Facebook messenger PM opines on Bot trends in 2017 and 3 of them involve having less conversations with Bots: The trends according to Facebook one of the leading messaging platforms: 1. More structure, (ed:to have) less conversation, 2. Mobile webview integrations (ed: to have web experiences) , 3. Bots that get social (ed: to talk to other people) and Trend 6. Hybrid Bots (which reintroduce humans into the flow)
When you launch a startup, the most common sentence you’ll hear from an investor is “ Looks interesting — call me back when you have some traction.” I heard it all the time with my first startup, Noospher, and I always left the meeting wondering, “Well, how do I know when we have traction?”
Peter’s a tutoring bot available through Facebook Messenger, always there and ready to help students with whatever questions they may have. The founders validate that old school virality techniques from the "velvet-ropes-at-clubs" to "tweet for special access" still work for Bots and messaging.
In the past couple of years, the number of chatbots - conversational user interfaces mimicking a chat partner - has exploded. Bots can serve as personal assistants, provide customer service and recommendations, answer questions and distribute information, entertain the user, or do pretty much anything else.
Bots being the trendy thing they are, however, they are being proposed as a solution to use cases that really would be best left botless. In particular, there are two kinds of chatbots that don’t need to exist.
Natural Language Processing, or NLP for short, is a very popular Data Science methodology that has gained a lot of traction over the past few years as more and more companies have realized that it’s easy to get access to text data and use it to derive valuable insights. Twitter, for example, offers a rich data stream which when processed properly, can yield important insights on topics or brands, using just NLP as a paid resource. However, NLP wouldn’t have gone far if it weren’t for A.I., since the latter allows it to go beyond the rudimentary statistical models that NLP has in its toolkit.