Calibrate Ventures’ Post

In the era of AI, ML, and NLP, many organizations are still not leveraging these technologies. Instead, they prefer the old-fashioned way of doing things. Access to these tools is restricted, and prompt engineering is not considered an important skill. Efforts are not being made to generalize these technologies, with only one or two Proof of Concepts (POCs) being made and boasted about AI and ML.

NLP in healthcare is always part of the end goal to get deeper insights from unstructured data. I have been talking to clinical researchers for years about vector edge strength. We're thrilled to announce the integration of native vector search into the @InterSystems IRIS platform. Unlocking the power of vector embeddings, this breakthrough enhances NLP, text, and image analysis capabilities. Discover how this feature is transforming AI application development.  https://lnkd.in/gtgNAyuC #AI #vectorsearch

The #GenAI revolution is here, and WhizAI leads the way with domain-tuned LLM designed for #lifesciences. Users can ask complex questions in simple, everyday language, and WhizAI’s intent-ready NLP and domain-tuned LLM decode them and deliver precise, contextual insights for clinical trials, drug development, and commercial operations in real-time. You’ll get instant access to insights, visual dashboards, anomaly detection, and next-best-action recommendations within seconds. No coding or training is required. Ready to experience the GenAI difference? Learn more: https://lnkd.in/dRbxsbJ5 #GenAI #LLMDifferentiator #GenAIAnalytics #ConversationalAnalytics

🔥 Unveiling the future of #DataScience! Dive into 2024's game-changing trends: Augmented Analytics 🌐 From chatbots to translation, NLP advancements bring technology closer to human understanding, paving the way for intuitive interactions. 📊 Trends to watch: Augmented Analytics automates insights, NLP extracts data in natural language, and AutoML democratizes machine learning. ⚙️ AI as a Service (AIaaS) democratizes access to advanced AI tools, fostering innovation across sectors. 🔍 Data democratization breaks barriers, enabling informed decision-making across organizations. #AI #Innovation #TechTrends #DataScience #DataScientist

Improving Language models (LM) across multiple capabilities is often a non-trivial task, specially with limited compute availability. The blog post by Marktechpost Media Inc. explains the recipe behind 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝗦𝘁𝗼𝗿𝗺-𝟴𝗕: 1. Self-curation: Find high-quality training examples. 2. Targeted fine-tuning: Improved model performance by selecting more relevant layers. 3. Model Merging: Combine multiple models and improve overall performance Above 3-steps were core ingredients in building Llama-3.1-Storm-8B model that led to consistent improvements over AI at Meta's Llama-3.1-8B-Instruct and Hermes-3.1-8B. - Model: https://lnkd.in/d3zvFAw4 - Ollama: https://lnkd.in/dezQgtJR - 🤗 Blog: https://lnkd.in/g7tbfe62 Thank you Asif Razzaq for finding our work interesting! 🙏

Llama-3.1-Storm-8B: A Groundbreaking AI Model that Outperforms Meta AI’s Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B Models on Diverse Benchmarks Artificial intelligence (AI) has witnessed rapid advancements over the past decade, with significant strides in NLP, machine learning, and deep learning. Among the latest and most notable developments is the release of Llama-3.1-Storm-8B by Ashvini Kumar Jindal and team. This new AI model represents a considerable leap forward in language model capabilities, setting new benchmarks in performance, efficiency, and applicability across various industries. One of the standout features of Llama-3.1-Storm-8B is its scale. With 8 billion parameters, the model is significantly more powerful than many competitors. This massive scale allows the model to capture subtle nuances in language, making it capable of generating text that is not only contextually relevant but also grammatically coherent and stylistically appropriate. The model’s architecture is based on a transformer design, which has become the standard in modern NLP due to its ability to handle long-range dependencies in text data. Llama-3.1-Storm-8B has been optimized for performance, balancing the trade-off between computational efficiency and output quality. This optimization is particularly important in scenarios requiring real-time processing, such as live chatbots or automated transcription services. The model’s ability to generate high-quality text in real-time without significant latency makes it an ideal choice for businesses looking to implement AI-driven solutions that require quick and accurate responses.... Read our full take on this: https://lnkd.in/gZB9UdtY Model: https://lnkd.in/gYd5s6AS Ashvini Jindal Ankur Parikh Pawan Rajpoot

🎭AI behind the scenes. Exploring Retrieval-Augmented Generation (RAG): A Powerful NLP Technique. RAG blends traditional info retrieval with generative models, enhancing NLP by combining your data with LLM skills. Let's break it down: Step 1: The user submits a question or query, seeking information from a vast internal knowledge base. Step 2: RAG’s retrieval system searches through the available internal knowledge and retrieves the most relevant documents based on the user’s query. Step 3: The retrieved documents are then fed into a Language Model (like Llama 3.1), which uses this specific, up-to-date information to generate an accurate and relevant response. Step 4: The language model provides a well-informed answer, grounded in the retrieved data, ensuring the response is aligned with the internal knowledge base. Step 5: Thanks to the model's short-term memory, the user can continue asking follow-up questions, maintaining a conversation with the RAG system based on the same documents or internal knowledge. #GasparAI #WorkplaceAutomation #AI #Efficiency #WishThatWasYou #SmarterSupport #Monday #JiraServiceManagement #SlackIntegration #Automation #Productivity #JSM #Helpdesk #TaskAgent #Boss #IT #CTO #CEO #Tech #SaaS #TechInnovation #APIs #Workflow #FutureOfWork

NuExtract by NuMind (YC S22) is a good open-source foundation model for structured extraction. - Lightweight text-to-JSON LLM (0.5B-7B parameters) - Extracts complex information from text into structured data - Comparable performance to much larger LLMs - Usable in zero-shot setting or fine-tunable - MIT licensed Applications range from parsing technical documents to powering conversational AI. It's an interesting example of task-specific foundation models in NLP. Release blog: https://lnkd.in/eezn8_rQ Demo space: https://lnkd.in/evtr-_UR Model Collection: https://lnkd.in/ejP8Xeky

Discover the efficiency of sub-7B AI models for document categorization. Smaller size, high performance - a game changer in NLP! #AI #DocumentClassification https://lnkd.in/dGunREHh

During the second week of the AI Solution Architect course at ELVTR, we moved beyond the theoretical and functional documentation and checklists. We engaged in practical exercises that helped us better understand the AI Classification diagram. We also conducted a comprehensive executive code walkthrough of the NLP Friendly Text Moderation app using Jupyter Notebook and HuggingFace. The exercise was not just about coding, as we explored the intricacies of NLP applications, discussing how they are used in moderating but not censoring online content and the issues of biases and fairness. The lessons were engaging and sparked lively discussions and innovative ideas in the Q&A session and on Discord, showcasing the impressive capabilities and enthusiasm of everyone involved. #AI #ML #NLP #ELVTR, #AISA, #DucHaba