Interview BigML.com

Here is an interview with Charlie Parker, head of large scale online algorithms at http://bigml.com

Ajay-  Describe your own personal background in scientific computing, and how you came to be involved with machine learning, cloud computing and BigML.com

Charlie- I am a machine learning Ph.D. from Oregon State University. Francisco Martin (our founder and CEO), Adam Ashenfelter (the lead developer on the tree algorithm), and myself were all studying machine learning at OSU around the same time. We all went our separate ways after that.

Francisco started Strands and turned it into a 100+ million dollar company building recommender systems. Adam worked for CleverSet, a probabilistic modeling company that was eventually sold to Cisco, I believe. I worked for several years in the research labs at Eastman Kodak on data mining, text analysis, and computer vision.

When Francisco left Strands to start BigML, he brought in Justin Donaldson who is a brilliant visualization guy from Indiana, and an ex-Googler named Jose Ortega who is responsible for most of our data infrastructure. They pulled in Adam and I a few months later. We also have Poul Petersen, a former Strands employee, who manages our herd of servers. He is a wizard and makes everyone else’s life much easier.

Ajay- You use clojure for the back end of BigML.com .Are there any other languages and packages you are considering? What makes clojure such a good fit for cloud computing ?

Charlie- Clojure is a great language because it offers you all of the benefits of Java (extensive libraries, cross-platform compatibility, easy integration with things like Hadoop, etc.) but has the syntactical elegance of a functional language. This makes our code base small and easy to read as well as powerful.

We’ve had occasional issues with speed, but that just means writing the occasional function or library in Java. As we build towards processing data at the Terabyte level, we’re hoping to create a framework that is language-agnostic to some extent. So if we have some great machine learning code in C, for example, we’ll use Clojure to tie everything together, but the code that does the heavy lifting will still be in C. For the API and Web layers, we use Python and Django, and Justin is a huge fan of HaXe for our visualizations.

 Ajay- Current support is for Decision Trees. When can we see SVM, K Means Clustering and Logit Regression?

Charlie- Right now we’re focused on perfecting our infrastructure and giving you new ways to put data in the system, but expect to see more algorithms appearing in the next few months. We want to make sure they are as beautiful and easy to use as the trees are. Without giving too much away, the first new thing we will probably introduce is an ensemble method of some sort (such as Boosting or Bagging). Clustering is a little further away but we’ll get there soon!

Ajay- How can we use the BigML.com API using R and Python.

Charlie- We have a public github repo for the language bindings. https://github.com/bigmlcom/io Right now, there there are only bash scripts but that should change very soon. The python bindings should be there in a matter of days, and the R bindings in probably a week or two. Clojure and Java bindings should follow shortly after that. We’ll have a blog post about it each time we release a new language binding. http://blog.bigml.com/

Ajay-  How can we predict large numbers of observations using a Model  that has been built and pruned (model scoring)?

Charlie- We are in the process of refactoring our backend right now for better support for batch prediction and model evaluation. This is something that is probably only a few weeks away. Keep your eye on our blog for updates!

Ajay-  How can we export models built in BigML.com for scoring data locally.

Charlie- This is as simple as a call to our API. https://bigml.com/developers/models The call gives you a JSON object representing the tree that is roughly equivalent to a PMML-style representation.

About-

You can read about Charlie Parker at http://www.linkedin.com/pub/charles-parker/11/85b/4b5 and the rest of the BigML team at

https://bigml.com/team

 

Software Review- Google Drive versus Dropbox

Here are some notes from reviewing Google Drive  https://drive.google.com/ vs Dropbox https://www.dropbox.com/.

1) Google Drive gives more free space upfront  than Dropbox.5GB versus 2GB

2) Dropbox has a referral system 500 mb per referral while there is no referral system for Google Drive

3) The sync facility with Google Docs makes Google Drive especially useful for prior users of Google Docs.

4) API access to Google Drive is only for Chrome apps which is intriguing!

https://developers.google.com/drive/apps_overview

Apps will not have any API access to files unless users have first installed the app in Chrome Web Store.

You can use the Dropbox API much more easily –

See the platforms at

https://www.dropbox.com/developers/start/core

Choose your platform:

iOS Android Python Ruby

But-

(though I wonder if you set the R working directory to the local shared drive for Google Drive it should sync up as well but of course be slower –http://scrogster.wordpress.com/2011/01/29/using-dropbox-with-r-2/)

5) Google Drive icon is ugly (seriously, dude!) , but the features in the Windows app is just the same as the Dropbox App. Too similar 😉

 

6) Upgrade space is much more cheaper to Google Drive than Dropbox ( by Google Drive prices being exactly  a quarter of prices on Dropbox and max storage being 16 times as much). This will affect power storage users. I expect to see some slowdown in Dropbox new business unless G Drive has outage (like Gmail) . Existing users at Dropbox probably wont shift for the small dollar amount- though it is quite easy to do so.

 

Install Google Drive on your local workstation and cut and paste your Dropbox local folder to the Google Drive local folder!!

7) Dropbox deserves credit for being first (like Hotmail and AOL) but Google Drive is almost better in all respects!

Google Drive

Free
5 GB of Drive (0% used)
10 GB of Gmail (48% used)
1 GB of Picasa (0% used)

Upgrade:

25 GB
2,49 $ / Month
+25 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

100 GB
4,99 $ / Month
+100 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

 Need more storage?

Up to 16 TB available

Dropbox–

Current account type

Large DropboxDropbox Badge greenFree
Free
Up to 18 GB (2 GB + 500 MB per referral)
Account info 

Other account types

Large DropboxDropbox Badge orange50 GB +
Pro 50
+1 GB per referral, up to +32 GB
$9.99/month or $99.00/year Upgrade to Pro 50
Large DropboxDropbox Badge purple100 GB +
Pro 100
+1 GB per referral, up to +32 GB
$19.99/month or $199.00/year Upgrade to Pro 100
Triple DropboxDropbox For Teams Badge1 TB +
Teams
Plans starting at 1 TB
Large shared quota, centralized admin and billing, and more!

 

 

 

Software Review- BigML.com – Machine Learning meets the Cloud

I had a chance to dekko the new startup BigML https://bigml.com/ and was suitably impressed by the briefing and my own puttering around the site. Here is my review-

1) The website is very intutively designed- You can create a dataset from an uploaded file in one click and you can create a Decision Tree model in one click as well. I wish other cloud computing websites like  Google Prediction API make design so intutive and easy to understand. Also unlike Google Prediction API, the models are not black box models, but have a description which can be understood.

2) It includes some well known data sources for people trying it out. They were kind enough to offer 5 invite codes for readers of Decisionstats ( if you want to check it yourself, use the codes below the post, note they are one time only , so the first five get the invites.

BigML is still invite only but plan to get into open release soon.

3) Data Sources can only be by uploading files (csv) but they plan to change this hopefully to get data from buckets (s3? or Google?) and from URLs.

4) The one click operation to convert data source into a dataset shows a histogram (distribution) of individual variables.The back end is clojure , because the team explained it made the easiest sense and fit with Java. The good news (?) is you would never see the clojure code at the back end. You can read about it from http://clojure.org/

As cloud computing takes off (someday) I expect clojure popularity to take off as well.

Clojure is a dynamic programming language that targets the Java Virtual Machine (and the CLR, and JavaScript). It is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language – it compiles directly to JVM bytecode, yet remains completely dynamic. Every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.

Clojure is a dialect of Lisp

 

5) As of now decision trees is the only distributed algol, but they expect to roll out other machine learning stuff soon. Hopefully this includes regression (as logit and linear) and k means clustering. The trees are created and pruned in real time which gives a slightly animated (and impressive effect). and yes model building is an one click operation.

The real time -live pruning is really impressive and I wonder why /how it can ever be replicated in other software based on desktop, because of the sheer interactive nature.

 

Making the model is just half the work. Creating predictions and scoring the model is what is really the money-earner. It is one click and customization is quite intuitive. It is not quite PMML compliant yet so I hope some Zemanta like functionality can be added so huge amounts of models can be applied to predictions or score data in real time.

 

If you are a developer/data hacker, you should check out this section too- it is quite impressive that the designers of BigML have planned for API access so early.

https://bigml.com/developers

BigML.io gives you:

  • Secure programmatic access to all your BigML resources.
  • Fully white-box access to your datasets and models.
  • Asynchronous creation of datasets and models.
  • Near real-time predictions.

 

Note: For your convenience, some of the snippets below include your real username and API key.

Please keep them secret.

REST API

BigML.io conforms to the design principles of Representational State Transfer (REST)BigML.io is enterely HTTP-based.

BigML.io gives you access to four basic resources: SourceDatasetModel and Prediction. You cancreatereadupdate, and delete resources using the respective standard HTTP methods: POSTGET,PUT and DELETE.

All communication with BigML.io is JSON formatted except for source creation. Source creation is handled with a HTTP PUT using the “multipart/form-data” content-type

HTTPS

All access to BigML.io must be performed over HTTPS

and https://bigml.com/developers/quick_start ( In think an R package which uses JSON ,RCurl  would further help in enhancing ease of usage).

 

Summary-

Overall a welcome addition to make software in the real of cloud computing and statistical computation/business analytics both easy to use and easy to deploy with fail safe mechanisms built in.

Check out https://bigml.com/ for yourself to see.

The invite codes are here -one time use only- first five get the invites- so click and try your luck, machine learning on the cloud.

If you dont get an invite (or it is already used, just leave your email there and wait a couple of days to get approval)

  1. https://bigml.com/accounts/register/?code=E1FE7
  2. https://bigml.com/accounts/register/?code=09991
  3. https://bigml.com/accounts/register/?code=5367D
  4. https://bigml.com/accounts/register/?code=76EEF
  5. https://bigml.com/accounts/register/?code=742FD

Book Review- Machine Learning for Hackers

This is review of the fashionably named book Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly ). The book is about hacking code in R.

 

The preface introduces the reader to the authors conception of what machine learning and hacking is all about. If the name of the book was machine learning for business analytsts or data miners, I am sure the content would have been unchanged though the popularity (and ambiguity) of the word hacker can often substitute for its usefulness. Indeed the many wise and learned Professors of statistics departments through out the civilized world would be mildly surprised and bemused by their day to day activities as hacking or teaching hackers. The book follows a case study and example based approach and uses the GGPLOT2 package within R programming almost to the point of ignoring any other native graphics system based in R. It can be quite useful for the aspiring reader who wishes to understand and join the booming market for skilled talent in statistical computing.

Chapter 1 has a very useful set of functions for data cleansing and formatting. It walks you through the basics of formatting based on dates and conditions, missing value and outlier treatment and using ggplot package in R for graphical analysis. The case study used is an Infochimps dataset with 60,000 recordings of UFO sightings. The case study is lucid, and done at a extremely helpful pace illustrating the powerful and flexible nature of R functions that can be used for data cleansing.The chapter mentions text editors and IDEs but fails to list them in a tabular format, while listing several other tables like Packages used in the book. It also jumps straight from installation instructions to functions in R without getting into the various kinds of data types within R or specifying where these can be referenced from. It thus assumes a higher level of basic programming understanding for the reader than the average R book.

Chapter 2 discusses data exploration, and has a very clear set of diagrams that explain the various data summary operations that are performed routinely. This is an innovative approach and will help students or newcomers to the field of data analysis. It introduces the reader to type determination functions, as well different kinds of encoding. The introduction to creating functions is quite elegant and simple , and numerical summary methods are explained adequately. While the chapter explains data exploration with the help of various histogram options in ggplot2 , it fails to create a more generic framework for data exploration or rules to assist the reader in visual data exploration in non standard data situations. While the examples are very helpful for a reader , there needs to be slightly more depth to step out of the example and into a framework for visual data exploration (or references for the same). A couple of case studies however elaborately explained cannot do justice to the vast field of data exploration and especially visual data exploration.

Chapter 3 discussed binary classification for the specific purpose for spam filtering using a dataset from SpamAssassin. It introduces the reader to the naïve Bayes classifier and the principles of text mining suing the tm package in R. Some of the example codes could have been better commented for easier readability in the book. Overall it is quite a easy tutorial for creating a naïve Bayes classifier even for beginners.

Chapter 4 discusses the issues in importance ranking and creating recommendation systems specifically in the case of ordering email messages into important and not important. It introduces the useful grepl, gsub, strsplit, strptime ,difftime and strtrim functions for parsing data. The chapter further introduces the reader to the concept of log (and affine) transformations in a lucid and clear way that can help even beginners learn this powerful transformation concept. Again the coding within this chapter is sparsely commented which can cause difficulties to people not used to learn reams of code. ( it may have been part of the code attached with the book, but I am reading an electronic book and I did not find an easy way to go back and forth between the code and the book). The readability of the chapters would be further enhanced by the use of flow charts explaining the path and process followed than overtly verbose textual descriptions running into multiple pages. The chapters are quite clearly written, but a helpful visual summary can help in both revising the concepts and elucidate the approach taken further.A suggestion for the authors could be to compile the list of useful functions they introduce in this book as a sort of reference card (or Ref Card) for R Hackers or atleast have a chapter wise summary of functions, datasets and packages used.

Chapter 5 discusses linear regression , and it is a surprising and not very good explanation of regression theory in the introduction to regression. However the chapter makes up in practical example what it oversimplifies in theory. The chapter on regression is not the finest chapter written in this otherwise excellent book. Part of this is because of relative lack of organization- correlation is explained after linear regression is explained. Once again the lack of a function summary and a process flow diagram hinders readability and a separate section on regression metrics that help make a regression result good or not so good could be a welcome addition. Functions introduced include lm.

Chapter 6 showcases Generalized Additive Model (GAM) and Polynomial Regression, including an introduction to singularity and of over-fitting. Functions included in this chapter are transform, and poly while the package glmnet is also used here. The chapter also introduces the reader formally to the concept of cross validation (though examples of cross validation had been introduced in earlier chapters) and regularization. Logistic regression is also introduced at the end in this chapter.

Chapter 7 is about optimization. It describes error metric in a very easy to understand way. It creates a grid by using nested loops for various values of intercept and slope of a regression equation and computing the sum of square of errors. It then describes the optim function in detail including how it works and it’s various parameters. It introduces the curve function. The chapter then describes ridge regression including definition and hyperparameter lamda. The use of optim function to optimize the error in regression is useful learning for the aspiring hacker. Lastly it describes a case study of breaking codes using the simplistic Caesar cipher, a lexical database and the Metropolis method. Functions introduced in this chapter include .Machine$double.eps .

Chapter 8 deals with Principal Component Analysis and unsupervised learning. It uses the ymd function from lubridate package to convert string to date objects, and the cast function from reshape package to further manipulate the structure of data. Using the princomp functions enables PCA in R.The case study creates a stock market index and compares the results with the Dow Jones index.

Chapter 9 deals with Multidimensional Scaling as well as clustering US senators on the basis of similarity in voting records on legislation .It showcases matrix multiplication using %*% and also the dist function to compute distance matrix.

Chapter 10 has the subject of K Nearest Neighbors for recommendation systems. Packages used include class ,reshape and and functions used include cor, function and log. It also demonstrates creating a custom kNN function for calculating Euclidean distance between center of centroids and data. The case study used is the R package recommendation contest on Kaggle. Overall a simplistic introduction to creating a recommendation system using K nearest neighbors, without getting into any of the prepackaged packages within R that deal with association analysis , clustering or recommendation systems.

Chapter 11 introduces the reader to social network analysis (and elements of graph theory) using the example of Erdos Number as an interesting example of social networks of mathematicians. The example of Social Graph API by Google for hacking are quite new and intriguing (though a bit obsolete by changes, and should be rectified in either the errata or next edition) . However there exists packages within R that should be atleast referenced or used within this chapter (like TwitteR package that use the Twitter API and ROauth package for other social networks). Packages used within this chapter include Rcurl, RJSONIO, and igraph packages of R and functions used include rbind and ifelse. It also introduces the reader to the advanced software Gephi. The last example is to build a recommendation engine for whom to follow in Twitter using R.

Chapter 12 is about model comparison and introduces the concept of Support Vector Machines. It uses the package e1071 and shows the svm function. It also introduces the concept of tuning hyper parameters within default algorithms . A small problem in understanding the concepts is the misalignment of diagram pages with the relevant code. It lastly concludes with using mean square error as a method for comparing models built with different algorithms.

 

Overall the book is a welcome addition in the library of books based on R programming language, and the refreshing nature of the flow of material and the practicality of it’s case studies make this a recommended addition to both academic and corporate business analysts trying to derive insights by hacking lots of heterogeneous data.

Have a look for yourself at-
http://shop.oreilly.com/product/0636920018483.do

Using R for Cloud Computing – made very easy and free by BioConductor

I really liked the no hassles way Biocnoductor has put a cloud AMI loaded with RStudio to help people learn R, and even try using R from within a browser in the cloud.

Not only is the tutorial very easy to use- they also give away 2 hours for free computing!!!

Check it out-

Step 1

Step 2

Step 3

and wow! I am using Google Chrome to run R ..and its awesome!

Interesting- check out two hours for free — all you need is a browser and internet connection

http://www.bioconductor.org/help/cloud/

Interview: Hjálmar Gíslason, CEO of DataMarket.com

Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com  . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.

 

Ajay-  Describe your journey as an entrepreneur and techie in Iceland. What are the 10 things that surprised you most as a tech entrepreneur.

HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!

Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”

They are:
* Do NOT generalize – especially not to begin with
* Prioritize – and find a work-flow that works for you
* Meet people – face to face
* You are a sales person – whether you like it or not
* Technology is not a product – it’s the entire experience
* Sell the current version – no matter how amazing the next one is
* Learn from mistakes – preferably others’
* Pick the right people – good people is not enough
* Tell a good story – but don’t make them up

I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far 😉

9 things nobody told me about the start-up business

Ajay-

Both Amazon  and Google  have entered the public datasets space. Infochimps  has 14,000+ public datasets. The US has http://www.data.gov/

So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still. 

How does DataMarket intend to address this market in a unique way to differentiate itself from others.

HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link: http://datamarket.com/data/list/?q=provider:eiu)

DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.

We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.

 

Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.

HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.

Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.

Ajay-  How is the technology entrepreneurship and venture capital scene in Iceland. What things work and what things can be improved?

HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.

There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.

I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.

We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.

Ajay- Describe your hobbies when you are not founding amazing tech startups.

HG- Most of my time is spent working – which happens to by my number one hobby.

It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.

I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.

About-

http://datamarket.com/p/about/team/

Management

Hjalmar GislasonHjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.

hjalmar.gislason@datamarket.com

To know more, have a quick  look at  http://datamarket.com/

Interview Michal Kosinski , Concerto Web Based App using #Rstats

Here is an interview with Michal Kosinski , leader of the team that has created Concerto – a web based application using R. What is Concerto? As per http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Concerto is a web based, adaptive testing platform for creating and running rich, dynamic tests. It combines the flexibility of HTML presentation with the computing power of the R language, and the safety and performance of the MySQL database. It’s totally free for commercial and academic use, and it’s open source

Ajay-  Describe your career in science from high school to this point. What are the various stats platforms you have trained on- and what do you think about their comparative advantages and disadvantages?  

Michal- I started with maths, but quickly realized that I prefer social sciences – thus after one year, I switched to a psychology major and obtained my MSc in Social Psychology with a specialization in Consumer Behaviour. At that time I was mostly using SPSS – as it was the only statistical package that was taught to students in my department. Also, it was not too bad for small samples and the rather basic analyses I was performing at that time.

 

My more recent research performed during my Mphil course in Psychometrics at Cambridge University followed by my current PhD project in social networks and research work at Microsoft Research, requires significantly more powerful tools. Initially, I tried to squeeze as much as possible from SPSS/PASW by mastering the syntax language. SPSS was all I knew, though I reached its limits pretty quickly and was forced to switch to R. It was a pretty dreary experience at the start, switching from an unwieldy but familiar environment into an unwelcoming command line interface, but I’ve quickly realized how empowering and convenient this tool was.

 

I believe that a course in R should be obligatory for all students that are likely to come close to any data analysis in their careers. It is really empowering – once you got the basics you have the potential to use virtually any method there is, and automate most tasks related to analysing and processing data. It is also free and open-source – so you can use it wherever you work. Finally, it enables you to quickly and seamlessly migrate to other powerful environments such as Matlab, C, or Python.

Ajay- What was the motivation behind building Concerto?

Michal- We deal with a lot of online projects at the Psychometrics Centre – one of them attracted more than 7 million unique participants. We needed a powerful tool that would allow researchers and practitioners to conveniently build and deliver online tests.

Also, our relationships with the website designers and software engineers that worked on developing our tests were rather difficult. We had trouble successfully explaining our needs, each little change was implemented with a delay and at significant cost. Not to mention the difficulties with embedding some more advanced methods (such as adaptive testing) in our tests.

So we created a tool allowing us, psychometricians, to easily develop psychometric tests from scratch an publish them online. And all this without having to hire software developers.

Ajay -Why did you choose R as the background for Concerto? What other languages and platforms did you consider. Apart from Concerto, how else do you utilize R in your center, department and University?

Michal- R was a natural choice as it is open-source, free, and nicely integrates with a server environment. Also, we believe that it is becoming a universal statistical and data processing language in science. We put increasing emphasis on teaching R to our students and we hope that it will replace SPSS/PASW as a default statistical tool for social scientists.

Ajay -What all can Concerto do besides a computer adaptive test?

Michal- We did not plan it initially, but Concerto turned out to be extremely flexible. In a nutshell, it is a web interface to R engine with a built-in MySQL database and easy-to-use developer panel. It can be installed on both Windows and Unix systems and used over the network or locally.

Effectively, it can be used to build any kind of web application that requires a powerful and quickly deployable statistical engine. For instance, I envision an easy to use website (that could look a bit like SPSS) allowing students to analyse their data using a web browser alone (learning the underlying R code simultaneously). Also, the authors of R libraries (or anyone else) could use Concerto to build user-friendly web interfaces to their methods.

Finally, Concerto can be conveniently used to build simple non-adaptive tests and questionnaires. It might seem to be slightly less intuitive at first than popular questionnaire services (such us my favourite Survey Monkey), but has virtually unlimited flexibility when it comes to item format, test flow, feedback options, etc. Also, it’s free.

Ajay- How do you see the cloud computing paradigm growing? Do you think browser based computation is here to stay?

Michal – I believe that cloud infrastructure is the future. Dynamically sharing computational and network resources between online service providers has a great competitive advantage over traditional strategies to deal with network infrastructure. I am sure the security concerns will be resolved soon, finishing the transformation of the network infrastructure as we know it. On the other hand, however, I do not see a reason why client-side (or browser) processing of the information should cease to exist – I rather think that the border between the cloud and personal or local computer will continually dissolve.

About

Michal Kosinski is Director of Operations for The Psychometrics Centre and Leader of the e-Psychometrics Unit. He is also a research advisor to the Online Services and Advertising group at the Microsoft Research Cambridge, and a visiting lecturer at the Department of Mathematics in the University of Namur, Belgium. You can read more about him at http://www.michalkosinski.com/

You can read more about Concerto at http://code.google.com/p/concerto-platform/ and http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm