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Running AI is so expensive that Amazon will probably charge you to use Alexa in future, says outgoing exec (www.businessinsider.com)
submitted 2 years ago by [M, B] to c/technology@lemmy.world
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Running AI is so expensive that Amazon will probably charge you to use Alexa in future, says outgoing exec::In an interview with Bloomberg, Dave Limp said that he "absolutely" believes that Amazon will soon start charging a subscription fee for Alexa

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[–] 10 points 2 years ago (2 children)

Can you share details? Been thinking of doing this with a new PC build. Curious what your performance and specs are.

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  • [–] 1 point 2 years ago (1 child)

    You shouldn’t need anything really, all the components run via cloud services so you just need a network connection.

    That’s why it’ll run just fine on a cheap pi model

    Essentially the script in Python just sends api requests directly to OpenAI and returns the AI response. Next I just pass that response to the elevenlabs api and play that audio binary stream via any library that supports audio playback.

    (That last bit is what I’ll have to toy around with on a pi but, I’m not worried about finding a suitable option, there’s lots of libraries out there)

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  • [–] 1 point 2 years ago* (1 child)

    Oh wait, I think I misunderstood. I thought you had local language models running on your computer. I have seen that be discussed before with varying results.

    Last time I tried running my own model was in the early days of the Llama release and ran it on an RTX 3060. The speed of delivery was much slower than OpenAI's API and the material was way off.

    It doesn't have to be perfect, but I'd like to do my own API calls from a remote device phoning home instead of OpenAI's servers. Using my own documents as a reference would be a plus to, just to keep my info private and still accessible by the LLM.

    Didn't know about Elevenlabs. Checking them out soon.

    Edit because writing is hard.

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  • [–] 1 point 2 years ago

    That could be fun! I’ve made and trained my own models prior but I find that getting the right amount of data (in terms of both size and diversity to ensure features are orthogonal out of the gate) can be pretty tough.

    If you don’t get that right balance of size and diversity in your data, that efficacy upper limit is gonna be way lower than you’d like, but you might have some good data sets laying around I got no clue ^_^

    Lemmy know how it goes!

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