Some Java based AI Frameworks : Encog, JavaML, Weka

While working through I am working through Programming Collection Intelligence I found myself sending a lot of time translating the Python code to java, being typically impatient at my slow progress, I went searching for alternatives.

I found 3:
Encog - Heaton Research
JavaML
Weka

This is by no means an in-depth investigation, I simply downloaded what the relevant projects had available and quickly compared what was available to me to learn and implement AI related samples / applications.
 

Encog

Advantages

1. You Tube video tutorials
2. E-Books available for both Java and .Net
3. C# implementation
4. Closure wrapper
5. Seems active

Disadvantages

1. Quite large code base to wrap your head around, this is probably due to the size of the domain we are looking at, but still much more intimidating to start off with vs. the Java ML library.

JavaML

Advantages

1. Seems reasonably stable
2. Well documented source code
3. Well defined simple algorithm implementations

Disadvantages

1. Lacks the tutorial support for a AI newbie like myself

Weka

Advantages

1. E-Book Data Mining Practical Techniques

Disadvantages

1. Could not install Weka 3-7-9 dmg... kept on giving me a "is damaged and can't be opened error, so left it there, as Sweet Brown says: "Ain't nobody got time for that". 

So no surprise I went with Encog, and started on their video tutorials....
A couple hours later, first JUnit test understanding, training and testing a Hopfield neural network using the Encog libs.

Similarity Score Algorithms

As per my previous post, I am working through Programming Collection Intelligence the first couple algorithms described in this book are regarding finding a similarity score, the methods they work through are Euclidean Distance and the Pearson Correlation Coefficient. The Manhattan distance score is also mentioned but some what I could find it seems that it is just the sum of the (absolute) differences of their coordinates, instead of Math.pow 2 used in Euclidean distance.

I worked through this and wrote/found some java equivalents for future use:

Euclidean Distance:

Pearson Correlation Coefficient:

Venture into AI, Machine Learning and all those algorithms that go with it.

It's been a 4 months since my last blog entry, I took it easy for a little while as we all need to do from time to time... but before long my brain got these nagging ideas and questions:

How hard can AI and Machine learning actually be?
How does it work?
I bet people are just over complicating it..
How are they currently trying to solve it?
Is it actually that difficult?
Could it be done it differently?

So off I went search the internet, some of useful sites I came across:
http://www.ai-junkie.com
Machine-learning Stanford Video course
Genetic algorithm example

I also ended up buying 2 books on Amazon:

Firstly, from many different recommendations:
Programming Collective Intelligence

I will be "working" through this book. While reading I will be translating, implementing and blogging the algorithms defined (in Python) as well as any mentioned that I will research separately in Java. Mainly for my own understanding and for the benefit of reusing them later, and an excuse to play with Java v7.

However, since I want to practically work through that book, I needed another for some "light" reading before sleep, I found another book from an article on MIT technology review Deep Learning, a bit that caught my eye was:

For all the advances, not everyone thinks deep learning can move artificial intelligence toward something rivaling human intelligence. Some critics say deep learning and AI in general ignore too much of the brain’s biology in favor of brute-force computing.

One such critic is Jeff Hawkins, founder of Palm Computing, whose latest venture, Numenta, is developing a machine-learning system that is biologically inspired but does not use deep learning. Numenta’s system can help predict energy consumption patterns and the likelihood that a machine such as a windmill is about to fail. Hawkins, author of On Intelligence, a 2004 book on how the brain works and how it might provide a guide to building intelligent machines, says deep learning fails to account for the concept of time. Brains process streams of sensory data, he says, and human learning depends on our ability to recall sequences of patterns: when you watch a video of a cat doing something funny, it’s the motion that matters, not a series of still images like those Google used in its experiment. “Google’s attitude is: lots of data makes up for everything,” Hawkins says.

So the second book I purchased - On Intelligence
So far (only page upto page 54) 2 things have from this book have imbedded themselves in my brain:
"Complexity is a symptom of confusion, not a cause" - so so common in the software development world.
&
"AI defenders also like to point out historical instances in which the engineering solution differs radically from natures version"
...
"Some philosophers of mind have taken a shine to the metaphor of the cognitive wheel, that is, an AI solution to some problem that although entirely different from how the brain does it is just as good"

Jeff himself believes we need to look deeper into the brain for a better understanding, but could it be possible to have completely different approach to solve the "intelligence" problem?