technology – Page 4 – DECISION STATS

Chrome Extension- MafiaaFire

The chrome extension MafiaaWire basically gives you an updated list of redirected websites. So the next time , your evil highness shuts down your favorite website- the list promises to give you an update. While obviously entertainment intellectual property is a very obvious site category for such redirects, in some cases these extensions can be used for simple things like hosting dissents or protesters against govt corruption in non US countries .

Basically under the new SOPA act (an oline version of pepper spray http://en.wikipedia.org/wiki/Stop_Online_Piracy_Act) even browsers like Firefox and Chrome would be liable for any such extension that can be used to download American Intellectual property illegally.

In the meantime – this is an interesting and creative use case of technology and sociology merging in the brave new world.

You can read about it here-

http://en.wikipedia.org/wiki/MAFIAAFire_Redirector

MAFIAAFire works by downloading a list which contains the names of the “blocked” sites as well as the sites to redirect to. This list is downloaded every time Firefox starts up or every two days on the Chrome version (although the user has the choice to force an update on the Chrome version instead of waiting for two days).

When a user types in a domain name from the list of blocked domains, the add-on recognizes this and automatically redirects the user to the secondary site. Since this happens before the browser connects to the DNS server, this renders any DNS blocks useless.

Although the add-on checks for which sites are entered into the address bar every time (as it needs to check if that site is on its block list), it does not log these requests nor send these requests to any central server. In other words: it does not track the user.

Download it from

https://chrome.google.com/webstore/detail/hnifiobpjihmmjgiokkaalgomddebhng

Interesting times indeed!

Related-

Encryption

http://poemsforkush.wordpress.com/2011/12/17/encryption/

My Digital Trail

Someone I know recently mentioned that I have an extensive Digital Trail. I do.

I have 7863 connections at http://www.linkedin.com/in/ajayohri, 31 likes at https://www.facebook.com/ajayohri and 19 likes at https://www.facebook.com/pages/Ajay-Ohri/157086547679568, 409 friends (and 13 subscribers) at https://www.facebook.com/byebyebyer .On twitter I have 499 followers at http://twitter.com/0_h_r_1 and 344 followers at http://twitter.com/rforbusiness , and even on Google Plus some 617 people circling me at https://plus.google.com/116302364907696741272 (besides 6 other pages on G+)

Even my Youtube channel at http://www.youtube.com/decisionstats is more popular than I am in non-digital life. my non existant video blog at http://videosforkush.blogspot.com/ and my poetry blog at http://poemsforkush.wordpress.com/, and my comments on other social media, and my blurbs on my tumblr http://kushohri.tumblr.com/, and you get a lot of my psych profile.

Why do I do leave so much trail digitally?

For one reason- I was a bit of introvert always and technology set me free, the opportunity to think and yet be relaxed in anonymous chatter.

For the second reason- I am divorced and my wife got my 4 yr old son’s custody. Even though I talk to him once a day for a couple of minutes, somehow I hope when he grows, he reads my digital trail , maybe even these words, on the kind of man I was and the phases and seasons of life I went through.

That is all.

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.

When does the class start?The class will start in January 2012 and will last approximately ten weeks.
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.
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.
Do I need to watch the lectures live?No. You can watch the lectures at your leisure.
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.
Will other Stanford resources be available to online students?No.
How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
Do I need to buy a textbook for the course?No.
How much does it cost to take the course?Nothing: it’s free!
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.

What are you thankful for?

I am thankful for-1) God and Jesus Christ and Rama and Allah and Buddha and their priests and intermediaries and everybody taking care of me. or atleast trying hard to take care of me.2) Earth my planet for nourishing me despite me polluting her, her fresh air and her delicious fruit and marvellous wine and hearty bread.

3) Fellow Human Beings for being nice to me when they feel curt, for displaying civilized manners, and working together in a vast invisible web of commerce, trade and exchange to meet our needs.

4) Scientists and Engineers who create wonderful technology by spending hours , months , years of their lives and giving it up for free on the Internet.

5) Powerful people who take time to mentor unknown wild cards, and young people to rejuvenate with new exciting ideas.

6) people who appreciate my poetry and people who appreciate my technology. and people who criticize only in the intention of me striving to create something better.

Continue reading “What are you thankful for?”

HANA Oncolyzer

An interesting use case of technology for better health is HANA Oncolyzer at http://epic.hpi.uni-potsdam.de/Home/HanaOncolyzer

“Build on the newest in-memory technology the HANA Oncolyzer is able to analyze even huge amounts of medical data in shortest time”, says Dr. Alexander Zeier, Deputy Chair of EPIC. Research institutes and university hospital support from HANA Oncolyzer by building the basis for a flexible exchange of information about efficiency of medicines and treatments.

In near future, the tumor’s DNA of all cancer patients needs to be analyzed to support specific patient therapies. These analyses result in medical data in amount of multiple terabytes. “These data need to be analyzed regarding mutations and anomalies in real-time”, says Matthias Steinbrecher at SAP’s Innovation Center in Potsdam. As one of the aims the research prototype HANA Oncolyzer was developed at our chair in cooperation with SAP’s Innovation Center in Potsdam. “The ‘heart’ of our development builds the in-memory technology that supports the parallel analysis of million of data within seconds in main memory”, saysMatthieu Schapranow, Ph.D. cand. at the HPI.

and

research activities result in 500.000 or more data points per patient.

and

With the help of a dedicated iPad application medical doctors can access all data mobile at any location anytime.

Kindle as a Tablet for 109$ and how to order it if you in India

I was just blown away by the price and functionality of the Kindle, including the browser and in built Wi-Fi- ( though the 40$ leather bag was a bit sneaky as an accessory, I mean seriously, dude)

And unlike some media technology companies (like Hulu,Spotify , even some Youtube channels)

who offer products to Asia only after a delayed lag, it is just as easy to order Kindle sitting from India.

Thank you Amazon!