AI, Bots, NLP, Slack and Alexa
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Sounds like buzzword bingo but this is for real. Our product relies heavily on AI and sometimes it was hard to sell the idea until we started to add a conversational interface named DAVIS to it. Building on Slack or Alexa is fairly easy but how do we understand what the user really wants from us. Join me on my journey from our initial prototype to where we are now - a Node.js based personal assistant that sometimes already sounds like a real person.
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AI, Bots, NLP, Slack and Alexa
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- 2. confidential Before we start … This talk • contains live samples • requires a working internet connection • requires an working audio connection • requires a working backend • … relies on a lot of stuff actually • uses non-deterministic algorithms
- 3. confidential Talking Bots CinemaJS Barcelona, March 30th 2017 @dkhan
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- 11. confidential AI Understand how parts of a system interact Detect anomalies and problems by learning from the history and past events Isolate the root cause of a problem by analyzing thousands of single events
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- 14. confidential “Tell me about user activity”
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- 24. confidential ngrok ... ngrok is awesome
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- 27. confidential intent: { name: 'DoCommand', slots: { command: { name: 'command', value: 'what does the cat say' } } }
- 28. confidential what does the cat say what do cats say how do cats sound what does a dog say
- 29. confidential what does the cat say subject | predicate Syntax The Structure
- 30. confidential what does the cat say intent: say slot: animal, value: cat Semantics The Meaning
- 31. confidential Natural Language Processing (NLP)
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- 34. confidential monkeys !== monkey && cat !== cats
- 35. confidential Fuzzy & Phonetic Matching
- 36. confidential Metaphone Two quick brown foxes jump over three lazy dogs TWKKBRNFKSSJMPFR0RLSTKS Same key for similar sounding words Two quick brown foxes jomp over three lazy dougs TWKKBRNFKSSJMPFR0RLSTKS Amazon did this for us already!
- 37. confidential Porter Stemmer Two quick brown foxes jump over three lazy dogs Two quick brown fox jump over three lazi dog Normalizes terms by removing or harmonizing morphological and inflexional endings.
- 39. confidential Levenshtein Distance lv(monkey, monkeys) = 0 lv(monkey, monkeys) = 1 lv(monkey, dog) = 6 How similar are two strings or how many transformations does it take to get from string A to string B.
- 43. confidential alexaAnimalSounds: https://github.com/danielkhan/alexaAnimalSounds Dynatrace Davis: https://github.com/Dynatrace/davis-server Dynatrace Free Trial: https://dynatrace.com/free-trial
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Editor's Notes
- I’m technology strategist at dynatrace. We do performance monitoring. This means we monitor how well applications are doing. We are the market leader in that and we have around 2.000 employees worldwide
- In our products we use artifical intelligence to find the root cause of problems.
- So we know what the user said but how can we make sense of it? Use regex? That would be a mess. Because the syntax of language is really complicated.
- So first we have to understand the syntax of language but what does does say cat mean anyways?
- So how can we achieve that?
- Alexa might use something like that to match against a dictionary.