Some official statistics on social media from the owners themselves
Date -17 Nov 2011
Check out the new Google Plus Hangout with Extras
Hangouts with Extras is a simple and easy way to connect and collaborate with your colleagues in real time. With Hangouts with Extras you can:
Connect with multiple people simultaneously: With group video chat and web conferencing you can connect with multiple people around the world at the same time.
Share your screen: Ever look at something that you couldn’t quite put into words? Well, with screen sharing you give other people the ability to view what’s on your computer screen. You can choose an open window screen on your computer and give everyone in your meeting the ability to look at it. Learn More
Collaborate in real time: You can meet, share notes, and even work on documents at the same time.Learn More
For enterprises- you can throw out your video conferencing software and collaboration tools and get a new mobile app for free.
Small drawbacks in the Google Plus-
lack of integration with Youtube (it is one way integration from youtube to hangouts but not the other way round fixed ), lack of a whiteboard for sketches- (like again a shortcut to a google doc 🙂 ) or even bundling the record from your web cam to record your desktop.
Ultimately enterprises want to know how they can use this stuff for e-learning modules or webcasts.
Check out NY Met Museum with Friends (thanks to Google Art Project)
Play Linkin Park Playlist (100 videos) with Friends btw. great graphic redesign of Youtube icons!! Now if we could only convince the Google Docs to get more integrated with Open Office or LibreOffice templates
or even set up a DJ table session using Google Hangouts. with Extras of course.
But as it stands it may be good to go for webcasts !!
Operating systems of Robots may be the future cash cow of Microsoft , while the pirates of Silicon Valley fight fascinating cloudy wars! 🙂
Microsoft Robotics Developer Studio 4 beta (RDS4 beta) provides a wide range of support to help make it easy to develop robot applications. RDS4 beta includes a programming model that helps make it easy to develop asynchronous, state-driven applications. RDS4 beta provides a common programming framework that can be applied to support a wide variety of robots, enabling code and skill transfer.
RDS4 beta includes a lightweight asynchronous services-oriented runtime, a set of visual authoring and simulation tools, as well as templates, tutorials, and sample code to help you get started.
This release has extensive support for the Kinect sensor hardware throug the Kinect for Windows SDK allowing developers to create Kinect-enabled robots in the Visual Simulation Environment and in real life. Along with this release comes a standardized reference spec for building a Kinect-based robot.
Concurrency and Coordination Runtime (CCR) helps make it easier to handle asynchronous input and output by eliminating the conventional complexities of manual threading, locks, and semaphores. Lightweight state-oriented Decentralized Software Services (DSS) framework enables you to create program modules that can interoperate on a robot and connected PCs by using a relatively simple, open protocol.
Visual Programming Language (VPL) provides a relatively simple drag-and-drop visual programming language tool that helps make it easy to create robotics applications. VPL also provides the ability to take a collection of connected blocks and reuse them as a single block elsewhere in your program. VPL is also capable of generating human-readable C#.
DSS Manifest Editor (DSSME) provides a relatively simple creation of application configuration and distribution scenarios.
The DSS Log Analyzer tool allows you to view message flows across multiple DSS services. DSS Log Analyzer also allows you to inspect message details.
Visual Simulation Environment (VSE) provides the ability to simulate and test robotic applications using a 3D physics-based simulation tool. This allows developers to create robotics applications without the hardware. Sample simulation models and environments enable you to test your application in a variety of 3D virtual environments.
Here is a short video I created on my experiences in using the new features in youtube video editing.
Whats new in Youtube videos-
1) My account can now upload more than 15 minutes of video
2) I can edit the videos online without any software at http://www.youtube.com/editor
4) I thought but eventually decided not to use the Animation features (for free) at
http://goanimate.com/signup ( I can login using my Google Account !)
5) Since I hope to keep my videos seperate- I created a new video at the awesome new features in Blogspot at
http://videosforkush.blogspot.com/ (seperate blog post on that later) and then I just share my videos using the Share feature in Blogspot (big discovery- the Twitter button has been demoted from the share this by you-tube button hierarchy)
5.6) I can preview the features side by side as well
6) I still wish Youtube has some feature to help me capture the screen so I can make training videos at a faster rate so I no longer have to use Camtasia
Video killed the Radio Star- Yup
Heres the final video-
I was looking at the site http://www.google.com/adplanner/static/top1000/index.html
and I saw this list (Below) and using a Google Doc at https://docs.google.com/spreadsheet/pub?hl=en_US&hl=en_US&key=0AtYMMvghK2ytdE9ybmVQeUxMeXdjWlVKYzRlMkxjX0E&output=html.
I then decided to divide pageviews by users to check the maths
Facebook is AAAAAmazing! and the Russian social network is amazing too!
The maths is wrong! (maybe sampling, maybe virtual pageviews caused by friendstream refresh)
but the average of 1,136 page views per unique visitor per month means 36 page views /visitor a Day!
Rank Site Category Unique Visitors (users) Page Views Views/Visitors
1 facebook.com Social Networks 880000000 1000000000000 1,136 29 linkedin.com Social Networks 80000000 2500000000 31 38 orkut.com Social Networks 66000000 4000000000 61 40 orkut.com.br Social Networks 62000000 43000000000 694 65 weibo.com Social Networks 42000000 2800000000 67 66 renren.com Social Networks 42000000 3300000000 79 84 odnoklassniki.ru Social Networks 37000000 13000000000 351 90 scribd.com Social Networks 34000000 140000000 4 95 vkontakte.ru Social Networks 34000000 48000000000 1,412
Rank Site Category Unique Visitors (users)Page Views Page Views/Visitors 1 facebook.com Social Networks 880000000 1000000000000 1,136 2 youtube.com Online Video 800000000 100000000000 125 3 yahoo.com Web Portals 590000000 77000000000 131 4 live.com Search Engines 490000000 84000000000 171 5 msn.com Web Portals 440000000 20000000000 45 6 wikipedia.org Dict 410000000 6000000000 15 7 blogspot.com Blogging 340000000 4900000000 14 8 baidu.com Search Engines 300000000 110000000000 367 9 microsoft.com Software 250000000 2500000000 10 10 qq.com Web Portals 250000000 39000000000 156
Here is an interview with Dan Steinberg, Founder and President of Salford Systems (http://www.salford-systems.com/ )
Ajay- Describe your journey from academia to technology entrepreneurship. What are the key milestones or turning points that you remember.
Dan- When I was in graduate school studying econometrics at Harvard, a number of distinguished professors at Harvard (and MIT) were actively involved in substantial real world activities. Professors that I interacted with, or studied with, or whose software I used became involved in the creation of such companies as Sun Microsystems, Data Resources, Inc. or were heavily involved in business consulting through their own companies or other influential consultants. Some not involved in private sector consulting took on substantial roles in government such as membership on the President’s Council of Economic Advisors. The atmosphere was one that encouraged free movement between academia and the private sector so the idea of forming a consulting and software company was quite natural and did not seem in any way inconsistent with being devoted to the advancement of science.
Ajay- What are the latest products by Salford Systems? Any future product plans or modification to work on Big Data analytics, mobile computing and cloud computing.
Dan- Our central set of data mining technologies are CART, MARS, TreeNet, RandomForests, and PRIM, and we have always maintained feature rich logistic regression and linear regression modules. In our latest release scheduled for January 2012 we will be including a new data mining approach to linear and logistic regression allowing for the rapid processing of massive numbers of predictors (e.g., one million columns), with powerful predictor selection and coefficient shrinkage. The new methods allow not only classic techniques such as ridge and lasso regression, but also sub-lasso model sizes. Clear tradeoff diagrams between model complexity (number of predictors) and predictive accuracy allow the modeler to select an ideal balance suitable for their requirements.
The new version of our data mining suite, Salford Predictive Modeler (SPM), also includes two important extensions to the boosted tree technology at the heart of TreeNet. The first, Importance Sampled learning Ensembles (ISLE), is used for the compression of TreeNet tree ensembles. Starting with, say, a 1,000 tree ensemble, the ISLE compression might well reduce this down to 200 reweighted trees. Such compression will be valuable when models need to be executed in real time. The compression rate is always under the modeler’s control, meaning that if a deployed model may only contain, say, 30 trees, then the compression will deliver an optimal 30-tree weighted ensemble. Needless to say, compression of tree ensembles should be expected to be lossy and how much accuracy is lost when extreme compression is desired will vary from case to case. Prior to ISLE, practitioners have simply truncated the ensemble to the maximum allowable size. The new methodology will substantially outperform truncation.
The second major advance is RULEFIT, a rule extraction engine that starts with a TreeNet model and decomposes it into the most interesting and predictive rules. RULEFIT is also a tree ensemble post-processor and offers the possibility of improving on the original TreeNet predictive performance. One can think of the rule extraction as an alternative way to explain and interpret an otherwise complex multi-tree model. The rules extracted are similar conceptually to the terminal nodes of a CART tree but the various rules will not refer to mutually exclusive regions of the data.
Ajay- You have led teams that have won multiple data mining competitions. What are some of your favorite techniques or approaches to a data mining problem.
Dan- We only enter competitions involving problems for which our technology is suitable, generally, classification and regression. In these areas, we are partial to TreeNet because it is such a capable and robust learning machine. However, we always find great value in analyzing many aspects of a data set with CART, especially when we require a compact and easy to understand story about the data. CART is exceptionally well suited to the discovery of errors in data, often revealing errors created by the competition organizers themselves. More than once, our reports of data problems have been responsible for the competition organizer’s decision to issue a corrected version of the data and we have been the only group to discover the problem.
In general, tackling a data mining competition is no different than tackling any analytical challenge. You must start with a solid conceptual grasp of the problem and the actual objectives, and the nature and limitations of the data. Following that comes feature extraction, the selection of a modeling strategy (or strategies), and then extensive experimentation to learn what works best.
Ajay- I know you have created your own software. But are there other software that you use or liked to use?
Dan- For analytics we frequently test open source software to make sure that our tools will in fact deliver the superior performance we advertise. In general, if a problem clearly requires technology other than that offered by Salford, we advise clients to seek other consultants expert in that other technology.
Ajay- Your software is installed at 3500 sites including 400 universities as per http://www.salford-systems.com/company/aboutus/index.html What is the key to managing and keeping so many customers happy?
Dan- First, we have taken great pains to make our software reliable and we make every effort to avoid problems related to bugs. Our testing procedures are extensive and we have experts dedicated to stress-testing software . Second, our interface is designed to be natural, intuitive, and easy to use, so the challenges to the new user are minimized. Also, clear documentation, help files, and training videos round out how we allow the user to look after themselves. Should a client need to contact us we try to achieve 24-hour turn around on tech support issues and monitor all tech support activity to ensure timeliness, accuracy, and helpfulness of our responses. WebEx/GotoMeeting and other internet based contact permit real time interaction.
Ajay- What do you do to relax and unwind?
Dan- I am in the gym almost every day combining weight and cardio training. No matter how tired I am before the workout I always come out energized so locating a good gym during my extensive travels is a must. I am also actively learning Portuguese so I look to watch a Brazilian TV show or Portuguese dubbed movie when I have time; I almost never watch any form of video unless it is available in Portuguese.
Dan Steinberg, President and Founder of Salford Systems, is a well-respected member of the statistics and econometrics communities. In 1992, he developed the first PC-based implementation of the original CART procedure, working in concert with Leo Breiman, Richard Olshen, Charles Stone and Jerome Friedman. In addition, he has provided consulting services on a number of biomedical and market research projects, which have sparked further innovations in the CART program and methodology.
Dr. Steinberg received his Ph.D. in Economics from Harvard University, and has given full day presentations on data mining for the American Marketing Association, the Direct Marketing Association and the American Statistical Association. After earning a PhD in Econometrics at Harvard Steinberg began his professional career as a Member of the Technical Staff at Bell Labs, Murray Hill, and then as Assistant Professor of Economics at the University of California, San Diego. A book he co-authored on Classification and Regression Trees was awarded the 1999 Nikkei Quality Control Literature Prize in Japan for excellence in statistical literature promoting the improvement of industrial quality control and management.
His consulting experience at Salford Systems has included complex modeling projects for major banks worldwide, including Citibank, Chase, American Express, Credit Suisse, and has included projects in Europe, Australia, New Zealand, Malaysia, Korea, Japan and Brazil. Steinberg led the teams that won first place awards in the KDDCup 2000, and the 2002 Duke/TeraData Churn modeling competition, and the teams that won awards in the PAKDD competitions of 2006 and 2007. He has published papers in economics, econometrics, computer science journals, and contributes actively to the ongoing research and development at Salford.
More Google Plus stuff
1) The logic is undeniable