TechGist05

This is a guest post by Luigi Muzii that focuses mostly on the various questions that surround Language Data, which by most “big data” definitions and volumes is really not what most in the community would consider big data. As the world hurtles into the brave new world that is being created by a growling volume of machine learning and AI applications, the question of getting the data right is often brushed aside. Most think the data is a solved problem or presume that data is easily available. However, those of us who have been working at MT seriously over the last decade understand this is far from a solved problem. Machines learn from data and smart engineers can find ways to leverage the patterns in data in innumerable ways. Properly used it can make knowledge work easier, or more efficient e.g. machine translation, recommendation, and personalization. The value and quality of this pattern learning can only be as good as the data used, and however exciting all this technology seem...

Another guest post by Luigi who covers a variety of subjects here: AI, Big Data, NMT Hype, and more. Luigi attempts on a regular basis to clarify the conflation that seems rampant in the translation industry and makes my life easier by producing what I perceive as interesting content to keep this blog relevant and current.  AI is a much-misunderstood term and thus I think it is worth a closer look to further reduce the conflation that surrounds it.  The graphic below from a presentation I made on "Linguistic AI" on behalf of SDL, describes what I think a real AI should do. However, the reality is still quite far from the broad promise made by the use of the word intelligence, and most of what we see today is narrowly focused ML deployments that indeed do seem to perform some kind of cognitive function around carefully selected data.  There is also a lot of confusion about what machine learning (ML) is and how it relates to AI. Thus I think this graphic below is also usefu...