AI

How to draft proposals from approved commercial evidence and convert decisions into clean CRM and delivery handoffs.

A practical framework for understanding the economic trade-offs of AI systems, including model cost, response speed, review effort, and business payoff.

How to introduce AI into daily work without hiding role changes, reviewer load, incentives, or correction responsibility.

A blueprint for running fixed tests, comparing versions, recording reviewer evidence, monitoring production signals, and enforcing release gates.

A compact historical reference for the major phases, breakthroughs, and shifts that shaped modern AI.

A curated guide to textbooks, authors, websites, and papers for readers who want to study transformer internals, attention math, fine-tuning, GPU optimization, and benchmarking in more depth.

TL;DR for operators A utility forecasting hundreds of heterogeneous customers and distribution transformers faces a model-management decision: maintain one shared forecaster, train separate local models, or specialize a common model without multiplying its full parameter set. Li, Cheng, and Weng demonstrate a third architecture. Their method keeps a probabilistic forecasting backbone fixed and constructs customer-specific predictive mean and variance heads from a shared bank of low-rank adaptations. Customers can combine several adapters according to their load characteristics rather than receiving an independently trained model. ...

TL;DR for operators Recommendation platforms already compute detailed information about users, items, and competing candidates when making ranking decisions. Much of that contextual information disappears when an encounter becomes another item in a user’s historical sequence. PRISM1 proposes retaining a compressed, personalized representation of each historical scoring encounter. On Meta’s Surface 1, the resulting history features reduce average offline normalized entropy by 0.15%, compared with 0.07% for VISTA, an alternative that caches user-history summaries. ...

TL;DR for operators An inventory manager can obtain accurate forecasts of stock and backlog yet still choose an inferior replenishment schedule. Forecasting future conditions, predicting how those conditions change under alternative actions, and ranking the available plans are different capabilities. ActTime: Action-Conditioned World Modeling for Time Series1 addresses this distinction by learning controlled dynamics from historical action-outcome trajectories. It then simulates alternative futures from the same observed history, without requiring paired counterfactual outcomes during training. ...

TL;DR for operators A speech-recognition system can transcribe nearly every word correctly while changing what a customer requests or what a voice assistant is supposed to do. Word Error Rate (WER) cannot distinguish those consequences when the edit counts are identical. SITAR, proposed by Lu and colleagues,1 measures situational distortion: whether transcription errors alter expression, understanding, or intended outcomes. It converts offline contextual judgments into a reusable neural scorer. ...