Encore Group ’s Post

What are the anti-patterns to avoid in projects? A rigorous function leader watches for anti-patterns in project planning, execution, and completion, such as Death by Planning Pattern, Fire Drill Pattern, and Throw It Over the Wall Pattern, and guides the team out of these anti-patterns as early as possible. *Death by Planning Pattern: A plan is overly complicated and not trackable against execution. *Fire Drill Pattern: Completing an initiated but delayed project in desperation while compromising on scope and quality. *Throw It Over the Wall Pattern: Code-complete but weakly tested and documented projects, developed by one team and intended to be operated by another. 101 gem insights are included in this book by Jike Chong and Yue Cathy Chang published by Manning Publications Co. Share and follow How to Lead in Data Science for more! The videos are from our Manning Early Access Program (MEAP) days! #artificialintelligence #machinelearning #ai #ml #leadingds #datascience #analytics

🚀 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐮𝐩𝐝𝐚𝐭𝐞! We are soon launching a new seminar series where leading academics and professionals will explore the latest trends, research, and innovations in machine learning. 𝐅𝐢𝐫𝐬𝐭 𝐒𝐞𝐦𝐢𝐧𝐚𝐫 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐥𝐞𝐫𝐭! 🗓️ Join us for our first session with Alberto Rossi and Rohit Singh on September 10th where we will dive deep into “Real-time Machine Learning in the Cross-Section of Stock Returns”. 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐣𝐨𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧? ➡️ The authors challenge the existing literature and show that unlocking the potential of machine learning in investment strategies falls short of previous expectations, as real-time implementation reveals weaker out-of-sample performance. ➡️ Their findings suggest a more nuanced view of economic gains, emphasizing the need for cautious optimism in this evolving field. 𝐎𝐮𝐫 𝟏-𝐡𝐨𝐮𝐫 𝐬𝐞𝐦𝐢𝐧𝐚𝐫 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 1️⃣ 𝐁𝐫𝐢𝐞𝐟 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 ✅ Overview of the format and topic ✅ Meet our experts ✅ Expectations from our audience 2️⃣ 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 ✅ Cutting-edge research: short paper presentation ✅ Expert Dialogue: Investigating Relevance and Identifying New Application Areas ✅ Interactive Q&A 3️⃣ 𝐖𝐫𝐚𝐩-𝐮𝐩 & 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞a𝐰𝐚𝐲𝐬 Don't miss out—register for the first session and visit our website for details using the link in the comment section below.👇 #AI #ResearchIntoPractice #Finance #DigitalTransformation Frankfurt School of Finance & Management Georgetown University McDonough School of Business martini.ai Centre for European Transformation Birgit Frankenberg Paulina Verhoff

How does the mind make sense of the world? How can our teams make sense of their world? The answer may lie in the unexpectedly simple world of Predictive Processing. Find out how your teams can use their brains' particular predictictions to provide pathways, powerful pictures and plans, presto. It's all on my Agile On The Mind substack where I explore Cognitive Science, Organisational Intelligence, and Agility. Do check it out.

🚀 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐮𝐩𝐝𝐚𝐭𝐞! We are soon launching a new seminar series where leading academics and professionals will explore the latest trends, research, and innovations in machine learning. 𝐅𝐢𝐫𝐬𝐭 𝐒𝐞𝐦𝐢𝐧𝐚𝐫 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐥𝐞𝐫𝐭! 🗓️ Join us for our first session with Alberto Rossi and Rohit Singh on September 10th where we will dive deep into “Real-time Machine Learning in the Cross-Section of Stock Returns”. 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐣𝐨𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧? ➡️ The authors challenge the existing literature and show that unlocking the potential of machine learning in investment strategies falls short of previous expectations, as real-time implementation reveals weaker out-of-sample performance. ➡️ Their findings suggest a more nuanced view of economic gains, emphasizing the need for cautious optimism in this evolving field. 𝐎𝐮𝐫 𝟏-𝐡𝐨𝐮𝐫 𝐬𝐞𝐦𝐢𝐧𝐚𝐫 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 1️⃣ 𝐁𝐫𝐢𝐞𝐟 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 ✅ Overview of the format and topic ✅ Meet our experts ✅ Expectations from our audience 2️⃣ 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 ✅ Cutting-edge research: short paper presentation ✅ Expert Dialogue: Investigating Relevance and Identifying New Application Areas ✅ Interactive Q&A 3️⃣ 𝐖𝐫𝐚𝐩-𝐮𝐩 & 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞a𝐰𝐚𝐲𝐬 Don't miss out—register for the first session and visit our website for details using the link in the comment section below.👇 #AI #ResearchIntoPractice #Finance #DigitalTransformation Frankfurt School of Finance & Management Georgetown University McDonough School of Business martini.ai Centre for European Transformation Birgit Frankenberg Paulina Verhoff

Building the complexity science in an environment is an art of problem solving, making out the relationship between the lessons learnt and the unknown is an art of decision making and this art bridges the gap between the heuristic and the algorithmic lake. #musigma #analytics A thought of making out solution from a problem is the capability of making out another problem from the solution and that's where we get into the complexity science and its essence of interconnectedness. Decision science is a journey of bringing out solutions from a complex problem but rather I would see it as discovering a new problem from the solution and making the industry future ready. LLMs can be optimized and made more capable once you know the problem and the solutions can be a key towards a better solution and a better decision making. d = D #decisonscience #capability #genai #thought

In the age of AI, we are constantly bombarded with mis-(information) from all directions. Our decisions are influenced by our biases and the information stored in our brains. Our rational minds struggle in this era of shortened attention spans, driven by social media consumption. How can we uncover our biases and remain rational and critical in any situation? Here is a great course for everyone—not just for those in the AI field, but for anyone who wants to grow intellectually. I am enjoying this Coursera course by Fractal and Analytics Vidhya, taught by Srikanth Velamakanni. Course Link: https://lnkd.in/gRypktXn We need more critical thinkers in this AI age. #criticalthinking #coursera #AI

Alchemy 2024-25, the Annual Management Conclave of SIBM Bengaluru, is forthcoming. This is the time to delve into the invaluable insights of our esteemed faculty. Their experiences and knowledge will enhance our understanding of the themes of Alchemy as well as the dynamic business trends. SIBM Bengaluru proudly presents Episode 2 of Faculty Speaks with Dr. swarnalakshmi umamaheswaran, Assistant Professor, highlighting how AI is transforming analytical practices and its applications in decision making and strategic forecasting. #LifeAtSIBMB #SIBMBengaluru #MBALife #AlchemyAtSIBMB

Some very interesting data here, courtesy of Benjamin F. Jones, Brian Uzzi, and Dashun Wang of the Kellogg School of Management at Northwestern University. They highlight complexity science as a means of better understanding how an organization functions. Complexity science uses the tools of big data, network science, and AI to look at complex systems holistically, such as looking at a flock of birds rather than describing the behavior of the individual birds. They argue that a similar approach makes sense for a business: while you can learn a lot from studying the impact of individuals within an organization, the organization as a whole also operates almost as an organism in its own right. Additionally, markets and organizations could also be analyzed in the same way, meaning that companies could gain valuable insights by looking at them through the lens of complexity science. It will be interesting to see how this approach could potentially shape business strategy over time. #ComplexityScience #BusinessStrategy

Interested in knowing more about the advantages of human-AI collaboration? Log on for our next presentation in the Data Science Lunch & Learn Series, which will be held Tuesday, March 6, at 12:30 p.m. Meng Zhu, marketing professor in Virginia Tech - Pamplin College of Business, has conducted research in human-AI collaboration, discovering that AI can be an advantageous collaborator in domains that have profound social and personal consequences. But are humans able to recognize AI-human complementarity? For more information about the talk and the series (including details on how to log on and watch prior discussions), check out the link: https://lnkd.in/eXamV9wz #datascience #lunchandlearn #artificialintelligence Virginia Tech College of Science  Virginia Tech College of Engineering

I'm excited to announce the successful leadership and completion of an ambitious project that I spearheaded from ideation to execution. Our endeavor aimed to revolutionize House Price Prediction through the integration of Satellite Imagery and state of the art Machine Learning methodologies. Leveraging my expertise in Computer Vision and Project Management, I meticulously orchestrated every aspect of the project, ensuring seamless coordination among team members and adherence to rigorous technical standards. From end-to-end process automation to the fusion of Computer Vision with Regression Analysis, our project exemplifies the seamless integration of cutting-edge technologies for transformative solutions in Real Estate valuation. Our journey began with the acquisition of Raw Satellite Imagery, meticulously annotating key features relevant to property valuation and introducing pixel noise to enhance model robustness. Utilizing state-of-the-art object detection models such as YOLOv8, we extracted and localized significant features laying the foundation for comprehensive data analysis. One of the pivotal phases involved extensive data augmentation and regression modeling to enhance the diversity and robustness of our dataset. We devised innovative strategies to calculate property prices based on detected features, incorporating factors such as location and amenities. Through meticulous analysis and categorization, we tiered properties into distinct price segments, enabling targeted analysis and precise valuation across different market segments. The culmination of our efforts manifested in the successful deployment of various regression algorithms, including Linear Regression, Random Forest Regression, Gradient Boosting Regression, and KNN Regression. We meticulously evaluated the performance of each model using appropriate metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) score, ensuring accuracy and effectiveness in estimating house prices across diverse market segments. This project exemplifies my ability to lead complex technical initiatives, leveraging cutting-edge technologies and methodologies to deliver transformative solutions. I'm immensely proud of our team's accomplishments and excited to continue exploring the frontiers of innovation at the intersection of AI and Real Estate. I want to take a moment to express my sincere gratitude to Professor Olin Hartin for inspiring and guiding me throughout this journey. If you're interested in learning more about our project or discussing potential collaborations, feel free to reach out! #TechnicalLeadership #MachineLearning #DataScience #RealEstate #Innovation #AI #Leadership #ProjectManagement #ArtificialIntelligence #ArizonaStateUniversity #ASU