executed in the user’s web browser. When used on the server side, it is run on a remote computer. You can learn more about the difference between frontend and backend programming here. JavaScript isn’t only used to create websites. It can also be used to build browser-based games and, with the help of certain frameworks, mobile apps for different operating systems. The creation of new libraries and frameworks is also making it possible to build backend programs with JavaScript, such as web apps and server apps. Is it still worth learning JavaScript in 2021? The world of web development is constantly moving. With so many new tools popping up all the time, it can be extremely difficult to know where you should focus your efforts. As an aspiring developer, you’ll want to make sure that what you’re learning is still relevant in today’s industry. If you’re having doubts about JavaScript, it’s important to know that, since its creation in 1995, JavaScript is pretty much everywhere on the web—and that’s not likely to change any time soon. According to the 2020 StackOverflow developer survey, JavaScript is the most commonly used programming language for the eighth year in a row. It is currently used by 94.5% of all websites and, despite originally being designed as a client-side language, JavaScript has now made its way to the server-side of websites (thanks to Node.js), mobile devices (thanks to React Native and Ionic) and desktop (courtesy of Electron). As long as people are interacting with the web, you can assume that JavaScript is highly relevant—there’s no doubt that this is a language worth knowing! With that in mind, let’s look at some of the key benefits of becoming a JavaScript expert. Why learn JavaScript? The most obvious reason for learning JavaScript is if you have hopes of becoming a web developer. Even if you haven’t got your heart set on a tech career, being proficient in JavaScript will enable you to build websites from scratch—a pretty useful skill to have in today’s job market! If you do want to become a web developer, here are some of the main reasons why you should learn JavaScript: JavaScript experts are versatile JavaScript is an extremely versatile language. Once you’ve mastered it, the possibilities are endless: you can code on the client-side (frontend) using Angular and on the server-side (backend) using Node.js. You can also develop web, mobile, and desktop apps using React, React Native, and Electron, and you can even get involved in machine learning. If you want to become a frontend developer, JavaScript is a prerequisite. However, that’s not the only career path open to you as a JavaScript expert. Mastering this key programming language could see you go on to work in full-stack development, games development, information security software engineering, machine learning, and artificial intelligence—to name just a few! Ultimately, if you want any kind of development or engineering career, proficiency in JavaScript is a must. JavaScript experts are in-demand (and well-paid) JavaScript is the most popular programming language in the world, so it’s no wonder that JavaScript is one of the most sought-after skills in the web development industry today. According to the Devskiller IT Skills and Hiring Report 2020, 72% of companies are looking to hire JavaScript experts. Enter the search term “JavaScript” on job site Indeed and you’ll find over 40,000 jobs requiring this skill (in the US). Run the same search on LinkedIn and the results are in excess of 125,000. At the same time, the global demand for JavaScript seems to outweigh the expertise available on the market. According to this 2018 HackerRank report, 48% of employers worldwide need developers with JavaScript skills, while only 42% of student developers claim to be proficient in JavaScript. And, in their most recent report for 2020, HackerRank once again reports that JavaScript is the most popular language that hiring mangers look for in a web developer candidate. Not only are JavaScript experts in demand—they are also well-paid. In the United States, JavaScript developers earn an average yearly salary of $111,953 per year. We’ve covered this topic in more detail in our JavaScript salary guide, but as you can see, learning JavaScript can really boost your earning potential as a developer. JavaScript is beginner-friendly Compared to many other programming languages, JavaScript offers one of the more beginner-friendly entry points into the world of coding. The great thing about JavaScript is that it comes installed on every modern web browser—there’s no need to set up any kind of development environment, which means you can start coding with JavaScript right away! Another advantage of learning JavaScript as your first programming language is that you get instant feedback; with a minimal amount of JavaScript code, you’ll immediately see visible results. There’s also a huge JavaScript community on sites like Stack Overflow, so you’ll find plenty of support as you learn. Not only is JavaScript beginner-friendly; it will also set you up with some extremely valuable transferable skills. JavaScript supports object-oriented, functional, and imperative styles of programming—skills which can be transferred to any new language you might learn later on, such as Python, Java, or C++. JavaScript provides a crucial introduction to key principles and practices that you’ll take with you throughout your career as a developer. Should you learn plain JavaScript first or can you skip to frameworks and libraries? When deciding whether or not to learn JavaScript, what you’re really asking is whether or not you should learn “vanilla” JavaScript. Vanilla JavaScript just means plain JavaScript without any libraries or frameworks. Let’s explore what this means in more detail now. What is meant by vanilla JavaScript, libraries, and frameworks? If you research the term “vanilla JavaScript”, you might run into some confusion; however, all you need to know is that vanilla JavaScript is used to refer to native, standards-based, non-extended JavaScript. There is no difference between vanilla JavaScript and JavaScript—it’s just there to emphasize the usage of plain JavaScript without the use of libraries and frameworks. So what are libraries and frameworks? JavaScript libraries and frameworks both contain sets of prewritten, ready-to-use JavaScript code—but they’re not the same thing. You can think of a framework as your blueprint for building a website: it gives you a structure to work from, and contains ready-made components and tools that help you to build certain elements much quicker than if you were to code them from scratch. Some popular JavaScript frameworks include Angular, React, Vue, and Node.js. Frameworks also contain libraries. Libraries are smaller than frameworks, and tend to be used for more specific cases. A JavaScript library contains sets of JavaScript code which can be called upon to implement certain functions and features. Let’s imagine you want to code a particular element into your website. You could write, say, ten lines of JavaScript from scratch—or you could take the condensed, ready-made version from your chosen JavaScript library. Some examples of JavaScript libraries include jQuery, Lodash, and Underscore. The easiest way to understand how frameworks and libraries work together is to imagine you are building a house. The framework provides the foundation and the structure, while the library enables you to add in ready-made components (like furniture) rather than building your own from scratch. You can learn more about the relationship between languages and libraries in this post explaining the main differences between JavaScript and jQuery. For now, let’s go back to our original question: How important is it to learn vanilla JavaScript? Should you learn vanilla JavaScript first? When it comes to learning JavaScript, it can be tempting to skip ahead to those time-saving frameworks and libraries we just talked about—and many developers do. However, there are many compelling arguments for learning plain JavaScript first. While JavaScript frameworks may help you get the job done quicker, there’s only so far you can go if you don’t understand the core concepts behind these frameworks. Frontend developer Abhishek Nagekar describes how not learning vanilla JavaScript came back to bite him when he started learning the JavaScript frameworks Node and Express: “As I went to write more and more code in Node and Express, I began to get stuck at even the tiniest problems. Suddenly, I was surrounded with words like callbacks, closures, event loop and prototype. It felt like I got a reintroduction to JavaScript, but this time, it was not a toddler playing in its cradle, it was something of a mysterious monster, challenging me on every other step for not having taken it seriously.” The above Tweet references a long-running joke within the developer community, and although it dates way back to 2015, it’s still highly relevant today. If you want to become a developer who can innovate, not just execute, you need to understand the underlying principles of the web—not just the shortcuts. This means learning vanilla JavaScript before you move on to frameworks. In fact, understanding plain JavaScript will help you later on when it comes to deciding whether to use a framework for a certain project, and if so, which framework to use. Why Study JavaScript? JavaScript is one of the 3 languages all web developers must learn: 1. HTML to define the content of web pages 2. CSS to specify the layout of web pages 3. JavaScript to program the behavior of web pages Learning Speed In this tutorial, the learning speed is your choice. Everything is up to you. If you are struggling, take a break, or re-read the material. Always make sure you understand all the "Try-it-Yourself" examples. The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code ! Commonly Asked Questions How do I get JavaScript? Where can I download JavaScript? Is JavaScript Free? You don't have to get or download JavaScript. JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone. JavaScript is free to use for everyone.

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. 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"Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". 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In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). 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[230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

Bitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto satoshin@gmx.com www.bitcoin.org Abstract. A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone. 1. Introduction Commerce on the Internet has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments. While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model. Completely non-reversible transactions are not really possible, since financial institutions cannot avoid mediating disputes. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions, and there is a broader cost in the loss of ability to make non-reversible payments for non- reversible services. With the possibility of reversal, the need for trust spreads. Merchants must be wary of their customers, hassling them for more information than they would otherwise need. A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties can be avoided in person by using physical currency, but no mechanism exists to make payments over a communications channel without a trusted party. What is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes. 1 2. Transactions We define an electronic coin as a chain of digital signatures. Each owner transfers the coin to the next by digitally signing a hash of the previous transaction and the public key of the next owner and adding these to the end of the coin. A payee can verify the signatures to verify the chain of ownership. Transaction Hash Transaction Hash Transaction Hash Owner 1's Public Key Owner 2's Public Key Owner 3's Public Key Owner 0's Signature Owner 1's Signature The problem of course is the payee can't verify that one of the owners did not double-spend the coin. A common solution is to introduce a trusted central authority, or mint, that checks every transaction for double spending. After each transaction, the coin must be returned to the mint to issue a new coin, and only coins issued directly from the mint are trusted not to be double-spent. The problem with this solution is that the fate of the entire money system depends on the company running the mint, with every transaction having to go through them, just like a bank. We need a way for the payee to know that the previous owners did not sign any earlier transactions. For our purposes, the earliest transaction is the one that counts, so we don't care about later attempts to double-spend. The only way to confirm the absence of a transaction is to be aware of all transactions. In the mint based model, the mint was aware of all transactions and decided which arrived first. To accomplish this without a trusted party, transactions must be publicly announced [1], and we need a system for participants to agree on a single history of the order in which they were received. The payee needs proof that at the time of each transaction, the majority of nodes agreed it was the first received. 3. Timestamp Server The solution we propose begins with a timestamp server. A timestamp server works by taking a hash of a block of items to be timestamped and widely publishing the hash, such as in a newspaper or Usenet post [2-5]. The timestamp proves that the data must have existed at the time, obviously, in order to get into the hash. Each timestamp includes the previous timestamp in its hash, forming a chain, with each additional timestamp reinforcing the ones before it. Hash Hash Owner 2's Signature Owner 1's Private Key Owner 2's Private Key Owner 3's Private Key Block Item Item ... 2 Block Item Item ... Verify Verify Sign Sign 4. Proof-of-Work To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proof- of-work system similar to Adam Back's Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block's hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include redoing all the blocks after it. The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they're generated too fast, the difficulty increases. 5. Network The steps to run the network are as follows: 1) New transactions are broadcast to all nodes. 2) Each node collects new transactions into a block. 3) Each node works on finding a difficult proof-of-work for its block. 4) When a node finds a proof-of-work, it broadcasts the block to all nodes. 5) Nodes accept the block only if all transactions in it are valid and not already spent. 6) Nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash. Nodes always consider the longest chain to be the correct one and will keep working on extending it. If two nodes broadcast different versions of the next block simultaneously, some nodes may receive one or the other first. In that case, they work on the first one they received, but save the other branch in case it becomes longer. The tie will be broken when the next proof- of-work is found and one branch becomes longer; the nodes that were working on the other branch will then switch to the longer one. 3 Block Nonce Tx Tx ... Block Nonce Tx Tx ... Prev Hash Prev Hash New transaction broadcasts do not necessarily need to reach all nodes. As long as they reach many nodes, they will get into a block before long. Block broadcasts are also tolerant of dropped messages. If a node does not receive a block, it will request it when it receives the next block and realizes it missed one. 6. Incentive By convention, the first transaction in a block is a special transaction that starts a new coin owned by the creator of the block. This adds an incentive for nodes to support the network, and provides a way to initially distribute coins into circulation, since there is no central authority to issue them. The steady addition of a constant of amount of new coins is analogous to gold miners expending resources to add gold to circulation. In our case, it is CPU time and electricity that is expended. The incentive can also be funded with transaction fees. If the output value of a transaction is less than its input value, the difference is a transaction fee that is added to the incentive value of the block containing the transaction. Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation free. The incentive may help encourage nodes to stay honest. If a greedy attacker is able to assemble more CPU power than all the honest nodes, he would have to choose between using it to defraud people by stealing back his payments, or using it to generate new coins. He ought to find it more profitable to play by the rules, such rules that favour him with more new coins than everyone else combined, than to undermine the system and the validity of his own wealth. 7. Reclaiming Disk Space Once the latest transaction in a coin is buried under enough blocks, the spent transactions before it can be discarded to save disk space. To facilitate this without breaking the block's hash, transactions are hashed in a Merkle Tree [7][2][5], with only the root included in the block's hash. Old blocks can then be compacted by stubbing off branches of the tree. The interior hashes do not need to be stored. Block Hash0 Hash1 Hash2 Hash3 Tx0 Tx1 Tx2 Tx3 Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Block Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Hash2 Hash3 Tx3 Transactions Hashed in a Merkle Tree After Pruning Tx0-2 from the Block A block header with no transactions would be about 80 bytes. If we suppose blocks are generated every 10 minutes, 80 bytes * 6 * 24 * 365 = 4.2MB per year. With computer systems typically selling with 2GB of RAM as of 2008, and Moore's Law predicting current growth of 1.2GB per year, storage should not be a problem even if the block headers must be kept in memory. 4 8. Simplified Payment Verification It is possible to verify payments without running a full network node. A user only needs to keep a copy of the block headers of the longest proof-of-work chain, which he can get by querying network nodes until he's convinced he has the longest chain, and obtain the Merkle branch linking the transaction to the block it's timestamped in. He can't check the transaction for himself, but by linking it to a place in the chain, he can see that a network node has accepted it, and blocks added after it further confirm the network has accepted it. Longest Proof-of-Work Chain Block Header Block Header Block Header Prev Hash Nonce Prev Hash Nonce Prev Hash Nonce Merkle Root Merkle Root Merkle Root Hash01 Hash23 Merkle Branch for Tx3 Hash2 Hash3 Tx3 As such, the verification is reliable as long as honest nodes control the network, but is more vulnerable if the network is overpowered by an attacker. While network nodes can verify transactions for themselves, the simplified method can be fooled by an attacker's fabricated transactions for as long as the attacker can continue to overpower the network. One strategy to protect against this would be to accept alerts from network nodes when they detect an invalid block, prompting the user's software to download the full block and alerted transactions to confirm the inconsistency. Businesses that receive frequent payments will probably still want to run their own nodes for more independent security and quicker verification. 9. Combining and Splitting Value Although it would be possible to handle coins individually, it would be unwieldy to make a separate transaction for every cent in a transfer. To allow value to be split and combined, transactions contain multiple inputs and outputs. Normally there will be either a single input from a larger previous transaction or multiple inputs combining smaller amounts, and at most two outputs: one for the payment, and one returning the change, if any, back to the sender. It should be noted that fan-out, where a transaction depends on several transactions, and those transactions depend on many more, is not a problem here. There is never the need to extract a complete standalone copy of a transaction's history. 5 Transaction In Out In ... ... 10. Privacy The traditional banking model achieves a level of privacy by limiting access to information to the parties involved and the trusted third party. The necessity to announce all transactions publicly precludes this method, but privacy can still be maintained by breaking the flow of information in another place: by keeping public keys anonymous. The public can see that someone is sending an amount to someone else, but without information linking the transaction to anyone. This is similar to the level of information released by stock exchanges, where the time and size of individual trades, the "tape", is made public, but without telling who the parties were. Traditional Privacy Model Identities Transactions New Privacy Model Identities Transactions As an additional firewall, a new key pair should be used for each transaction to keep them from being linked to a common owner. Some linking is still unavoidable with multi-input transactions, which necessarily reveal that their inputs were owned by the same owner. The risk is that if the owner of a key is revealed, linking could reveal other transactions that belonged to the same owner. 11. Calculations We consider the scenario of an attacker trying to generate an alternate chain faster than the honest chain. Even if this is accomplished, it does not throw the system open to arbitrary changes, such as creating value out of thin air or taking money that never belonged to the attacker. Nodes are not going to accept an invalid transaction as payment, and honest nodes will never accept a block containing them. An attacker can only try to change one of his own transactions to take back money he recently spent. The race between the honest chain and an attacker chain can be characterized as a Binomial Random Walk. The success event is the honest chain being extended by one block, increasing its lead by +1, and the failure event is the attacker's chain being extended by one block, reducing the gap by -1. The probability of an attacker catching up from a given deficit is analogous to a Gambler's Ruin problem. Suppose a gambler with unlimited credit starts at a deficit and plays potentially an infinite number of trials to try to reach breakeven. We can calculate the probability he ever reaches breakeven, or that an attacker ever catches up with the honest chain, as follows [8]: p = probability an honest node finds the next block q = probability the attacker finds the next block qz = probability the attacker will ever catch up from z blocks behind Trusted Third Party q ={ 1 if p≤q} z q/pz if pq 6 Counterparty Public Public Given our assumption that p > q, the probability drops exponentially as the number of blocks the attacker has to catch up with increases. With the odds against him, if he doesn't make a lucky lunge forward early on, his chances become vanishingly small as he falls further behind. We now consider how long the recipient of a new transaction needs to wait before being sufficiently certain the sender can't change the transaction. We assume the sender is an attacker who wants to make the recipient believe he paid him for a while, then switch it to pay back to himself after some time has passed. The receiver will be alerted when that happens, but the sender hopes it will be too late. The receiver generates a new key pair and gives the public key to the sender shortly before signing. This prevents the sender from preparing a chain of blocks ahead of time by working on it continuously until he is lucky enough to get far enough ahead, then executing the transaction at that moment. Once the transaction is sent, the dishonest sender starts working in secret on a parallel chain containing an alternate version of his transaction. The recipient waits until the transaction has been added to a block and z blocks have been linked after it. He doesn't know the exact amount of progress the attacker has made, but assuming the honest blocks took the average expected time per block, the attacker's potential progress will be a Poisson distribution with expected value:  = z qp To get the probability the attacker could still catch up now, we multiply the Poisson density for each amount of progress he could have made by the probability he could catch up from that point: ∞ ke−{q/pz−k ifk≤z} ∑k=0 k!⋅ 1 ifkz Rearranging to avoid summing the infinite tail of the distribution... z ke− z−k 1−∑k=0 k! 1−q/p  Converting to C code... #include <math.h> double AttackerSuccessProbability(double q, int z) { double p = 1.0 - q; double lambda = z * (q / p); double sum = 1.0; int i, k; for (k = 0; k <= z; k++) { double poisson = exp(-lambda); for (i = 1; i <= k; i++) poisson *= lambda / i; sum -= poisson * (1 - pow(q / p, z - k)); } return sum; } 7 Running some results, we can see the probability drop off exponentially with z. q=0.1 z=0 P=1.0000000 z=1 P=0.2045873 z=2 P=0.0509779 z=3 P=0.0131722 z=4 P=0.0034552 z=5 P=0.0009137 z=6 P=0.0002428 z=7 P=0.0000647 z=8 P=0.0000173 z=9 P=0.0000046 z=10 P=0.0000012 q=0.3 z=0 P=1.0000000 z=5 P=0.1773523 z=10 P=0.0416605 z=15 P=0.0101008 z=20 P=0.0024804 z=25 P=0.0006132 z=30 P=0.0001522 z=35 P=0.0000379 z=40 P=0.0000095 z=45 P=0.0000024 z=50 P=0.0000006 Solving for P less than 0.1%... P < 0.001 q=0.10 z=5 q=0.15 z=8 q=0.20 z=11 q=0.25 z=15 q=0.30 z=24 q=0.35 z=41 q=0.40 z=89 q=0.45 z=340 12. Conclusion We have proposed a system for electronic transactions without relying on trust. We started with the usual framework of coins made from digital signatures, which provides strong control of ownership, but is incomplete without a way to prevent double-spending. To solve this, we proposed a peer-to-peer network using proof-of-work to record a public history of transactions that quickly becomes computationally impractical for an attacker to change if honest nodes control a majority of CPU power. The network is robust in its unstructured simplicity. Nodes work all at once with little coordination. They do not need to be identified, since messages are not routed to any particular place and only need to be delivered on a best effort basis. Nodes can leave and rejoin the network at will, accepting the proof-of-work chain as proof of what happened while they were gone. They vote with their CPU power, expressing their acceptance of valid blocks by working on extending them and rejecting invalid blocks by refusing to work on them. Any needed rules and incentives can be enforced with this consensus mechanism. 8 References [1] W. Dai, "b-money," http://www.weidai.com/bmoney.txt, 1998. [2] H. Massias, X.S. Avila, and J.-J. Quisquater, "Design of a secure timestamping service with minimal trust requirements," In 20th Symposium on Information Theory in the Benelux, May 1999. [3] S. Haber, W.S. Stornetta, "How to time-stamp a digital document," In Journal of Cryptology, vol 3, no 2, pages 99-111, 1991. [4] D. Bayer, S. Haber, W.S. Stornetta, "Improving the efficiency and reliability of digital time-stamping," In Sequences II: Methods in Communication, Security and Computer Science, pages 329-334, 1993. [5] S. Haber, W.S. 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