Source: O’Reilly Radar, Apr 2012
The challenge in building great products with ML lies not in just understanding basic ML theory, but in understanding the domain and problem sufficiently to operationalize intuitions into model design. Interesting problems don’t have simple off-the-shelf ML solutions.
Progress in important ML application areas, like NLP, come from insights specific to these problems, rather than generic ML machinery. Often, specific insights into a problem and careful model design make the difference between a system that doesn’t work at all and one that people will actually use.
Defining the problem
But I think there’s an even bigger barrier beyond ingenious model design and engineering skills. In the case of machine translation and speech recognition, the problem being solved is straightforward to understand and well-specified. Many of the NLP technologies that I think will revolutionize consumer products over the next decade are much vaguer. How, exactly, can we take the excellent research in structured topic models, discourse processing, or sentiment analysis and make a mass-appeal consumer product?
if you want to build a rich ML product, you need to have a rich product/design/research/engineering team.
