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Some query patterns I use in Google that you might be interested in Translate {words} to {language} My flights My schedule My packages What time is at {city}? How tall/old/heavy is {important person}? {description of a photos} in my photos (like 'ocean in my photos') Note: if you have G+ photos. description doesn't need to be typed in the photo meta info compare {food} and {food} (nutritions)
123 {currency} in {currency}
How do such personal agents work? I mean after the speech recognition and NLP (part-of-speech tagging, named-entity recognition, etc): Do they use template databases for different topics? (like afaik WolframAlpha) Do they use (scientific) ontologies or are the templates more flat and stored in a SQL database? [and web search results as fallback]
> Do they use template databases for different topics? (like afaik WolframAlpha) What do you mean by a template database?
"How tall [object]?" / "[object] height"
"Becoming a [job]"
"How much is a [unit] of [object]?"
"How old is [object]"
"When is the next [event]"
My question is: Do Siri/Cortana/GoogleNow use such flat templates (per topic) or do the use an IR ontology? http://en.wikipedia.org/wiki/Ontology_(information_science)
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There are approximate conversions for recipes, since many American recipes use volume measures and expect you to have measuring cups, while European recipes expect you to have a kitchen scale. But yes, there isn't any single conversion, since density varies: there's one cups/grams ratio for granulated sugar, one for powdered sugar, one for sifted flour, one for water, etc.
Which, as a geek, I find superconfusing and generally insane. I wish cooking was treated as chemistry (which it de facto is) and at least used precise units and proper measuring tools.
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> The big problem for open source speech recognition is training data. What exactly is needed for training - audio recordings with transcripts, human validation of recognized text? There are successful crowdsourced efforts for proofreading of OCR'ed text. Archive.org could host a CC-licensed archive of sound & transcripts. Recognition of the human voice is almost like writing, hopefully everyone could have access. Ed…
For example, the Switchboard corpus (300h, 8khz, transcribed audio) is about 16GB. That is a common size for LVCSR, and you need something around that area to get good performance (maybe minimum 100h). In academic papers by Google, they usually use their own private training data set, with e.g. 1900h. (E.g.: http://arxiv.org/pdf/1402.1128.pdf ) Some crowdsourced effort to collect transcribed audio under a CC-licence…
Maybe also: https://librivox.org - has audiobooks read by volunteers, plus the book text.
FWIW, I just switched from Windows Phone to iOS -- from the 1020 to iPhone6+. Personally, I preferred Cortana over Siri. And it basically comes down to speed and responsiveness. Cortana is much quicker to launch, and much quicker to find results. I haven't noticed any discernable difference in their query results. And actually, for one of the my most used queries - "What is the weather today?" -- I dont even use it o…
> Personally, I preferred Cortana over Siri. And it basically comes down to speed and responsiveness. Cortana is much quicker to launch, and much quicker to find results. I haven't used Cortana, and have only had disappointing experiences with Siri (after coming from Google Voice Search); how does the performance on Google Voice Search Compare? I wonder if the performance rankings are just a complete reversal of the…
And usually I'm trying to decide between a one sentence typing task or asking Siri to do it. The five second wait really throws off my time "profit margin".
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I've actually taken to dictating most of my texts on Android. It's faster for me than typing or swype-style keyboards and it's really very accurate until I need to use a strange proper name it doesn't understand. I'd say it easily gets about 90% of what I say, and it figures out the correct context for words like "there" "their" "they're" and "to" and "too" so far completely correct. edit thought I'd add this. I just…
How do you write text messages in private and not have your messages overheard? I think I would be quite self concious that I'm talking into my phone and not directing it at anyone (I rarely use Siri for this reason)
A while back I created a toy IRC bot that answers questions like this just by searching reddit and taking the top comment. It works surprisingly well for questions that are likely to have been asked before. And when it does work, the result is much better than a dry wikipedia excerpt. I then added some simple machine learning to filter search results for the most relevant threads which improved it quite a bit.