Generative AI’s Post

𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜'𝐬 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 Last year, prompt engineering was all the buzz in AI, with developers meticulously fine-tuning prompts. Anthropic has simplified the process. 🪄 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: ✅ Claude 3.5 Sonnet makes generating, testing, and refining prompts a breeze. 🛠️ ✅ Rapid feedback for quick optimisation. 📊 ✅ Suitable for both beginners and pros, enhancing project efficiency. 🚀 Describe your task, and Claude 3.5 Sonnet generates a high-quality prompt. Compare outputs, create new versions, and grade response quality to refine your results. #ai #generativeai #promptengineering

Most AI failures aren't due to innovation or tech. they're failing due to 𝗶𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴. Rushing the early stages. Skimping on rigorous tests. Patching things up as we go. Sound familiar? These shortcuts lead to... ❌ Inconsistent performance across different use cases. ❌ Inability to handle edge cases effectively. ❌ Slow response to user feedback and evolving data inputs. Why does this matter? Because thorough testing isn’t just a hoop to jump through. You 𝗻𝗲𝗲𝗱 a streamlined evaluation process. Hamel Husain made a mental model for improving AI systems, the Virtuous Cycle. ⬇️ Initially, Lucy handled simple tasks well. But as responsibilities grew, performance issues began to surface. This problem-solving turned into a game of whack-a-mole. Lucy had reached its limits with the existing setup. To improve Lucy, he focused on enhancing the evaluation process: 1️⃣ Unit Tests: developed rigorous unit tests to quickly identify errors in Lucy's responses. 2️⃣ Model & Human Eval: integrated deeper human reviews and model evaluations to pinpoint where Lucy was lacking. 3️⃣ A/B testing: used insights from evaluations to continuously refine prompts and fine-tune responses. The result? Lucy transformed from just another AI assistant into a powerful tool that significantly improved user experience and operational efficiency. Want a more detailed exploration of how to implement these principles in your AI projects? Read the full article by Hamel Husain: https://lnkd.in/dcNftbMr #LLM #ML #artificialintelligence #innovation #data For more insights into where AI is heading next, follow me.

🚀 Exciting News: Improving RAG Answer Quality with Re-ranker 🌟 Artificial intelligence keeps advancing, and so do the tools that empower it. Re-ranker algorithm, a key addition to our Retrieval Augmented Generation (RAG) pipeline. Let's dive into this groundbreaking development by experts. 🤖 What's Re-ranker? Re-ranker, also known as the "learning-to-rank approach," is the answer to enhancing Q&A accuracy. It meticulously sifts through responses generated by retrievers, like Watson Discovery or Elastic Search, and ranks them based on their relevance. Imagine a smart assistant that provides you with not just an answer but the best answer! 🔍 Why is Re-ranker a Game-Changer? First-stage retrievers can sometimes be less than perfect, ranking irrelevant documents highly and vice versa. Re-ranker comes to the rescue by refining these results and ensuring that you get the most relevant answers every time. 🌐 Unlock the Potential The impact of Re-ranker technology extends beyond RAG. You can easily implement it for various purposes, opening up new possibilities for leveraging AI in your applications.🚀 #AI #MachineLearning #ReRanker #GenerativeAI #Innovation #Technology

𝐀𝐈 I𝐬 𝐀 𝟏,𝟎𝟎𝟎-𝐘𝐞𝐚𝐫 𝐋𝐞𝐠𝐚𝐜𝐲 Artificial Intelligence isn’t a shiny new toy; it’s the culmination of a thousand-year-old quest. From ancient automata to modern machine learning, AI’s roots run deep. Techno-Business leaders need to appreciate this rich history to understand its potential and avoid repeating past mistakes. The development of automata in ancient #Greece and medieval #Europe showcases humanity’s long-standing fascination with intelligent machines. The works of Hero of Alexandria and Al-Jazari are prime examples. Research on the Jacquard loom and Ada Lovelace’s notes on the #Analytical #Engine provide a direct link to contemporary AI, demonstrating its evolution over centuries. Understanding the long history of AI helps techno-business leaders recognize its enduring value and potential.It’s not just a passing trend; it’s a well-established field with deep roots and vast implications for business. 𝐒𝐨 𝐖𝐡𝐚𝐭, 𝐅𝐨𝐫 𝐘𝐨𝐮 & 𝐘𝐨𝐮𝐫 𝐓𝐨𝐝𝐚𝐲 ? 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐎𝐥𝐝 & 𝐍𝐞𝐰 Businesses can leverage AI’s historical context to innovate while respecting time-tested principles. 𝘍𝘰𝘳 𝘦𝘹𝘢𝘮𝘱𝘭𝘦, 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘯𝘨 𝘳𝘦𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘵𝘢𝘴𝘬𝘴 (𝘢 𝘤𝘰𝘯𝘤𝘦𝘱𝘵 𝘧𝘳𝘰𝘮 𝘢𝘯𝘤𝘪𝘦𝘯𝘵 𝘎𝘳𝘦𝘦𝘤𝘦) 𝘳𝘦𝘮𝘢𝘪𝘯𝘴 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘵𝘰𝘥𝘢𝘺, 𝘧𝘳𝘦𝘦𝘪𝘯𝘨 𝘶𝘱 𝘩𝘶𝘮𝘢𝘯 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘪𝘵𝘺. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐒𝐭𝐞𝐩𝐬 Implement AI by focusing on automation and efficiency, but also use it to drive new insights and strategic decisions. 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦 𝘈𝘐 𝘪𝘯 𝘸𝘢𝘺𝘴 𝘵𝘩𝘢𝘵 𝘤𝘰𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵 𝘩𝘶𝘮𝘢𝘯 𝘴𝘬𝘪𝘭𝘭𝘴, 𝘫𝘶𝘴𝘵 𝘢𝘴 𝘢𝘯𝘤𝘪𝘦𝘯𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴 𝘥𝘪𝘥 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦𝘪𝘳 𝘤𝘳𝘦𝘢𝘵𝘪𝘰𝘯𝘴. 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈 𝐒𝐡𝐢𝐧𝐞𝐬 AI excels in data-intensive industries like finance, healthcare, and logistics, where its ability to process and analyze vast amounts of information can drive significant improvements. 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈 𝐒𝐭𝐫𝐮𝐠𝐠𝐥𝐞𝐬 Small businesses with limited data and resources may find it challenging to implement AI effectively. For them, simpler, more cost-effective solutions might be more appropriate. As we move forward, we’re not just creating new technology; we’re continuing a legacy of human innovation that began with the earliest thinkers and tinkerers. #aiforgood #aihistory #origins #ai #aiagents

EIGHT STEPS of AI In today's fast-moving world, where AI advancements are all around us, understanding the importance of each step is key to grasping the bigger picture. With AI becoming increasingly integrated into our daily lives, knowing how it works and the significance of its development steps allows us to gain a global perspective. Let's explore these fundamental steps together to truly comprehend the transformative potential of AI. 🎯 Problem Definition: Start by clearly defining the problem and desired outcomes. This sets the stage for effective AI solutions. 🔍 Data Collection and Preparation: Gather relevant data, clean it, and split it into training and testing sets for model development. 🤖 Model Selection and Algorithm Development: Choose the right AI technique and develop a suitable algorithm or model architecture. ⚙️ Model Training: Feed data into the model, adjust parameters, and monitor performance to ensure optimal learning. 📊 Model Evaluation: Test the trained model on unseen data and assess its performance using predefined metrics. 🛠️ Model Fine-tuning and Optimization: Continuously refine the model through adjustments and iterative improvements. 🚀 Model Deployment: Integrate the trained model into applications and systems, monitoring its performance in real-world scenarios. 👩⚖️ Ethical Considerations: Ensure fairness, transparency, and accountability in AI systems, addressing potential biases and following data privacy guidelines. Embrace the power of AI to innovate and shape a brighter future! Let's continue to explore its limitless possibilities together. #artificialintelligence #ai #innovation #techrevolution #technology

This is really awesome! The whole Gen AI development is still in its infancy, but it is remarkable what is already possible today. By creating our own GPTs, we are entering a completely new dimension. I found one by chance at the weekend - many thanks to Colin Gillingham - with the focus on giving a trading view based on any chart. Procedure (Video🔻): 1️⃣ Go to your own GPT - here Market View 2️⃣ Insert your own chart - here S&P 500 from Bloomberg 3️⃣ Prompt - here a very simple - "Analyse this chart for entry/exit points" 4️⃣ Result - must be interpreted by a professional. It is a very simple example, but it is intended to show where the journey will take us. Many functions in a #CIO office - whether in a multifamily office or a private bank - will change radically in the near future. In the future, AI will be able to perform boring, repetitive tasks faster and better. The most important thing at the end - the human being is irreplaceable, as he is both the creative input provider (what do I want to analyze) and must interpret the output for the client. This is the only way we can improve the customer experience, and that is our number one goal. #genai #ai #transformation #ciovision #lookingbeyond #thomaswille

When it comes to prompt engineering, there are two primary methods apart from various other techniques : Few-shot and Zero-shot prompting. Each approach has its own set of pros and cons. 🤖 ➡ Few-shot prompting involves providing the AI with a few examples within the prompt, illustrating the desired outcome. This method leads to improved accuracy, flexibility, and reduced ambiguity. However, longer prompts can be cumbersome, and the quality of the examples heavily influences the outputs. Additionally, the model might rely too much on the examples, reducing its ability to generalize. ➡ On the other hand, zero-shot prompting involves asking the AI to perform a task without examples, relying on its pre-existing knowledge. This method is efficient, has broad applicability, and encourages the model to leverage its broader knowledge. However, it may lead to lower accuracy, context dependence, and limited specificity. 🌟 The choice between these methods hinges on balancing accuracy, flexibility, and efficiency based on the task at hand. In summary, prompt engineering, whether through few-shot or zero-shot prompting, is a powerful tool for leveraging AI capabilities. How have you utilized prompt engineering in your work, and what challenges have you faced in achieving the desired AI responses ❓Share your experiences and insights in comments ! #AI #engineering #promptengineering #AIcapabilities #fewshotprompting #zeroshotprompting #artificialintelligence #machinelearning

🤖🔧 Embracing the AI Mindset in Engineering: Building Tomorrow's Innovations Today 🤖🔧 In the ever-evolving landscape of engineering, there's a game-changer that's transforming the way we design, create, and innovate - Artificial Intelligence. 🌟 The AI mindset in engineering isn't just about using AI tools; it's about a fundamental shift in how we approach problem-solving and innovation. Embracing the AI mindset isn't just an option; it's a necessity for engineers who want to lead in our tech-driven world. Let's not fear the future but shape it with our creativity and AI expertise. 🚀 How are you incorporating AI into your engineering journey? Share your thoughts and experiences below! 👇💡 #AIinEngineering #EngineeringInnovation #AI #EngineeringMindset #FutureofEngineering

📢 AI Framework Stanford suggests typical AI (or GenAI) follows FURM framework - FURM means "Fair", "Useful", "Reliable" and "Monetizable" Models. It needs to follow life cycle which is different than usual software life cycle in order to adopt FURM.- #1. Define Problem Statement #2. Ethical Concerns Review #3. Business Case #4. Use Case Definition #5. Utility Assessment #6. Workload Analysis #7. Model Formulation #8. Model Development- performance #9. Model Development -fairness #10. IT feasibility #11. Workflow Integration #12. Monitoring #13. Prospective Evaluation. #datascience #ai #genai #innovation #softwareinnovation #ushealthcare

Exciting news in the world of AI - #Claude3 model family just launched! This is a significant leap forward in setting industry standards for cognitive tasks. The family proudly introduces three groundbreaking models: → Haiku → Sonnet, and → Opus. What stands out about this innovation? - Unmatched intelligence 🧠 - Speed ⚡ - Cost-effectiveness 📉 They're taking AI performance to new heights with Opus leading the charge with its human-like comprehension and efficiency. These models foster impressive capabilities in - 1. Analysis & Forecasting ↳ Maneuvers through data like never before. 2. Content creation ↳ From articles to crisp social media posts, it's got it all covered. 3. Code generation and ↳ Automatically generates desired code in real-time. 4. Mastering multilingual conversations. ↳ Seamless interactions in multiple languages, making it truly global. They've managed to facilitate live customer interactions and real-time data processes with near-instant results - setting a high bar indeed. To top it off, they have completely revolutionized visual processing capabilities. From pictures and graphs to complex technical diagrams, these models have the dexterity to handle it all, making them an absolute dream for enterprise clients. #Sonnet and #Opus are now available for use in 159 countries via https://drp.li/jTg9l and the Claude API, with the promising #Haiku soon to follow. As an ardent follower of AI advancements, I'm thrilled to see such progress in this domain and can't wait to see how #Claude3 will impact various industries. Check out https://drp.li/jTg9l to explore this innovative offering from Claude. The future of AI looks promising! 🌟🚀💡