Dewey’s Post

💡 Ever wondered how AI models generate accurate and reliable responses? Following yesterday's discussion on Small Language Models (SLMs), many of you reached out, curious to delve into Retrieval-Augmented Generation (RAG). Enter Retrieval-Augmented Generation (RAG), a groundbreaking AI framework introduced in a 2020 research paper by Meta (then Facebook). RAG elevates the capabilities of Large Language Models (LLMs) by incorporating external information through a combination of pre-trained dense retrieval (DPR) and Seq2Seq models. ✅ At the core of RAG are 3 steps: 🔅 Retrieve: Embed the user query into a vector space along with additional context from an external knowledge source. Utilize an embedding model for this process, enabling a similarity search. The top k closest data objects from the vector database are then retrieved. 🔅 Augment: Combine the user query and retrieve context within a prompt template. 🔅 Generate: Feed the retrieval-augmented prompt into the Large Language Model (LLM) for response generation. ✅ RAG finds its stride across various applications: 🔅 Question Answering: Enhancing accuracy in LLM-based question-answering systems by grounding models on up-to-date and reliable external knowledge. 🔅 Content Creation: Elevating the quality of generated text content, including articles, summaries, and product descriptions, by retrieving relevant information from external sources. 🔅 Chatbots: Improving chatbot responses by grounding models on external information, ensuring more accurate and informative interactions with users. 🔅 Medical Diagnosis: Boosting the accuracy of medical diagnoses through the retrieval of pertinent information from sources like medical journals and databases. While RAG holds immense promise, it comes with its set of challenges. The need for extensive training data and significant computational resources poses hurdles, particularly in resource-constrained environments. Additionally, RAG's performance may vary, especially in handling complex tasks or large-scale datasets Retrieval-augmented generation (RAG) stands as a transformative AI framework, enhancing the precision of LLM-generated responses by leveraging external information. In simpler words, it's like bringing your study materials or books to an open-book exam. You have the necessary information with you to answer the questions. 😀 #OpenBookExamAI #RetrievalAugmentedGeneration #GenerativeAI #AIInLaymansTerms #KnowledgeEnhancement #AIInsights #UnderstandingRAG #InnovationsInAI

🚀 Exciting news from CeADAR Ireland - we have launched a suite of free tools designed to revolutionise businesses' understanding and application of generative AI. 1) LLMXplorer: Evaluate over 155 Large Language Models (LLMs) and access 50+ datasets for LLM training. 2) Building Trust in Conversational AI Report: Insights into the evolving conversational AI landscape, emphasizing ethical and transparent AI systems. 3) AIVision360: A domain-specific dataset enhancing LLMs in AI news discussions. 4) NewsConnect 7B & 13B: Open-source LLMs for AI news trend analysis, focusing on technology and media sectors. These tools, spearheaded by Dr. Arsalan Shahid and his team, are a leap forward in ethical AI use, aligning with the highest standards of trust. They're a part of CeADAR's commitment to accelerating AI adoption in Irish industries, bolstering international competitiveness. 🌟 Dive into the future of AI with CeADAR's innovative tools – a game changer for AI in business and research! https://lnkd.in/eJcFiv9D #CeADAR #AIInnovation #GenerativeAI

Pleased to share the release of our latest set of free GenAI tools and domain-specific LLMs at CeADAR Ireland. In today’s digital landscape, the integration of generative AI into businesses is essential for value creation and productivity. Yet, the trustworthiness and ethical use of AI remain our North Star. Our recent release of open source tools stands as a testament to CeADAR’s commitment to open research, ensuring businesses can swiftly adapt in this dynamic AI landscape. As we refine domain-specific large language models for sectors like finance, healthcare and the legal domain, our mission is clear: to empower industries with AI tools that are not only cutting-edge but also align with the highest standards of trust and ethics. Chan Le Van Ahtsham Zafar Venkatesh B P Aafaq Iqbal Khan Saad Shahid #LLMs #GenerativeAI #LLMXplorer #KnowledgeGraphs #AIInnovation

Small language models (SLMs) are changing the game in AI applications, packing the punch of their larger counterparts into a compact, flexible framework. This streamlined technology empowers businesses of all sizes to deploy AI swiftly and adaptively, enhancing their operational agility and broadening the scope of possibilities. Ideal for dynamic environments, SLMs ensure that any organization can harness the full power of AI efficiently. Explore the future of AI with us! 🚀 Read more here: https://bit.ly/3JZKEYM #FutureOfAI #SLMpower #AITechnology #InnovationInAI #SmallLanguageModels

I highly recommend engaging with the WSB CPED to connect on your digital strategy. Large language models (LLMs) and generative AI are rapidly transforming the business landscape, empowering organizations to enhance customer service, personalize marketing, optimize operations, and generate creative content. By automating tasks, analyzing data, and producing original content, these technologies are streamlining processes, improving decision-making, and fueling innovation across diverse industries. From personalized customer interactions to accelerated research and development, LLMs and generative AI are revolutionizing the way businesses operate and driving growth in the digital age. https://lnkd.in/gJvjMMgR

The webinar “Innovate With Current: A Live Users’ Guide to Generative AI Tools” dove into the heart of how people in public media are experimenting with AI technology.

Are you curious about the latest breakthroughs in artificial intelligence? Dive into our recent blog post where we explore the fascinating world of Multimodal Large Language Models (MLLMs). From enhancing customer interactions to automating data analysis, these advanced systems are reshaping the way we perceive AI's capabilities. #GenerativeAI #AIInnovation #MultimodalAI #BusinessTransformation #MLLMs #TechInnovation #AIApplications #DigitalRevolution #DevSecOps

💡 A Leap Forward in Multimodal Large Language Models (MLLMs:) A recent study introduces MM1 – a groundbreaking multimodal large language model that sets new benchmarks in understanding and integrating multimodal (image-text) data. 🔍 What is MM1? MM1 isn't just another AI model; it represents a significant leap in understanding and integrating multimodal data. By carefully analyzing the architecture components, data choices, and the pre-training process, MM1 demonstrates unparalleled proficiency in few-shot learning across various benchmarks. This means MM1 can understand and generate content by integrating both text and image inputs more effectively than ever before. 🔧 Why Does It Matter? The implications are vast. From improving AI's accessibility by better understanding visual content to enhancing automated customer support with more intuitive responses, MM1's advancements open new doors. For sectors relying on image-text data, such as healthcare for patient records analysis or retail for product descriptions, MM1 offers a new level of AI efficiency and accuracy. 👨🔬 Key Takeaways: - High Impact on Few-Shot Learning: MM1 excels in learning from a limited number of examples, making it more adaptable and efficient. - Superior Multimodal Integration: By effectively combining image and text data, MM1 offers a more holistic understanding, crucial for applications like automated content creation or detailed image descriptions. - Future-Proof: With its robust pre-training and fine-tuning methods, MM1 sets a solid foundation for future AI models to build upon. 🤔 Looking Ahead The MM1 model opens exciting possibilities for AI's role in various industries, enhancing how machines understand and interact with human language and visuals. It's not just about building smarter AI but about making technology more intuitive and aligned with human cognition. 🔗 Learn More For an in-depth look at MM1's methodology, performance benchmarks, and potential applications, check the link in the comments! 📢 Your Thoughts? How do you see MM1 impacting your industry or the broader AI landscape? Share your views below! #AI #MachineLearning #MultimodalAI #Innovation #TechnologyTrends

📢 ProCIS: A Large-Scale Dataset for Proactive Conversational Information Seeking In the rapidly evolving field of conversational AI, ProCIS addresses a significant gap by providing a standardized large-scale benchmark for evaluating proactive retrieval models in open-domain conversations. 🗣️🔍 Key highlights of ProCIS: ✅ Over 2.8 million multi-party conversations collected from Reddit threads ✅ Enriched with external links to Wikipedia articles for context ✅ High-quality relevance judgments obtained through depth-k pooling ✅ Annotations for conversation parts related to each document We also introduce the Language Model Grounded Retrieval (LMGR) framework, a novel baseline model that outperforms existing ad-hoc retrieval models in the reactive setting by a big margin, showcasing the potential of using large language models (in)directly for retrieval. 🚀💡 ProCIS opens up exciting research opportunities, including: 🔹 Development of proactive retrieval models 🔹 Improvement of dense retrieval models 🔹 Advanced pooling methods 🔹 Explainability and query generation 🔹 Synthetic data generation 🔹 Generative retrieval models We believe ProCIS will inspire further advancements in proactive conversational search (and not only), enhancing user experiences and unlocking the full potential of conversational AI agents across various domains. 🌟🔓 Read our full paper to learn more about ProCIS and its implications for the future of conversational information seeking: https://lnkd.in/eRxUNf3x Many thanks to my faculty advisor Hamed Zamani #ConversationalAI #InformationRetrieval #ProactiveSearch #LargeLanguageModels #Dataset

Hugging Face : The Platform that Powers the Future of AI #Artificial_Intelligence (AI) is transforming the world in unprecedented ways. From natural language processing to computer vision, from speech recognition to text generation, AI is enabling new possibilities and applications across various domains and industries. However, developing and deploying AI models is not an easy task. It requires a lot of expertise, resources, and data. That's where Hugging Face comes in. Hugging Face is a startup that aims to democratize and advance artificial intelligence through open source and open science. It is becoming the GitHub of AI, where the machine learning community collaborates on models, datasets, and applications. In this post, I will explain what Hugging Face does, how it works, and why it is getting popular among AI enthusiasts. What Hugging Face does Hugging Face provides a platform that simplifies and accelerates the development and deployment of AI models. It offers three main products: - Transformers - Datasets - Spaces How Hugging Face works Hugging Face leverages the power of open source and open science to create a collaborative and innovative AI ecosystem. It works by: - Sharing: Hugging Face hosts a large repository of pre-trained models and datasets that are freely available for anyone to use. Users can also upload their own models and datasets to share with the community. - Improving: Hugging Face continuously improves its models and datasets by incorporating feedback, suggestions, and contributions from the community. Users can also fine-tune existing models or create new ones using Hugging Face tools. - Deploying: Hugging Face makes it easy to deploy AI models to various platforms such as web, mobile, edge, or cloud. Users can also integrate Hugging Face models with other services such as AWS, Google Cloud, or Microsoft Azure. Why Hugging Face is getting popular Hugging Face is getting popular among AI enthusiasts because it offers several benefits such as: - Accessibility: Hugging Face lowers the barriers to entry for AI development by providing easy-to-use tools and resources that anyone can access and learn from. - Quality: Hugging Face delivers high-quality models and datasets that are based on the latest research and best practices in the field of AI. - Diversity: Hugging Face supports a diverse range of languages, domains, and tasks that cater to different needs and interests of the users. - Community: Hugging Face fosters a vibrant and supportive community of AI practitioners, researchers, educators, and enthusiasts who collaborate, exchange ideas, and help each other.