Kenneth Donau

𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) refer to a class of artificial intelligence models designed to understand and generate human-like language. These models are characterized by their enormous size, typically containing tens or hundreds of billions of parameters. One of the breakthroughs in this field is represented by models like OpenAI's GPT-3 (Generative Pre-trained Transformer 3). Here are key features and aspects of Large Language Models: 𝟭. 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: LLMs are usually pre-trained on massive datasets containing parts of the internet, books, articles, and more. During this phase, the model learns to predict the next word in a sentence, capturing grammar, context, and semantic relationships. 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: LLMs often use the transformer architecture, which allows them to capture long-range dependencies in the data efficiently. Transformers have become a standard for various natural language processing tasks. 𝟯. 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴: After pre-training, LLMs can be fine-tuned on specific tasks. This fine-tuning process tailors the model for tasks like language translation, summarization, question answering, and more. 𝟰. 𝗗𝗶𝘃𝗲𝗿𝘀𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: LLMs exhibit versatility and can be applied to a wide range of natural language processing tasks, from text completion and generation to chatbots, language translation, summarization, and code generation. 𝟱. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: Despite their capabilities, LLMs face challenges such as biases present in the training data, ethical concerns related to the generation of inappropriate content, and the substantial computational resources required for training and inference. 𝟲. 𝗚𝗣𝗧-𝟯 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: OpenAI's GPT-3, released in 2020, is one of the most prominent LLMs. With 175 billion parameters, it demonstrates remarkable performance on various language-related tasks, and it is often used via API for developers to integrate into their applications. 𝟳. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Continued research is dedicated to enhancing the capabilities of LLMs, addressing their limitations, and exploring ways to make them more interpretable, ethical, and aligned with human values. Large Language Models have significantly advanced natural language understanding and generation, contributing to breakthroughs in various fields. However, their deployment also raises important considerations about responsible and ethical AI use. Credit : @ If this post insightful please share, repost and follow or connect with me for more …meer

Thursday 8 February 2024 | Evoluon - Eindhoven events.opentext.com

Join us for an entire day dedicated to recounting meaningful customer experiences. Discover the new OpenText and hear customers and experts discuss how they overcame the kinds of challenges you face today. …meer

The Future is Vector: Why Vector Databases Are Critical for GenAl As we move into the era of generative Al, vector databases are becoming an essential backend technology. In this post, We'll explore what vector databases are, how they work, and why they are so critical for next-generation Al like GenAl. What Are Vector Databases? While traditional databases store data in rows and columns, a vector database stores data as math vectors. Each piece of data is represented as a point in high-dimensional space, with hundreds or thousands of dimensions. This allows very sophisticated relationships between data points to be captured. Searching and analyzing vector databases relies on vector mathematics and similarity calculations. By comparing vector positions, highly relevant results can be returned, even if there are no exact keyword matches. Why Are Vector Databases Important for Al? Vector databases are ideal for managing and extracting insights from the enormous datasets required to train modern Al models. Key advantages include: - Efficient similarity search - easily find related vectors for pattern recognition - Support for incremental learning - new data can be continuously added - Flexible schema - new types of data can be easily incorporated - Scalability to massive datasets crucial for Al training As GenAl moves into mainstream applications, the role of vector databases will only grow. Their ability to organize and structure knowledge in a format tailored for Al aligns with the needs of next-gen generative models. The combination of vector databases and transformers allows GenAl to understand language meaning rather than just keywords. This next-generation Al capability powered by vector math is what delivers such natural, intelligent conversations. The Road Ahead As Al progresses, expect vector databases to play an increasingly important role. GenAl is just one example of how vector DBs enable complex, human-like Al - the potential applications are vast. Follow RAKESH GODARI for more such amazing content :) …meer

Understand your data with OpenText™ Knowledge Discovery events.opentext.com

Webinar Understand your data with OpenText™ Knowledge Discovery On-demand webinar Check out this webinar to discover how OpenText Knowledge Discovery can deliver complete, correct, authentic, and controlled information—ensuring it’s ready for the AI innovations that are impacting business processes and customer interactions. Explore the solution and learn how you can start understanding your data. …meer

Arrived at the Opentext Knowledge Discovery booth with Bernie Rowan and Erica Tarenzi. Great event. …meer