that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. (source) circa 1950s first major advances in the 1980s
a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. (source) circa 1966 (ELIZA) first major advances in the early 2000s
W3C Community specification was published in 2012 SpeechRecognition interface currently only supported in Chrome, experimental feature Uses Google's servers to convert speech to text (requires Internet connection)
real people speak." (source) Publicly open dataset Upload recordings of your voice Help reduce bias in Natural Language Processing (NLP) & machine learning
• lightweight database (e.g. Amazon DynamoDB) • CI server of choice (e.g. Travis, Jenkins, etc.) • unit testing framework of choice (e.g. Jest) • TypeScript…?
you • Certification process similar to mobile app store submissions ◦ ~48-hour turnaround on average ◦ Feedback is unpredictable! ◦ Respect existing brands • Can share a "beta" version with co-workers, friends, etc. ◦ Great for QA as well as hobby projects
◦ Learning about the platform limitations ◦ Managing stakeholder expectations ◦ Understanding & changing user behaviors ◦ QA • It's not just an engineering challenge!
but they're not! • It's clear the companies designing these platforms are still focused primarily on Text-to-Speech (TTS) • The Actions on Google audio player is almost unusable • The Alexa audio player has many features but is very unintuitive when you're first working with it
Dialogflow can reject a user's request ◦ If that happens, your app is not notified at all! ◦ Logs/analytics can't tell the whole story ◦ Users often don't understand why it failed • Real user testing is critical!