[galenko]

Sadly, the NLP world is full of hot air. I've seen so many companies get funding for complete "written by a 12-year old" dogshit "industry leading IP", it's not even funny anymore.

The hype has gone down and some are actually doing great work, but 90% of the people who say they do NLP/AI stuff don't even fundamentally understand what NLP/AI is.

[fnl]

Sadly, I'd fully agree to this. Things are possible now that were not 10 years ago. But mostly, only performance increased on things we could do 10 years ago, while hardly any new abilities came along. Machine translation, linguistic parsing, etc. came a long way. But we still can't do satisfactory abstractive summarization or create a conversational agent for more than an extremely narrow domain. Yet, at least the things we can do can be done at levels that are "production ready".

[tanilama]

I still hold hope. However, it seems naively exploit the function approximation capacity we have with deep learning can only go that far to understand our own language.

Maybe we need to look back and start from beginning and ask ourself: How does human learn, exactly? How do we learn with so few examples? How do we jointly learn image/audio/video/language with only one brain?

[amelius]

Perhaps we should consider working only on techniques which improve as more computational power is thrown at it.

[glup]

Computation won't help if we don't have the right representations. Arguably computation can help us discover the right representations but the space of possible representations is very, very large.