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.
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AI's pervasive reach in our modern world often belies the complex challenges lurking beneath its polished surface. When we cast our minds back to the 1970s, we're reminded of the optimism that surrounded 'expert systems'. Packaged as the beacon of progress, these AI systems were sold as the elixir to corporate relevance - a narrative eagerly consumed by CEOs anxious not to fall behind. 💼🚀 Fast forward to the 80s, and an impressive two-thirds of Fortune 500 companies had already taken the plunge, eager to stay competitive. 📈 However, the expectation of these systems' infallibility was as flawed then as it is now. The dilemma persists: the relentless evolution of knowledge demands AI's ceaseless adaptation, a feat more intricate than merely flipping a switch. 🧠 Providing these systems with the latest information is a Herculean task. The most astute minds, the wellsprings of this information, often remain elusive, guarded behind the high walls of their expertise. Moreover, as the knowledge base burgeons, so does the requirement for computational power, translating into spiralling costs. 💻💰 But what looms largest is the shadow of error - an inevitable but often underestimated aspect of AI systems. 🛑 The crux of the matter is not merely the occurrence of errors; rather, it's the anticipated reaction to these missteps. Consider this: when AI systems err, there's a sluggishness in our response, a slow-dawning realization married to actions already set astray. Hindsight may be 20/20, but in the moment, AI-induced mistakes can slip undetected under the radar of human perception. 💡 When humans falter, interrogation paves the way to understanding. Shouldn't AI be held to a similar standard? If an AI system cannot articulate the 'why' behind its conclusions, its market readiness should be questioned. After all, in a world ever reliant on the judgements of intelligent machinery, accountability must evolve alongside capability. 🤖🔍 This reflection is not a critique of our technological ambitions but rather a cautionary tale reminding us to bridge the gap between expectation and reality. As we harness the power of AI, let's tread thoughtfully, acknowledging the limitations and ensuring that when systems falter, we're prepared to catch the fall. #AIaccountability #ExpertSystems #TechnologyEthics
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AI Tools: Focus on Needs, Not Just Features We've all been there. That new AI tool with a killer demo – it's hard to resist, right? But jumping headfirst into every shiny new thing is a recipe for lost time, wasted resources, and misaligned tech. Here's why a tool-focused approach can backfire: Shiny Object Syndrome: Distracts you from the real problems you need to solve. Innovation Theater: Using flashy AI for show, not actual impact. Tech-First Trap: Seeing every problem as a nail for your shiny new AI hammer. 🔨 Flip the script: Think problems, not tools. 🎯 Ask the tough question: "How exactly will this tool move the needle for our customers and business?" 🙅♀️ Don't be afraid to ditch a tool if it's not delivering measurable results. Before you get swept away by the demo, ask yourself: 🤔 Integration: How does this actually fit into our workflows? 🔐 Security and Safety: Is this robust and ethical at scale? 💰 ROI: Will this be truly cost-effective long-term? AI has the potential to be transformative – but only when it's in service of real needs. Let's stay focused on the outcomes, not the tools themselves! #GenAI #LLM #Prompt #promptengineering #AI #innovation #ml #finetuning
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𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜'𝐬 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 Last year, prompt engineering became the talk of the AI world. Developers everywhere were fine-tuning their prompts, tweaking every word to get the perfect response from their AI models. It was a painstaking process, but Anthropic just made it a whole lot easier. 🪄 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: ✅ With Claude 3.5 Sonnet, generating, testing, and refining prompts is now a breeze. No more endless trial and error. 🛠️ ✅ The new tools offer rapid feedback, helping you quickly identify what works best. 📊 ✅ Whether you're new to AI or a seasoned pro, these features are designed to help you streamline your projects and get better results. 🚀 Crafting high-quality prompts is crucial yet challenging in AI applications. Anthropic has made this easier with new features in the Anthropic Console. Now, you can simply describe your task, and Claude 3.5 Sonnet will generate a high-quality prompt for you. You can compare outputs side by side, create new versions, and re-run test suites to quickly iterate and improve results. Additionally, you can grade response quality on a 5-point scale to see whether your changes have improved the performance. More information: https://lnkd.in/dUFwvpWE Have you tried these new tools? Share your experiences in the comments below. #ai #generativeai #promptengineering
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Did you know? 🤔 In the ever-evolving world of AI, two exciting concepts are gaining traction: Retrieval-Augmented Generation (RAG) and the Agentic Framework. These innovations are shaping the future of intelligent systems in remarkable ways. 🔍 Retrieval-Augmented Generation (RAG): RAG combines the power of retrieval and generation. It enhances language models by integrating a retrieval mechanism that fetches relevant documents or data, which the model then uses to generate more accurate and contextually enriched responses. This hybrid approach improves the quality and relevance of AI-generated content, making it a game-changer for applications in customer support, content creation, and beyond. 🤖 Agentic Framework: The Agentic Framework empowers AI to operate more autonomously. It structures AI behavior around agents—autonomous entities that can perform tasks, make decisions, and interact with their environment. By breaking down complex tasks into smaller, manageable actions, this framework boosts the efficiency and versatility of AI systems, leading to smarter and more adaptive solutions. Together, RAG and the Agentic Framework are pushing the boundaries of what AI can achieve. They are paving the way for smarter, more efficient, and contextually aware systems that can truly understand and respond to our needs. Stay tuned as these technologies continue to evolve and transform the AI landscape. The future of AI is not just about smarter machines—it's about creating intelligent systems that enhance our capabilities and drive innovation across industries. 🚀 #AI #Innovation #TechTrends #MachineLearning #FutureTech
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Managing Director at BCG | Technology Executive | Driving Digital Transformation & Innovation | GenAI Strategy | Digital & Data platforms | IT Architecture | Cloud Solutions | Guest Lecturer at UGA | GTA Advisory Board
Generative AI, while transformative, isn't a universal key to all business dilemmas. The essence of effective problem-solving lies in a discerning selection among diverse analytics techniques, each specifically matched to the challenge being addressed. This selection process becomes more nuanced when it integrates a thorough consideration of the organization's unique context, encompassing operational realities, regulatory requirements, and budgetary constraints. At the core of this approach is the clear identification of the actual problem, a step that guarantees the chosen technology not only tackles the issue efficiently but also harmonizes with the organization's wider strategies and limitations, ensuring a coherent and strategic application of technology solutions. #Technology #ProblemSolving #GenAI
Find the AI Approach That Fits the Problem You’re Trying to Solve
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Senior Product Leader | Driving Operational Excellence & Innovation | Designing Strategies for Global Business Success Through Local Market Preferences
Read this if you fear AI might take over your role next! In an era where AI and cutting-edge technologies are revolutionizing the way we work, it's crucial to remember! Behind every data-driven decision, there stands a human being, balancing logic with intuition. While AI excels in automating routine tasks, complex decision-making remains a distinctly human domain, where both logic and reasoning are paramount. Let's delve into why, despite the metrics and algorithms, the final call still relies on human judgment. #strategygrams #productgrams #gauravgrams #generatieveai
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Artificial Intelligence (AI) is everywhere and the industry is booming. In 2022, the global AI market was valued at $136.6 billion. In 2023, that number grew to an estimated $500 billion. By 2030, we are looking at a nearly $2 trillion industry. Today, there are countless AI platforms that perform all sorts of tasks and processes. And more are added an a daily basis. AI and automation will profoundly impact the way we shop, learn, communicate, create, and do business for the foreseeable future. If you are a business owner who has yet to embrace the promise and possibilities of AI in your organization, it is time to dig a little deeper. To start, read “Ay Yi Yi: The Fascinating World of AI” at The Alternative Board (TAB). https://lnkd.in/dEyqFAyE #tabboards #businesscoaching #artificialintelligence
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Research Operations & Project Mgmt at Barclays, specializing in Program & Change Mgmt, Business Intelligence, Power BI, DAX, enabling effective business analysis. Passionate about strategic initiatives & results.
Progress Update on Company Research AI POC I am pleased to report that significant progress has been made on the Company Research AI POC. By the end of next week, we aim to have the first draft ready to demo. The backend infrastructure has been successfully developed to connect to the API, and the front end is now operational. Users can select a company from a dropdown menu, and within 5 seconds, the AI generates research content(at this point only basic company update). Currently, i am in the process of refining and polishing the output. The system pulls in a summary update and displays a selection of key financial metrics on the right-hand side for easy reference. This personal project marks a significant step forward in automating research processes and enhancing analytical capabilities. Im quite buzzed about the potential impact this AI tool can have on streamlining research workflows and improving decision-making processes. #ai #airesearch #equityresearch #researchanalyst #barclays #investmentbanking
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AI & ML Innovator | Transforming Data into Revenue | Expert in Building Scalable ML Solutions | Ex-Microsoft
3moIt's clear that thorough testing is crucial for AI success. Rushing or skipping steps can lead to inconsistent performance and slow response to feedback. Hamel Husain's Virtuous Cycle highlights the importance of rigorous evaluation, from unit tests to human reviews. This approach can transform AI systems, improving user experience and efficiency. Implementing these principles can be challenging but ultimately rewarding. It ensures AI systems meet user needs effectively. Thanks for sharing this valuable insight into AI development processes!