Asimov in Modern Science

. Monday, September 22, 2008

Today I've read about PHRIENDS project. Funded by EU (2.16 € million), PHRIENDS tries to force robots to respect Asimov's laws. Asimov seems to be a great scientific since even his most futuristic ideas are influencing the actual science.

I spent a great part of my adolescence reading Asimov's books and dreaming about intelligent robots that make use of the 3 laws. Now, it seems that, sometime, I could even work with a robot that implements those laws and that is so cool... :D

P.D: For Spanish speakers, I've posted a larger post about the project in my spanish scientific blog.

CFPS on Social Networks

. Thursday, September 11, 2008

In recent months, I've been developing software for my new company, a social network for videogamers, Wipley. That has awaken interests on all the stuff related to Social Network analysis. Last days, I've received a couple of call for papers related to this topic, that seems very interesting:

Workshop on Machine Learning Open Source 2008

. Wednesday, September 10, 2008

I like Open Access and Open Software, in fact, I'm meber of the local LUG of my University (GLUEM) and some of my posts are refered to these topics. In ML-News list, I've seen a call for submissiones for the a Workshop on Machine Learning Open Source (MLOSS), that will be held at NIPS, December 12th. I this this kind of workshops are a very good idea to promote the use of Open Software in ML, and give extra benefits to those developers that let the community use their software, allowing other researchers a faster development of their experiments.

The NIPS workshop on Workshop on Machine Learning Open Source Software (MLOSS) will held in Whistler (B.C.) on the 12th of December, 2008.

Important Dates

* Submission Date: October 1st, 2008
* Notification of Acceptance: October 14th, 2008
* Workshop date: December 12 or 13th, 2008

Call for Contributions

The organizing committee is currently seeking abstracts for talks at MLOSS 2008. MLOSS is a great opportunity for you to tell the community about your use, development, or philosophy of open source software in machine learning. This includes (but is not limited to) numeric packages (as e.g. R,octave,numpy), machine learning toolboxes and implementations of ML-algorithms. The committee will select several submitted abstracts for 20-minute talks. The submission process is very simple:

* Tag your project with the tag nips2008

* Ensure that you have a good description (limited to 500 words)

* Any bells and whistles can be put on your own project page, and of course provide this link on

On 1 October 2008, we will collect all projects tagged with nips2008 for review.

Note: Projects must adhere to a recognized Open Source License (cf. ) and the source code must have been released at the time of submission. Submissions will be reviewed based on the status of the project at the time of the
submission deadline.


We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of machine learning. The goal of this workshop is to further support the current developments in this area and give new impulses to it. Following the success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the Journal of Machine Learning Research (JMLR) has started a new track for machine learning open source software initiated by the workshop's organizers. Many prominent machine learning researchers have co-authored a position paper advocating the need for open source software in machine learning. Furthermore, the workshop's organizers have set up a community website where people can register
their software projects, rate existing projects and initiate discussions about projects and related topics. This website currently lists 123 such projects including many prominent projects in the area of machine learning.

The main goal of this workshop is to bring the main practitioners in the area of machine learning open source software together in order to initiate processes which will help to further improve the development of this area. In particular, we have to move beyond a mere collection of more or less unrelated software projects and provide a common foundation to stimulate cooperation and interoperability between different projects. An important step in this direction will be a common data exchange format such that different methods can exchange their results more easily.

This year's workshop sessions will consist of three parts.

* We have two invited speakers: John Eaton, the lead developer of Octave and John Hunter, the lead developer of matplotlib.

* Researchers are invited to submit their open source project to present it at the workshop.

* In discussion sessions, important questions regarding the future development of this area will be discussed. In particular, we will discuss what makes a good machine learning software project and how to improve interoperability between programs. In addition, the question of how to deal with data sets and reproducibility will also be addressed.

Taking advantage of the large number of key research groups which attend NIPS, decisions and agreements taken at the workshop will have the potential to significantly impact the future of machine learning software.

Invited Speakers

* John D. Hunter - Main author of matplotlib.

* John W. Eaton - Main author of Octave.

Tentative Program

The 1 day workshop will be a mixture of talks (including a mandatory demo of the software) and panel/open/hands-on discussions.

Morning session: 7:30am - 10:30am

* Introduction and overview
* Octave (John W. Eaton)
* Contributed Talks
* Discussion: What is a good mloss project?
o Review criteria for JMLR mloss
o Interoperable software
o Test suites

Afternoon session: 3:30pm - 6:30pm

* Matplotlib (John D. Hunter)
* Contributed Talks
* Discussion: Reproducible research
o Data exchange standards
o Shall datasets be open too? How to provide access to data sets.
o Reproducible research, the next level after UCI datasets.

Program Committee

* Jason Weston (NEC Princeton, USA)
* Gunnar Rätsch (FML Tuebingen, Germany)
* Lieven Vandenberghe (University of California LA, USA)
* Joachim Dahl (Aalborg University, Denmark)
* Torsten Hothorn (Ludwig Maximilians University, Munich, Germany)
* Asa Ben-Hur (Colorado State University, USA)
* William Stafford Noble (Department of Genome Sciences Seattle, USA)
* Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
* Geoff Holmes (University of Waikato, New Zealand)
* Alain Rakotomamonjy (University of Rouen, France)


* Soeren Sonnenburg
Fraunhofer FIRST Kekuléstr. 7, 12489 Berlin, Germany

* Mikio Braun
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587
Berlin, Germany

* Cheng Soon Ong
ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland


The workshop is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)

Dashboards, pointing the way of Business Intelligence

. Monday, September 08, 2008

I use to read an interesting spanish blog related to BI world, called TodoBI, which pointed me to an article about the importance of dashboards in Business Intelligence, written by Tom Gonzalez. In this article, Tom exposes his vision about the future of Business Intelligence. Tom believes that BI should focus on dashboards, adopting a user-centric approach instead of a more data-centric one. In Tom's words

So where does that leave us today, and what does this all mean for the future of BI? I think dashboards represent just the first step for the next major phase in BI both from a technology and a methodology perspective. For lack of a better term I will label this next phase the "BI user experience" as represented by user interfaces that information workers and business executives interact with to "experience" their data [...] Your ability to process that information and the inherent relationships within that data is exponentially higher and faster with the bar chart. This is one area where the human brain still far exceeds the power of technology-driven computation in its ability to recognize and process patterns composed of large volumes of information.

I totally agree with Tom's vision, which fits in my vision of the connection between Machine Learning, Data Mining and Business Intelligence. For me, ML, DM and BI can be seen as 3 different areas, but they can also be seen as a chain where each one plays an important role. DM is data centric as it focuses on data, BI is user centric as it should deal with users needs and ML is the intelligence behind the process (althought not every need needs an intelligent process).

In the figure, ML is represented inside DM and DM inside BI. From the BI point of view, DM is like glacé cherry, a turn of the screw from the statistical processes behind BI. ML is inside DM as it is the engine for processing all the data in DM processes.

WWW Tracks

. Monday, September 01, 2008

This year, WWW Conference is held at 10 minutes from my work, at Universidad Europea de Madrid. There are several interesting tracks dealing with different aspects of the Web. The most interesting, for me, are the following tracks: "Data Mining", "Social Networks and Web 2.0", "Semantic/Data Web".

I knew about the CFP but the last time I visited the web there was no info about the tracks. A post in Hurst's blog, reminded me to refresh the info about the WWW Conference.