Machines, Lost In Translation: The Dream Of Universal Understanding : All Tech Considered : NPR
It was early 1954 when computer scientists, for the first time, publicly revealed a machine that could translate between human languages. It became known as the Georgetown-IBM experiment: an “electronic brain” that translated sentences from Russian into English.
The scientists believed a universal translator, once developed, would not only give Americans a security edge over the Soviets but also promote world peace by eliminating language barriers.
They also believed this kind of progress was just around the corner: Leon Dostert, the Georgetown language scholar who initiated the collaboration with IBM founder Thomas Watson, suggested that people might be able to use electronic translators to bridge several languages within five years, or even less.
The process proved far slower. (So slow, in fact, that about a decade later, funders of the research launched an investigation into its lack of progress.) And more than 60 years later, a true real-time universal translator — a la C-3PO from Star Wars or the Babel Fish from The Hitchhiker’s Guide to the Galaxy — is still the stuff of science fiction.
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Stimulating Machines’ Brains
After decades of jumping linguistic and technological hurdles, the technical approach scientists use today is known as the neural network method, in which machines are trained to emulate the way people think — in essence, creating an artificial version of the neural networks of our brains.
Neurons are nerve cells that are activated by all aspects of a person’s environment, including words. The longer someone exists in an environment, the more elaborate that person’s neural network becomes.
With the neural network method, the machine converts every word into its simplest representation — a vector, the equivalent of a neuron in a biological network, that contains information not only about each word but about a whole sentence or text. In the context of machine learning, a science that has been developed over the years, a neural network produces more accurate results the more translations it attempts, with limited assistance from a human.
Though machines can now “learn” similarly to the way humans learn, they still face some limits, says Yoshua Bengio, a computer science professor at the University of Montreal who studies neural networks. One of the limits is the sheer amount of data required — children need far less to learn a language than machines do.