Lars Rønn’s Post

AI is reducing the value of these skills: 🔻 Statistical analysis 🔻 Machine Learning 🔻 Data Manipulation 🔻 Programming

AI is changing data science. This graph explains how AI is shifting the role of the Data Scientist to a new role I call the AI Business Scientist. Let me explain. AI is here, whether you like it or not. That means the landscape of business needs is shifting, and it means your skills are changing in value. Some are increasing. Some are declining. As I look towards the future of data science, I see a shift to a new role called the AI Business Scientist. Let me break this down by time: Pre-2023: Businesses wanted people to come in and analyze their data, predict what was going to happen, and make better decisions. Post-2023: In 2023 that all changed with AI. ChatGPT made technical skills much easier to obtain as long as you could ask good questions. 📉 These technical skills became less important to master: 1. Statistical Analysis 2. Machine Learning 3. Data Manipulation 4. Programming As long as you know the right questions to ask and how to check ChatGPT's work, you can create decent solutions quickly. But something else changed. Businesses saw the power of AI, production, and problem-solving. Many soft skills became more important along with a new batch of technical skills. 📈 These skills gained importance: 1. Generative AI (new technical skill) 2. Web Apps (production) 3. Project Management (soft) 4. Problem Solving (soft) 5. Strategic Thinking (soft) 6. Domain Knowledge (soft) The winners of this new AI era will become builders of AI. Or what I call AI Business Scientists. They will be able to understand business problems, architect solutions with AI and ML, and put business solutions into production themselves. Ready to become an AI Business Scientist? On May 30th, I'm launching my new AI course that is designed to teach you the exact Generative AI skills that companies need this new breed of AI Business Scientists to possess. 👉 Register for my AI course waitlist here (seats are limited): https://lnkd.in/ePcscx4k

Yesterday, MIT CSAIL unveiled an intriguing look into AI's evolving role in our workplaces. Their study reveals that just about 23% of tasks involving computer vision are economically viable for AI automation right now. This paints a picture of AI’s integration into our work lives as more gradual and multifaceted than we have been taught to believe from recent media headlines. While the emphasis on computer vision is important, it’s worth noting the study's limited scope. In the vast AI landscape, areas like data analysis, natural language processing, and decision-making play a crucial role in the everyday activities of knowledge workers, perhaps more so than computer vision tasks. There's also room to question these findings, especially with the swift advancements in generative AI. For instance, with the current growth trajectory of OpenAI’s ChatGPT, the upcoming ChatGPT 5 model might easily end up being able to do 50% of computer vision tasks at half the cost. This study, honing in on computer vision, kickstarts a broader conversation about AI's expansive potential in various sectors. It highlights the layered and intricate path of weaving AI into our professional spheres, opening doors to new discoveries and insights.

Have you heard about the treasure trove of ChatGPT prompts for data science? These 600+ prompts can supercharge your data analysis, visualization, and exploration. Imagine transforming how you approach data cleaning, deep learning, and model deployment. Stick around; I’ll reveal how to access this valuable resource for free, even if you're just starting out. ☑ Data Cleaning Prompts ↳ Streamline your data preparation process with ease. ↳ Identify and fix inconsistencies like a pro. ↳ Prepare clean datasets for accurate analysis. ☑ Visualization Techniques ↳ Create compelling visual representations of your data. ↳ Use prompts to generate clear, impactful charts. ↳ Communicate your findings effectively. ☑ Exploratory Data Analysis ↳ Uncover hidden patterns within your datasets. ↳ Generate insights quickly with tailored queries. ↳ Make better decisions based on solid data understanding. ☑ Deep Learning Strategies ↳ Leverage advanced techniques with clear guidance. ↳ Experiment and refine your models using AI-generated prompts. ↳ Master the complexities of deep learning easily. ☑ Natural Language Processing ↳ Unlock the potential of text data analysis. ↳ Create effective models for sentiment and context understanding. ↳ Automate data extraction and processing tasks. ☑ Access Instructions ↳ Sign up for a free Notion account. ↳ Clone the template for your personal use. ↳ Start using the prompts today without any costs. The takeaway? These prompts can elevate your data science game dramatically. How will you utilize AI to enhance your data processes? #AI #DataScience #ChatGPT #DataAnalytics #DataVisualization #MachineLearning #Notion

AI Literacy: The New Essential Skill The AI revolution is here, and the workforce is scrambling to keep up. With ChatGPT's debut, AI literacy has become a global imperative: - 66% of leaders won't hire without AI skills (Microsoft) - 94% of employees want to learn AI (Accentute) - GenAI course enrollments up 1,061% globally (Coursera) Yet only 5% of companies have implemented large-scale reskilling. The message is clear: Upskill in AI or risk being left behind. Are you prepared for the AI-driven future?

How Faithful are RAG Models? This AI Paper from Stanford Evaluates the Faithfulness of RAG Models and the Impact of Data Accuracy on RAG Systems in LLMs https://lnkd.in/deCRK4Vf “`html Retrieval-Augmented Generation (RAG) in Large Language Models Practical Solutions and Value RAG technology enhances large language models (LLMs) by integrating external information with existing model knowledge, improving accuracy particularly for queries about recent or nuanced data not in their training set. This addresses limitations of LLMs and supports precise responses. Enhancing Accuracy and Relevance Effective RAG systems seamlessly integrate a model’s internal knowledge with accurate, timely external data, improving response precision and navigating conflicting information while maintaining reliability. Real-Time Data Retrieval and Factual Accuracy The RAG model and the Generation-Augmented Retrieval framework enhance generative models with real-time data retrieval, significantly improving factual accuracy in responses. Commercial models like ChatGPT and Gemini utilize retrieval-augmented approaches to enrich user interactions with current search results. Evaluating RAG Systems Efforts to assess RAG systems include rigorous benchmarks and automated evaluation frameworks to focus on operational characteristics and reliability in practical applications. Understanding Adaptability and Reliance Stanford researchers analyze how LLMs, specifically GPT-4, integrate and prioritize external information retrieved through RAG systems. The focus is on the interplay between a model’s pre-trained knowledge and the accuracy of external data, providing insights into adaptability in practical applications. Effectiveness and Limitations The study found that RAG systems significantly improve response accuracy with correct data but their effectiveness diminishes with inaccurate external information. This highlights the importance of enhancing RAG system designs for better discrimination and integration of external data. AI Solutions for Your Business Evolve your company with AI to stay competitive. Identify automation opportunities, define KPIs, select AI solutions, and implement gradually. Connect with us at [email protected] for AI KPI management advice. Practical AI Solution: AI Sales Bot Consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. “` List of Useful Links: AI Lab in Telegram @aiscrumbot – free consultation Twitter –  @itinaicom #artificialintelligence #ai #machinelearning #technology #datascience #python #deeplearning #programming #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #business #engineering #robot #datascient...

📌 Top 23 Key AI Terms & What They Mean for CIOs & Business Leaders Navigating the world of generative AI comes with its own vocabulary. Here’s a quick rundown of the top AI terms every business leader should know for strategic and operational success. 🧠 Agentic Systems: Autonomous AI agents collaborating on tasks. 🧩 Alignment: Ensuring AI systems align with company values. 🔍 Black Box: When a model’s decision-making is hidden. 💡 Context Window: Defines how much text or data an AI model can process at once. ⚙️ Distillation: Making smaller, efficient versions of larger models. 📊 Embeddings: Data representations that group similar items. 🎯 Fine-Tuning: Customizing a pre-trained model with specific data. 📂 Foundation Models: Large models used as the base for various tasks. 🏗️ Grounding: Providing AI with specific data for accurate responses. 🚨 Hallucinations: False or misleading answers from AI. 👥 Human in the Loop: Adding human oversight to AI outputs. 💬 Inference: The process of using trained models for responses. 🔓 Jailbreaking: Circumventing an AI's safety guardrails. 📚 Large Language Model (LLM): Massive models like ChatGPT for complex tasks. 🎥 Multimodal AI: Models handling various data types (text, images, audio). 📝 Prompt: The question or instruction given to an AI. 🧑🏫 Prompt Engineering: Crafting prompts to get better results. 🔎 Retrieval Augmented Generation (RAG): Adding data to enhance response accuracy. 🔒 Responsible AI: Ethical, fair, and transparent AI. 📉 Small Language Model: Smaller, cost-effective models for targeted tasks. 📊 Synthetic Data: Artificial data for model training. 🔗 Vector Database: Storing data for fast, context-based AI responses. 🕹️ Zero-Shot Prompting: Using AI without examples for guidance. 𝗢𝘂𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: 𝗦𝘁𝗮𝗳𝗳𝗶𝗻𝗴: We offer contract, contract to hire, direct hire, remote global hiring, SOW projects and managed services. https://lnkd.in/g6bddCHa 𝗥𝗲𝗺𝗼𝘁𝗲 𝗛𝗶𝗿𝗶𝗻𝗴: We offer U.S. companies the opportunity to hire IT professionals from our India-based talent network. https://lnkd.in/gN2A4c-Y 𝗖𝘂𝘀𝘁𝗼𝗺 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: We offer Web/Mobile Development, UI/UX Design, QA & Automation, API Integration, DevOps services and Product Development. https://lnkd.in/dcKsvxAu 𝗢𝘂𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 𝗭𝗲𝗻𝗕𝗮𝘀𝗸𝗲𝘁 :An E-commerce platform to sell your products online to a large user base with custom features. https://getzenbasket.com/ 𝗭𝗲𝗻𝘆𝗼 𝗣𝗮𝘆𝗿𝗼𝗹𝗹: An automated payroll application that helps companies in India process their employees' payroll. https://lnkd.in/gvDg-Uds 𝗭𝗲𝗻𝘆𝗼 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲: Simplifying all HR processes and maximizing productivity by automating routine tasks. https://lnkd.in/grcEACXM #AI #GenerativeAI #CIO #BusinessTransformation #TechInnovation #AITrends #MachineLearning #DataScience #EnterpriseAI #DigitalTransformation #Centizen #ZenBasket

Let's try a Yes-No fact-check question for reality and trust: "Is there any data products you know, that can interpret and answer the following realistic Chinese-English multilingual questions of BI?" With our IP, a copyrighted multilingual metadata, we can provide real time answers, by census geographical locations, as evidence for policy/decision making, since 2009. "Who, in the Ontario province of Canada, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇‘’ province of China, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, NVIDIA chips, supercomputers, etc. https://lnkd.in/g-aJFnXR Our IP can also make your information service UNIQUE in the world.

Prompt engineering is the key to unlocking the true potential of AI, transforming it from a tool into a strategic partner for your business. Master this skill and join the future of work – explore our educational AI course "AI For Everyone" https://lnkd.in/gSsb68Qh Use the Code: RUSSELL for 15% off

I posted a new essay on my (infrequently updated) tech blog. I'm going to be publishing much more now that I don't have a corporate tech blog platform to write for. https://lnkd.in/ervz25C2 Take a look and feel free to tell me why I am wrong. TL:DR - A lot of organizations won't reap any real benefits of AI / ML because they are not data driven currently. - If they have no data to train and refine models, all they will be able to use is generic content. - Nonetheless, there will be people trying to sell them "AI enablement" which will have little positive impact. - At the same time, managers and execs who don't actually know what AI is have been convinced it's a magic wand to cut workforce and transform their businesses.