Week 5 - BALT 4364 - CH 5

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I have always wondered how our phones can guess the next word or phrase we are going to type. Natural Language Processing (NLP) seems to be the answer to that, as it is the reason that computers can actually understand and work with human language. I am still impressed with the process, because it is either accurate or very close to predicting the rest of our sentences. I believe iPhones even adapt to the person who is using the phone and begin to recognize the owner of the phone's way of talking/lingo. Learning about text classification, sentiment analysis, and language modeling helped me see how AI can make sense of huge amounts of text data. 

Using NLP to sort emails or analyze social media posts is a good example of how these models work and are used in real life. I also found it helpful to learn about important steps like tokenization, padding, and embedding, which prepare language data for machine learning. It makes sense that models like ChatGPT are built using these same techniques to predict and generate human-like responses. A few other examples would include sentiment analysis helping a business understand what customers think of their products, while text classification could automatically organize support tickets or financial documents. Even simple conveniences like autocorrect, translation apps, and voice assistants all rely on the same NLP concepts from this chapter. Understanding these tools better makes me appreciate how much they improve efficiency, whether the purpose is to automate tasks at work or just to make technology easier to interact with in everyday life.



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