<story><title>What it takes to build great machine learning products </title><url>http://radar.oreilly.com/2012/04/great-machine-learning-products.html</url></story><parent_chain></parent_chain><comment><author>gfodor</author><text>A great and insightful article. A common theme I&apos;ve seen in practice is folks who have a deep understanding of ML often run straight to applying the most sophisticated algorithms possible on raw data. On the other hand, people who know a bit about ML but understand the domain better start by applying intuition to data cleansing and then follow up with simpler algorithms. Without fail the latter group ends up with better results.</text></comment>
<story><title>What it takes to build great machine learning products </title><url>http://radar.oreilly.com/2012/04/great-machine-learning-products.html</url></story><parent_chain></parent_chain><comment><author>tgflynn</author><text>I agree that the big wins in machine learning/(weak)AI are probably going to come more from figuring out how to better  apply existing models and algorithms to real problems rather than from improving the performance of the algorithms themselves.&lt;p&gt;That said one shouldn&apos;t underestimate the amount of commonality between problems that to some people may appear unrelated.  For example this post talks about the gains in machine translation performance from including larger contexts.  The same principle applies to many other sequence learning problems.  For example you have a very similar issue with handwriting recognition where it is often not possible (even for a human) to determine the correct letter classification for a given handwritten character without seeing it within the context of the word.</text></comment>