Is there an advantage to using less irregular human languages - say German - in machine learning?
Analyzing any real language (not Esperanto) is going to be extremely difficult. I would think English is marginally easier than say Korean where everything is based around context, including dropping the subject itself and particles. Otoh, some aspects of Korean are probably easier than English. And all 3 have irregularities despite Sejong the Great purposefully creating Korean Hangul.
If your message being get across is predicated on successfully transmitting an arbitrarily large sequence of symbols where every one of them might be crucial (supposing that word frequency is inversely proportional to meaningfulness, which is rather reasonable), this "talking" thing would get hard pretty fast.
That's what interested me enough to want to share it here.
I'll use more discretion next time i submit something.
Here's what you need to do before publishing an article on AI:
1. Hire an expert that actually knows this stuff 2. Have him read the article 3. Throw the article out when he tells you to throw it out 4. Feel shame
Stop writing these imbecilic, clickbaity shit articles about AI. This leaves people that don't know anything about AI or neural networks thinking that we're somehow developing skynet, instead of just writing a computer program which essentially implements a mathematical model that continually adjusts itself based on some metric.
Then why are we putzing about with strings in the first place? Let them use binary formats if that's the goal.
I still assert that a relative black box will lie at the core of any "strong" AI.
