Very useful info if your selling in an international market. But I am not, thankfully. But thanks for the good info.
http://denversinsurance.com
http://denversinsuranceblog.com
Google Prediction API
71–80 of 88 posts
Re: Google Prediction API
#72I'd love to see how well it could predict comment ratings from Hacker News. The following data would be a good start: 1. Text of comment 2. How many points the comment has 3. How many points the article has 4. Time article was posted 5. Time comment was posted I'd also be interested to see what kind of user bias there is. If you don't provide user names, you could see what kind of rating a comment should have based o…
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Re: Google Prediction API
#73"Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications." I can understand the necessity of this, but that'll be some serious lock-in.
Not necessarily. Unless I'm misunderstanding, you're not transforming your historical data in Google Storage. So as long as you kept it backed up outside of Google Storage, then you shouldn't have any issues.
Re: Google Prediction API
#74Are there any input/output samples?
Here's one: http://code.google.com/apis/predict/docs/language_id.txt
Linked to from this page: http://code.google.com/apis/predict/docs/getting-started.htm...
Re: Google Prediction API
#75Earlier quoted context omitted.
I had this idea myself, basically machine learning as a service about 3 years or more ago. Somehow, I also knew Google would implement something like this. So while I still consider this a viable startup idea, I knew it would be tough to compete against a behemoth that already has tons of data and experience training countless machine learning algorithms.
But three years ago google didn't have this, you could have cornered the market in those three years. Not doing something because 'google might do it' is not a very good reason.
well, that's really not the reason i didn't do it. amongst other things (my lack of preparation at the time, primarily) it just wasn't easy for anyone but a few big companies because the biggest requirement is that you have not only scale, but loads of data. this is no longer true. you now have aws, hadoop, etc, so it's easier and cheaper to scale, but this is why i knew google would do it. they had the capacity, and they still do. very smart machine learners, petabytes of data, scale, etc.
Re: Google Prediction API
#76Earlier quoted context omitted.
You scratched the itch I procrastinated to scratch. Others (à la Postrank) tried, you just seem you have tried better! 1 Kudo :) EDIT: Ah, you're the guy behind Raphaël Vector lib, have one more Kudo!
Raphaël was written by Dmitry Baranovskiy. I only made very minor contributions to the library (animation easing, which are pretty ubiquitous in the examples now) and presented the library at the SVG Open 2009 at Google. I made Protopub to scratch the itch I think a LOT of us have. I am about a month away from a v1.0, and that's when I'll announce it on HN. Until then, I'm tweaking AI algorithms, fixing UI bugs, and…
I wish you the best! You'll perhaps have some competition if it still itches in spite of your job, after Broodwar AI Competition is over though. :)
Re: Google Prediction API
#77Earlier quoted context omitted.
I had this idea myself, basically machine learning as a service about 3 years or more ago. Somehow, I also knew Google would implement something like this. So while I still consider this a viable startup idea, I knew it would be tough to compete against a behemoth that already has tons of data and experience training countless machine learning algorithms.
The "AI API" is the dream application. Just imagine: you get to implement (and even discover) cutting-edge prediction algorithms, make them scale, and expose them via your protocol-of-choice. No frontends to write, no Joe Luser to support, just beautiful math and hardcore infrastructure engineering. There are reasons this hasn't been done before. I don't think it's a viable startup idea. Think about the capital you w…
interesting. i actually envisioned a simple web interface that anyone, including joe luser, could use. the idea was to empower anyone to be more data driven, from the individual business owner in africa, to the small and medium business owners everywhere. i did recently run into directed edge. there's also data applied (their ui is too complex though).
by implementing a web app that anyone could use (mobile or not), i also envisioned a sort of community/market place where people could post their data and do simple stuff and/or have experts try to tackle it for a service fee. and whatever algorithms came out of that would be made available for future data that has similar features. i recently came across a similar site. can't remember the name.
anyway, i know this is not necessarily a viable startup idea. and if it is, it's ultimately all about execution. i'm still dreaming though and was psyched google launched their predict api.
Re: Google Prediction API
#78Re: Google Prediction API
#79This is easily the most interesting announcement so far. Machine learning has so many applications, but its use is constrained by the high barriers to entry. Recommendation engines, for example, are huge sales drivers, but few among even the largest ecommerce stores use them. A simple prediction interface that's built on the ML expertise at Google is a win for everyone.
Directed Edge, a promising YC startup, makes recommendation engines surprisingly easy: http://www.directededge.com/ It's quite a bit higher-level than what Google is offering here, with all the benefits and drawbacks that entails.
Thanks for the friendly plug :-)
You can find the full documentation on our developer site at http://developer.directededge.com/, and we offer a free developer account for non-commercial purposes: http://www.directededge.com/signup-developer.html
If anybody is giving the Google Prediction API a whirl for recommendations, we'd love to hear about your findings!
Re: Google Prediction API
#80Are there any (preferably FOSS) libraries that does anything like this?
there is nothing out there which would scale to terabytes out-of-the-box
VFDT (Very Fast Decission Trees), maybe?
You can also run those distributively without much problems.
Original paper: http://www.cs.washington.edu/homes/pedrod/papers/kdd00.pdf
You should also look on http://www.cs.washington.edu/dm/vfml/