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WeCloudData provides customized learning paths to improve technical proficiency in large language models (LLM) and generative artificial intelligence (gen AI). Whether you’re a student exploring career options, a professional looking to upskill, or a business owner seeking innovative solutions, understanding Generative AI and LLMs is important in today’s fast-paced AI world.
This blog gives a brief overview of what gen AI is, its use cases and role of large language models in automating the world. Let’s get started!
Generative AI is a sub category of artificial intelligence that creates text, videos, images, and audio based on the patterns and data it is trained on. Consider Generative Artificial intelligence as a graduate student. Like pursuing a master’s or doctoral degree, Gen AI goes beyond the fundamentals. Gen AI takes creativity to a new level unlike traditional artificial intelligence. To learn more about generative AI check out our introductory blog on Generative AI here.
Generative artificial intelligence has multiple practical applications across various domains. Some of the dominant applications are listed here:
Large Language Models are the powerhouse of Gen AI. They are advanced AI systems trained on large volumes of data from books, websites, academic papers, and more to understand and then generate human-like text.
These are the major reasons why LLMs are consider as the powerhouse of gen AI:
Let’s explore how Gen AI and LLMS are revolutionizing innovations across multiple industries:
Tools like GitHub Copilot assist developers by providing assistance in creating and debugging code. This practical application dramatically reduces development time, improves accuracy and boosts developers motivation.
One of the most important sectors influenced by AI is education. AI-powered platforms are used to deliver personalized learning materials to the students and instructors making education more inclusive and effective.
Generative AI has multiple applications in business, specifically in the customer support industry. For example chatbots like ChatGPT can handle millions of customer queries daily, streamlining operations and improving customer satisfaction.
Generative AI is a game-changing technology in the healthcare industry. Gen AI models can assist in summarizing medical records, generating reports, and even helping with diagnosis.
Transformer- The Math Behind LLMs
Transformer was first presented by Google in a research paper “Attention Is All You Need.” . It addressed major drawbacks of earlier AI models, such as LSTMs (Long Short-Term Memory networks) and RNNs (Recurrent Neural Networks), which had trouble with long-term dependencies and sequential text processing. Transformer became the base for the LLMs by providing solutions for the shortcoming of LSTMs and RNNs.
Transformers depend on three major components that allow LLMs to handle large datasets and produce high-quality outputs:
Attention mechanism allows LLMs to focus on the most relevant parts of a sentence while equally understanding the relationships between words. For example: In this sentence “Cat chased the mouse because it was hungry,” the model uses attention to determine that “it” refers to “cat.”
Transformers use positional encoding to give meaning to the word order as they handle text in a non-sequential way. This guarantees the integrity of the sentence structure.
Unlike older AI models, transformers process all words simultaneously, allowing LLMs to analyze longer sequences and work much faster.
LLMS trains and fine-tunes on vast amounts of data using transformers. Here is the brief explanation for the process for your understanding.
Pre-training means the model is trained on unlabeled data over different tasks. Pre-training is a resource intensive process. This stage teaches the model to understand patterns, grammar, and contextual relationships in language. For example: Model learns that “Paris” and “France” are related concepts, allowing it to answer questions like, “What’s the capital of France?”
Fine-tuning offers a resource-efficient and faster way to adapt pre-trained models to specific tasks. LLMs are fine-tuned on smaller, domain-specific datasets to specialize in tasks like:
When a prompt is given to LLMs like ‘write a description of pencil’. The large language model uses decoder from the transformer to predict the next word and generate coherent, contextually accurate responses.
Transformers brought several key innovations to LLMs:
1.Capturing Context
It was difficult for traditional AI models to extract context from lengthy statements. The reason is that as the model processes data in a sequential manner, the words that come before it fade from its memory when it moves on to the next statement. Transformers solve this issue with self-attention, allowing LLMs to analyze relationships between all words simultaneously.
2.Scalability
Transformers offer scalability as they can scale to billions or trillions of parameters, enabling models like GPT-4 to handle highly complex inputs and tasks.
3.Multimodal Capabilities
Models like Google Gemini combine text, images, and video processing, making them suitable for diverse use cases.
WeCloudData simplifies complex concepts through expert-led courses that empower learners to understand and use them practically. Our LLM Course Track provides hands-on experience in fine-tuning AI models for real-world applications.
At WeCloudData, we take pride in making advanced AI technologies both accessible and understandable. Our comprehensive offerings include:
Unlock the full potential of Generative AI with WeCloudData. Visit our website today to discover how we can help you thrive in an AI-driven future.
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