Tanat Tonguthaisri, CISSP®’s Post

Using colour in guides and client information sheets really isn't rocket science, but it's easy to get it wrong. Here's a blog with some top tips ✍️ https://lnkd.in/eZzwhHhr

A member recently came to the group for advice on running their first tree test. But in the setup of their question revealed some critical details that made tree testing feel like the shadier choice. See what we did there? ;) This sparked a conversation about the role of tree testing, which is ideally in the testing of a navigational device that is based on a taxonomy that we are already feeling solid about. If we are testing both the taxonomy and the navigation at the same time, we have introduced too many variables. This makes our analysis harder, if not (depending on scale) near impossible. The group talked about how other research methods like open, and then closed card sorting might provide more insight into the quality and applicability of the terms and hierarchies chosen, before then testing the final arrangement through the lens of a navigational task. — If this sounds like a discussion you wished you had taken part in, we are ready to welcome you at our next discussion. Check our website for our monthly schedule.

Great insights on evaluation metrics! 🌟 #AI #RAG This blog is a must-read for anyone in AI! 🚀 #MachineLearning #DataScience

Over the past few weeks I have working on my latest blog about 𝐡𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐩𝐫𝐞𝐩𝐚𝐫𝐞 𝐟𝐨𝐫 𝐟𝐮𝐭𝐮𝐫𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬. Dive in now to discover how this incredible process works in your brain and how you can use it to your advantage! 🧠 😀 Read the blog: https://lnkd.in/efFQXVng

Wondering how the most popular metrics for evaluating Retrieval Augmented Generation systems work? Wonder no longer! This new blog explores precision@K, recall@K, MAP@K, MRR@K, and NDCG@K as evaluation methods for information retrieval systems - how they work, and which are the most popular 🚀 Read more: https://lnkd.in/eYURjwgK Leonie Monigatti is a pro at explaining complicated concepts simply 🫶 Super awesome read

🔎 What new findings would you uncover if you tracked the growth of every experiment? Imagine having a real-time pulse on your research—watching changes unfold, catching patterns early, and identifying opportunities for optimization that may otherwise go unnoticed. With automated tracking, we can move from static snapshots to a dynamic, data-rich understanding of each experiment, fueling insights at every step. So, what would it mean for your lab to bring that level of visibility to every sample, every plate, and every experiment?

The Google landscape has undergone significant changes as we wrap up the March 2024 Core update, and it’s time to start looking at where we landed 🤔 https://lnkd.in/gTTxfD3s #algorithm #googleupdate #google

Can you solve this number grid logic puzzle? Find the missing digit and challenge your mind! 🧩💡 Share your answer in the comments below! 👇🏻 The quiz results will be featured in the upcoming issue of the Blueprint newsletter tomorrow : https://ie.social/VMKXJ

Just attended Darko Medin and Justin Bélair's insightful seminar on causal inference. A very useful heads up they gave was that there is a dedicated CRAN resource of R packages used for causal inference, which I was not aware of until today. For those interested, I refer you to this link, which lists all of the packages and their descriptions: https://lnkd.in/eZ2vE4sg #statistics #research #researchmethods #causality #r #rprogramming #causalinference