Not just Java, other languages (like python) were also released around/near 95, and also seemed to get to "mature" respect status. much quicker.
Java was first released in mid-1995, just 6 months before JS.
By 98, when I went to college, Java was already used for all the first level courses in the CS program.
How did it catch on so quickly (just 3 years) to shift university curriculum, which is usually so slow/behind?
Not just Java, other languages (like python) were also released around/near 95, and also seemed to get to "mature" respect status. much quicker.
it sure seems like the industry (and academics) just decided Java and C++ were the stable mature ones and langs like JS were toys.
Why wouldn't a university consider teaching JS alongside Java and C++ (and python), given they were all roughly the same age?
They're the "Java" and "C++" of today. They've been chosen.
More from Tech
The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
"I really want to break into Product Management"
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
"I really want to break into comics"
— Ed Brisson (@edbrisson) December 4, 2018
make comics.
"If only someone would tell me how I can get an editor to notice me."
Make Comics.
"I guess it's impossible and I'll never break into the industry."
MAKE COMICS.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
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.
You May Also Like
1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
