Training data is perhaps one of the most integral pieces of machine learning and artificial intelligence. Without it, machine learning and artificial intelligence would be impossible. Models would not be able to learn, make predictions, or extract useful information without learning from training data. It’s safe to say that training data is the backbone of machine learning and artificial intelligence.
Similar to how humans learn from past experiences, artificial intelligence uses training data to learn and develop intelligence to make decisions. Because of that, a model is only as good as the quality of the training data. This means that training data of higher quality will yield a better-trained model and more accurate results, reducing the chances of a model with a high bias or high variance.
Through training data, artificial intelligence and machine learning have changed the world we live in and continue to do so. Tasks that would take humans hours or even years can now be solved within seconds. These capabilities have led to incredible advancements in healthcare, finance, retail, business, and many more other industries. Humans today rely on artificial intelligence without even knowing it. For example, email spam detection or predictive typing platforms are ways in which artificial intelligence and machine learning have shaped our day-to-day lives. This would not be possible without the presence of training data to enable these technologies.
Husna is a data scientist and has studied Mathematical Sciences at University of California, Santa Barbara. She also holds her master’s degree in Engineering, Data Science from University of California Riverside. She has experience in machine learning, data analytics, statistics, and big data. She enjoys technical writing when she is not working and is currently responsible for the data science-related content at TAUS.
The AI scene of the 2010s was shaped by breakthroughs in vision-enabled technologies, from advanced image searches to computer vision systems for medical image analysis or for detecting defective parts in manufacturing and assembly. The 2020s, however, are foreseen to be all about natural language technologies and language-based AI tasks. NLP, NLG, NLQ, NLU… The list of abbreviations starting with NL (Natural Language) seems to grow each day. Regardless of the technology domain, it’s observed that natural language technologies will be in a field-shaping position in a variety of areas from business intelligence and healthcare to fintech.
This is the third article in my series on Translation Economics of the 2020s. In the first article published in Multilingual, I sketched the evolution of the translation industry driven by technological breakthroughs from an economic perspective. In the second article, Reconfiguring the Translation Ecosystem, I laid out the emerging new business models and ended with the observation that new smarter models still need to be invented. This is where I will now pick up the thread and introduce you to the next logical translation solution. I call it: Data-Enhanced Machine Translation.