Tag: software

Ads Alliance on Internet

Just saw

the Digital Advertising Alliance’s (DAA) Self-Regulatory Program for Online Behavioral Advertising.

Multi-Site Data Collection Principles Broaden Self Regulation Beyond Online Behavioral Advertising
WASHINGTON, D.C., NOVEMBER 7, 2011

The new Principles consist of the following specific requirements:

  1. Transparency and consumer control for purposes other than OBA – The Multi-Site Data Principles call for organizations that collect Multi-Site Data for purposes other than OBA to provide transparency and control regarding Internet surfing across unrelated Websites.
  2. Collection / use of data for eligibility determination – The Multi-Site Data Principles prohibit the collection, use or transfer of Internet surfing data across Websites for determination of a consumer’s eligibility for employment, credit standing, healthcare treatment and insurance.
  3. Collection / use of children’s data – The Multi-Site Data Principles state that organizations must comply with the Children’s Online Privacy Protection Act (COPPA).
  4. Meaningful accountability – The Multi-Site Data Principles are subject to enforcement through strong accountability mechanisms.

http://www.aboutads.info/principles

The DAA Self-Regulatory Principles

 

The cross-industry Self-Regulatory Principles for Multi-Site Data augment the Self-Regulatory   Principles for Online Behavioral Advertising  (OBA)  by covering the prospective  collection of Web site   data beyond that collected for OBA purposes.  The existing OBA  Principles and definitions  remain in   full force and effect and are not limited by the new  principles.

The cross-industry Self-Regulatory Principles for Online Behavioral Advertising was developed by   leading industry associations to apply  consumer-friendly standards to online  behavioral advertising  across the Internet. Online behavioral advertising increasingly supports the convenient access to  content, services, and applications over the Internet that consumers have come to expect at no cost   to them.

The Education Principle calls for organizations to participate in efforts to educate individuals and businesses about online behavioral advertising and the Principles.

The Transparency Principle calls for clearer and easily accessible disclosures to consumers about data collection and use practices associated with online behavioral advertising. It will result in new, enhanced notice on the page where data is collected through links embedded in or around advertisements, or on the Web page itself.

The Consumer Control Principle provides consumers with an expanded ability to choose whether data is collected and used for online behavioral advertising purposes. This choice will be available through a link from the notice provided on the Web page where data is collected.

The Consumer Control Principle requires “service providers”, a term that includes Internet access service providers and providers of desktop applications software such as Web browser “tool bars” to obtain the consent of users before engaging in online behavioral advertising, and take steps to de-identify the data used for such purposes.

The Data Security Principle calls for organizations to provide appropriate security for, and limited retention of data, collected and used for online behavioral advertising purposes.

The Material Changes Principle calls for obtaining consumer consent before a Material Change is made to an entity’s Online Behavioral Advertising data collection and use policies unless that change will result in less collection or use of data.

The Sensitive Data Principle recognizes that data collected from children and used for online behavioral advertising merits heightened protection, and requires parental consent for behavioral advertising to consumers known to be under 13 on child-directed Web sites. This Principle also provides heightened protections to certain health and financial data when attributable to a specific individual.

The Accountability Principle calls for development of programs to further advance these Principles, including programs to monitor and report instances of uncorrected non-compliance with these Principles to appropriate government agencies. The CBBB and DMA have been asked and agreed to work cooperatively to establish accountability mechanisms under the Principles.

 

Ajay- So why the self regulations?

Answer- Shoddy Maths in behaviorally targeted ads is leading to a very high glut in targeted ads, more than can be reasonably expected to click based on consumer spending. On the internet- unlike on television- cost is less of a barrrier to OVER ADVERTISING.

 

Pune Hackathon

message from Jimmy Wales and friends-

 

Pune Wikimedia hackathon.

Date: 10-12 February 2012
Venue: Symbiosis Institute of Computer Studies & Research (SICSR) at
Symbiosis International University, Pune,India
Extremely rough event page, soon to get more details:
https://www.mediawiki.org/wiki/Pune_Hackathon_Feb_2012

As you know, a Wikimedia hackathon is a chance to learn how to develop
using MediaWiki, Phonegap, and our other technologies, and to work
alongside experts. Software engineers, designers, and translators are
welcome. We’re tentatively planning to focus on internationalisation and
localisation, mobile Wikipedia access, and the JavaScript-based gadgets
framework.

Registration link: http://is.gd/rjpNOA

If you’re interested, please register to request an invitation, and feel
free to publicize.  Thanks!

Occupy the Internet

BORN IN THE USA

Continue reading “Occupy the Internet”

JMP and R – #rstats

An amazing example of R being used sucessfully in combination (and not is isolation) with other enterprise software is the add-ins functionality of JMP and it’s R integration.

See the following JMP add-ins which use R

http://support.sas.com/demosdownloads/downarea_t4.jsp?productID=110454&jmpflag=Y

JMP Add-in: Multidimensional Scaling using R

This add-in creates a new menu command under the Add-Ins Menu in the submenu R Add-ins. The script will launch a custom dialog (or prompt for a JMP data table is one is not already open) where you can cast columns into roles for performing MDS on the data table. The analysis results in a data table of MDS dimensions and associated output graphics. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix. Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table in R).

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis

Chernoff Faces Add-in

One way to plot multivariate data is to use Chernoff faces. For each observation in your data table, a face is drawn such that each variable in your data set is represented by a feature in the face. This add-in uses JMP’s R integration functionality to create Chernoff faces. An R install and the TeachingDemos R package are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Data Visualization

Support Vector Machine for Classification

By simply opening a data table, specifying X, Y variables, selecting a kernel function, and specifying its parameters on the user-friendly dialog, you can build a classification model using Support Vector Machine. Please note that R package ‘e1071’ should be installed before running this dialog. The package can be found from http://cran.r-project.org/web/packages/e1071/index.html.

Submitted by: Jong-Seok Lee SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis/Mining

Penalized Regression Add-in

This add-in uses JMP’s R integration functionality to provide access to several penalized regression methods. Methods included are the LASSO (least absolutee shrinkage and selection operator, LARS (least angle regression), Forward Stagewise, and the Elastic Net. An R install and the “lars” and “elasticnet” R packages are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Regression

MP Addin: Univariate Nonparametric Bootstrapping

This script performs simple univariate, nonparametric bootstrap sampling by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available in JMP 9. The boot package in R is used to call the boot() function and the boot.ci() function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform.

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Basic Statistics

Software as a Religion ( SaaR)

The decline of organized religion and debate about such matters in the Western Hemisphere has been co-related to the increase in debates and arguments (again mostly) in the Western Hemisphere on software. Be it the PC vs Mac, the Microsofties vs Open Sourcers, the not so evil Google versus fans of Facebook, considerable activity is now being done by human beings in terms of social interaction on the merit’s and demerit’s of each software bundle. Perhaps for the first time in human history these interactions are being captured digitally on medium (that is hopefully longer lasting than papyrus).

Will this lead to newer branches of psychologists, sociologists (Goodwin’s law is too simplistic but an effort)

Even software as a religion is plausible, all they need is another college drop-put whizkid  to find a way to make it effective.

Religion as a software has of course been around for several millennium.

Also see http://goo.gl/smISa

Free Machine Learning at Stanford

One of the cornerstones of the technology revolution, Stanford now offers some courses for free via distance learning. One of the more exciting courses is of course- machine learning

 

 

http://jan2012.ml-class.org/

About The Course

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

The Instructor

Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.

 

  1. When does the class start?The class will start in January 2012 and will last approximately ten weeks.
  2. What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
  3. Will the text of the lectures be available?We hope to transcribe the lectures into text to make them more accessible for those not fluent in English. Stay tuned.
  4. Do I need to watch the lectures live?No. You can watch the lectures at your leisure.
  5. Can online students ask questions and/or contact the professor?Yes, but not directly There is a Q&A forum in which students rank questions and answers, so that the most important questions and the best answers bubble to the top. Teaching staff will monitor these forums, so that important questions not answered by other students can be addressed.
  6. Will other Stanford resources be available to online students?No.
  7. How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
  8. Do I need to buy a textbook for the course?No.
  9. How much does it cost to take the course?Nothing: it’s free!
  10. Will I get university credit for taking this course?No.Interested in learning machine learning-

    Well here is the website to enroll http://jan2012.ml-class.org/

Interview Zach Goldberg, Google Prediction API

Here is an interview with Zach Goldberg, who is the product manager of Google Prediction API, the next generation machine learning analytics-as-an-api service state of the art cloud computing model building browser app.
Ajay- Describe your journey in science and technology from high school to your current job at Google.

Zach- First, thanks so much for the opportunity to do this interview Ajay!  My personal journey started in college where I worked at a startup named Invite Media.   From there I transferred to the Associate Product Manager (APM) program at Google.  The APM program is a two year rotational program.  I did my first year working in display advertising.  After that I rotated to work on the Prediction API.

Ajay- How does the Google Prediction API help an average business analytics customer who is already using enterprise software , servers to generate his business forecasts. How does Google Prediction API fit in or complement other APIs in the Google API suite.

Zach- The Google Prediction API is a cloud based machine learning API.  We offer the ability for anybody to sign up and within a few minutes have their data uploaded to the cloud, a model built and an API to make predictions from anywhere. Traditionally the task of implementing predictive analytics inside an application required a fair amount of domain knowledge; you had to know a fair bit about machine learning to make it work.  With the Google Prediction API you only need to know how to use an online REST API to get started.

You can learn more about how we help businesses by watching our video and going to our project website.

Ajay-  What are the additional use cases of Google Prediction API that you think traditional enterprise software in business analytics ignore, or are not so strong on.  What use cases would you suggest NOT using Google Prediction API for an enterprise.

Zach- We are living in a world that is changing rapidly thanks to technology.  Storing, accessing, and managing information is much easier and more affordable than it was even a few years ago.  That creates exciting opportunities for companies, and we hope the Prediction API will help them derive value from their data.

The Prediction API focuses on providing predictive solutions to two types of problems: regression and classification. Businesses facing problems where there is sufficient data to describe an underlying pattern in either of these two areas can expect to derive value from using the Prediction API.

Ajay- What are your separate incentives to teach about Google APIs  to academic or researchers in universities globally.

Zach- I’d refer you to our university relations page–

Google thrives on academic curiosity. While we do significant in-house research and engineering, we also maintain strong relations with leading academic institutions world-wide pursuing research in areas of common interest. As part of our mission to build the most advanced and usable methods for information access, we support university research, technological innovation and the teaching and learning experience through a variety of programs.

Ajay- What is the biggest challenge you face while communicating about Google Prediction API to traditional users of enterprise software.

Zach- Businesses often expect that implementing predictive analytics is going to be very expensive and require a lot of resources.  Many have already begun investing heavily in this area.  Quite often we’re faced with surprise, and even skepticism, when they see the simplicity of the Google Prediction API.  We work really hard to provide a very powerful solution and take care of the complexity of building high quality models behind the scenes so businesses can focus more on building their business and less on machine learning.