Hello everyone, this is Vishal Khadi. Thank you for being here today. In this session, we will explore transformative NLP in how we interact with technology. Let's dive in. today's talk, we'll cover five major sections. First, an introduction to what NLP is and why it matters. Next, we will discuss some key NLP techniques such as sentiment analysis, language generation, and name entity recognition or NER. Following that, we will look at real world applications where NLP has made a significant impact as well as the major challenges it still Finally, we will wrap up with a look at the future of NLP and what lies ahead. Now, let's start with the basics. Natural language processing lies at the intersection of artificial intelligence, linguistics, and cognitive psychology. Its primary aim is to enable computers to understand, interpret, and generate human language in a meaningful way. NLP compasses a variety of tasks from speech recognition, text analysis to sentiment analysis and language translation. It plays a crucial role in bridging the communication gap between humans and machines, allowing for more intuitive and effective interactions across various applications such as virtual assistant and automated customer support. As we go forward, I will explain some of the key techniques that enable NLP to work. To function effectively, explore some core techniques that make NLP work. The first is sentiment analysis, which involves detecting the emotional tone behind a piece of text to classify it as positive, negative, or neutral. It's widely used in customer service, social media monitoring, and market research. Next, we have language generation. This technique enables computers to produce human like text. Advanced models such as GPT 3, GPT 4 are capable of generating responses that are contextually relevant and making them useful for applications like chatbots. content creation, and even creative writing. Lastly, name entity recognition, or NER, identifies and categorizes specific entities such as names, locations, and dates within a text. This technique is essential for tasks like information extraction and enhancing search engine responses. Now let's look more closely at each technique, starting with the sentiment analysis. Sentiment analysis is also known as opinion mining, determines the emotional tone behind a piece of text. The process typically starts with text preprocessing where the data is cleaned and then standardized. Next, features are extracted to identify the key linguistic traits following by classification using machine learning or deep learning models such as BERT. This method helps companies understand customer sentiment in previews, social media posts, and surveys. It's a valuable tool for businesses to gauge the public perception and make the data driven decisions. Let's move on to language generation. Language generation allows machines to produce human like text. This is often achieved using models based on transformer architectures, such as GPD3, GPD4, which learn from vast amount of text data to generate meaningful and coherent responses. Language generation has many practical applications, including chatbots, automated reporting, summarizing the content. It even extends into creative fields, where AI assists in writing stories, scripts, and poetry. While generating core text is a challenge, ongoing advancements are continuously pushing the boundaries of what's possible. Now let's discuss named entity recognition. Named entity recognition or NER focuses on identifying entities within a text such as people, places, organizations, and dates. NER can be implemented using rule based approaches, machine learning models, or deep learning techniques linked by LSTM networks. It's a powerful tool for automating the task, such as extracting relevant information from large data sets or improving the accuracy of search engines. For instance, NER enables better query understanding by identifying keywords and categorizing them appropriately. Now let's look at how NLP impacts the real world applications. NLP has made a significant impact in various industries. Virtual assistants like Siri and Alexa use NLP for voice recognition and generating context based responses. Translation services such as Google Translate utilize NLP for accurate real time translations. In healthcare, NLP helps analyze critical notes, adding in diagnosis and treatment recommendations. And in finance, sentiment analysis is used to understand market trends and inform trading decisions. These are just a few examples that highlight NLP's potential to transform industries and improve efficiency. However, there are still significant challenges to address, which we will see in the coming slides. So NLP faces several hurdles. such as language ambiguity is a major issue. As the meaning of the word can change based on the context, this makes it difficult for machines to accurately interpret certain phrases. There is also the challenge of multi lingual processing, especially for low resource languages that lack large data sets for training. Moreover, ethical issues like bias in language models and privacy concerns must be addressed to ensure fairness and responsible use of NLP. Navigating these challenges is essential for developing reliable and transparent NLP systems. As we look ahead, let's discuss where the future of NLP is heading. The future of NLP holds many exciting prospects. Model efficiency will be a major focus with efforts to make models smaller and faster while maintaining high accuracy. This is often referred to as green AI. NLP models are also expanding into multilevel integration. Combining text and visual and audio data to create a feature more nuanced understanding. For instance, this could improve the content analysis by analyzing the text and images and the audio all together. Common sense reasoning is another area where NLP is evolving, aiming to make models more capable of understanding everyday scenarios like humans. Additionally, there is a growing focus on making NLP models more explainable and transferable. These advancements. We'll continue to push the boundaries of what NLP can achieve, enabling even more seamless and intelligent human computer interactions. Now we come to the conclusion part of it. In conclusion, NLP is revolutionizing the way humans interact with the machines, making these interactions more natural and effective. From sentiment analysis to language generation and NER, we have covered the core techniques that power modern NLP systems, while there are still challenges to overcome. The future looks promising with new advancements in model efficiency, multi bottom capabilities, which are paying the way for the future. Thank you for your time and I look forward to the questions you may have.