PinnedInAi-Ai-OHbyKalle Georgiev·Oct 15, 2024What Do The Robots Say About Us When We’ve Left The Building?A post-human water cooler conversationA response icon40A response icon40
InAi-Ai-OHbyKalle Georgiev·Sep 21Pseudo-Imperatives For Humans (and the Machines Imitating Them)Why LinkedIn sounds like the Bible, and why everyone blames AIA response icon5A response icon5
InData Science CollectivebyKalle Georgiev·Sep 13Measuring the Unmeasurable for HumansHow do you rate a system you can’t switch off, copy, or crash on purpose?A response icon2A response icon2
InData Science CollectivebyKalle Georgiev·Jun 29Embeddings for Humans: How AI Turns Words Into a Map of MeaningAnd why “king minus man plus woman equals queen” is not a party trickA response icon7A response icon7
InData Science CollectivebyKalle Georgiev·Jun 1Correlation vs. Causation for Humans: Why Your Data Is Lying to YouAnd why roosters don’t cause sunrisesA response icon2A response icon2
InData Science CollectivebyKalle Georgiev·May 26Survivorship Bias For Humans: How to Spot the Data That Isn’t ThereHow the data you can’t see affects everything you canA response icon12A response icon12
InData Science CollectivebyKalle Georgiev·Apr 16Context Windows for Humans: How AI Loses the ThreadAnd why there’s a limit to every AI conversationA response icon6A response icon6
Kalle Georgiev·Mar 15The Law of Large Numbers for HumansWhy luck isn’t random, it’s just incomplete mathA response icon9A response icon9
InData Science CollectivebyKalle Georgiev·Feb 22Gradient Descent for HumansHow we learn from our mistakes and why we sometimes settle for “good enough”A response icon9A response icon9
InData Science CollectivebyKalle Georgiev·Jan 18Stock Markets for Humans: Thinking in Uncertain SystemsWhy math makes market prediction so hardA response icon31A response icon31