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great - somewhere in your game plan, please recognize that not everyone is going to use opaque, trained-elsewhere binary blobs as base models; nor does everyon
by mistrial9 5y ago
great - somewhere in your game plan, please recognize that not everyone is going to use opaque, trained-elsewhere binary blobs as base models; nor does everyone require CNN/DeepLearning to do useful, real research or invention. Things like python skLearn, an internally built supervised model or a few hundred of them; analysis runs that are done without logging into a cloud with AUTH linked to some records behind a closed glass door somewhere..
If you are FAIR, then you let people try things without being attached to you. agree?
- adamsmith143 5y ago>great - somewhere in your game plan, please recognize that not everyone is going to use opaque, trained-elsewhere binary blobs as base models; nor does everyone require CNN/DeepLearning to do useful, real research or invention. Shocking that a company focused on Deep Learning, NLP and CV might focus their efforts on people who do Deep Learning, NLP and CV??
- k8si 5y agoYou can turn off the part where it logs into the cloud and insert your own logging backend fairly easily. Also if your research/biz needs are satisfied by sklearn, then why not just use sklearn? But for a lot of NLP systems, BERT is actually really really useful. And if you don't want to use a pretrained BERT, you can easily initialize their BERT implementation randomly and train it on your own data.
- visarga 5y agoCan't use sklearn so much for NLP. Sure, it's great for small ML tasks, but you need big honking transformer models to compete in the field. On the other hand prompting GPT-3 or fine-tuning text transformers is quite accessible now. About as easy as using sklearn.