Mubarak Ali’s Post

We set out to build an NLP model that truly understands our users’ needs—and simply put, GPT-3 wasn’t cutting it. So, we built our own. This graphic shows how our model is trained and refined to give you the best AI-powered writing assistance possible. Here’s a quick look at our approach: 🔹 Massive Data Processing Our model processes data from blogs, Twitter, Reddit, Wikipedia, and more. 🔹 Purpose-Built for Compose AI We move from a general-purpose NLP model to a domain-specific model to give you more relevant and accurate suggestions. 🔹 Real-Time Assistance Our model delivers real-time support tailored to each user. Want to be part of the future of AI-powered writing? We’re opening up our Community Round, and now’s your chance to invest! Head over to https://zurl.co/NGfx and learn more about our pitch.

I had the opportunity to write an early review for this awesome piece from Jeff Vestal and Bahaaldine A. . Natural Language Processing is an important element for vector search. For a beginner and professional trying to grasp the fundamentals, this book gives the rudiments including vector dimensions, cosine similarity, embeddings, vector database, NLP and integration with OpenAl and hugging face models. Elasticsearch makes it easy to have your ML instance running for your models. https://lnkd.in/drMbJwRw Thanks to Packt and Meghal Patel for this opportunity. #LearningIsAContinuum

RAG can bit overwhelming for beginners to understand Here's how to approach it better... 📌 Start by learning the basics of Generative AI (Free resources in the comments). 📌 Understand the fundamentals of NLP (Natural Language Processing). 📌 Moving on, Start learning the basic workflow for RAG. ↳ This includes learning about Retrieval and Generators + their best workflow practices. (I've attached the best workflow practices for RAG in the comments 😊). 📌 Then learn about components used in Retrieval and Generator. ↳ Study retrieval components like Opensearch, FAISS, BM25 and others. Similarly for the generator, study components like LLMs. 📌 For hands-on practice, Utilize AWS or even Hugging Face's Transformers and Haystack. These will help you create a basic RAG Pipeline. ↳ After this, just learn. Build your knowledge to become better at creating complex RAG Pipelines. BTW 📌 If you want to learn in-depth, I am creating an in-depth Video about "Building RAG on AWS". So make sure to hit that notification button! Hope you liked our analogy in this! Make sure to follow for more content like this! #RAG #LLM #GenAI

Public discourse shapes narratives, opinions, and decisions on countless topics worldwide. In response to this, I developed "You Sentiment," an AI-powered application designed to analyze the sentiment of discussions surrounding a particular topic. Leveraging the Google Developer API, the tool fetches videos from YouTube to extract and analyze comments using advanced Natural Language Processing techniques and Machine Learning models. "You Sentiment" classifies sentiments into Positive, Negative, or Neutral, providing detailed statistics and highlighting the most impactful comments across these categories. Beyond analyzing public sentiment, the application enables users to input their own comments, offering insights into how their statements may align with or diverge from the broader sentiment landscape. Special thanks to Haris Humayon and Rafay Ul Haq for their contributions.

DSPy, developed by the Stanford NLP group, transforms the manual task of prompt engineering into a structured machine learning workflow. It separates the logic of prompt engineering from its textual representation, making the process reproducible and LLM-agnostic. DSPy follows two strategies: separating program flow (modules) from parameters (LM prompts and weights), and introducing LM-driven optimizers that adjust these parameters to maximize a specified metric for each module. For a detailed exploration, visit Jina AI's article.

🚀 LLMs Terms are confusing 🤯 🧐 Model size? RLHF? Fine-tuning? RAG? Token? Inference? Let me explain Are you intrigued by Large Language Models (LLMs) but confused about some basic concepts? Let's break it down: ⭐ Model Size: LLMs come in various sizes, ranging from millions to billions of parameters. The size often correlates with their performance and complexity. ⭐ Token: In NLP (Natural Language Processing), a token refers to the basic unit of text, such as a word or subword. LLMs tokenize input text into smaller units for processing. ⭐ Inference: In the context of LLMs, inference refers to the process of generating predictions or responses based on input data after the model has been trained. ⭐ RLHF (Reinforcement Learning by Human Feedback): RLHF involves integrating human feedback into the learning process to improve model performance and adaptability, making it a crucial aspect of LLM development. ⭐ Fine-Tuning: Fine-tuning involves adjusting a pre-trained LLM on a specific task or domain by further training it on task-specific data. This process helps enhance the model's performance on specific tasks. ⭐ RAG (Retrieval-Augmented Generation): RAG combines retrieval-based methods with generative models to improve the relevance and coherence of generated text by incorporating information retrieved from a large corpus. Curious to learn more? Stay tuned for insights and explanations that will level up your understanding! 💡 #ArtificialIntelligence #GenerativeAI #ApplicableAI #LLMs #TechTalks #StayCurious

Everyone who has studied data analytics or research knows the distinction between quantitative and qualitative research. Each has its strengths and weaknesses: Quantitative Research ✅ Scalable and objective, giving you the hard numbers to generalize findings. ❌ Often lacks the "why," missing the motivations and emotions behind the patterns. Qualitative Research ✅ Rich in depth, uncovering stories and unmet needs that drive behavior. ❌ Labor-intensive and hard to scale, making it challenging for large datasets. Until recently, bridging the two felt impossible. Older tools like NLTK NLP were like the "stone age" of text analysis—great for spotting patterns but unable to grasp meaning. Enter LLMs (Large Language Models), the first tools to truly combine natural language processing with human-level understanding. For more detail, read the full article from the DVJ Insights newsletter and consider signing up to keep up-to-date.

Did you know Google’s search algorithm is getting smarter about understanding why we search, not just what we type? It’s not just matching keywords anymore, Google’s using some pretty advanced tools to decode our search intent! Here’s a breakdown of how it’s doing this: Vector Embeddings Imagine each word or phrase as a tiny data point with context and meaning. This helps Google find results that are relevant, even if they don’t contain the exact words you used. BERT This deep-learning model is all about understanding the full context of words in a sentence. It’s especially useful when we ask long, conversational questions. NLP With Natural Language Processing, Google “reads” online content like a human would, ranking pages that best answer your search intent. All of this means it’s time to think of your pages as “answer engines.” By aligning with Google’s AI-powered approach and keeping an eye on layout best practices, you can create content that really connects with your audience’s needs! Noticing any changes in your search results lately? I’d love to hear if Google’s updates have made a difference for you—drop your thoughts below! #GoogleAlgorithm #SEO #AI #DigitalMarketing #ContentStrategy #SemanticSearch #UserIntent #AnswerEngine #AdvancedSEO #ContentMarketing

SEO TIP: You can use Google's Gemini tool to see what Google "might" extract from your web pages using NLP (Natural Language Processing). Use Gemini and the following prompt to see what Entities, Topics, Sentiment & Keywords Google "might" extract using NLP. "What kind of information will Google's NLP extract from this website: [WEBSITE]" You will get an output like the screenshot attached. I have noticed that the outputs are quite specific, which is very useful to see what Google understands about your website/entities. One website: People: Experts in eye health, researchers on blue light. Another: People: Founders, researchers, or key personnel associated with [COMPANY] Try it out and let me know what you think or how you can use this to your advantage! #SEO #Google #GoogleGemini #Prompt #AI #SEO

Variational Autoencoders (VAEs) are well-known for image generation, but did you know they’re also used in NLP? While not as popular as Transformer models like GPT, VAEs offer unique flexibility. They sample from a continuous latent space, allowing for creative variations in meaning and style. Applications in NLP: - Text generation: VAEs can generate diverse and coherent text. - Sentence representation: Useful for document clustering and topic modeling. - Paraphrase generation: VAEs can rephrase sentences with varied meaning. - Data augmentation: Helps create additional data in low-resource tasks. So, why aren’t VAEs more common? While VAEs are great for diversity, their text outputs are often less fluent and coherent compared to GPT models, which excel at capturing long-range dependencies in text. Here's an example: VAE: "The sky was filled with echoes of forgotten dreams." GPT: "The sky was filled with a tapestry of stars, twinkling like distant beacons." VAEs offer flexibility, but GPT often delivers more polished, structured text. Which one you choose depends on your task—diversity vs. fluency.