Comment by mastermojo

5 years ago

I'm on the team at Sapling Intelligence, a deep-learning AI Writing Assistant. A lot of privacy and security conscious folks don't like the idea of a keylogger, so we have self-hosted/on-premise/cloud-premise options for businesses. We have a list of available offerings here: https://sapling.ai/comparison/onprem. Sapling deployments can also be configured for no data retention, sacrificing some model customization.

Cost-wise, it doesn't make sense for individuals to host a neural-network based grammar checker, though some of the rule-based options may work. There's a future where if we can maintain some sort of Moore's law scaling we will be able to run these language models on individual computers as opposed to the cloud.

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mastermojo

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TheRealPomax  5 years ago

> Cost-wise, it doesn't make sense for individuals to host a neural-network based grammar checker

Why?

We already do the same in the photography world in the form of apps like Topaz Labs' denoise/sharpen/gigapixel, as well as video enhance. Why would I care how many gigs of disk space and even a GPU might be required for an NN grammar checker if it literally makes back the money by improving the writing that influences my career? Hell, I can expense what is needed to run this if the payoff to my company is "the quality of work is better, and more secure".

  • hombre_fatal  5 years ago

    Well, I think you'd first have to know the resource requirements, and it's reasonable that so few people would be willing/capable of running it that it doesn't make much business sense to focus on that as an option.

    I'm certainly curious to know.

    • omnicognate  5 years ago

      I'm not an expert and would appreciate being corrected if I'm wrong, but I'm under the impression using a neural network after it has been trained typically requires relatively little computation and data. It's training it that takes the big compute and requires lots of data.

      I think many services like Grammarly would be perfectly possible to implement without sending your data off device. There are just massive incentives not to.

    • TheRealPomax  5 years ago

      Not really, that's an assumption based on a misunderstanding about how NN actually work.

      While the training (e.g. the part necessary in order to even have a product) is incredibly resource intensive, and will yield a model that's typically hundreds of megabytes, actually applying the resultant model to new data takes milliseconds at most for plain text analysis.