jkestelyn

They don't have to be awful; most of them are just poorly designed. Another Area 120 startup, Chatbase, is a service for designing voice bots that are much smarter and versatile than the norm by anticipating what questions people ask based on precedent (and all the different ways they ask them). Most voice bots can't do that.

[Disclaimer: I work on the Chatbase team]

"But researchers must also deal with the unexpected"

You'd think from this article that deploying a bot is like letting a dog off its leash and you just have to hope that it doesn't bite anyone. In fact there are several common-sense ways to prevent a bot from going rogue, including monitoring how it responds to intents and then making changes as needed.

So analytics are only useful for successful techs with wide adoption? I think many of those techs got that way because of analytics.

I don't disagree but how is that relevant?

"The technology to make AI-powered assistants truly useful is still far out of reach, and people aren't rushing to close that gap by adapting their behavior."

Exactly; if you build a bot with the intention of relying 100% on NLP, you're asking for trouble. But I fail to see how that fact leads to chatbots being useless when there are plenty of tools available for guiding necessary optimizations that can lead to a much better experience.

When websites were bad we turned to tools like Google Analytics to make website development data driven -- we didn't stop building them.

We value and appreciate those first VoiceLabs users who have made the jump to Chatbase; we're committed to ensuring you don't miss a beat!

I don't disagree; my suggestion was tongue-in-cheek. There is no value in optimizing an application that is so flawed that it shouldn't exist to begin with.

Those flaws derive from a wildly optimistic use case for the technology, though. A much cleaner use case would have been a bot intended for Facebook Help (instead of, or to complement, a KB -- assuming people still need that).

More ambitious maybe, but perhaps not impossible, would be a bot that looks for signs of suicidal tendencies in posts or comments and engages the user in therapeutic conversation. (?)

Leaving the clickbait-y headline aside, this story is really problematic because it conflates technology with an application of that technology.

1. When reasonably scoped (i.e., to a specific use case) and iteratively optimized over time, chatbots can meet user expectations quite well.

2. If ostensibly intended by their builders to handle every type of request on the fly via ML pixie dust, chatbots can be miserable failures.

Both things can be equally true.

(Disclaimer: I work for Chatbase, a service for analyzing and optimizing bots. Maybe Facebook should have looked at that. :) )

Sure, but past performance is not a predictor of future results. Just because many of them are built poorly today doesn't mean they have to be built that way.

Building bots is hard: there are few tools available of any sophistication, leading to a vicious cycle of trial and error (with users getting the worst of it).

Remember what the first websites looked like? Everyone was just making it up as they went along. We're in a similar situation today.

"Your chatbot should be purposeful, reflective of your product’s voice, and simpatico with your users"

Agreed 100% but IMO, this is not a function of "personality" but rather a function of deeply understanding user intents. A bot cannot be purposeful if its own designers don't know its purpose from a user-centric perspective.

(Disclaimer: I work on Chatbase, a service for analyzing and optimizing bots)

Relevant to this topic: Description of exactly-once implementation in Google Cloud Dataflow + what "exactly once" means in context of streaming:

https://cloud.google.com/blog/big-data/2017/05/after-lambda-...

(Google Cloud emp speaking)

GCP offers a $300 credit that expires after 1 year. It's a good way to get your feet wet with Hadoop and Spark via Cloud Dataproc. (Google Cloud employee speaking.)