131–140 of 296 posts
"Hey Siri, remind me in 2 hours to do X"
"Hey Siri, remind me when I get home to do Y"
"Hey Siri, remind me next time I go to Costco to buy Z"
It's pretty quick to set up reminders by voice, especially location-based reminders. Almost anything else I'd rather do via a UI.
Earlier quoted context omitted.
I use my voice assistant on my phone to set timers. That's it.
Weather is also useful! Sometimes reminders, which are basically timers. But yea that's it.
"What's the weather in Yosemite" and "Yosemite weather" can get you two drastically different results. As of right now (10/26/2020 10:35 AM) the two results I get are 43F and 32F.
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> I can set my own alarms, thank you. And not only that, but a specialized user interface is often preferable to voice even when the assistant passes the Turing test. There's a reason that people use apps to get food delivery rather than calling.
It's funny, but for years I've felt like "Hey Siri, set an alarm for 7am" was 1000x easier than using the clunky Clock UI and it's almost exclusively what I use Siri for. Tasks that are so perfectly well-defined are exactly what this "smart" tech is useful for. Except that recently, Siri screwed me. I said "hey Siri, set an alarm for 6:30am" and her response was "Ok, your 6:30am alarm is on," but what she actually me…
Earlier quoted context omitted.
> I can set my own alarms, thank you. And not only that, but a specialized user interface is often preferable to voice even when the assistant passes the Turing test. There's a reason that people use apps to get food delivery rather than calling.
It's funny, but for years I've felt like "Hey Siri, set an alarm for 7am" was 1000x easier than using the clunky Clock UI and it's almost exclusively what I use Siri for. Tasks that are so perfectly well-defined are exactly what this "smart" tech is useful for. Except that recently, Siri screwed me. I said "hey Siri, set an alarm for 6:30am" and her response was "Ok, your 6:30am alarm is on," but what she actually me…
It doesn't even support 24 hour time - at least in English.
Earlier quoted context omitted.
It's funny, but for years I've felt like "Hey Siri, set an alarm for 7am" was 1000x easier than using the clunky Clock UI and it's almost exclusively what I use Siri for. Tasks that are so perfectly well-defined are exactly what this "smart" tech is useful for. Except that recently, Siri screwed me. I said "hey Siri, set an alarm for 6:30am" and her response was "Ok, your 6:30am alarm is on," but what she actually me…
Alarms and reminders are my most frequent uses of Siri (and to entertain/frustrate the kids...). It's also pretty good for hands-free quick texts while driving and hands-free calling, though I don't do those often. Your alarm example is frustrating. Of course, you could say "set alarm for tomorrow at 6:30 am" which would work, but then you're back in the realm not of natural language commands, but formal commands tha…
Edit: also, your "tomorrow at 6:30am" example is also open to interpretation if you're saying it two minutes after midnight. I'd really like it to recognize these sort of ambiguities and prompt me for clarification. "It's after midnight. By 'tomorrow at 6:30am', do you mean you want your alarm to go off about 6 hours from now or the next day?"
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Which is exactly why I prefer formal query languages over NLP queries. In both cases (at least with most state of the art NLP techniques), you have to learn certain patterns and ways to phrase a query so that the system will reliably understand it. With formal query languages, these patterns are well-defined, can be looked up and will most likely not change significantly (so there is value in memorizing them). With N…
Well, to extend the GUI/console metaphor, it means that at some point soon, we'll all be using NLP because it's dramatically more user-friendly for the vast majority of people.
In those aspects of UX, voice interfaces have the same drawbacks as console apps when compared to a good GUI.
Also, they have to work within the "bandwidth bottleneck" of audio - just imagine a phone system that tells you all the options you have, "Press 1 for something, Press 2 for another thing..." - they are so annoying because they are slow and inherently linear; a GUI can show the same options all at once, and you can read it much faster than listen to them.
So NLP as such is not dramatically more user-friendly unless it is at the "do what I mean" level which likely requries full human-level general artificial intelligence; before that it's just a voice equivalent of a console app, sharing all the problems of discoverability and needing to remember what the system can do and how should it be invoked.
A big problem with the assistants is that as soon as they fail at a query they seem stupid, I feel stupid, and I stop using them for a long time. Last weekend I had the following failed queries: "OK Google, what is the air quality like at Mt. Shasta today?" "OK Google, add a waypoint for the last gas station before the mountain pass" "OK Google, what percentage of people can you detect to be wearing masks on recent I…
Ok, so you need the assistant to:
* Already have a trained dataset of people wearing masks
* Fetch ALL instagram pictures it can find
* Not only detect if there's a mask in the picture, but count them
* Fetch the location of Mount Shasta
* Calculate a 10 mile radius around it
* Apply that calculation to the people counted in the first step
* Calculate a percentage of people wearing masks which needs:
* Count people not wearing masks.
Those steps are sub-optimal. So you need not only understand that, but to run some sort of 'query planner' in order to get the results you are looking for.
You are thinking of Jarvis from Iron Man. Forget assistants. Allow someone to construct a query like this in a couple of minutes and you have a great product you can sell. Existing ones require quite a bit of domain knowledge and setup. Not even things like Wolfram Alpha would be able to parse this query.
This reminds me of: https://xkcd.com/1425/
There are good use cases apart from Dictation for Speech tech: 1. Voice based operations in factories, construction workers who want to have both the hands free but want to navigate via a device 2. Use cases while Driving. E.g. A Driver who is delivering goods. 3. Call centre - Analytics of audio calls etc. Can have many use cases 4. Voice assistant like Alexa, Siri. Mind that Alexa, Siri have vision to do more than…
They can't understand speech properly in a quiet environment, and you want them to get your commands on a factory floor or construction site? :)
A big problem with the assistants is that as soon as they fail at a query they seem stupid, I feel stupid, and I stop using them for a long time. Last weekend I had the following failed queries: "OK Google, what is the air quality like at Mt. Shasta today?" "OK Google, add a waypoint for the last gas station before the mountain pass" "OK Google, what percentage of people can you detect to be wearing masks on recent I…
Which is exactly why I prefer formal query languages over NLP queries. In both cases (at least with most state of the art NLP techniques), you have to learn certain patterns and ways to phrase a query so that the system will reliably understand it. With formal query languages, these patterns are well-defined, can be looked up and will most likely not change significantly (so there is value in memorizing them). With N…
People like to reference Star Trek for stuff like NLP queries, but if you go back to TNG and pay close attention to the verbal queries to the computer, much of the time it isn't natural language. They seem to actually use some sort of formal query language that fits English a bit closer, but is still distinct from when the characters speak to each other.