Google Earth is a Java-based GIS application that allows users to find places on the face of the Earth. The users can zoom from space down to street level and combine imagery, 3D geography, maps, and business data to get the total picture in seconds.

I love it! If your computer meets these requirements, give it a try.

It took me 15 mins to find the place where I used live in Hong Kong in Google Earth.

This is a fictionary 8-mintue mini-movie speculating the evolution of media from 1984 up until 2014 .

• ‘2005 – In response to Google’s recent moves, Microsoft buys Friendster. ‘
• ‘2008 sees the alliance that will challenge Microsoft’s ambitions. Google and Amazon join forces to form Googlezon.’
• ‘In 2011, the slumbering Fourth Estate awakes to make its first and final stand. The New York Times Company sues Googlezon…’
• ‘In year 2014, New York Times has gone offline.’

source: http://www.robinsloan.com/epic/; transcript

Trading virtual objects may sound zany, but it seems people can get motivated enough to kill for them, like this tragic incident.

Who owns virtual resources? Can there be rights over objects/artifacts in virtual gaming worlds and for that matter the Internet? Do we own email messages sent or received on Hotmail or Gmail? Is this really different from privacy? Is this DRM?

$9m trade revenues on eBay for such artifacts, gives an idea of the scope of the problem.

IBM seems to have captured the gaming console market, becoming the sole chip provider for PS3, XBox 360 and Nintendo.

Apple moves on in search of greener pastures, switching to Intel x86 chips. Mac users will probably see dropped prices and increasing support for x86 applications by end of 2007. Seems to be win-win situation for Intel, Apple and Mac enthusiasts! We might finally see non-linux robust software on x86 after all ;).

So the major players have joined in the WiMax game. This report from the Washington Post describes Intel coming to DC area to release their new WiMax chipset.

There is an interesting paper that describes how TiVo computes its recording recommendations.

The abstract:

We describe the TiVo television show collaborative recommendation system which has been fielded in over one million TiVo clients for four years. Over this install base, TiVo currently has approximately 100 million ratings by users over approximately 30,000 distinct TV shows and movies. TiVo uses an item-item (show to show) form of collaborative filtering which obviates the need to keep any persistent memory of each user�s viewing preferences at the TiVo server. Taking advantage of TiVo�s client-server architecture has produced a novel collaborative filtering system in which the server does a minimum of work and most work is delegated to the numerous clients. Nevertheless, the server-side processing is also highly scalable and parallelizable. Although we have not performed formal empirical evaluations of its accuracy, internal studies have shown its recommendations to be useful even for multiple user households. TiVo�s architecture also allows for throttling of the server so if more server-side resources become available, more correlations can be computed on the server allowing TiVo to make recommendations for niche audiences.

See PVRBLog