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If you bleed red,white and blue and know some geo-spatial analysis ,social network analysis and some supervised and unsupervised learning (and unlearning)- here is a chance for you to put your skills for an awesome project
For this challenge, Darpa will lodge a selected six to eight teams at George Mason University and provide them with an initial $10,000 for equipment and access to unclassified data sets including “ground-level video of human activity in both urban and rural environments; high-resolution wide-area LiDAR of urban and mountainous terrain, wide-area airborne full motion video; and unstructured amateur photos and videos, such as would be taken from an adversary’s cell phone.” However, participants are encouraged to use any open sourced, legal data sets they want. (In the hackathon spirit, we would encourage the consumption of massive quantities of pizza and Red Bull, too.)
DARPA Innovation House Project
Proposals must be one to three pages. Team resumes of any length must be attached and do not count against the page limit. Proposals must have 1-inch margins, use a font size of at least 11, and be delivered in Microsoft Word or Adobe PDF format.
Proposals must be emailed to InnovationHouse@c4i.gmu.edu by 4:00PM ET on Tuesday, July 31, 2012.
Proposals must have a Title and contain at least the following sections with the following contents.
- Team Members
Each team member must be listed with name, email and phone.
The Lead Developer should be indicated.
The statement “All team members are proposed as Key Personnel.” must be included.
- Capability Description
The description should clearly explain what capability the software is designed to provide the user, how it is proposed to work, and what data it will process.
In addition, a clear argument should be made as to why it is a novel approach that is not incremental to existing methods in the field.
- Proposed Phase 1 Demonstration
This section should clearly explain what will be demonstrated at the end of Session I. The description should be expressive, and as concrete as possible about the nature of the designs and software the team intends to produce in Session I.
- Proposed Phase 2 Demonstration
This section should clearly explain how the final software capability will be demonstrated as quantitatively as possible (for example, positing the amount of data that will be processed during the demonstration), how much time that will take, and the nature of the results the processing aims to achieve.
In addition, the following sections are optional.
- Technical Approach
The technical approach section amplifies the Capability Description, explaining proposed algorithms, coding practices, architectural designs and/or other technical details.
- Team Qualifications
Team qualifications should be included if the team?s experience base does not make it obvious that it has the potential to do this level of software development. In that case, this section should make a credible argument as to why the team should be considered to have a reasonable chance of completing its goals, especially under the tight timelines described.
Other sections may be included at the proposers? discretion, provided the proposal does not exceed three pages.
Web users in India are once again able to access video and file-sharing sites, including The Pirate Bay.The country’s Madras High Court has changed its earlier censorship order which centred on the issue of internet copyright
It states that only specific web addresses – URLs – carrying the pirated content should be blocked, but not the entire website.
“The order of interim injunction dated 25/04/2012 is hereby clarified that the interim injunction is granted only in respect of a particular URL where the infringing movie is kept and not in respect of the entire website,” reads the updated decision.
I kind of liked the fact that Google Drive has a lot of apps already- even though it is quite young.
Especially the mechanical engineer in me liked the AutoCAD app and the video editing apps, the online bitcoin wallet, free project scheduling app, the cloud’s first (?) open office document reader and etc
Developers would especially like playing with the OAuth Playground app for Google Drive on the Google Chrome platform.
Check out for yourself.
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.
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
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.
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/
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.
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.