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Seems like these chat bots have A LOT of lock in. So the question is which one to pick? This one? IBM Watson? Others? Which horse are you betting on and wh

Seems like these chat bots have A LOT of lock in. So the question is which one to pick? This one? IBM Watson? Others? Which horse are you betting on and why? Amazon making this is great because it's developer friendly and will surely fall in price and improve in quality over time. They also have to make it work well because it's a critical ordering channel for them going forward. Seems like IBM has been focused on this for years though? Worried they will try to make this a profit center though... rather than continually dropping the price. Other companies working on this I should be aware of? Thoughts?

ronack 10y ago
You're right, lack of standardization and portability is a major problem. I would not personally choose IBM due to the pricing model and previous bad experiences. Google, Microsoft, and Facebook (and a bunch of startups) have all built or acquired conversational services very much like Lex. None of them is a clear winner at this point in my opinion.
- AJ007 10y ago
  I would prefer to log everything on an owned system and test out bots through third-party APIs. Poking around with options now, but will probably just bootstrap with human operators and pre-canned responses until something makes sense. It's also worth pointing out there is a huge difference between using the bot for a critical piece of your product vs as a supplement to customer support.
- chiefalchemist 10y ago
  Call me naive/stupid but how is this any different than a backend DB? Can't an interface prevent any sorta lock in?
olavgg 10y ago
I'm working for a startup called Boost.ai where we have decided to build our own deep learning model and create our own training data. We use several models that predicts what word comes next, take care of mispellings and dialects. We also have memory so it can remember contexts when you ask a new question or it predicts that you want to start with a new context. If you want something kick ass, I highly recommend building it yourself. The bar set by IBM's Watson, Nuance's Nina or IPSoft's Amelia isn't actually very high. For non english languages, anyone who has some knowledge about NLP and deep learning will easily surpass them.
- choxi 10y ago
  Any pointers on where to get started? I've messed with Karpathy's RNN example using Shakespeare and PG essays, but don't know where to go from there.
  - olavgg 10y ago
    Karpathy's example tries to predict the next character, there is a fork of it that tries to predict the next word. https://github.com/larspars/word-rnn https://github.com/larspars/word-rnn RNN is great approach if you want to play around with text generation with deep learning. But for a chatbot, deep learning alone is not there yet. We create our own intents based on the domain and predict the intent of each question. We have also made it looks smarter by creating an intent hierarchy where we try to do multiple predictions for a question with a goal to drill the question down in the intent tree. In that way we know that the question is about a bank card and can figure out if you want a new one, block it, increase credit, set limits and so on.
- chiefalchemist 10y ago
  Re: "If you want something kick ass, I highly recommend building it yourself." That might be true, but the ability to spin something up without all that overhead is any MVP'ers wet dream, yes?
Qworg 10y ago
Microsoft has their Bot Framework + LUIS. I've found both to be easy to work with. Disclosure: I work at Microsoft AI and Research.
flukus 10y ago
> Seems like these chat bots have A LOT of lock in. So the question is which one to pick? This one? None of the above? We had desktop voice to text 20 years ago (that was about on par with google now for me) so I don't see why everything has to go through cloud services.
- smiled0g 10y ago
  I don't think desktop solutions (like Microsoft speech synthesizer) does a great job at producing "natural" and life-like speeches. Sure, it does read the text, but that's about it. To properly produce speech, the synthesizer needs to take into account context of around words to determine how to pronounce ambiguous words or how to pace the speech. What about the tone? How about making the speech sounds more engaging? These are the things that need data model to work great. However, it's just difficult to cram that into a desktop app. And why would any company do that when they can put the service online and charge for it (which is totally fair)?
  - flukus 10y ago
    I was thinking more about the other way because that's the hard part, we can understand synthesized voices better than they can understand us. Apple seems far and away the best desktop solution. For other OS's though, it's frustrating that they haven't improved on what computers of the early eighties could do. Now you've made me nostalgic though, I'll never forget the first time I heard my amiga 500 read my words.

ronack 10y ago

You're right, lack of standardization and portability is a major problem. I would not personally choose IBM due to the pricing model and previous bad experiences. Google, Microsoft, and Facebook (and a bunch of startups) have all built or acquired conversational services very much like Lex. None of them is a clear winner at this point in my opinion.

AJ007 10y ago
I would prefer to log everything on an owned system and test out bots through third-party APIs. Poking around with options now, but will probably just bootstrap with human operators and pre-canned responses until something makes sense. It's also worth pointing out there is a huge difference between using the bot for a critical piece of your product vs as a supplement to customer support.
chiefalchemist 10y ago
Call me naive/stupid but how is this any different than a backend DB? Can't an interface prevent any sorta lock in?

olavgg 10y ago

I'm working for a startup called Boost.ai where we have decided to build our own deep learning model and create our own training data. We use several models that predicts what word comes next, take care of mispellings and dialects. We also have memory so it can remember contexts when you ask a new question or it predicts that you want to start with a new context. If you want something kick ass, I highly recommend building it yourself. The bar set by IBM's Watson, Nuance's Nina or IPSoft's Amelia isn't actually very high. For non english languages, anyone who has some knowledge about NLP and deep learning will easily surpass them.

choxi 10y ago
Any pointers on where to get started? I've messed with Karpathy's RNN example using Shakespeare and PG essays, but don't know where to go from there.
- olavgg 10y ago
  Karpathy's example tries to predict the next character, there is a fork of it that tries to predict the next word. https://github.com/larspars/word-rnn https://github.com/larspars/word-rnn RNN is great approach if you want to play around with text generation with deep learning. But for a chatbot, deep learning alone is not there yet. We create our own intents based on the domain and predict the intent of each question. We have also made it looks smarter by creating an intent hierarchy where we try to do multiple predictions for a question with a goal to drill the question down in the intent tree. In that way we know that the question is about a bank card and can figure out if you want a new one, block it, increase credit, set limits and so on.
chiefalchemist 10y ago
Re: "If you want something kick ass, I highly recommend building it yourself." That might be true, but the ability to spin something up without all that overhead is any MVP'ers wet dream, yes?

Qworg 10y ago

Microsoft has their Bot Framework + LUIS. I've found both to be easy to work with. Disclosure: I work at Microsoft AI and Research.

flukus 10y ago

> Seems like these chat bots have A LOT of lock in. So the question is which one to pick? This one? None of the above? We had desktop voice to text 20 years ago (that was about on par with google now for me) so I don't see why everything has to go through cloud services.

smiled0g 10y ago
I don't think desktop solutions (like Microsoft speech synthesizer) does a great job at producing "natural" and life-like speeches. Sure, it does read the text, but that's about it. To properly produce speech, the synthesizer needs to take into account context of around words to determine how to pronounce ambiguous words or how to pace the speech. What about the tone? How about making the speech sounds more engaging? These are the things that need data model to work great. However, it's just difficult to cram that into a desktop app. And why would any company do that when they can put the service online and charge for it (which is totally fair)?
- flukus 10y ago
  I was thinking more about the other way because that's the hard part, we can understand synthesized voices better than they can understand us. Apple seems far and away the best desktop solution. For other OS's though, it's frustrating that they haven't improved on what computers of the early eighties could do. Now you've made me nostalgic though, I'll never forget the first time I heard my amiga 500 read my words.