Your commentary on the dangers of silicon valley coming under the spell of wokeness and cancel culture is spot on. I know this to be true because in Bitcoin we have already lived through a similar culture war against crypto anarchists.
For example have a look at this excellent presentation on @twetchapp https://t.co/BR8sXguBRT
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I think about this a lot, both in IT and civil infrastructure. It looks so trivial to “fix” from the outside. In fact, it is incredibly draining to do the entirely crushing work of real policy changes internally. It’s harder than drafting a blank page of how the world should be.
I’m at a sort of career crisis point. In my job before, three people could contain the entire complexity of a nation-wide company’s IT infrastructure in their head.
Once you move above that mark, it becomes exponentially, far and away beyond anything I dreamed, more difficult.
And I look at candidates and know-everything’s who think it’s all so easy. Or, people who think we could burn it down with no losses and start over.
God I wish I lived in that world of triviality. In moments, I find myself regretting leaving that place of self-directed autonomy.
For ten years I knew I could build something and see results that same day. Now I’m adjusting to building something in my mind in one day, and it taking a year to do the due-diligence and edge cases and documentation and familiarization and roll-out.
That’s the hard work. It’s not technical. It’s not becoming a rockstar to peers.
These people look at me and just see another self-important idiot in Security who thinks they understand the system others live. Who thinks “bad” designs were made for no reason.
Who wasn’t there.
The tragedy of revolutionaries is they design a utopia by a river but discover the impure city they razed was on stilts for a reason.
— SwiftOnSecurity (@SwiftOnSecurity) June 19, 2016
I’m at a sort of career crisis point. In my job before, three people could contain the entire complexity of a nation-wide company’s IT infrastructure in their head.
Once you move above that mark, it becomes exponentially, far and away beyond anything I dreamed, more difficult.
And I look at candidates and know-everything’s who think it’s all so easy. Or, people who think we could burn it down with no losses and start over.
God I wish I lived in that world of triviality. In moments, I find myself regretting leaving that place of self-directed autonomy.
For ten years I knew I could build something and see results that same day. Now I’m adjusting to building something in my mind in one day, and it taking a year to do the due-diligence and edge cases and documentation and familiarization and roll-out.
That’s the hard work. It’s not technical. It’s not becoming a rockstar to peers.
These people look at me and just see another self-important idiot in Security who thinks they understand the system others live. Who thinks “bad” designs were made for no reason.
Who wasn’t there.
Machine translation can be a wonderful translation tool, but its uses are widely misunderstood.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
