ARTICLES / TWO WAYS TO READ
Learn the ideas.
Follow the developments.
Understand how AI works, or explore what new developments mean for manufacturing and business. Choose the question that brings you here.
CHOOSE YOUR READING PATH
01
Clear explanations, worked examples and practical skills.
02
Reported developments, industry context and decisions to consider.
CLEAR EXPLANATIONS & PRACTICAL GUIDES
Latest in AI Learning
Build understanding with explanations and examples. Each article is labelled with its learning topic.
Decision Trees Explained: Will This Order Arrive on Time?
Learn how a decision tree predicts delivery outcomes through one simple question and 12 illustrative orders. Two clear diagrams, no code or…
Predictive Maintenance Pilot: A Practical Evaluation Checklist
A practical guide to evaluating a predictive maintenance pilot, with a data checklist, alert-metric example and a first-month plan for manufacturing teams.
How a Prediction Is Decomposed Into Baseline and Feature Contributions
Understand SHAP values through an industrial risk example: baseline output, feature contributions, background data, output scale, and limits on causal interpretation.
How Industrial AI Preserves Entity Type, Relationship Meaning, and Event Time
Represent machines, orders, materials, and events with heterogeneous temporal graphs that preserve relationship meaning, event time, and prediction-time boundaries.
How AI Learns From Connected Machines, Orders, Materials, and Events
Learn how graph neural networks use message passing to connect manufacturing context, with practical checks for graph schemas, data leakage, and provenance.
Why the Objective Determines the Behavior an Agent Learns
Design industrial AI reward functions with explicit throughput, delay, energy, and quality trade-offs, then test weights, time horizons, shaping, and constraints.
NEWS, RESEARCH & INDUSTRY ANALYSIS
Latest in Explore
Follow reporting, research and practical implications across all five industry collections.
AI Agent Execution Contract: Why a Managed Harness Still Needs Control
The Agents API reduces harness engineering, but accountable operations still require a portable, revocable, and replayable execution contract.
ASML’s BIC North Bet: Why a Flow Factory Must Become a Manufacturing Operating System
BIC North creates physical capacity options; a Flow Factory must synchronise suppliers, material flow, assembly, qualification, infrastructure, and learning to convert space…
Semiconductor Workforce Value Sharing: Beyond Record Rewards
Exceptional rewards can recognize contribution, but qualified capacity also requires transparent sharing, skill depth, sustainable rosters, and shared operating evidence.
AI Welding Agents Need an Execution Contract, Not Just Zero Teaching
FANUC’s AI Welding Agent can turn drawings into weld settings and robot motion, but production use still needs validation, approval, traceability, and…
AI Inference Rack Integration: Why Chip Access Is Not Yet Deployable Throughput
d-Matrix plans to connect Raptor inference processors to NVIDIA rack infrastructure through NVLink Fusion. The strategic test is qualified service, not interface…
Financial AI Decision Lineage: Why Better Research Tools Need an Evidence-to-Approval Workflow
ChatGPT for Financial Services integrates data, analysis, models, and client materials. Institutions still need a reproducible evidence-to-approval operating control.
ESSENTIAL READING
Three guides to keep within reach.
Start with readiness, then plan a pilot and decide how people will review its actions.
01 / CHECK READINESS
Choose a problem worth solving.
Check data, workflow, ownership and evaluation before choosing a pilot.
02 / DESIGN THE PILOT
Make the next 90 days useful.
Connect the pilot’s preparation, evidence and operational review.
03 / DEFINE CONTROL
Know when an agent may act.
Examine the evidence, permissions and oversight required for trustworthy actions.