The Future of Machine Learning in Translation
Before moving into the core of this article, we need to shed a light on what machine learning and artificial intelligence are.
Artificial Intelligence (AI), in simple terms, means giving machines the processing capabilities of a human brain. These are the processes in AI:
Information Acquisition: Just like humans, an artificially intelligent device can ‘learn’ new information.
Reasoning: A machine should be able to conclude if the information it gained is right.
Self-Correction: If a machine somehow becomes wrong, it should know how to correct its task and do the right thing.
Machine learning is a part of artificial intelligence. It lets a computer perform a certain task without explicit instruction. Instead, the machine operates on its experience and the interferences. For example, if you create a waiter robot that automatically goes to the table of guests when they arrive without any input, we can say that the robot has the capability of machine learning.
So, Where Does Translation Fit in the Picture?
According to Alan Turing, a pioneer in artificial intelligence studies, a computer should have four basic capabilities to become intelligent:
- Natural Language (Human Language) Processing
- Machine Learning
- Knowledge Representation
- Automated Reasoning
Natural Language Processing and Machine Learning come side by side. Machine learning means the ability to adapt to new circumstances. To adjust, a machine should first get at least some sort of information from the user about how they need to act. And a user, who is a person, will provide instruction in human language.
Look at the picture- in the future, a device; say a robot, will act, not how the company who made it instructs it to, but it will follow the orders of its owner.
There are roughly 6,500 spoken languages in the world. Any invention in the computing world, be it phones, software, TVs, etc. are to be made accessible to everyone in the world. This also goes the same for artificial intelligence.
Thus, for a perfect artificially intelligent system to become usable by everyone in the world, it should be able to process every existing language. This is how translation becomes of the utmost importance in the field.
The Problem with Machine Translation Right Now
You know Google translate, right? As of now, the feature can convert more than 100 languages in the world. It is pretty efficient if you ask an average user.
Machine translation means giving machines the ability to translate between different languages.
The only (and the biggest) challenge in machine translation is that it is not perfect. There are various phrases, local slangs, different grammatical rules in different languages, etc. After all, human language is a very complicated subject.
You see, it is just overwhelming for a machine of today’s computing power to perfect millions, if not, billions of human language information. Machine translation, for now, say Google translate can be pretty efficient when a human uses it. Even if the grammar is wrong, people’s mind can somehow process the information. This is because of their ability for reasoning.
But in the case of machines, in some cases, a simple fault or misinterpretation while translating a language can give a totally different instruction. This will make the machine act in a different way than the user wanted it to.
Wrapping up, we can say that translation, being an integral part of human language processing, is a huge aspect of machine learning and AI. However, for now, machine translation is nowhere as efficient and accurate as human translation. This might change in the future, but it will still take years of innovation and discoveries.
Hence, there already are millions of rules and ways of using existing languages — moreover, even the way humans use their language changes according to the time. Thus, in today’s time, there is almost no way a software machine can translate information like a human can do.