Slowly Delving into AI

This year seems to be a big year for AI development. Deep Learning approaches are going to be applied to more areas and I expect most of the big name tech companies will continue to expand their research in the area.

The encouraging thing for the rest of us developers is going to be the opening up of core technologies.  The algorithms themselves are not significantly complicated. And the true value comes from the data used to train these models.  So there is some incentive for companies like Google to open-source their AI tooling.  It will enable more developers the chance to push the boundaries of AI techniques, while the companies themselves maintain ownership of the critical training data used to get the best results from these models.

What that means is that this year there will be more than a few new start-ups trying to turn these AIs into web services, or sell trained libraries as tools you can use in your own code.

Take for instance, something like sentiment analysis.  There are already quite a few APIs you can easily tie into to get this sort of analysis added to your own projects.

This year I expect this will expand into a large variety of areas.

Spell checking is prime for disruption.  For too long spell checking has relied on simple dictionary lookups and Levenshtein distance to guess at correct spelling.  These are relatively crude compared to the ability to understand context within a sentence and give much more probable corrections.

Google has open-sourced TensorFlow, and it has already gotten some significant attention from the developer community.  As more developers learn how to use these tools this year, you’ll see a lot of very interesting developments.