It would seem that the continued progress of machine translation now hinges on the development of deep learning and neural networks. The advantage of neural machine translation is the vast amount of linguistic data it can use to continually hone the accuracy of its translations with much less human supervision than previous models. And this data is ever increasing in volume.
AI is becoming more able to imitate the way the brain works through deep learning neural network technology. This progress owes much to the deep learning revolution which, as mentioned earlier, was in turn made possible by the increased computing power afforded to us by advances in computing hardware.
The phones that fit in the palm of our hand today are hundreds oman mobile database of times more powerful than ENIAC. Computers are becoming faster and costing less to produce. Humanity’s ambitions for technological development have always only been hindered by the capability of hardware, but now we’re seeing hardware improve at an accelerated pace.
There’s much to be optimistic about today. But on the other side of the coin, history has already shown the result of unbridled optimism. Who’s to say that we won’t experience a third AI winter? What if there’s a limit to what deep learning can do for machine translation?
These are questions we should consider soberly as well, but they shouldn’t lead us all the way over to the other side, into the province of complacency and nihilism. It may not be soon, but the singularity is inevitable, and we need to think of it as such. And a world where machine translation can perfect itself is something we need to think about, if only to answer the question—what does such a world mean for translators?