Be honest. You dread email. It's a volley that never ends, and one that's not exactly efficient, either. Plus, all those Effective Habits of Getting Things Done can help, but they're not panaceas.
What if machine learning could help instead? What if statistical models about which email is important to you, based not only on things like volume of correspondence, but also observed behavior, could be applied to separate your email wheat from the chaff?
That's the thinking behind Skimbox, a product incubated at Watertown, MA-based SoftArtisans. Skimbox provides a native iOS client for Gmail- and Exchange Server-based email that intercepts and segregates your "skim" -- email that you'd likely rather skip, or read later.
Skimbox is built on the premise of ferreting out "signals" from your email content itself, and from your treatment of it. Here are some factors that are particularly germane:
- Header content (especially sender, of course)
- Monograms, bigrams and trigrams (one-, two- and three-word phrases) that are common in important messages and their subject lines
- Determining which emails are read
- Determining which emails are categorized
The result? An inbox with your important mail separated from everything else, the latter being your skimbox and the former being your mainbox:
Curious about the technology used to build Skimbox? I was, especially since I happen to know the company weighed its database options very carefully. Ultimately, the team settled on MongoDB for the database, Ruby for the back end, node.js for Web service tier, and Objective C for the iOS native app (natch!). Skimbox's classification engine, meanwhile, is written in Python, using NLTK (the Natural Language Toolkit), with neural net functionality implemented using the Enthought Python Distribution.
Will natural language processing and neural nets be enough to tame the email beast? Time will tell...or maybe you will, as you can download Skimbox from the App Store now.
Disclosure: I've done work in the past for Skimbox's incubator-parent, SoftArtisans.