ARK Invest Europe的动态

?? Excited to share my visualization on RAG Evaluation Metrics! While working with RAG systems, I noticed we often lack clarity on how to properly evaluate them. Through research and hands-on experience, I identified these three fundamental metrics that together paint a complete picture of RAG performance: Completeness: Does it capture all required information? Relevance: Is it focused on what matters? Faithfulness: Does it stay true to sources? The beauty lies in their mathematical simplicity - each metric is a straightforward ratio that tells us something crucial about the system's performance. What fascinates me most is how these metrics complement each other. A system might be complete but irrelevant, or relevant but unfaithful! ?? Would you be interested in a follow-up post about automated evaluation techniques for these metrics? It's an area that deserves more attention in our field. #AI #RAG #MachineLearning #DataScience #LLMs

83 of the 100 Best MBA Business Schools of the Financial Times' 2023 Ranking have INFORMS PubsOnLine Suite in their collections. https://bit.ly/3A8rVbZ #INFORMS #datascience #AI #ML #marketingscience #orms #analytics #transportation #optimization #mathematics #research #operationsresearch #managementscience #servicescience

What is Catastrophic forgetting? Catastrophic forgetting is the term where a LLM tends to lose previously acquired knowledge as it learns new information. Why Catastrophic ? Forgetting is called "catastrophic" because the model's performance on previous tasks doesn't just degrade slightly—it often drops dramatically or even completely. This sudden and severe loss of knowledge is what makes it "catastrophic." Instead of gradually forgetting or adapting, the model may entirely forget how to perform tasks it was once good at, leading to a sharp decline in its overall usefulness. #AI #data #science

?? ?????????????????? ???????????????????? ???????? ???????? ?????????? ?????? ???????????????? ??????????????! ?? Imbalanced datasets can seriously impact model performance. SMOTE (Synthetic Minority Over-sampling Technique) and ensemble methods are powerful tools to tackle this. ???? ?? Step 1: SMOTE generates synthetic samples to balance classes, improving model generalization. ?? Step 2: Ensemble methods split data into balanced subsets and use majority voting for the final prediction. ? ? Key Takeaways: ?? Improved accuracy ?? Better handling of class imbalance ?? Enhanced precision for minority classes #DataScience #MachineLearning #SMOTE #ImbalancedData #EnsembleLearning #AI

Reading financial reports often involves hours of meticulous analysis, connecting data dots, and extracting insights. With Anthropic Claude 3.5 Sonnet, this process becomes much more manageable and insightful. It's incredibly reader-friendly and allows us to generate demo dashboard in just a few minutes. Prompt: Create an interactive Business intelligence infographics report of this Schr?dinger Q1 2024 Update with financial and drug discovery highlights #FinancialAnalysis #AI #Anthropic #ProductivityHack

One thing that data scientists have fooled businesses for years is forecast/predictive accuracy is of so much importance and that too point forecast , point forecast will never be as accurate as your evaluation window accuracy because you have no control over future so what’s the fuss all about ? It’s about predicting confidence intervals accurately and planning best and worst case dynamics ( which actually businesses have followed for years with experience and ballparking these upper and lower limits ) #datascience #AI #supplychainplanning

#Data exploration generally has a much lower “hit” rate than #Analytics development with well-defined goals. But that is often where the low-hanging fruits, the bigger bangs for the buck, and even breakthroughs lie. #Statistics #DataScience #MachineLearning #AI #AdvancedAnalytics

#Data exploration generally has a much lower “hit” rate than #Analytics development with well-defined goals. But that is often where the low-hanging fruits, the bigger bangs for the buck, and even breakthroughs lie. #Statistics #DataScience #MachineLearning #AI #AdvancedAnalytics

Hello ???? Had a great time sharing insights on the transformative power of Generative AI in Business Analytics at ISME yesterday! It was a pleasure interacting with Shantanu Biswas and discussing how AI is revolutionizing the way we analyze data and make informed decisions. Key takeaways from the session: -How generative AI can enhance data exploration and discovery -The potential of AI-powered predictive analytics -Ethical considerations and responsible AI usage Thank you Sanjit Ghosh Let's continue the conversation! Feel free to connect and share your thoughts. #generativeAI #AI #businessanalytics #datascience #machinelearning #futureofwork #innovation

?? K-Means Clustering: Finding the Optimal K ?? Lately, I've been exploring K-means clustering, with a focus on identifying the optimal number of clusters (K). Two key methods have proven invaluable in this pursuit: ?? Elbow Method: Plotting inertia against K helps pinpoint the "elbow" point where inertia reduction slows, though interpreting an ambiguous elbow can be subjective. ?? Silhouette Score: This method evaluates how well each point aligns with its cluster, offering insights into cluster quality, yet it can face challenges with overlapping or imbalanced clusters. Key Challenges: - Cluster Imbalance: Real-world data often leads to clusters of varying sizes, complicating analysis. - High Dimensionality: Defining clear cluster boundaries becomes complex with numerous features. - Interpreting Results: Understanding the business relevance of each cluster post-K determination is a significant challenge. Determining the optimal K involves a dynamic process, balancing algorithm effectiveness with data insights. How do you approach selecting the best K in your projects? I'm eager to hear your insights! ?? #DataScience #MachineLearning #KMeansClustering #AI #UnsupervisedLearning #Tech