00:00–00:40 · UNDERSTANDStart with an actual concept.
Open the expanded machine-learning guide: show the intuitive mechanism, a concrete example, the original reference and links to related concepts.
Be precise: 1,345 concepts have stable pages, but only 21 guides are expanded; the rest display their original concise entries.
Follow the concept →
00:40–01:25 · DECIDECompare tools, not marketing.
Search the technology catalog for PyTorch, select another suitable tool, and compare best-fit applications, constraints, alternatives and linked sources.
Be precise: 300 original profiles are available; these comparisons are editorial, not experimental benchmark results.
Open the technology search →
01:25–02:15 · PRACTICETurn knowledge into a game.
Open Games inside the original AI Updates sidebar. Try a multiple-choice quiz or matching round, filter the deck by topic, and open a missed concept directly in the existing AI Updates record dialog.
Be precise: both modes use the same verified dictionary; no AI grades free-form paraphrases.
Open Games in AI Updates →
02:15–03:00 · VERIFYShow what is known—and pending.
Open a safety record and point out whether the evidence is a controlled demonstration or an incident report. Then show the dated What’s New archive and its actual publication status.
Be precise: imported review labels are not newly audited. An empty news archive means the first reviewed edition is pending, not that nothing happened in AI.
Inspect safety evidence →Check the dated news archive →
THE EXTENDED ROUTE · ABOUT FIVE MORE MINUTES
Follow the evidence in more depth.
The steps below give a longer alternative for conversations about research methods, safety incidents, engineering playbooks and publication integrity.
START / CONNECT THE CONCEPTSML → DL → language models.
Search the dictionary, open a permanent concept page, and follow its prerequisites, related concepts, examples and sources. All 1,345 concepts have reference pages; 12 now have expanded explanations.
Point to make: distinguish imported concise entries from the first expanded guides.
Open the machine learning guide →
THEN / COMPARE THE OPTIONSFind a tool for the workload.
Search for PyTorch, add it and another tool to a comparison, and inspect the differences in purpose, execution, caveats and evidence status.
Point to make: these are editorial comparisons; no head-to-head benchmark is claimed.
Search technology profiles →Open the compact technology browser
01 / ASK A RESEARCH QUESTIONStart with reward hacking.
Search the paper library. Open a record and show its summary, limitations, review label, and link to the original paper.
Point to make: the explanation and its evidence stay together.
Open the research search →
02 / INSPECT A SAFETY CLAIMKeep the context attached.
Open the safety observatory. Use the report-type filter, inspect a record's environment and timeline, and explain whether it describes a real-world report or a controlled evaluation.
Point to make: a concerning demonstration is not automatically a confirmed deployment incident.
Explore the safety reports →
03 / MAKE IT PRACTICALMove from reading to a plan.
Browse the engineering playbooks. Choose one related to the conversation and show its suggested approach, first step, validation plan, and tradeoffs.
Point to make: the hub is intended to support experiments and decisions, not just definitions.
Open the problem playbooks →
04 / SHOW WHAT IS REALFinish with an honest inventory.
Open publication progress. It separates what is published from what is merely prepared. Use the counts in this release instead of quoting an old screenshot.
Point to make: the catalog is incomplete, evidence labels have limits, and a scheduled development task is not a live research feed.
Show publication progress →