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Ask HN: machine learning success stories?

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Ask HN: machine learning success stories?

#1
There seems to be a bit of a buzz around machine learning these days. The combination of cheap compute clusters and lots of easy available, potentially mineable data from social networking, e-commerce etc seems to present some juicy new opportunities for these techniques.

So, are there some good examples of successes with this kind of approach lately or is this the overpromise and underdeliver of AI all over again?

Re: Ask HN: machine learning success stories?

#7
At least basic machine learning seems to have become ingrained enough in a lot of areas that it's not really a separate thing anymore, just part of how people do things, which I suppose is one kind of success. E.g. lots of business-analytics departments will use things that at least 10 or 20 years ago would've been considered "machine learning", but now are just part of how you analyze and visualize data.

Re: Ask HN: machine learning success stories?

#8
post #6

FlightCaster ( http://flightcaster.com/ )

I came to this thread to check if Bradford replied. Not yet. Hopefully he will pitch in.

The uninitiated will benefit from digging into his blog:

http://measuringmeasures.com/

Also, the new ML Q&A site:

http://metaoptimize.com/

Re: Ask HN: machine learning success stories?

#10
Some fairly well-known consumer-facing things that use machine learning are spam filters, recommendation engines, speech recognition systems (speech-to-text or customer service stuff), internet advertising, news clustering (Google News), related stories, handwriting recognition, questionable content identification, automatic closed captioning, and machine translation. These are not all equally successful or sophisticated, but are ML-based and mostly good enough to use.

There are a number of examples that are not consumer-facing, like credit card fraud detection, snail mail routing, quantitative trading, market segmentation analysis, demand prediction for inventory control, and other things. It is also used for scientific data analysis in several areas, with bioinformatics being the really big one. There are other examples.

There are also applications that are not considered machine learning, but use the same ideas for different purposes. An example would be modern codes, which are used for things like compression and satellite communications, and are based on the same `graphical models' pervasive in machine learning.

There is hype, and some applications need only a little bit, but it is at least used in some real stuff.

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