Google Adds Roundtables to Its Video Library


Google has just posted the first three videos in an ongoing series of technology roundtables that it is hosting. The discussions are pretty esoteric at times, but anyone interested in advanced computing topics such as working with web search, the cloud, clusters, and speech technology will find these of interest.

The videos feature Google engineers, and provide a glimpse into what Google is working on. In this post, I’ll also discuss some much more down-to-earth and practical videos and presentations from Google, which many web workers may want to sit in on.

The three initial videos in the video roundtable series are: Large Scale Search System Infrastructure and Search Quality, Map Reduce, and Applications of Human Language Technology. I’ve been sampling these roundtables, and watched the whole discussion on Map Reduce. If you’re unfamiliar with Map Reduce, it’s considered by many to be Google’s secret weapon–a distributed way of processing queries and parts of queries across clusters of computers. In the video Google engineer Sanjay Ghemawat describes it as a “library which helps people write distributed programs.” Several other engineers weigh in on applications for Map Reduce.

The Applications of Human Language roundtable discussion features speech technology experts from Google. Still, whenever I hear these speech technology experts telling me that we’ve come such a long way with machine translation and the like, I’m always a little skeptical.

In July, over on OStatic, we did a post on a collection of much more practical and hands-on videos from Google that many web developers will find of interest. The presentations, mostly from Google Developer’s Days held around the world are here.

Among them, you can learn how to design your own YouTube player, create a client-side search engine, host open source software projects on Google Code, and use Google App Engine. The most hands-on and practical videos in this series are from the Google I/O Sessions, with the whole collection found here.



I apologize for the huge number of typos in my earlier comment. I hope the crux of my post was not as murky as its composition :)



I am a Ph.D. student and my research is in the area of statistical machine translation (SMT). I know it sounds like the field of machine translation has remained stagnant over the last 10 years but take it from someone who is looking at the research side of this. While it may seem to end users that not much has changed in this area, the amount of research that’s been done over the last 10 years is absolutely phonomenal and the current research systems are SO much better than. Google Translate is a somewhat good proxy for such improvements but there are systems being developed right now (thanks primarily to DARPA’s GALE program) that can beat easily beat Google given the same amount of data.

I know it seems frustrating that not many of these improvements make it over to the consumer side of the equation, but I am confident that it will soon get better. As I tell me students and colleagues, translation is a good example of a problem that’s easy for a human unless the human is telling a machine how to do it.

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