A semi-accurate yet plausible history of Machine Translation.

1960's/70's
Bad Lip Reading - An introduction.
We all know them. We grew up with Kung Fu movies thinking they were the best movies to ever grace the 13" black and white TV screen. You know the ones... mouth moving a million miles an hour, yet only "Watch Out!" is heard.
Obviously a high 'FAIL' on the Turing Test, this was our first introduction of localized media to the American market.
1980's/90's
But do all your bases belong to this guy?
Obviously, the next evolution in Machine Translation, this guy above gave many the gamer a big laugh when Zero Wing was released in 1989 showing off the translation talents of the Japanese. It was at this point, Alta Vista realized they needed to step in and released the first publically available translation platform Babel Fish
I mean, something had to be done to correct this wild west of translation, and for that, we thank you, Babel Fish
2000's - Enter modern Neural Machine Translation
Google Translate shows us how it could possibly be done.
Enter modern neural machine translation (NMT) systems that differ from their rule- and stat-based predecessors by having the ability to improve and learn with each subsequent translation.
Blah, blah, blah, yada, yada, yada.
Thanks to NMT, translations are getting better. Following Babel Fish, Google released its online translation engine built into its Chrome browser, making translations more accurate and accessible. Using highly trained datasets, Google, for the most part, had a mostly accurate system and appeased the majority of the peasant populous with an easy-to-use translation interface that was right 90% of the time.
But there were still major gaps for localization that needed to be filled.
The next evolution of translation tools: reSpeak - The filler of gaps.
Things just got WAY easier.
It's a common problem. Virtually every person involved in content creation has wondered, “Man, what if I could show this to the world?” Even a multi-national corporation needs its 3rd quarter earnings report presentation to be shown to investors worldwide in their natural language within minutes of the meeting.
This is what inspires us to close the gap and eliminate language barriers.
While reSpeak uses current state-of-the-art Neural Machine Translation, its AI still has some challenges (cultural differences, gender, etc...), we've added an interface to make the last mile fast and accurate, saving humans time and money while getting to solid results.
Finally, someone gets it.

1960's/70's
Bad Lip Reading - An introduction.
We all know them. We grew up with Kung Fu movies thinking they were the best movies to ever grace the 13" black and white TV screen. You know the ones... mouth moving a million miles an hour, yet only "Watch Out!" is heard.
Obviously a high 'FAIL' on the Turing Test, this was our first introduction of localized media to the American market.
But do all your bases belong to this guy?
Obviously, the next evolution in Machine Translation, this guy above gave many the gamer a big laugh when Zero Wing was released in 1989 showing off the translation talents of the Japanese. It was at this point, Alta Vista realized they needed to step in and released the first publically available translation platform Babel Fish
I mean, something had to be done to correct this wild west of translation, and for that, we thank you, Babel Fish
1980's/90's
2000's - Enter modern Neural Machine Translation
Google Translate shows us how it could possibly be done.
Enter modern neural machine translation (NMT) systems that differ from their rule- and stat-based predecessors by having the ability to improve and learn with each subsequent translation.
Blah, blah, blah, yada, yada, yada.
Thanks to NMT, translations are getting better. Following Babel Fish, Google released its online translation engine built into its Chrome browser, making translations more accurate and accessible. Using highly trained datasets, Google, for the most part, had a mostly accurate system and appeased the majority of the peasant populous with an easy-to-use translation interface that was right 90% of the time.
But there were still major gaps for localization that needed to be filled.
Things just got WAY easier.
It's a common problem. Virtually every person involved in content creation has wondered, “Man, what if I could show this to the world?” Even a multi-national corporation needs its 3rd quarter earnings report presentation to be shown to investors worldwide in their natural language within minutes of the meeting.
This is what inspires us to close the gap and eliminate language barriers.
While reSpeak uses current state-of-the-art Neural Machine Translation, its AI still has some challenges (cultural differences, gender, etc...), we've added an interface to make the last mile fast and accurate, saving humans time and money while getting to solid results.
Finally, someone gets it.
The next evolution of translation tools: reSpeak - The filler of gaps.

If you want a more accurate portrayal of the history of machine translation, this will help.