11–20 of 201 posts
The DGX-1 is available for a cool $129k: http://www.nvidia.com/object/deep-learning-system.html Correct me if I'm wrong, but I think it's basically a couple hundred NVIDIA 10-series cards strapped together with a full custom NVIDIA software stack.
They should limit it to top comments only, and for training, you might as well assume 90% of top comments are sarcastic/tongue in cheek. Or let a user dial the sarcasm/wittiness/seriousness as they want it, kind of like TARS from 'Interstellar'.
The Reddit comment corpus is an awesome dataset. There's relatively little mark-up to scrub out, low duplication, good metadata, and a variety of topics. We used it to train a syntax-enriched word2vec model. Write up and demo: https://explosion.ai/blog/sense2vec-with-spacy Btw, the above was run on CPU in a couple of days, because spaCy doesn't use GPUs yet. I've applied for a grant from NVidia so I can fix that. If…
"it was gone" meaning the association between Carrot Top and Kate Mara? So after better training, who is now most_similar(['Carrot_Top|PERSON'])?
EDIT: RTFA, used the interactive demo. most_similar() is now a category I would describe as "actors/comedians popular in the 90's: "Bill Murray, Gary Busey, David Spade, Charlie Sheen, Ashton Kutcher, Chris Farley".
As a frequent Redditor, this AI is going to be very witty. They should limit it to top comments only, and for training, you might as well assume 90% of top comments are sarcastic/tongue in cheek. Or let a user dial the sarcasm/wittiness/seriousness as they want it, kind of like TARS from 'Interstellar'.
As a frequent Redditor, this AI is going to be very witty. They should limit it to top comments only, and for training, you might as well assume 90% of top comments are sarcastic/tongue in cheek. Or let a user dial the sarcasm/wittiness/seriousness as they want it, kind of like TARS from 'Interstellar'.
That was first thought. Curious How does NN or any other model capture sentiment ?
Didn't msft do the same thing with twitter and end up with racist bot? I am not sure how this will turn out.
The Reddit comment corpus is an awesome dataset. There's relatively little mark-up to scrub out, low duplication, good metadata, and a variety of topics. We used it to train a syntax-enriched word2vec model. Write up and demo: https://explosion.ai/blog/sense2vec-with-spacy Btw, the above was run on CPU in a couple of days, because spaCy doesn't use GPUs yet. I've applied for a grant from NVidia so I can fix that. If…
> But when we ran the model on more data, and it was gone and soon forgotten. Just like Carrot Top. "it was gone" meaning the association between Carrot Top and Kate Mara? So after better training, who is now most_similar(['Carrot_Top|PERSON'])? EDIT: RTFA, used the interactive demo. most_similar() is now a category I would describe as "actors/comedians popular in the 90's: "Bill Murray, Gary Busey, David Spade, Char…
Weirdly there's a bug that's dropped PERSON from the sense list. Fixing.
Edit: Fixed.
Edit2: Ah this is super misleading atm. I'll have a think about how to do this better. auto is case insensitive, but if you specify a sense, it's case sensitive. So you need to do "Carrot Top" and set PERSON. Btw, contrast with carrot top "NOUN".
Didn't msft do the same thing with twitter and end up with racist bot? I am not sure how this will turn out.
My second reaction was, "at least they're not using 4chan."
[0]: http://www.theverge.com/2016/3/24/11297050/tay-microsoft-cha...