Big news from the Observe.AI team! We're excited to announce that 3 of our research papers have been accepted at the NAACL 2024 conference! ?? Our work focuses on pushing the boundaries of AI in the customer service industry, and we’re proud to contribute cutting-edge insights to the academic community. ?? Check out the blog to learn more about the innovative research we're presenting at #NAACL2024: https://lnkd.in/gez8qa84
Observe.AI的动态
最相关的动态
-
Director @ WTW | Industry Risk Monitoring, Solutions Development/GB Regional Lead for TMT ??????
A useful term to understand ,especially when making AI processes more efficient.
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
?? At HOORON.AI we have implemented RAG to let you CHAT WITH YOUR COMPANY DATA FAST, just like on WhatsApp! ?? You can have a fast and user-friendly AI tool designed for Managers and Employees to instantly address internal questions, pulling information from your database or operating system about your Sales, Administrative, and Supply Chain data. ?? Alex Wang's recent LinkedIn post provides a compelling overview of the evolution of Retrieval-Augmented Generation (RAG) models, supported by a detailed visual timeline. ?? This is structured to show three stages of RAG model development: * Pre-training * Fine-tuning * and Inference ?? RAG models differentiate themselves from traditional Large Language Models (LLMs) by fetching relevant external information to improve response accuracy. ?? While LLMs generate answers based solely on their training data, RAG models retrieve up-to-date facts from various sources. ?? This capability makes RAG models particularly valuable in business applications where accuracy and timeliness are crucial. ?? DM me for more details. #artificialintelligence #ai #business #marketing #sales
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
I'm often asked about where in the hype cycle is GenAI and how enterprises can effectively scale its usage. My response: While all technologies go through a journey in the hype cycle we're witnessing a whirlwind of innovation unlike anything before! ?? But to fully capture the capability at scale we need to strike a balance between risk management and bold experimentation that lets us uncover the unknowns so that we can create a path forward. And that's what gets me excited about RAG Models – a technique for advancing accuracy and reliability of GenAI models. For example, leveraging RAG internally for tailored employee training and beyond! Just seeing this visual from Alex Wang is a great reminder. I'm curious, what do you think about the capabilities of RAG in order to make it work reliably? ?? #CIOInsights #GenAI #RAGModels #innovation #techleadership
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
Project Lead & Research Assistant at KDD | Innovator in Edge Computing and IoT Solutions | Worked with ESP32, raspberry pi , Coral Tpu, Google MediaPipe, and MicroPython
?? Just finished creating a cutting-edge language model project! ?? Utilizing LangChain's powerful tools and models, I built a RAG (Retrieval-Augmented Generation) model to enhance document retrieval and generation. ?? Here's a quick rundown of the tech stack: ?? Document Loaders: Leveraged UnstructuredPDFLoader for local PDFs and OnlinePDFLoader for web-based documents. ?? Text Splitters: Employed RecursiveCharacterTextSplitter for efficient document chunking. ?? Embeddings: Integrated OllamaEmbeddings for semantic understanding. ?? Vector Stores: Stored document vectors with Chroma for fast retrieval. ?? Chat Models: Implemented ChatOllama (phi3) for conversational interactions. ?? Query Retrievers: Utilized MultiQueryRetriever for retrieving documents based on multiple perspectives. ?? Prompt Templates: Created ChatPromptTemplate for generating RAG prompts. ?? FastAPI Integration: Developed a FastAPI server for seamless interaction. Excited to see how this project transforms document processing and AI assistance! ?? #NLP #AI #LangChain #RAGModel
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
Gen AI Advisor | Certified Enterprise & Multi-Cloud Solution Architect | Enterprise Strategy | Technology Leader | AI Evangelist | AI Safety | AI Ops | Automation | FinOps | Agile | Technical Debt ??
Another good representation around Evolution of RAG models. Of course its not as simple as it sounds but just to simplify the complexity and diversity of approach , I see a lot of companies these days are switching towards a combination of RAG, RLHF , Domain Specific Models ( DSM) & Multi-Modal Models to improve the accuracy of LLM's . Alex Wang thanks for sharing.
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
Data Scientist | Data Analyst | Ex-Intern @ IIT | AI Contest Winner | Azure AI Certified | Expert in ML, NLP, DL | SQL, Tableau, Power BI, Advanced Excel
Evolution of RAG Models #rag #model #generativeai #machinelearning #datascience #ai
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
?? Data Scientist | ???? Technical Referral in Data Science, Python & Gen AI | ?? Gen AI Instructor | ???? Data Science Instructor |?? GDG Organizer | ? 1,200+ Hours of Training Delivered in Data Science
RAG is revolutionizing the field of generative AI by making models more accurate and reliable. It's fascinating to see how the integration of structured data and the exploration of self-retrieval are addressing some of the biggest challenges in AI. Looking forward to seeing more advancements in this area!
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
-
Excited to share this course “Generative AI: The Evolution of Thoughtful Online Search” by Ashley Kennedy!? Completed it today and gain knowledge regarding Generative AI.? #artificialintelligenceforbusiness #searchenginetechnology #linkedin
Certificate of Completion
linkedin.com
-
A great overview of all the noticeable RAG research that has been happening. Credits to one and only Alex Wang The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
A great overview of all the noticeable RAG research that has been happening. The term Retrieval-Augmented Generation (RAG) was first introduced in a paper by a team at Facebook AI Research in 2020, as a way to improve the accuracy and reliability of generative AI models by fetching facts from external sources. Initially, RAG focused on using unstructured data, but it has expanded to incorporate high-quality structured data, like knowledge graphs, to address issues like hallucinations and incorrect knowledge. Recently, researchers have also started exploring self-retrieval, which involves using LLMs' own knowledge to enhance their performance. The image is from the paper 'Retrieval-Augmented Generation for Large Language Models: A Survey', last revised on Mar 2024; which should be a great read. ____________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang #rag #generativeai #llms #machinelearning #datascience
-
Observe.AI | Conversational Intelligence Expert
2 个月????????????