Alexis Alulema - The first time someone hears about Natural Language Processing (NLP), they often don’t consider it to be overly complicated. After some additional consideration, though, they’ll realize how much the AI community has worked on the topic. The technology is even appearing in headlines like this one, as featured on sciencealert.com: Google’s AI has Learned to Become ‘Highly Aggressive’ in Stressful Situations. As the above article suggests, this technology could become dangerous in the wrong hands or if managed incorrectly. However, I believe that if we get a better understanding of the nuts-and-bolts of NLP, we will conclude that there is nothing ‘magic’ under the hood. Rather, there is a growing, rigorous body of scientific work on creating better algorithms to process data and generate impressive solutions. Neural Networks have started to become ubiquitous ever since Deep Learning experienced considerable growth with big data, cloud computing, and the appearance of Deep Learning frameworks like TensorFlow or PyTorch. These advancements are visible in areas like Computer Vision (CV), where predictions are incredibly precise and reach 95-98% accuracy. This precision is at the core of impressive advancements in face detection, artificial face generation, self-driving cars, and many other implementations.
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