31–40 of 71 posts
http://www.inference.phy.cam.ac.uk/dasher/
it uses this prediction to make it efficient for people who cannot type, type.
http://imgur.com/8YJIB.png Looks like it includes someone's source code, probably that of the page itself?
Neat! So is this just a fun project or does Google plan to use this technology in any of their products? Edit: You can use Scribe on any web page. From the help section: "Google Scribe can be used anywhere, on any web page, using the Google Scribe Bookmarklet. From the Google Scribe home page, drag the Google Scribe Bookmarklet (located below the text box) to Bookmarks toolbar (or Favorites toolbar depending on your…
It might make sense to use this as one predictor for transcribed text in Google Voice. Then again, once the results begin to diverge, this would amplify the problem.
That shouldn't be too hard to do, should it? :)
I'd actually like to see the exact opposite: an editor that warns me every time I use a too common and worn out phrase or sequence of words. That shouldn't be too hard to do, should it? :)
2 the exact opposite
3 worn out phrase
4 that shouldn't be too hard
5 should it?
it predicts what you're going to type next. this is really nothing new. perhaps the most interesting use of predicting input in this fashion is dasher: http://www.inference.phy.cam.ac.uk/dasher/ it uses this prediction to make it efficient for people who cannot type, type.
There are so many things to say about this. Thanks to Google for creating this tool which I must say still seems a bit random in its objectives though. I think the main issue here is that logic behind scribe is based on statistical frequency of co-occurrence of strings rather than any semantic logic. Granted, automating the semantics here could be tough and very expensive with the amounts of data available to Google'…
I doubt that a lot of people will use this, although the obvious place to deploy it is in mobile communications, where per-word prediction is still very slow and text entry is very inefficient. This seems 'good enough' for most functional communications. But I would imagine that it is or will be running in the background soon on all Google pages featuring text entry, like docs or mail, because that will provide a huge flow of data to refine their models against.
Interesting. It can also be used as a Markov chain text generator. Type one word, then just accept every suggestion. "In the case of these two types of information that is not appropriate for all users of the catalogue should also be noted that there is anything you would not believe how much I loved them."
There are so many things to say about this. Thanks to Google for creating this tool which I must say still seems a bit random in its objectives though. I think the main issue here is that logic behind scribe is based on statistical frequency of co-occurrence of strings rather than any semantic logic. Granted, automating the semantics here could be tough and very expensive with the amounts of data available to Google'…
Interesting, thanks. I'm not sure if I agree, though; I think our natural understanding of language is acquired mainly though statistical inference, even though it may be encoded in the brain using a semantic taxonomy for efficiency. I doubt that a lot of people will use this, although the obvious place to deploy it is in mobile communications, where per-word prediction is still very slow and text entry is very ineff…