This just in: Machine Learning is the Next Big Thing. It’s going to transform the way we work, the way we live, the way we do business. Basically– high caliber analysis here– machine learning will solve all our problems.
I’m kidding, of course. This isn’t news; machine learning’s life-changing (awe-inspiring, jaw-dropping, mind-blowing) potential has been the subject of anticipation and acclaim for some time. Google, for instance, announced its impact on most of the company’s core products and its hopes for future implementations of the technology as early as 2016.
Google “machine learning”, and the first page of news results alone lists a staggering array of uses. Machine learning is being used to locate the source of a Salmonella outbreak, develop new drugs, improve medical imaging, combat potholes, and find patterns in quantum fireworks.
One of the most vaunted applications of machine learning is machine translation. Historically, automated translation attempted to use encyclopedic collections of syntactic rules and vocabulary, with limited success. With machine learning, however, translation engines have improved drastically, with many predicting that effective automated translation could be achieved in mere years. Chief among these translation engines is Google Translate.
Yet as many a foreign language student has discovered, even Google Translate’s translations can be… iffy. Pass it anything more complicated than a few words, and an error is more likely than not. Google Translate’s strength is its ability to translate any word into (almost) any language, using its extensive database to map appropriate word pairings. But its grasp of context and connotation hasn’t, probably, replaced the need for dictionaries and thesauri in translation, as this article in the Atlantic last year illustrates.
This inherent inaccuracy has given rise to a whole new subgenre of comedy: making fun of Google Translate’s errors. The YouTube channel Translator Fails has over 800,000 subscribers, and The Tonight Show Starring Jimmy Fallon’s Google Translate Songs segments routinely rack up viewer counts in the millions.
Watch the video if you’re not convinced: whether in syntax or in substance, Google Translate’s translations ought not to be trusted.
Still, the tool’s accuracy is impressive considering the difficulty of the task at hand. What Google Translate has already accomplished would have been thought impossible at one point, and for some languages, its accuracy scores at over 90%. Is time the missing ingredient; will Google Translate in another ten or 20 years be what many have heralded already? Or is the engine failing to live up to expectations because those expectations are unfeasible; should we give up the ghost on machine translation?
For now, the answer might be c), none of the above. Machine translation tools can be incredibly valuable without replacing the role of a human translator. A start-up called Lilt is seeking to use machine translation tools, with machine learning at their core, to improve translator’s efficiency and effectiveness. The system provides suggestions for the next word or sentence; the human editor filters for quality, subtext, context, and style. This model combines the best of both worlds while assuring the accuracy that’s essential for translations in business, government, or publishing.
Maybe Machine Learning will solve the ages-old problem of automated translation– along with potholes– at some point, but for now, we need both man and machine.