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> you can't explain what's going on inside that black box Do you have a better solution for speech recognition? We all know how well speech recognition works i

> you can't explain what's going on inside that black box Do you have a better solution for speech recognition? We all know how well speech recognition works in reality, and we know language models can accomplish more complex tasks than setting your music and lights. These models are not state of the art, they are cheap versions for scaling up to millions of users. It's sad but we rarely get to see SOTA in a product.

mrguyorama 4y ago
My mom's $50 LG flip phone from 2000 did voice control better than any of the "assistants" from nowadays. Classical, non-ai methods work really really well if you are allowed to constrain the expected vocab, and with how little these voice assistants are actually capable of doing, constraining the vocab and showing users the actual phrases expected will make the user experience much better. Stop pretending to be general purpose AI and making an utter fool of it, and produce a product and functionality that actually works for the users.
BaseballPhysics 4y ago
> Do you have a better solution for speech recognition? Must it be necessary for anyone critiquing a technology to also come armed with a solution?
saiya-jin 4y ago
OP just described ridiculously bad behavior that consistently changes over time, not randomly random results. That's more a case of bad engineering (ie light sensor interfering with microphone data feed) or some stupid management decision then 'AI is complicated but we don't have anything better'
- visarga 4y ago
  I think it was management that chose to go with a cheaper model, not the engineers. They got to think about profits. You do realise that a neural net, given the same input, will always give the same output, if you set your random seeds of course. It doesn't depend on time of day unless you degrade the service when it is overloaded - say, you use a different model in the evening that is 10x faster but 2x worse.
  - JacobiX 4y ago
    Not sure about the cheaper model part, because one cannot transform trivially a big SOTA langage modeling models into a digital assistant. So maybe they are not using a cheap ML model after all ... Also, for a digital assistant the input is not only the spoken commands, it can also be: time of the day, weather, location, etc
  - counttheforks 4y ago
    Good luck always getting the same output from a microphone. Maybe the neural net works perfectly fine on crisp samples, but falls apart when some noise is introduced. Maybe it works fine with most kinds of noise, but not a specific type of noise generated in the evening.
JacobiX 4y ago
The fact that a machine learning model can solve a seemingly hard task A does not automatically translate to solving a lesser hard task B. Conversational AI that understand the intent remains a really hard problem even if speech recognition for English is basically solved.

mrguyorama 4y ago

My mom's $50 LG flip phone from 2000 did voice control better than any of the "assistants" from nowadays. Classical, non-ai methods work really really well if you are allowed to constrain the expected vocab, and with how little these voice assistants are actually capable of doing, constraining the vocab and showing users the actual phrases expected will make the user experience much better. Stop pretending to be general purpose AI and making an utter fool of it, and produce a product and functionality that actually works for the users.

BaseballPhysics 4y ago

> Do you have a better solution for speech recognition? Must it be necessary for anyone critiquing a technology to also come armed with a solution?

saiya-jin 4y ago

OP just described ridiculously bad behavior that consistently changes over time, not randomly random results. That's more a case of bad engineering (ie light sensor interfering with microphone data feed) or some stupid management decision then 'AI is complicated but we don't have anything better'

visarga 4y ago
I think it was management that chose to go with a cheaper model, not the engineers. They got to think about profits. You do realise that a neural net, given the same input, will always give the same output, if you set your random seeds of course. It doesn't depend on time of day unless you degrade the service when it is overloaded - say, you use a different model in the evening that is 10x faster but 2x worse.
- JacobiX 4y ago
  Not sure about the cheaper model part, because one cannot transform trivially a big SOTA langage modeling models into a digital assistant. So maybe they are not using a cheap ML model after all ... Also, for a digital assistant the input is not only the spoken commands, it can also be: time of the day, weather, location, etc
- counttheforks 4y ago
  Good luck always getting the same output from a microphone. Maybe the neural net works perfectly fine on crisp samples, but falls apart when some noise is introduced. Maybe it works fine with most kinds of noise, but not a specific type of noise generated in the evening.

JacobiX 4y ago

The fact that a machine learning model can solve a seemingly hard task A does not automatically translate to solving a lesser hard task B. Conversational AI that understand the intent remains a really hard problem even if speech recognition for English is basically solved.