51–60 of 66 posts
Why would FB work on such a tech?
Are you looking at Facebook with glasses from 2010? Because one would expect a almost-trillion-dollar company to work on more than a single-focus product. Why would a search engine (1997) make glasses (2013)?
It literally was just because Sergey Brin thought it was cool.
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Nope, like autonmous driving the problem will be the last 20%
Just because one overhyped tech is not working out does not mean all new tech will fail. We already have noise cancellation, good tiny batteries, fast wireless internet, and all the tech to learn spoken languages without supervision! Autonomous driving has a much higher bar for accuracy.
https://pangeanic.com/knowledge/the-worst-translation-mistak...
Language has great ambiguity, depends heavily on time period, context and tone.
https://www.reddit.com/r/funny/comments/2chfge/you_need_some... That could lead to problems even without translation.
I’m thinking language like this, but also labelled imagery for, for example, face detection, which works better on white people.
Has anyone attempted to create a way for people to create and donate labelled data to a dataset?
Earlier quoted context omitted.
Just because one overhyped tech is not working out does not mean all new tech will fail. We already have noise cancellation, good tiny batteries, fast wireless internet, and all the tech to learn spoken languages without supervision! Autonomous driving has a much higher bar for accuracy.
Are you sure? A translation error played part in the dropping of the atomic bomb on Hiroshima. https://pangeanic.com/knowledge/the-worst-translation-mistak... Language has great ambiguity, depends heavily on time period, context and tone. https://www.reddit.com/r/funny/comments/2chfge/you_need_some... That could lead to problems even without translation.
(Fwiw, if you read the comments at your second link, you'll find the image is a fake.)
Question: are there any efforts to communally create / crowd source training material for neural networks? I’m thinking language like this, but also labelled imagery for, for example, face detection, which works better on white people. Has anyone attempted to create a way for people to create and donate labelled data to a dataset?
The idea is that the smart contract is a learning algorithm, and people can donate data to a public repository stored on a blockchain. The learned model is publicly available for everyone. People can also receive incentives for donating data in some implementations.
I see they are using a GAN, and doing unsupervised training. But then they appear to compare their model to supervised-trained models.
How do they do this? Do they tack a supervised-trained model onto the end of their unsupervised model? I imagine they must do supervised training at some point, else how can they convert sounds to text?
Also : "The discriminator itself is also a neural network. We train it by feeding it the output of the generator as well as showing it real text from various sources that were phonemized."
Is the "real text from various sources that were phonemized" a manually labelized database? If Yes, that step is supervised, which makes the whole thing actually supervised to some extent
That's great, but it still performs 2 times worst than the best supervised model. Also : "The discriminator itself is also a neural network. We train it by feeding it the output of the generator as well as showing it real text from various sources that were phonemized." Is the "real text from various sources that were phonemized" a manually labelized database? If Yes, that step is supervised, which makes the whole th…
Maybe the computational benefits of such a simple algorithm outweighed the potentially bad clusters. Thoughts? I'll try to read the paper in more depth later in case they explain the choice and I missed it.
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Our main usages of speech rec are: - Understanding for videos for content moderation, ranking and recommendations - Captioning - Assistant tech in Portal, Oculus and upcoming glasses
For me the most scary part is not doing end-to-end encryption in Messenger, which means that the US government has a right to listen to my conversations on it.