AI North-Star Terminology
Summary
This page tracks high-level AI goal terms only when they affect the wiki’s time-series and world-model agenda. The current stance is conservative: terms such as AGI are too overloaded to guide technical synthesis unless the source defines measurable interfaces. Superhuman Adaptable Intelligence is useful because it shifts attention from a fixed human-centric capability checklist toward adaptation speed, specialization, and useful tasks.
For this wiki, the important translation is:
not "one model that does everything"
but "systems that maintain useful state and adapt quickly in important domains"Concept Map
flowchart LR AGI[AGI as overloaded generality] --> Problems[unclear scope, human-centric targets, weak assessment] SAI[SAI: specialization plus adaptation speed] --> Substrates[SSL, latent prediction, world models, modularity] Substrates --> TSFM[foundation time-series model agenda] TSFM --> Test[latent state, context, actions, useful futures]
What The Wiki Currently Believes
- AGI should not be used as a precise technical target unless a source defines its task scope, resource constraints, and measurement protocol.
- SAI is a better discussion handle than AGI for this wiki because it emphasizes specialization, fast adaptation, and utility. It is still not an operational benchmark by itself.
- The relevant bridge into time-series modeling is state and dynamics learning: a system should maintain latent state, use context, and eventually evaluate plausible futures under actions, control inputs, or interventions.
- The AMI lineage supplies the technical substrate adjacent to SAI: self-supervised predictive representations, world models, hierarchical planning, and latent or representation-space prediction.
- Alex’s foundation time-series agenda is narrower and more operational than SAI. The Foundation Time-Series Model Research Agenda asks whether a model closes concrete bottlenecks for numeric time series, event streams, context, streaming state, and action-conditioned world modeling.
Operational Reading
When a source uses a broad AI north-star term, map it into the wiki’s canonical interfaces before treating it as evidence:
| Source language | Wiki translation | Evidence required |
|---|---|---|
| General intelligence | Broad transfer or adaptation across task families | Defined task distribution, resource budget, and evaluation protocol |
| Superhuman performance | Performance above a human baseline | Clear baseline, domain, metric, and cost model |
| Adaptability | Fast acquisition of new skills | Few-shot, online, or transfer benchmark with data and compute budgets |
| World model | Predictive representation of state and dynamics | Future-state or action-consequence evaluation, not only passive forecasting |
| Specialization | Domain-targeted model capacity or routing | Matched-compute comparison against broader shared models |
Relation To Foundation TSFM Agenda
SAI is a useful north-star frame, but the Foundation Time-Series Model Research Agenda owns the measurable research target. The agenda should use SAI mainly as a warning against generic “one model for everything” claims. A foundation time-series model can still share broad representations, but its evidence should come from concrete capabilities: streaming latent-state updates, context use, rare-regime preservation, calibrated future distributions, dense numeric generation, and action-conditioned rollout.
In time-series language, SAI pushes the agenda toward specialist systems that can adapt to a particular system embodiment:
telemetry schema + context + event streams + typed action/control-input/intervention interface
-> maintained latent state
-> fast adaptation to useful operational tasksThat is compatible with broad pretraining, but the evaluation should reward useful adaptation and decision support rather than only average zero-shot forecasting.
Evidence Boundaries
AI Must Embrace Specialization via Superhuman Adaptable Intelligence is a credible position paper, not direct model evidence. It can justify terminology choices and research framing, but it should not be cited as empirical support that a particular self-supervised objective, JEPA variant, world model, or modular architecture works.
The paper also leaves utility and importance underdefined. For this wiki, those terms should be concretized through domain-specific objectives such as incident reduction, reliable control, scientific discovery, patient outcomes, business-process constraints, or another explicit outcome surface.
Open Questions
- What is the right adaptation-speed benchmark for high-dimensional multivariate time series?
- How should a TSFM benchmark score adaptation under changing telemetry schemas, context availability, and intervention surfaces?
- Which parts of a time-series/world-model stack should be general pretrained substrate versus specialist module?
- How should utility be defined for operational systems without reducing every task to short-term economic value?