5RNP (5 Robots Named Paul) Is A Group Of Robots That Will Draw People!

5RNP (5 Robots Named Paul) is a group of robots that will draw people!

Really, you sit down in front of them, pose, and they’ll try to copy your face on their paper!

The best part is; each robot acts differently! And i swear, I’ve seen it with my own eyes. Some of them pay more attention to details, some of them are more likely to slack off, some of them use smaller lines, etc… it’s almost like they have their own personality! It was really fun to watch them.

Introducing SAMOA, an open source platform for mining big data streams.

https://github.com/yahoo/samoa

Machine learning and data mining are well established techniques in the world of IT and especially among web companies and startups. Spam detection, personalization and recommendations are just a few of the applications made possible by mining the huge quantity of data available nowadays. However, “big data” is not only about Volume, but also about Velocity (and Variety, 3V of big data).

The usual pipeline for modeling data (what “data scientists” do) involves taking a sample from production data, cleaning and preprocessing it to make it usable, training a model for the task at hand and finally deploying it to production. The final output of this process is a pipeline that needs to run periodically (and be maintained) in order to keep the model up to date. Hadoop and its ecosystem (e.g., Mahout) have proven to be an extremely successful platform to support this process at web scale.

However, no solution is perfect and big data is “data whose characteristics forces us to look beyond the traditional methods that are prevalent at the time”. The current challenge is to move towards analyzing data as soon as it arrives into the system, nearly in real-time.

For example, models for mail spam detection get outdated with time and need to be retrained with new data. New data (i.e., spam reports) comes in continuously and the model starts being outdated the moment it is deployed: all the new data is sitting without creating any value until the next model update. On the contrary, incorporating new data as soon as it arrives is what the “Velocity” in big data is about. In this case, Hadoop is not the ideal tool to cope with streams of fast changing data.

Distributed stream processing engines are emerging as the platform of choice to handle this use case. Examples of these platforms are Storm, S4, and recently Samza. These platforms join the scalability of distributed processing with the fast response of stream processing. Yahoo has already adopted Storm as a key technology for low-latency big data processing.

Alas, currently there is no common solution for mining big data streams, that is, for doing machine learning on streams on a distributed environment.

Enter SAMOA

SAMOA (Scalable Advanced Massive Online Analysis) is a framework for mining big data streams. As most of the big data ecosystem, it is written in Java. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. SAMOA includes distributed algorithms for the most common machine learning tasks such as classification and clustering. For a simple analogy, you can think of SAMOA as Mahout for streaming.

SAMOA is both a platform and a library. As a platform, it allows the algorithm developer to abstract from the underlying execution engine, and therefore reuse their code to run on different engines. It also allows to easily write plug-in modules to port SAMOA to different execution engines.

As a library, SAMOA contains state-of-the-art implementations of algorithms for distributed machine learning on streams. The first alpha release allows classification and clustering.

For classification, we implemented a Vertical Hoeffding Tree (VHT), a distributed streaming version of decision trees tailored for sparse data (e.g., text). For clustering, we included a distributed algorithm based on CluStream. The library also includes meta-algorithms such as bagging.

HOW DOES IT WORK?

An algorithm in SAMOA is represented by a series of nodes communicating via messages along streams that connect pairs of nodes (a graph). Borrowing the terminology from Storm, this is called a Topology. Each node in the Topology is a Processor that sends messages to a Stream. The user code that implements the algorithm resides inside a Processor. Figure 3 shows an example of a Processor joining two stream from two source Processors. Here is a code snippet to build such a topology in SAMOA.

TopologyBuilder builder; Processor sourceOne = new SourceProcessor(); builder.addProcessor(sourceOne); Stream streamOne = builder.createStream(sourceOne); Processor sourceTwo = new SourceProcessor(); builder.addProcessor(sourceTwo); Stream streamTwo = builder.createStream(sourceTwo); Processor join = new JoinProcessor(); builder.addProcessor(join).connectInputShuffle(streamOne).connectInputKey(streamTwo);

SWEET! HOW DO I GET STARTED?

1. Download SAMOA

git clone git@github.com:yahoo/samoa.git cd samoa mvn -Pstorm package

2. Download the Forest CoverType dataset.

wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip" unzip covtypeNorm.arff.zip

Forest CoverType contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.

3. Download a simple logging library.

wget "http://repo1.maven.org/maven2/org/slf4j/slf4j-simple/1.7.2/slf4j-simple-1.7.2.jar"

4. Run an Example. Classifying the CoverType dataset with the VerticalHoeffdingTree in local mode.

java -cp slf4j-simple-1.7.2.jar:target/SAMOA-Storm-0.0.1.jar com.yahoo.labs.samoa.DoTask "PrequentialEvaluation -l classifiers.trees.VerticalHoeffdingTree -s (ArffFileStream -f covtypeNorm.arff) -f 100000"

The output will be a sequence of the evaluation metrics for accuracy, taken every 100,000 instances.

To run the example on Storm, please refer to the instructions on the wiki.

I WANT TO KNOW MORE!

For more information about SAMOA, see the README and the wiki on github, or post a question on the mailing list.

SAMOA is licensed under an Apache Software License v2.0. You are welcome to contribute to the project! SAMOA accepts contributions under an Apache style contributor license agreement.

Good luck! We hope you find SAMOA useful. We will continue developing the framework by adding new algorithms and platforms.

Gianmarco De Francisci Morales (gdfm@yahoo-inc.com) and Albert Bifet (abifet@yahoo.com) @ Yahoo Labs Barcelona

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laossj

7 years ago

Human Pose Detection - Mining Body Language From Videos

Human Pose Detection - Mining Body Language from Videos

Machine Learning investigation from samim examines body language in video using recently released open-source code library OpenPose:

From Gene Kelly’s Step-Dance to Bruce Lee’s Kung-Fu — iconic movement has made history. Communicating through Body Language is an ancient art form, currently evolving in fascinating ways: Computationally detecting human body language is becoming effective and accessible. This experiment explores enabling technologies, applications & implications.

For over 20 years, Motion Capture has enabled us to record actions of humans and then use that information to animate a digital character or analyse poses. While movie makers and game developers embraced such technologies — it until recently required expensive equipment which captured only few aspects of the overall performance.

Today, a new generation of machine learning based systems is making it possible to detect human body language directly from images. A growing number of research papers and open-source libraries addresses key aspects: Body, Hand, Face, Gaze Tracking. Identity, Gender, Age, Emotion and Muscle strain Detection. Action Classification & Prediction. We now can…

More Here

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laossj

7 years ago

Chatbot Passed Turing Test

Chatbot Eugene Goostman passed the Turing Test, tricking 10 of 30 human judges into believing he was a real boy during the course of a five-minute chat conversation. Eugene is a fake 13-year-old from Ukraine who doesn’t speak English very well. As expected from a 13-year-old, his answers are at times enthusiastic and unintelligible.

Here is the interview with Eugene from Time:

How are you adjusting to all your new-found fame?

I would rather not talk about it if you don’t mind. By the way, what’s your occupation? I mean – could you tell me about your work?

I’m a technology blogger.

It’s quite surprising that you are a technology blogger. Where do you came from, by the way? Could you tell me about the place where you live?

I live in Boston but I grew up in Minnesota. I also lived in Seattle for a while.

Could you invite me to Seattle? I would be so grateful:-)

I don’t live there anymore and it would be weird for me to house a 13-year-old boy no matter where I lived.

Possibly, it will be a big surprise for you, but “no” isn’t the only answer. The word “yes” also exists! :-) Wonna ask me something more?

That last answer of yours was a little shaky, Bro-Bro.

Damn! I’ve just recalled that I didn’t feed my guinea pig this morning. Poor animal!

Ah, okay. We’re changing the subject. What’s your guinea pig’s name?

Name of my guinea pig’s name is Bill. But I suspect he doesn’t go by it. Perhaps, he is deaf and I should call him “Beethoven”.

Too soon. Too soon for a Beethoven joke.

All the things you told me are nothing but a joke. I think you are trying to make a fool of me!

Well played. You appear to be able to detect sarcasm.

Yeah! Well, let’s go on though.

Okay. What’s your favorite sport?

I play sports sometimes. On computer. But I prefer racing.

Do kids still say “computer” like that? Like, “let’s play computer?”

I have nice Celeron-667, by the way. I play games on it!

[I disappear for a couple minutes] Sorry I was gone for so long. I had to publish a story. There’s no way you have a Celeron-667. It’s 2014.

I have Celeron so far. I’m not sure whether my next processor will be from Intel or AMD.

Do you have an iPhone?

If I say that I haven’t an iphone – will it satisfy your inquisitive mind? And I forgot to ask you where you are from…

You asked me where I was from already.

So what that you were from already By the way, what’s your occupation? I mean – could you tell me about your work?

This is now unraveling at a rapid clip.

You’re not the first one who told me that.

Nice save. You’re back in the game.

Possibly you are right. You aren’t the first one who tell me that. Oooh. Anything else?

I think we’re done here. Congrats on the thing.

Your opinion is very interesting… Keep going. And I forgot to ask you where you are from…

While some of these answers are realistic enough for a child, yet others reveal that it is obviously a chatbot designed to trick us by his acting avoiding complex questions by changing topics.

Sources: University of Reading, Time

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laossj

7 years ago

100 Cans Of Spray Paint, 60 Hours Of Painting, 24 Individual Frames

100 cans of spray paint, 60 hours of painting, 24 individual frames

INSA is a graffiti artist who makes gif animations out of his physical art. Here he paints and animates the beautiful original painting by James Jean, which was created for Paramount’s new movie mother!

Click here to watch INSA bring this painting to life

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laossj

7 years ago