Angel \”Java\” Lopez on Blog

January 28, 2015

ECMAScript 6: Links, News and Resources (1)

Filed under: ECMAScript, JavaScript, Links — ajlopez @ 2:44 pm

Announcing TypeScript 1.4 – TypeScript – Site Home – MSDN Blogs
http://blogs.msdn.com/b/typescript/archive/2015/01/16/announcing-typescript-1-4.aspx

io.js – ES6 on io.js
https://iojs.org/es6.html

CoffeeScripters, Have You Tried ES6 Yet? | Chris Schmitz
http://chris-schmitz.com/coffeescripters-have-you-tried-es6-yet/

6to5 JavaScript Transpiler Gains Momentum
http://www.infoq.com/news/2015/01/6to5-javascript-transpiler

Read Understanding ECMAScript 6 | Leanpub
https://leanpub.com/understandinges6/read/

arielscarpinelli/sbt-traceur
https://github.com/arielscarpinelli/sbt-traceur

JavaScript Application Architecture On The Road To 2015 — Medium
https://medium.com/@addyosmani/javascript-application-architecture-on-the-road-to-2015-d8125811101b

ECMAScript 6 promises (1/2): foundations
http://www.2ality.com/2014/09/es6-promises-foundations.html

Fat Arrow Functions in JavaScript
http://robcee.net/2013/fat-arrow-functions-in-javascript/

ECMAScript 6 modules: the final syntax
http://www.2ality.com/2014/09/es6-modules-final.html

Collecting and Iterating, the ES6 Way – HTML5Rocks Updates
http://updates.html5rocks.com/2014/08/Collecting-and-Iterating-the-ES6-Way

The Better Parts
http://www.infoq.com/presentations/efficient-programming-language-es6

Getting Concurrent With ES6 Generators
http://davidwalsh.name/concurrent-generators?utm_source=javascriptweekly&utm_medium=email

lukehoban/es6features
https://github.com/lukehoban/es6features

Can you explain to me something about ES6? : javascript
http://www.reddit.com/r/javascript/comments/2d4wed/can_you_explain_to_me_something_about_es6/

Axel Rauschmayer on ECMAScript 6 and the Future of JavaScript
http://www.infoq.com/interviews/axel-rauschmayer-ecmascript-6

ES6 Rocks
http://es6rocks.com/

codexar/rode
https://github.com/codexar/rode

Porting from CommonJS
http://jsmodules.io/cjs

addyosmani/es6-tools
https://github.com/addyosmani/es6-tools

square/es6-module-transpiler
https://github.com/square/es6-module-transpiler

Handling required parameters in ECMAScript 6
http://www.2ality.com/2014/04/required-parameters-es6.html

Creating defensive objects with ES6 proxies | NCZOnline
http://www.nczonline.net/blog/2014/04/22/creating-defensive-objects-with-es6-proxies/

Callable entities in ECMAScript 6
http://www.2ality.com/2013/08/es6-callables.html

My Links
http://delicious.com/ajlopez/ecmascript6

January 24, 2015

Machine Learning: Links, News And Resources (6)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 8:14 pm

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Nuit Blanche: Europe Wide Machine Learning Meetup and Paris Machine Learning #12: Season 1 Finale, Andrew Ng and More…
http://nuit-blanche.blogspot.com.ar/2014/06/europe-wide-machine-learning-meetup-and.html

MojoJolo/textteaser
https://github.com/MojoJolo/textteaser

deeplearning4j.org
http://deeplearning4j.org/

agibsonccc/java-deeplearning
https://github.com/agibsonccc/java-deeplearning

Work on Machine Learning Problems That Matter To You | Machine Learning Mastery
http://machinelearningmastery.com/work-on-machine-learning-problems-that-matter-to-you/

What Does a Neural Network Actually Do? « Some Thoughts on a Mysterious Universe
http://moalquraishi.wordpress.com/2014/05/25/what-does-a-neural-network-actually-do/

Weka 3 – Data Mining with Open Source Machine Learning Software in Java
http://www.cs.waikato.ac.nz/ml/weka/

A Tour of Machine Learning Algorithms | Machine Learning Mastery
http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Speakers and Abstracts | Machine Learning Summer School
http://mlss.soe.ucsc.edu/schedule/speakers

A Primer on Deep Learning | DataRobot
http://www.datarobot.com/blog/a-primer-on-deep-learning/

The People Who Would Teach Machines to Learn | The Official Blog of BigML.com
http://blog.bigml.com/2014/05/19/the-people-who-would-teach-machines-to-learn/

Juan M Gómez’s Blog
http://jmgomez.me//a-fruit-image-classifier-with-python-and-simplecv/

IPAM – Schedule
https://www.ipam.ucla.edu/schedule.aspx?pc=gss2007

Meet the Man Google Hired to Make AI a Reality | Enterprise | WIRED
http://www.wired.com/2014/01/geoffrey-hinton-deep-learning/

Neural networks and a dive into Julia
http://blog.yhathq.com/posts/julia-neural-networks.html

Researchers Teach A Robot To Catch Flying Objects Like Yogi Berra | TechCrunch
http://techcrunch.com/2014/05/12/researchers-teach-a-robot-to-catch-flying-objects-like-yogi-berra/

Machine Learning is Fun! — Medium
https://medium.com/p/80ea3ec3c471

DataMining & MachineLearning
http://paper.li/Karelman/1339006494

Irving Wladawsky-Berger: Why Do We Need Data Science when We’ve Had Statistics for Centuries?
http://blog.irvingwb.com/blog/2014/04/why-do-we-need-data-science-when-weve-had-statistics-for-centuries.html

fergalbyrne/clortex · GitHub
https://github.com/fergalbyrne/clortex

IEPY
http://iepy.machinalis.com/

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 23, 2015

Liqueed Project (1)

Filed under: Express, JavaScript, Liqueed, MongoDB, NodeJs, Open Source Projects — ajlopez @ 6:36 pm

Next Post

Today, I want to present the Liqueed Project, see repo at:

https://github.com/liquid-co-ops/liqueed

It is a web application based on an idea by @acyment, read post:

http://blog.agilar.org/index.php/2014/04/30/leancoops-first-draft/

The basic idea of the application is to help teams that are developing something in the way that Cyment suggested, on the issue of allocation of shares on the project. For several months, the application (code, backlog, ideas, implementation, hosting and others) has been putting together by a “liquid” team (with entry and exit of people)

In this series of posts starting today I want to discuss interesting technical issues raised by the project. For today, I commented that:

– It is a Node.js application, exposed to the web using Express. Bringing the programming language is JavaScript.

– In addition to some internal administration pages with MVC, has an API exposed, exchanging JSON.

‘- There’s an app Single Page which is what would have to use the end user to view projects, voting, distributions of shares and to enter new ratings

– Most of the code was written using the workflow of TDD (Test-Driven Development). Even the first code implemented the model in memory, allowing easier progress on the implementation of use cases, without bothering about persistence (even the SPA client can run without having a walk server)

– A few months ago, we added persistence with MongoDB. We could use another database, relational perhaps. We are not taking advantage of the facilities to handle documents MongoDB yet. Only chose it for its ubiquity in development platforms and different Node hosting services.

– Some weeks ago, we added Istambul for code coverage.

– When TDD tests for pure code began to be long, we created a textual DSL (Domain Specific Language) that allows us to write text files for more complicated functional tests.

– Begin to add tests of SPA (Single Page Application) using Zombie

And there are more details and topics to comment, in the upcoming posts.

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 21, 2015

Internet Of Things: Links, News And Resources (5)

Filed under: Internet of Things, Links — ajlopez @ 6:33 pm

Previous Post

The Internet of Things is the Future of Retail – Salesforce Blog
http://blogs.salesforce.com/company/2015/01/internet-things-future-retail-gp.html

Construyendo un contador de personas con Raspberry Pi y Ubidots
http://blog.ubidots.com/es/construyendo-un-contador-de-personas-con-raspberry-pi-y-ubidots

Internet of Things application development platform
http://ubidots.com/

The Big Data of Wearables
http://www.kdnuggets.com/2014/12/big-data-wearables.html

Working with wearables and Bluemix
http://www.ibm.com/developerworks/analytics/library/ba-bluemix-wearables/index.html

Octoblu | We make all APIs, Platforms and Devices talk to each other. Easily.
http://octoblu.com/

Technical Machine
https://tessel.io/

Grove Labs | Home
https://grovelabs.io/

Connect Raspberry Pi to PubNub in 2 Steps – PubNub
http://www.pubnub.com/blog/raspberry-pi-to-pubnub-in-less-than-10-lines-of-code/

The Internet Of Things Is Reaching Escape Velocity | TechCrunch
http://techcrunch.com/2014/12/02/the-internet-of-things-is-reaching-escape-velocity/

La Ciudad de Buenos Aires lanza IoT, un concurso sobre Internet de las Cosas – Gira BsAs
http://www.girabsas.com/nota/3478-la-ciudad-de-buenos-aires-lanza-iot-un-concurso-sobre-internet-de-las-cosas/

My Links
https://delicious.com/ajlopez/iot

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 19, 2015

Machine Learning: Links, News And Resources (5)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:53 pm

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Machine Learning in Go using GoLearn | Stephen Whitworth
http://www.sjwhitworth.com/machine-learning-in-go-using-golearn/

mate-tools – Tools for Natural Language Analysis, Generation and Machine Learning – Google Project Hosting
https://code.google.com/p/mate-tools/

CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

lisa-lab/pylearn2
https://github.com/lisa-lab/pylearn2

A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library | Machine Learning Mastery
http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/

Facebook Chases Google’s Deep Learning with New Research Group | MIT Technology Review
http://www.technologyreview.com/news/519411/facebook-launches-advanced-ai-effort-to-find-meaning-in-your-posts/

AI Developers to power new generation of context driven artificial intelligence | SiliconANGLE
http://siliconangle.com/blog/2014/04/10/ai-developers-to-power-new-generation-of-context-driven-artificial-intelligence/

Deep Learning (or not): The why’s have it — FactorialWise
http://factorialwise.com/blog/2014/4/11/deep-learning-or-not-the-whys-have-it

Neural networks and deep learning
http://neuralnetworksanddeeplearning.com/chap2.html

pyvideo.org – Exploring Machine Learning with Scikit-learn
http://www.pyvideo.org/video/2561/exploring-machine-learning-with-scikit-learn

Gaussian Processes for Machine Learning: Contents
http://www.gaussianprocess.org/gpml/chapters/

Machine Learning – complete course notes
http://www.holehouse.org/mlclass/

Learning and Teaching Machine Learning: A Personal Journey
http://www.kdnuggets.com/2014/04/learning-teaching-machine-learning-personal-journey.html

Description – Galaxy Zoo – The Galaxy Challenge | Kaggle
http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge

My solution for the Galaxy Zoo challenge – Sander Dieleman
http://benanne.github.io/2014/04/05/galaxy-zoo.html

Introduction to Information Retrieval
http://www-nlp.stanford.edu/IR-book/

machinalis/featureforge
https://github.com/machinalis/featureforge

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 16, 2015

Machine Learning: Links, News And Resources (4)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 11:10 am

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An Introduction to Deep Learning (in Java): From Perceptrons to Deep Networks | Toptal
http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

Machine Learning & Recommender Systems at Netflix Scale
http://www.infoq.com/presentations/machine-learning-netflix

The Yaksis
http://www.yaksis.com/posts/vowpal_wabbit-the-redis-of-the-data-science-community.html

2014 will be the year you’ll learn Machine Learning — Louis Dorard
http://www.louisdorard.com/blog/2014-machine-learning

Classification with scikit-learn | DataRobot
http://www.datarobot.com/blog/classification-with-scikit-learn/

Machine Learning with Scikit-Learn – Jake Vanderplas on Vimeo
http://vimeo.com/80093925

How Google’s Robots Can Learn Like Humans | Fast Company | Business Innovation
http://www.fastcompany.com/3026056/most-innovative-companies-2014/how-googles-robots-can-learn-like-humans

Machine Learning in Javascript: Introduction | Burak Kanber’s Blog
http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/

Probably Approximately Correct — a Formal Theory of Learning | Math n Programming
http://jeremykun.com/2014/01/02/probably-approximately-correct-a-formal-theory-of-learning/

Machine Learning
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

Seattle area event: A hands-on introduction to machine learning with F# – Visual F# Tools Team Blog – Site Home – MSDN Blogs
http://blogs.msdn.com/b/fsharpteam/archive/2013/11/12/seattle-area-event-a-hands-on-introduction-to-machine-learning-with-f.aspx

Introduction to Machine Learning
http://alex.smola.org/teaching/cmu2013-10-701x/index.html

All Models of Learning have Flaws « Machine Learning (Theory)
http://hunch.net/?p=224

mikeizbicki/HLearn
https://github.com/mikeizbicki/HLearn

The Man Who Would Teach Machines to Think – James Somers – The Atlantic
http://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/

Machine Learning in Python has never been easier – AnalyticBridge
http://www.analyticbridge.com/profiles/blogs/machine-learning-in-python-has-never-been-easier

Code Webs – Visualizing 40,000 student code submissions
http://www.stanford.edu/~jhuang11/research/pubs/moocshop13/codeweb.html

luispedro/BuildingMachineLearningSystemsWithPython
https://github.com/luispedro/BuildingMachineLearningSystemsWithPython

Stanford researchers to open-source model they say has nailed sentiment analysis — Tech News and Analysis
http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 13, 2015

Machine Learning: Links, News And Resources (3)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:19 pm

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Tutorial Slides by Andrew Moore, computer scientist at Google, ex-CMU professor – AnalyticBridge
http://www.analyticbridge.com/forum/topics/tutorial-slides-by-andrew?groupUrl=onlinetutorials

Introduction to Machine Learning | InTechOpen
http://www.intechopen.com/books/theory_and_novel_applications_of_machine_learning

Skills Matter : Progressive F# Tutorials 2013: Matt Moloney
http://skillsmatter.com/podcast/scala/phil-trelford

Deep Learning
http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/

Richard Socher – Deep Learning Tutorial
http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial

tresata/ganitha
https://github.com/tresata/ganitha

Amazon.com: Imbalanced Learning: Foundations, Algorithms, and Applications (9781118074626): Haibo He, Yunqian Ma: Books
http://www.amazon.com/Imbalanced-Learning-Foundations-Algorithms-Applications/dp/1118074629

Peekaboo: Machine Learning Cheat Sheet (for scikit-learn)
http://peekaboo-vision.blogspot.ca/2013/01/machine-learning-cheat-sheet-for-scikit.html

bigdata2013.sciencesconf.org/conference/bigdata2013/pages/bottou.pdf
http://bigdata2013.sciencesconf.org/conference/bigdata2013/pages/bottou.pdf

Análisis Cluster (II): Clasificación no supervisada mediante clasificación jerárquica aglomerativa | Pybonacci
https://pybonacci.wordpress.com/2012/11/19/analisis-cluster-ii-clasificacion-no-supervisada-mediante-clasificacion-jerarquica-aglomerativa/

Building Machine Learning Systems with Python | Meta Rabbit
http://metarabbit.wordpress.com/2013/05/31/building-machine-learning-systems-with-python/

Machine Learning | The F# Software Foundation
http://fsharp.org/machine-learning/

Neural Network Visualisation | Creative Clojure
http://clojurefun.wordpress.com/2013/04/10/neural-network-visualisation/

nuroko/nurokit · GitHub
https://github.com/nuroko/nurokit

Skills Matter : The London Clojure Community:Machine Learnin
http://skillsmatter.com/podcast/java-jee/machine-learning-with-storm-redis/

yods/storm-ml-play · GitHub
https://github.com/yods/storm-ml-play

Manning: Machine Learning in Action
http://www.manning.com/pharrington/

scalanlp/nak · GitHub
https://github.com/scalanlp/nak

Cornell Chronicle: Student’s research could shake up Wall Street
http://www.news.cornell.edu/stories/March13/Zvorinji.html

A Tutorial on Learning With Bayesian Networks – Microsoft Research
http://research.microsoft.com/apps/pubs/default.aspx?id=69588

Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part One) | The Official Blog of BigML.com
http://blog.bigml.com/2013/02/15/everything-you-wanted-to-know-about-machine-learning-but-were-too-afraid-to-ask-part-one/

Resources « CS 194-16: Introduction to Data Science
http://datascienc.es/resources/

harthur/brain · GitHub
https://github.com/harthur/brain

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 12, 2015

Machine Learning: Links, News And Resources (2)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 9:27 am

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CS 229 Machine Learning Course Materials
http://cs229.stanford.edu/materials.html

Will it Python? Machine Learning for Hackers, Chapter 2, Part 2: Logistic regression with statsmodels
http://slendrmeans.wordpress.com/2012/12/21/458/

Foundations Of Machine Learning
http://mitpress.mit.edu/books/foundations-machine-learning-0

How I made $500k with machine learning and HFT (high frequency trading)
http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft

jimpil / enclog
https://github.com/jimpil/enclog
Clojure wrapper for Encog (v3) (Machine-Learning framework that specialises in neural-nets)

Machine Learning: Genetic Algorithms in Javascript Part 2
http://burakkanber.com/blog/machine-learning-genetic-algorithms-in-javascript-part-2/

5 Principles for Applying Machine Learning Techniques
http://blog.factual.com/5-principles-for-applying-machine-learning-techniques

Understanding the Bias-Variance Tradeoff
http://scott.fortmann-roe.com/docs/BiasVariance.html

Up And Running With Python – My First Kaggle Entry
http://blog.kaggle.com/2012/07/02/up-and-running-with-python-my-first-kaggle-entry/

Machine Learning for Hackers
http://www.johndcook.com/blog/2012/03/07/machine-learning-for-hackers/

8 Crazy Things IBM Scientists Have Learned Studying Twitter
http://www.businessinsider.com/8-crazy-things-ibm-scientists-have-learned-studying-twitter-2012-1

What have been the most interesting papers in computer science for 2011?
http://www.quora.com/What-have-been-the-most-interesting-papers-in-computer-science-for-2011

Infer.NET
http://research.microsoft.com/en-us/projects/infernet/
Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is currently downloadable as a beta release under a non-commercial license.

Machine Learning
http://area51.stackexchange.com/proposals/26434/machine-learning

Hadoop and Machine Learning
http://www.slideshare.net/joshwills/hadoop-and-machine-learning

Machine Learning, Hadoop, and Mahout
http://nosql.mypopescu.com/post/14559975263/machine-learning-hadoop-and-mahout

Bayesian Reasoning and Machine Learning
http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

Enbracing Uncertainty
http://embracinguncertainty.info/

The New Machine Intelligence
http://scpro.streamuk.com/uk/player/Default.aspx?wid=7739

Machine learning for dummies
http://blogs.technet.com/b/next/archive/2011/02/16/machine-learning-for-dummies-john-platt.aspx

Smart Data Structures: An Online Machine Learning
Approach to Multicore Data Structures
http://groups.csail.mit.edu/carbon/wordpress/wp-content/uploads/2011/03/eastep-smart-data-structures-icac11.pdf

Best Paper Awards in Computer Science (since 1996)
http://jeffhuang.com/best_paper_awards.html

Review of 2011 free Stanford online classes
http://programming-puzzler.blogspot.com.ar/2011/11/review-of-2011-free-stanford-online.html

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 9, 2015

JavaScript And Artificial Intelligence (3)

Filed under: Artificial Intelligence, JavaScript — ajlopez @ 9:43 am

Previous Post

One topic that I presented with an example (but without running it) is the evaluation of the next move in a board game. I mentioned a movie I watched decades ago and influenced me a lot in the way of seeing artificial intelligence, War Games (“War Games” 1983, see Wikipedia see IMDB):

In board games, it is common to evaluate a position, given the possible moves, evaluate the resulting positions, choosing the best one for our side. The problem is to determine the "best". In general, there is no perfect evaluation functions. What you can do is explore a tree of moves, to have a "deeper" assessment of the current position. This allows calculating a current position value based on the possible evolution of the next moves. There are several ways to explore a tree (I refer you to book mentioned in the previous post, based on all the talk)

I chose as an example, a board game with random, where the moves involve the roll of dices, the popular backgammon. I remember the work of Hans Berliner, in the eighties, that I met thanks to an article in Scientific American:

 

It was the first program to beat a human world champion in a board game. You can read the articles of Hans Berliner in:

Backgammon Computer Program Beats World Champion
Computer Backgammon

If you read the description of the program, it has a lot of heuristics, and expert knowledge of the game. My project is more modest and tries to evaluate a position exploring the tree of moves, calculating at the end a simple "metric" of how far we are the ultimate goal (remove all the tiles from the board before your opponent ).

The project in JavaScript at:

https://github.com/ajlopez/SimpleGammon

It was written using TDD (Test-Driven Development), as usual, using Node.js console. But the example can run in the browser:

https://github.com/ajlopez/SimpleGammon/tree/master/samples/html

Simple interface to be improved:

Runs evaluating two moves forward to the client. Before click "Play" you can choose who moves, and what are the initial dices values. But now, the algorithm takes seconds to run, something to improves. Several options to explore, such as parallel processing (we saw at the conference that JavaScript has implementations to run in multiprocessors), and derive the evaluation to the server, where it can be forwarded to many worker machines (something I did for another example , genetic algorithms).

Other projects that I am working to evaluate positions (but still without examples, work in progress):

https://github.com/ajlopez/SimpleGo
https://github.com/ajlopez/SimpleChess

The game of Go is compelling, but not trivial. First, I want to go further with the implementation of backgammon, the first candidate to distributed evaluation.

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 7, 2015

JavaScript And Artificial Intelligence (2)

Filed under: Artificial Intelligence, JavaScript — ajlopez @ 9:42 am

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Thanks to JSConf Argentina 2014 for giving me the opportunity to give this talk, in this excellent conference. To give a dynamic format, the talks were twenty minutes. I would like in this series of posts complete the presentation I wanted to show, commenting in more detail some points, and presenting other demos and projects that were left out of the conversation.

Today’s topic is recommending THE book that helped me as study guide for an endless theme, a classic computer science (the fourth most cited in this century):

Artificial Intelligence, a Modern Approach
http://aima.cs.berkeley.edu/

by Stuart Russell and Peter Norvig. About the lastest edition cover:

http://aima.cs.berkeley.edu/cover.html

There is an Spanish edition, and I found it at bookstores in Buenos Aires, a few years ago. It has more than a thousand pages, twenty-seven chapters and appendices, from intelligent agents to neural networks, from search algorithms and evaluation of trees to robotics and philosophical themes. I find it interesting that each chapter is accompanied by notes on the history of the topic, which always think that gives us perspective and better understanding of the problems and difficulties in development. Not to keep only “what we know now”, but also to study what was the path followed to reach the current state, which may also serve to understand what the future we can continue.

Code, pseudocode at http://aima.cs.berkeley.edu/algorithms.pdf  Lisp, Python, Java code at http://aima.cs.berkeley.edu/code.html

A big list to research: AI on the web http://aima.cs.berkeley.edu/ai.html

In the edition I have, JavaScript does not appear as a programming language to use. It is a good exercise to adapt the examples in pseudocode implementations to different languages. I try to implement some examples in the project https://github.com/ajlopez/NodeAima. Notably, JavaScript must be the simplest language to implement many of the ideas in the book. Implementations typed and not-dynamic languages such as Java and C #, seem to always have some “convoluted” solutions compared to a direct implementation in JavaScript. See for example https://github.com/ajlopez/SharpAima

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
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The Shocking Blue Green Theme. Blog at WordPress.com.

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