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AI and Machine Learning, What Does It Really Mean? May 11, 2017

Posted by Anthony Mason in Machine Learning.

Before I enrolled in the Machine Learning seminar at Marquette I often saw the buzzwords Machine Learning (ML) and Artificial Intelligence (AI) being thrown around on the web. In some cases, these terms were used irresponsibly and utilized to simply generate hype amongst the masses. In other instances, many established experts in the field have gone to great lengths to discuss the realistic expectations and current progress that is being made in the sector. Now that I have completed this seminar I can confidently say that I am walking away with a solid understanding of what is realistically achievable in today’s machine learning and AI space. In addition, I think I have a better ability to gauge hype vs. reality when it comes to future machine learning and artificial intelligence advancements. With that being said, I have enjoyed learning about the topic and would like to speak to one article I found to be quite fascinating.

In one of our weekly discussions, our class explored the notion of an Interlingua language used by a Google AI Translation system. Google is currently developing an AI language translation tool that is demonstrating the ability to interpret and understand relationships between words in different languages. Devin Coldewey highlights the power of Google’s system in his article, Google’s AI Translation Tool Seems to Have Invented its Own Secret Internal Language, by explaining that the system can translate between two languages it has not been trained on by leveraging trained data sets between known languages.

For example, Google’s system can be trained to translate English words to Korean words and vice versa. Then the system can be trained to translate English words to Japanese words and vice-versa. Yet, what is fascinating is the system is capable of translating Japanese words to Korean words or vice-versa without ever being trained to translate between the two languages. The article credits the ability to infer the relationship between Japanese and Korean words to the system’s ability to define it’s own internal language, called Interlingua.

What I found most interesting was the rationale behind this line of thinking. Simply speaking, Google’s system deep neural networks are able to contextualize the meanings of words, to a certain degree. Using this knowledge the system is able to make logical fundamentals decisions when translating un-paired languages by referencing understood relationships between paired languages. This capability is absolutely jaw dropping to me and makes me really excited for what the future beholds.

If there is one thing I took away from this course is that Machine Learning and Artificial Intelligence applications are not only on the horizon but they are here and here to stay. I am excited to continue to read up more on the future advancements in this space.



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