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5 Hard Truths About Generative AI for Technology Leaders
The text discusses the challenges and potential of generative AI (GenAI) in driving business value. It highlights the importance of developing differentiated and valuable features, addressing data, technological, and infrastructure challenges, and involving key players like data engineers. It emphasizes the need for a strategic approach to leverage GenAI effectively in business.
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Why Do Data Teams Fail at Delivering Tangible ROI?
The text explores the obstacles faced by data teams in achieving tangible Return on Investment (ROI). It outlines steps for measuring ROI, such as establishing key performance indicators, improving them through data, and measuring the data’s impact. The article identifies various obstacles, including alignment with business priorities, setting realistic expectations, root cause analysis, taking action…
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AI Customer Support App: Semantic Search with PGVector, Llama2 with RAG, and Advanced Translation Models
The text is about leveraging AI in customer support for multilingual semantic search, advanced translation models, and RAG systems for enhanced communication in global markets. It covers mBART for machine translation, XLM-RoBERTa for language detection, and building a multilingual chatbot for customer purchasing support using Streamlit. The article presents a detailed technical approach and future…
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Bayesian Inference: A Unified Framework for Perception, Reasoning, and Decision-making
French mathematician Pierre-Simon Laplace recognized over 200 years ago that many problems we face are probabilistic in nature, and that our knowledge is based on probabilities. He developed Bayes’ theorem, influential in diverse disciplines and increasingly applied in scientific research and data science. Bayes’ reasoning has significant implications for perception, reasoning, and decision-making.
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What Makes A Strong AI?
Summary: The text discusses the concepts of mediators in causality, their impact on outcomes, and the need to distinguish direct and indirect effects. It also explores the challenges of estimating causal effects and the importance of combining causality with big data. Furthermore, it outlines the characteristics of a strong AI as highlighted in Judea Pearl’s…
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What Next? Exploring Graph Neural Network Recommendation Engines
The article discusses using a Graph Neural Network (GNN) approach to build a content recommendation engine. It explains GNN concept, graph data structures, and their application using PyTorch Geometric. The article then details the process of feature engineering, building a graph dataset, and training a GNN model. Finally, it evaluates the model’s performance with RMSE…
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2,778 researchers weigh in on AI risks – what do we learn from their responses?
A survey of 2,700 AI researchers revealed varied opinions on AI risks. Notably, 58% foresee potential catastrophic outcomes, while others predict AI mastering tasks by 2028 and surpassing human performance by 2047. Immediate concerns like deep fakes and misinformation also trouble over 70% of researchers. Balancing both short-term and long-term AI risks is highlighted.
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Generating value from enterprise data: Best practices for Text2SQL and generative AI
Generative AI has revolutionized AI, finding applications in text generation, code generation, summarization, and more. One evolving area is natural language processing (NLP) for intuitive SQL queries, aiming to make database querying more accessible to non-technical users. Key considerations include prompt engineering, architecture patterns, and optimization for efficient text-to-SQL systems using Large Language Models (LLMs).…
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Promotion Forecasting: Case Study with a Retail Giant
Using machine learning, NLP, and deep domain knowledge, Auchan Retail International achieved an impressive 18% reduction in out-of-stock items and overstock across national operations in just one year. Their dual-model strategy, extensive feature engineering, and close collaboration with stakeholders led to substantial operational improvements and efficiency in retail forecasting.
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Optimization or Architecture: How to Hack Kalman Filtering
The paper discusses the superiority of Kalman Filter (KF) over neural networks in some cases and the need to optimize KF parameters. Despite its 60-year-old linear architecture, the KF outperformed a fancy neural network after parameter optimization. The study emphasizes the importance of optimizing KF and not relying on its assumptions, offering a simple training…