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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.


"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.


To be fair, regular people use websites. Regular people do not use chatbots in significant numbers


A killer app (well, in this consumerism-driven world) would be a TV ad that ends with "Order now! Just tell your voice assistant 'buy me a pair of the pair of Jimmy Choo's Megan Fox wore in her ad!'".

And it would either have your shoe size saved in your profile, or if you're not the right gender, it will prompt "For who is this pair? Your wife has size 38. Your mistress Mandy has size 36."...


I don't disagree but how is that relevant?


Because even when websites were "bad," the web was still a successful platform with wide adoption. Google Analytics came about because the ecosystem was growing and thriving, not to save something that had failed to gain traction.


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


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!


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. :) )


I think the article overreaches in conflating the problems with chatbots -- which typically don't do the kind of things M promised -- with the cost/scope overrun of M. But I think it's too simplistic to say that Facebook should have just optimized their chatbots. The Facebook service has a much broader and diverse userbase and functionality -- think of the criticism that FB gets for seemingly taking over every aspect of our lives (everything from messaging friends, photo management, video broadcasting, news publishing, gaming, financial transactions).

What purpose would an optimized, limited-scope chatbot for Facebook even look like? Though come to think of it, I can think of a few usecases if Facebook wasn't out to dominate everything about real life. For example:

- When traveling to a new city: "Do I know anyone who lives here or is currently visiting?"

- When wanting to read about or discuss news topics, but only from my current network: "Are any of my friends talking about the election?"

- When bored: "What games are my friends playing?" (I'm thinking back to the time when FB was a games platform for things like Words with Friends)

All of these may be findable through a combination of searches, but I'm not a power user, and I bet most people aren't. I think if I go to the "New York, NY" location page, there's a section that lists friend connections, but a bot that processed a natural language query would be so much smoother.

And what about queries like: "What are my friends doing this weekend?". Searching that exact question brings up nothing of relevance. When I do a search for "weekend", the top results are for things like "Vampire Weekend". I have to scroll down to find a section for "Posts from Friends", and that only contains posts (even from months ago) that contain the literal word, "weekend".

I don't really know how to improve those results, without hurting some other kind of expected functionality. But a chatbot that purports to deal with everyday human questions might be the right interface for everyday quality-of-life questions


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. (?)


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.


And you know what? Some people are pining for those sites nowadays. The fact that you can write rich, computation-heavy applications and serve them over HTTP in a browser is amazing, but that doesn't mean every website needs to be a rich, computation-heavy application.

Same with chatbots. They might have some valid use somewhere, but most of the ones I have seen so far are just there for the sake of being there.


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.


"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.)


Reminiscent of Walker Percy's essay "The Delta Factor", in which he theorizes that the essence of human-ness derives from the "linguistic triangle" (thing + word + human brain).


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