Ingenious e-Brain ’s Post

Explore the power of combining Large Language Models (LLMs) with knowledge graphs in this insightful talk by Fanghua (Joshua) Yu, from last year's DataEngBytes Melbourne 2023. Learn how this innovative framework can bring context and explainability to generative AI, leveraging Neo4j graph databases. Watch this talk and discover how this technology can revolutionise enterprise and government applications by addressing concerns of reliability, control, and real-time updates. Watch the talk here: https://lnkd.in/gNixd56W #deb2023 #dataengineering #learning

Just completed the "Generative AI with Large Language Models" course. Excited to apply newfound knowledge in crafting innovative solutions and generating impactful content. Ready to push the boundaries of creativity and AI integration in my future endeavors! #GenerativeAI #LLMs #ArtificialIntelligence

At Innovation Hacks, our Gen AI solutions go beyond the typical limitations of large language models (LLMs) by addressing complex scenarios involving numerical data, time series (temporal) analysis, and cause-and-effect reasoning. We focus now on causal reasoning, where our systems excel at interpreting cause-and-effect relationships. Enter CausalLM's CEAdvanced, an innovative language model designed to blend high performance with efficiency. This compact yet powerful model stands out for making advanced AI capabilities more accessible and scalable, setting a new standard in the AI landscape.

Just completed the course “Introduction to Prompt Engineering for Generative AI”! #NaturalLanguageProcessing #GenerativeAI I'm excited to share that I’ve finished a course on prompt engineering for generative AI. I learned how to create effective prompts to get the best results from AI models like GPT-3 and GPT-4. Key things I learned: - Basic concepts of prompt engineering. - How to write clear and relevant prompts. - Ways to improve AI responses with better prompts. - Real-world uses of generative AI.

A recent piece of research demonstrates just how big of a jump the O series is at reasoning and planning when compared to the GPT series of models from Open AI (and all other traditional LLMS models) The chart shows the number of steps a benchmark planning problem has, and the accuracy of the model solving it... GPT is barely able to accurately solve a planning problem consisting of a couple of steps... O1 manages to solve problems at a success rate of over 50% that require planning out 6 to 8 steps. there is still a long way to go, but this demonstrates that while the GPT series is great at advice, creativity, copy, answering questions... the O Series shines with multi step thinking and reasoning.

Just finished the course “Generative AI Skills for Creative Content: Opportunities, Issues, and Ethics” by Ashley Kennedy! Generative AI brings the power of machine learning and artificial intelligence to everyone, making it incredibly easy to create media of all kinds—text, images, video, audio, and more. But for creative professionals, this power also brings important content considerations and ethical issues. In this course, Ashley Kennedy explores the benefits and issues behind different generative AI tools—unearthing the potential, but also investigating the bigger picture and ramifications of this incredible technology. Check it out: https://lnkd.in/dpijj7vM  #artificialintelligencefordesign.

Join our ML Conferences in Munich, New York and Berlin. The Event for Machine Learning Technologies & Innovations, Generative AI, Large Language Models, MLOps and even more topics to push your business forward. More information at www.mlconference.ai #MachineLearning #GenAI #LLM #MLOps #ML #Business #MLTools #MLDevelopment #GenerativeAI

New tutorial on Retrieval-Augmented Generation (RAG) using the LangChain framework. This Co-Lab notebook is designed to help you combine retrieval systems with generative models to produce highly accurate and contextually relevant responses. 🔑 Key Highlights: Introduction to RAG: Understand how RAG improves the accuracy of AI responses by combining retrieval systems with generative models. Data Preparation: Learn to load, chunk, and embed documents for effective retrieval. Integration with LLMs: Seamlessly connect retrieval systems with models like GPT-3.5, Llama, or Mistral for enriched output. Practical Applications: Explore how RAG can be applied in chatbots, question-answering, and content creation tasks. This tutorial is perfect for anyone looking to enhance their AI projects with more grounded and reliable outputs. Check out the notebook and let me know your thoughts! #AI #Langchain #Colab #RAG #MachineLearning #AIDevelopment #OpenAI #HuggingFace #GenerativeAI

🌟 Completed “Mastering Generative AI: Prompt Engineering Basics” Course! I’m thrilled to share that I’ve successfully completed the “Mastering Generative AI: Prompt Engineering Basics” course. 🚀 In this comprehensive course, I delved into the world of generative artificial intelligence (AI) and learned how to effectively guide AI models using prompt engineering techniques. From creating impactful prompts to understanding zero-shot and few-shot techniques, I gained practical skills to unlock the full potential of large language models. 🤖 #genAssist #Engineering #prompt #generativeai

#LFMs are bound to replace #LLMs ? Explained by itself: "A Liquid Foundation Model (LFM) is a type of machine learning model that is designed to handle sequential data. It's called "liquid" because it can adapt and change its structure based on the data it's processing, much like a liquid can change its shape to fit its container. This model is particularly useful in natural language processing tasks, where the order of the data (words in a sentence, for example) is important. It can remember information from earlier in the sequence and use it to inform its understanding of later data. In essence, it's a flexible and adaptable model that can learn from and make predictions based on sequences of data." https://lnkd.in/eXqWY7Ng