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Big Data at work: Google's speech recognition

Via oreillyradar:

Google’s impressive speech recognition is one of the best examples of big data’s practical application. Farhad Manjoo explains:

How does Android’s speech system work so well? The magic of data. Speech recognition is one of a handful of Google’s artificial intelligence programs—the others are language translation and image search—that get their power by analyzing impossibly huge troves of information. For the speech system, the data are a large number of voice recordings. If you’ve used Android’s speech recognition system, Google Voice’s e-mail transcription service, Goog411 (a now-defunct information service), or some other Google speech-related service, there’s a good chance that the company has your voice somewhere on its servers. And it’s only because Google has your voice—and millions of others—that it can recognize mine.

Filed under data big data Google

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This is why the Financial Times will be fine

Via oreillyradar:

From Reuters:

“We don’t want to lose our direct relationship with our subscribers. It’s at the core of our business model,” Rob Grimshaw told Reuters in an interview on Monday. He said he was hopeful of a positive outcome to negotiations with Apple, but added: “If it turns out that one or another channel doesn’t mix with the way we want to do business, there’s a large number of other channels available to us.”

No chest thumping. No talk of iPad “salvation.” Just a level-headed perspective of the current opportunities and how they may or may not benefit the Finaicial Times. This is how all publishers — news, books, film, etc. — should approach digital channels. Does it work for us or does it not?

Filed under Financial Times Apple data disruption

Notes

The Zen of Open Data, by Chris McDowall

Open is better than closed.
Transparent is better than opaque.
Simple is better than complex.
Accessible is better than inaccessible.
Sharing is better than hoarding.
Linked is more useful than isolated.
Fine grained is preferable to aggregated. (Although there are legitimate privacy and security limitations.)
Optimise for machine readability — they can translate for humans.
Barriers prevent worthwhile things from happening.
“Flawed, but out there” is a million times better than “perfect, but unattainable”.
Opening data up to thousands of eyes makes the data better.
Iterate in response to demand.
There is no one true feed for all eternity — people need to maintain this stuff.

The Zen of Open Data | seeing data

Filed under data open data open web