A programming language for AI

I am curious which programming language is more useful for Artificial Intelligence. “Choose the language that you are more proficient in”, it is not an option to me. Choose the right tool, for the right problem is better in my case.

I was looking in Quora and founding some results. “What is best programming language for Artificial Intelligence projects?” is one of the most interesting, I was reading the answers from there. And the conclusion among the results is: Python (because it is fast to develop things and there are interesting libraries), C/C++ (because the speed and performance) or Java.

Taking a look to google, I found a tutorial written by Günter Neumann, from the German Research Center for Artificial Intelligence, entitled Programming Languages in Artificial Intelligence. In the tutorial, you can read why functional programming languages and symbolic languages are more useful for AI and then you find an introduction to Lisp and a small part for Prolog.

It is a simple introduction to Lisp, but I couldn’t avoid to remember µlisp (an small Lisp interpreter that I did, based in another book Build Your Own Lisp). I built a simple version of Lisp using C. In that point there were no standard libraries, you have to build them by yourself and I was wondering, if in that point you can start to create a language that it helps you to represent the world.

As always that was a crazy idea. Create a programming language that experts tell it is useful for artificial intelligence and build the standard libraries to represent part of the world that the system have to work with. As far as you go with the idea, you know that you cannot represent the complete real world with that approach, but… could you do a mix of implement part of the world with the programming language and part of the world based in the experience somehow? That was my thought, maybe there would be a way to do it.

By the way, my mind took me to start reading the new book of Deep Learning by Ian Goodfellow, Aaron Courville and Yoshua Bengio. In the introduction, there is a reference to Cyc (Lenat and Guha, 1989) and knowledge base.

A computer can reason about statements in these formal languages automatically using logical inference rules. This is known as the knowledge base approach to artificial intelligence. None of these projects has lead to a major success. One of the most famous such projects is Cyc (Lenat and Guha, 1989)  [extracted from the draft]

I am still thinking that it could work, because my approach is not to write every single rule of the world with the programming language, if not, to have some base using the language prepared for that specific problem like a DSL, but going further and without any limitation from the language itself. Either way, it is just an idea, I will continue reading the book from Yoshua Bengio, about deep learning it looks really promising and I will take a look to the review of the Lenat & Guha book, maybe I can figure out more.

Deep learning in a large scale distributed system

Deep learning is interesting in many ways. But when you consider to do it in thousands of cores that can process millions of parameters, then the problem is more interesting and complex at the same time.

Google Datacenter (via Google)

Google was doing an interesting experiment, training a deep network with millions of parameters in thousands of CPUs. The goal was to train very large datasets without to limit the form of the model.

The paper describes the use of DistBelief, a framework created for distributed parallel computing applied to deep learning training. A collection of the features that the framework manage by itself are:

The framework automatically parallelises computation in each machine using all available core, and manages communication, synchronisation and data transfer between machines during both training and inference.

I couldn’t find too much information about it, only what it is written in the paper.

They have applied two algorithms: SGD (Stochastic Gradient Descent) and L-BFGS. These algorithms usually works well, but they doesn’t scale with very large data sets. That is because they introduce some modifications to them. The paper gives you more details about the optimisations in both algorithms that you can find interesting.

I was found really interesting the idea of distributed parallel computing working for very large datasets  in such algorithms.

You can read “Large Scale Distributed Deep Networks”, or if you are interested in the pdf version. Have fun!

Classifying documents using Apache Mahout

I was wondering how to do some text classification with Java and Apache MahoutIsabel Drost-Fromm gave a talk in the LuceneSolrRevolution Conference (Dublin – 2013) where she was speaking about the topic, how Apache Mahout and Lucene could help you.

It is a good an introduction to the topic. I have enjoyed too much what it was presented in the talk.

Lucene, Mahout and Hadoop (only a little bit) sound really great for a talk about how to do texts classifications.

The general idea behind the complete process to classify documents will follow the below steps:

HTML >> Apache Tika

Fulltext >> Lucene Analyzer

Tokenstream >> FeatureVectorEnconder

Vector >> Online Learner

Of course Isabel was giving the advice of reuse the libraries that you have in your hands, take an internal look to the algorithms used there and improve them, if you need it. As a first approach it is really good for me to see how things work.

Mahout is a really good library for machine learning, it was using map reduce to perfectly integrate with Hadoop (v1.0), although from April of 2014 they have decided to move forward:

The Mahout community decided to move its codebase onto modern data processing systems that offer a richer programming model and more efficient execution than Hadoop MapReduce. (You can read that in there web site).

At the end of the video there is a recommendation to everyone to participate in the project: bug fixing, documentation, reporting bugs… There are a lot of things to do in open source projects always. If you are using the libraries there, I recommend you to subscribe to the mailing lists if you are interested in the project.

I really recommend you to see the video if you are interested in the field, I think she was giving a good talk about a good topic. You can take a look to the slides too.

Two interesting books to start with Machine Learning

There are a lot of books in the field of Machine Learning, just a fast search in Amazon gives you more than 25.ooo books. I wanted to filter all those books an choose the most useful. I was looking in google, quora and reading some post that I found around internet. There a lot of people giving a list of 10 – 20 books about machine learning, statistical learning, reinforcement learning… I just wanted to find the two interesting books to go into the field.

With these books, it is possible to learn general aspects about the topic and later go more in deep in the part that sounds more interesting.



Machine Learning

The “book” that everyone recommend as a good point to start, written by Tom M. Mitchell (professor in the Carnegie Mellon University).

This is an introduction book for the field. You don’t need to have previous knowledge in Machine Learning.

Some topics that you will find in the book: decision tree learning, artificial neural networks, bayesian learning, computational learning, genetic algorithms, reinforcement learning and more.



Pattern Recognition and Machine Learning (Information Science and Statistics)

The author is Christopher M. Bishop, a Distinguished Scientist at Microsoft Research Cambridge, where he leads the Machine Learning and Perception group

This book will give you a really good approach to the commonly used algorithms in Machine Learning.



Both books are theoretical and will give you a good introduction. Of course there so many books in the area, some of then more practical, some about statistical learning… But I think it is good to have a simple point to start.

I have started with Tom M. Mitchell’s book. I will give you my impression when I have finished it.