Update pages/Introduction.py

pages/Introduction.py

@@ -110,3 +110,25 @@ Developed by Facebook, it extends Word2Vec by considering subword information, m
 **4. Transformers (Contextual Embeddings)**  
 Models like **BERT**, **ELMo**, and **GPT** generate embeddings based on the context in which a word appears, capturing nuanced meanings.
 """)
+
+st.subheader("Future of NLP")
+st.write("""
+The future of Natural Language Processing (NLP) is exciting, with advancements that aim to make machines understand and interact with human language more effectively. Here are key areas shaping the future of NLP:
+**1.Context-Aware Models:**
+- Enhanced Understanding of Context: Models like GPT and BERT have already revolutionized NLP. Future advancements will further refine their ability to comprehend nuanced context, sarcasm, and idioms.
+
+**2.Real-Time Multilingual NLP**
+- Instant Translations: Real-time and accurate translation across diverse languages, including low-resource ones.
+- Language Independence: NLP systems capable of handling any language seamlessly.
+
+**3.Conversational AI**
+- Human-like Conversations: Chatbots and virtual assistants will become more natural, empathetic, and intuitive in conversations.
+- Emotion Recognition: Understanding and responding to user emotions effectively.
+
+**4. Zero-shot and Few-shot Learning**
+- Minimal Data Requirement: Models will handle new tasks or languages with little to no additional training, making NLP accessible across domains with limited data.
+
+**5. Multimodal Learning**
+- Beyond Text: Integrating text with images, audio, and video for richer applications like understanding memes, videos, or interactive media.
+
+""")