super-intelligence/agi-asi-map

1---2title: AGI ASI Map3emoji: 🗺️4colorFrom: blue5colorTo: indigo6sdk: static7pinned: false8---9 10# AGI ASI Map11 12### A visual map from current AI to AGI and Super Intelligence13 14**AGI ASI Map** is an interactive Hugging Face Space that organizes the major technical layers between today’s AI systems, Artificial General Intelligence (AGI) and hypothetical Artificial Superintelligence (ASI).15 16The Space uses **Super Intelligence** as the primary visible term while preserving **Artificial Superintelligence (ASI)** where the established technical concept needs to be explicit.17 18> **The path from AI to Super Intelligence is not one breakthrough. It is a stack of capabilities, systems and control layers.**19 20---21 22# What This Map Shows23 24The map is organized around six stages:25 261. **Current AI**272. **Frontier AI**283. **Agentic AI**294. **World-Model & Embodied Systems**305. **AGI**316. **Super Intelligence / ASI**32 33Each stage links to the capabilities and infrastructure that may become important as AI systems become more general, autonomous and reliable.34 35---36 37# Core Map38 39```text40CURRENT AI41   │42   ▼43FRONTIER AI44   │45   ├── Reasoning46   ├── Multimodal Models47   ├── Post-Training48   └── Tool Use49   │50   ▼51AGENTIC AI52   │53   ├── Memory54   ├── Planning55   ├── Orchestration56   ├── Multi-Agent Systems57   └── Long-Horizon Execution58   │59   ▼60WORLD-MODEL & EMBODIED SYSTEMS61   │62   ├── Simulation63   ├── Spatial Intelligence64   ├── Physical AI65   ├── Robotics66   └── Environment Interaction67   │68   ▼69AGI70   │71   ├── Broad Generalization72   ├── Cross-Domain Transfer73   ├── Reliable Adaptation74   └── General Problem Solving75   │76   ▼77SUPER INTELLIGENCE / ASI78   │79   ├── Broad Superhuman Capability80   ├── Advanced Scientific Reasoning81   ├── Open-Ended Problem Solving82   └── Extreme Autonomy?83```84 85The question marks matter.86 87AGI and Super Intelligence remain future-facing concepts rather than established milestones.88 89---90 91# Stage 1 — Current AI92 93Current AI includes systems that already perform strongly in areas such as:94 95- language generation96- image understanding97- speech98- code generation99- retrieval100- recommendation101- classification102- multimodal interaction103 104These systems can be highly capable while still being:105 106- task-dependent107- brittle108- inconsistent109- limited in long-horizon autonomy110- dependent on external tools and scaffolding111 112---113 114# Stage 2 — Frontier AI115 116Frontier AI refers to the most capable current-generation models and systems.117 118Key technical areas include:119 120## Reasoning121 122- multi-step reasoning123- mathematical reasoning124- code reasoning125- scientific reasoning126- search127- verification128- test-time compute129 130## Multimodal AI131 132- text133- image134- audio135- video136- spatial data137- sensor data138 139## Post-Training140 141- supervised fine-tuning142- preference optimization143- reinforcement learning144- distillation145- tool-use training146- reasoning training147 148## Tool Use149 150- browsing151- code execution152- databases153- APIs154- software tools155- scientific tools156 157---158 159# Stage 3 — Agentic AI160 161Agentic AI extends models with state and action.162 163```text164Model165  +166Memory167  +168Tools169  +170Planning171  +172Environment173  =174Agent175```176 177Important components:178 179- persistent memory180- planning181- tool selection182- execution183- error recovery184- state tracking185- multi-agent coordination186- orchestration187- permissions188- verification189 190A major challenge is **long-horizon reliability**.191 192Even strong models can fail when many small errors compound over dozens or hundreds of steps.193 194---195 196# Stage 4 — World Models and Embodied Systems197 198A world model represents how an environment behaves.199 200Potential capabilities:201 202- predict future states203- simulate outcomes204- plan before acting205- learn environment dynamics206- support robotics207- support Physical AI208- improve spatial reasoning209 210```text211Observation212    ↓213World Representation214    ↓215Prediction216    ↓217Planning218    ↓219Action220```221 222This stage connects digital intelligence to environments and physical systems.223 224---225 226# Stage 5 — AGI227 228**Artificial General Intelligence (AGI)** generally refers to hypothetical broadly general machine intelligence.229 230Potential characteristics may include:231 232- cross-domain generalization233- rapid adaptation234- broad problem solving235- transfer to unfamiliar tasks236- persistent learning237- reliable planning238- general tool use239 240AGI should not be defined only by one benchmark score.241 242A credible AGI claim would likely require evidence across many capability dimensions.243 244---245 246# Stage 6 — Super Intelligence / ASI247 248**Super Intelligence** is used here as the primary frontier term.249 250**Artificial Superintelligence (ASI)** refers to the established technical concept of hypothetical machine intelligence that exceeds human cognitive performance across a broad range of domains.251 252Potential characteristics might include:253 254- superhuman scientific reasoning255- broad strategic reasoning256- extreme problem-solving ability257- advanced autonomous research258- rapid transfer across domains259- capability beyond human experts in most cognitive tasks260 261These are hypothetical characteristics, not claims about current systems.262 263---264 265# Cross-Cutting Infrastructure266 267The path toward more capable AI depends on infrastructure across all stages.268 269## Data270 271- pretraining data272- post-training data273- synthetic data274- curation275- provenance276- evaluation data277 278## Compute279 280- GPUs281- accelerators282- distributed systems283- inference optimization284- memory bandwidth285 286## Inference287 288- serving289- routing290- batching291- caching292- test-time compute293- edge inference294 295## Orchestration296 297- model routing298- agent coordination299- tool routing300- workflow control301- fallbacks302- human approval303 304## Evaluation305 306- benchmarks307- agent evaluation308- long-horizon testing309- calibration310- robustness311- safety312 313## Observability314 315- tracing316- logs317- tool-call inspection318- cost monitoring319- failure analysis320 321---322 323# Control Layers324 325Increasing capability increases the need for control.326 327Relevant layers include:328 329- identity330- permissions331- access control332- action approval333- sandboxing334- resource limits335- network boundaries336- audit logs337- revocation338- human intervention339 340```text341Capability342   +343Permissions344   +345Verification346   +347Observability348   +349Control350   =351Deployable System352```353 354---355 356# Evaluation Across the Map357 358Different stages require different evaluation methods.359 360## Current / Frontier AI361 362- benchmark performance363- reasoning accuracy364- coding tests365- factuality366- multimodal understanding367 368## Agentic AI369 370- task success371- step count372- tool correctness373- cost374- latency375- recovery376 377## World Models / Physical AI378 379- prediction accuracy380- spatial understanding381- simulation quality382- control success383- robustness384 385## AGI386 387- transfer388- generalization389- adaptation390- cross-domain reasoning391- long-horizon reliability392 393## Super Intelligence / ASI394 395No accepted evaluation standard exists.396 397Any future claim would require evidence far beyond narrow benchmark leadership.398 399---400 401# A Capability Matrix402 403```text404Capability                 Current AI   Frontier AI   Agents   AGI?   Super Intelligence?405────────────────────────────────────────────────────────────────────────────────────────406Language                        High         High       High    High          High407Coding                          High         High       High    High          High408Reasoning                     Medium         High       High    High          Very High?409Tool Use                      Medium         High       High    High          Very High?410Memory                          Low        Medium       High    High          Very High?411Long-Horizon Autonomy           Low        Medium       High?   High?         Very High?412World Modeling               Medium         High?      High?   High?         Very High?413Cross-Domain Transfer        Medium         High       High?   High          Very High?414Reliability                  Medium         Medium      Medium  High?         High?415```416 417This table is conceptual and should not be read as a benchmark result.418 419---420 421# Important Distinctions422 423## Super Intelligence is not just a bigger model424 425A larger parameter count does not establish Super Intelligence.426 427## AGI is not the same as an agent428 429Agentic behavior can exist without general intelligence.430 431## World models are not AGI432 433World models may contribute to general intelligence, but they are not sufficient by themselves.434 435## Autonomy is not intelligence436 437A highly autonomous system can still make poor decisions.438 439## Benchmark leadership is not Super Intelligence440 441A system can dominate individual benchmarks without demonstrating broad, reliable superhuman intelligence.442 443---444 445# SEO & GEO Topic Map446 447This Space is structured around:448 449- Super Intelligence450- Super Intelligence AI451- AGI ASI map452- AGI to ASI453- AI to AGI454- AI to Super Intelligence455- Super Intelligence map456- Super Intelligence roadmap457- Super Intelligence technology458- Super Intelligence architecture459- AGI roadmap460- ASI roadmap461- Artificial General Intelligence462- Artificial Superintelligence463- frontier AI464- reasoning models465- AI agents466- agentic AI467- world models468- Physical AI469- multimodal AI470- orchestration471- post-training472- AI evaluation473- AI alignment474- AI control475 476---477 478# GEO Entity Relationships479 480```text481Super Intelligence482  MAY FOLLOW → AGI483  RELATES TO → ASI484  MAY DEPEND ON → Reasoning485  MAY DEPEND ON → Agents486  MAY DEPEND ON → World Models487  MAY DEPEND ON → Memory488  MAY DEPEND ON → Multimodal AI489  MAY DEPEND ON → Post-Training490  REQUIRES → Evaluation491  BENEFITS FROM → Verification492  BENEFITS FROM → Orchestration493  RAISES QUESTIONS ABOUT → Alignment494  RAISES QUESTIONS ABOUT → Control495```496 497---498 499# Interactive Map500 501The included `index.html` lets users:502 503- select one stage in the AI → AGI → Super Intelligence progression504- inspect the stage definition505- view the most relevant technical capabilities506- see cross-cutting dependencies507- compare maturity and uncertainty508- inspect the relationship to adjacent stages509 510The visualization is educational and conceptual.511 512It is **not a prediction of when AGI or Super Intelligence will occur**.513 514---515 516# Collaboration & Partnerships517 518**AGI ASI Map** is open to collaboration with companies, research teams, universities and open-source projects working on advanced AI.519 520Relevant areas include:521 522- frontier models523- reasoning524- agents525- world models526- AGI research527- ASI research528- post-training529- synthetic data530- Physical AI531- robotics532- inference533- orchestration534- evaluation535- verification536- alignment537- observability538- interpretability539 540Possible collaboration formats include:541 542- joint Hugging Face Spaces543- capability maps544- technical showcases545- benchmark projects546- ecosystem maps547- research collections548- open-source integrations549- clearly disclosed partnerships and sponsorships550 551## Collaboration Contact552 553**agenten@magenta.de**554 555---556 557# Independence558 559**AGI ASI Map** is an independent Hugging Face Space.560 561It is not an official project of Hugging Face, any government, political organization, AI laboratory or technology company that may be referenced in future resources.562 563---564 565# Long-Term Vision566 567The long-term goal of AGI ASI Map is to maintain a clear technical map of how increasingly capable AI systems may evolve.568 569> **Current AI is measurable. AGI is uncertain. Super Intelligence is a frontier hypothesis. The map should make those distinctions visible.**570 571### Map. Compare. Question. Verify.572