Clicks are a cost centre. In a 3D annotation tool, deleting an unnecessary bounding box may take one or two seconds. Creating a missed vehicle annotation from scratch takes about 23 seconds. Correcting a poorly positioned box falls somewhere in between. These actions may all count as model errors. They do not cost the same amount of human time. ...
Topic modeling has a small, annoying question hiding inside a very large workflow: How many topics should the model use? Not what the topics mean. Not whether the dashboard looks elegant. Not whether management will discover a “strategic insight” after renaming a cluster from miscellaneous complaints to emerging customer sentiment. Just the integer: 10 topics, 30 topics, 80 topics, 200 topics? ...
Cloud sharing sounds easy until the people sharing it are not one company, not one data center, not one legal jurisdiction, and not even one scientific discipline. Inside a single enterprise, “AI platform” usually means a controlled environment: one cloud vendor, one identity system, one billing model, one preferred deployment stack, and one procurement department quietly pretending this is all strategic. In scientific research, the picture is messier. A climate group may have data in one national infrastructure, compute in another, collaborators across several countries, and privacy restrictions that prevent raw data from moving at all. A bioimaging team may want to publish a model, let others inspect its lineage, deploy it on external infrastructure, and still retain enough metadata for the next researcher to reproduce the result rather than merely admire the abstract. ...
TL;DR for operators A deletion request is not a prompt. It is not a “please forget” instruction, a fine-tuning vibe, or a compliance-flavoured model apology. The useful idea in Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models is much less mystical: make training reproducible enough that deletion can be executed like systems recovery.1 The paper treats training as a deterministic program, logs the minimal control inputs needed to replay that program, and then removes the requested data during replay. Under strict preconditions, the resulting parameters are bit-identical, in the training dtype, to the model that would have been produced if the forgotten examples had never been included. ...
TL;DR for operators Most organisations do not have an AI capability problem. They have an AI allocation problem. They send too many routine, repetitive, low-risk tasks to large frontier models because the demo looked impressive and the invoice arrived later. The slingshot strategy is the opposite instinct: break a workflow into smaller decisions, assign the cheap and reliable parts to specialised models or rules, and escalate only the uncertain or high-value cases to stronger LLMs. The point is not to worship small models. That would be merely replacing one superstition with a smaller, cheaper superstition. The point is to allocate model capacity like an operating resource. ...