--- title: "Mga Entidad · mga isip at posibleng isip, nakaayos ayon sa ebidensya · Hypothesis ng Simulasyon" url: https://simulation-hypothesis.com/fil/entities description: "Gabay na may pinagmulan sa mga isip: hayop na may nasukat na neuron, kolektibo at artipisyal na sistema, nailathalang pagsusuri sa pagkadama at kamalayan." license: https://creativecommons.org/licenses/by/4.0/ credit: "Ross Allan Dahl, Hypothesis ng Simulasyon (simulation-hypothesis.com)" --- # Mga Entidad · mga isip at posibleng isip, nakaayos ayon sa ebidensya Kung simulasyon ang mundo, maaaring ang mga isip ang ilan sa pinakamahirap i-compute rito. Inililista ng gabay na ito ang mga kilalang kaso, mula sa mga nag-iisang cell hanggang sa mga tao. Saklaw din nito ang mga artipisyal na sistemang nabuo na, at ang mga isip na iminungkahi ng mga siyentipiko at pilosopo pero wala pang nakakita. May pinagmulan at tag ng kumpiyansa ang bawat numero. Ang nasukat ay direktang bilang, ang pagtataya ay kinuwenta mula sa ibang datos, at ang hindi binilang ay nangangahulugang walang naglathala ng bilang. Hiwalay na tanong kung may karanasan ang alinman sa mga sistemang ito. Nasa ilalim ng [mga pamantayan ng pagkadama](https://simulation-hypothesis.com/fil/entities/sentience) at [mga indikasyon ng kamalayan](https://simulation-hypothesis.com/fil/entities/indicators) ang ebidensya para roon. ## Mga indibidwal na isip ## Mga hayop: mga neuron sa cortex at sa buong utak Cortex o pallium Buong utak Lahat Mga mammal Mga ibon Mga neuron sa cerebral cortex (mga mammal) o sa pallium (mga ibon), isang malawakang ginagamit na anatomikong panukat ng kakayahang pangkaisipan. Logarithmic na iskala; mga bilang ang solidong bar, mga tantiya o pinagtatalunang bilang (†) ang may guhit na bar. Hindi ipinapakita ang mga reptilya at invertebrate dahil walang maihahambing na bilang. 1. Orca † 43.1 bilyon 2. Long-finned pilot whale † 37.2 bilyon 3. [Tao](https://simulation-hypothesis.com/fil/entities/human)▲1 16.3 bilyon 4. Harbour porpoise † 14.9 na bilyon 5. [Bottlenose dolphin †](https://simulation-hypothesis.com/fil/entities/dolphin) 12.7 bilyon 6. Cuvier’s beaked whale 9.1 bilyon 7. Gorilla 8.5 bilyon 8. Orangutan 8.5 bilyon 9. [Chimpanzee](https://simulation-hypothesis.com/fil/entities/chimp) 7.4 na bilyon 10. Common dolphin 6.7 bilyon 11. Harp seal 6.1 bilyon 12. [Elepanteng Aprikano](https://simulation-hypothesis.com/fil/entities/elephant)▼1 5.6 na bilyon 13. Fin whale 5.2 bilyon 14. Blue whale 5.1 bilyon 15. Minke whale 3.2 bilyon Ipakita lahat ng 88 10^6 10^7 10^8 10^9 10^10 10^11 neurons Ipakita ang mga numero | # | Hayop | Cortex o pallium | Buong utak | | -- | ------------------------- | ---------------- | ---------------- | | 1 | Orca † | 43.1 bilyon | | | 2 | Long-finned pilot whale † | 37.2 bilyon | | | 3 | Tao | 16.3 bilyon | 86.1 bilyon | | 4 | Harbour porpoise † | 14.9 na bilyon | | | 5 | Bottlenose dolphin † | 12.7 bilyon | | | 6 | Cuvier’s beaked whale | 9.1 bilyon | | | 7 | Gorilla | 8.5 bilyon | | | 8 | Orangutan | 8.5 bilyon | | | 9 | Chimpanzee | 7.4 na bilyon | | | 10 | Common dolphin | 6.7 bilyon | | | 11 | Harp seal | 6.1 bilyon | | | 12 | Elepanteng Aprikano | 5.6 na bilyon | 257 bilyon | | 13 | Fin whale | 5.2 bilyon | | | 14 | Blue whale | 5.1 bilyon | | | 15 | Minke whale | 3.2 bilyon | 57.4 na bilyon | | 16 | Olive baboon | 2.88 bilyon | 10.95 bilyon | | 17 | Beluga | 2.7 bilyon | | | 18 | Pygmy sperm whale | 2.02 bilyon | | | 19 | Blue-and-yellow macaw | 1.92 bilyon | 3.14 na bilyon | | 20 | Rhesus macaque | 1.71 bilyon | 6.39 na bilyon | | 21 | Giraffe | 1.67 bilyon | 11.21 bilyon | | 22 | Squirrel monkey | 1.34 na bilyon | 3.25 bilyon | | 23 | Eurasian eagle-owl | 1.33 bilyon | 1.9 na bilyon | | 24 | Kea | 1.28 bilyon | 2.15 bilyon | | 25 | Snowy owl | 1.27 bilyon | 1.81 bilyon | | 26 | Karaniwang uwak | 1.2 bilyon | 2.17 bilyon | | 27 | Tawny owl | 1.15 bilyon | 1.58 bilyon | | 28 | Capuchin monkey | 1.14 na bilyon | 3.69 na bilyon | | 29 | Sulphur-crested cockatoo | 1.14 na bilyon | 2.12 bilyon | | 30 | African grey parrot | 850 milyon | 1.57 bilyon | | 31 | Rook | 820 milyon | 1.51 bilyon | | 32 | Greater kudu | 793.07 milyon | 6.09 na bilyon | | 33 | Long-eared owl | 673 milyon | 991.31 milyon | | 34 | Goffin's cockatoo | 599 na milyon | 1.16 na bilyon | | 35 | Lion | 545.24 na milyon | 4.67 bilyon | | 36 | White-tailed eagle | 542.93 milyon | 1.65 bilyon | | 37 | Eurasian jay | 529 na milyon | 1.09 na bilyon | | 38 | Alagang aso | 526 na milyon | | | 39 | Striped hyena | 495.28 milyon | 3.88 bilyon | | 40 | Western jackdaw | 492 milyon | 967.99 na milyon | | 41 | Common ostrich | 479.41 milyon | 1.63 bilyon | | 42 | Eurasian magpie | 443 milyon | 897.27 milyon | | 43 | Saker falcon | 442.95 milyon | 1.2 bilyon | | 44 | Emu | 439 na milyon | 1.34 na bilyon | | 45 | Raccoon | 437.94 na milyon | 2.15 bilyon | | 46 | Barn owl | 437 milyon | 689.54 na milyon | | 47 | Little owl | 400.82 milyon | 585.92 milyon | | 48 | Black vulture | 398.9 na milyon | 1.01 bilyon | | 49 | Eurasian pygmy owl | 364 na milyon | 533.26 na milyon | | 50 | Common buzzard | 351.7 milyon | 1 bilyon | | 51 | Greater rhea | 330.34 na milyon | 1.03 bilyon | | 52 | Mute swan | 323.66 na milyon | 1 bilyon | | 53 | Alagang baboy | 293 milyon | 2.22 bilyon | | 54 | Greylag goose | 258.65 milyon | 738.23 milyon | | 55 | Brown bear | 250.97 milyon | 9.59 na bilyon | | 56 | Alagang pusa | 250 milyon | 1.22 bilyon | | 57 | Common marmoset | 245 milyon | 638 milyon | | 58 | Common kestrel | 237.9 na milyon | 624.58 milyon | | 59 | Eurasian teal | 167.29 na milyon | 375.41 milyon | | 60 | Budgerigar | 149 na milyon | 321.82 milyon | | 61 | Red-breasted goose | 148.62 milyon | 482.71 milyon | | 62 | Wood duck | 138.21 milyon | 305.82 milyon | | 63 | Pygmy falcon | 131.9 na milyon | 350.37 milyon | | 64 | Marbled duck | 130.14 na milyon | 386.92 milyon | | 65 | Indian peafowl | 129.62 milyon | 570.93 milyon | | 66 | Yellow-knobbed curassow | 124.62 milyon | 652.99 na milyon | | 67 | Victoria crowned pigeon | 118.45 milyon | 578.7 milyon | | 68 | Mallard | 112.26 na milyon | 367.47 milyon | | 69 | Wild turkey | 105.65 milyon | 492.87 milyon | | 70 | Eurasian sparrowhawk | 87.83 milyon | 401.75 milyon | | 71 | Reeves’s pheasant | 80.69 na milyon | 387.17 milyon | | 72 | Kalapati | 72 milyon | 310 milyon | | 73 | European rabbit | 71.5 milyon | 513 milyon | | 74 | Red junglefowl | 60.7 milyon | 220.84 na milyon | | 75 | Chilean tinamou | 59.13 milyon | 241.37 milyon | | 76 | Zebra finch | 55.2 milyon | 135.98 milyon | | 77 | Elegant crested tinamou | 51.38 milyon | 217.77 milyon | | 78 | Common wood pigeon | 51.33 milyon | 258.68 milyon | | 79 | Quebracho crested tinamou | 48.29 na milyon | 235.67 milyon | | 80 | African collared dove | 40 milyon | 139.27 milyon | | 81 | Northern bobwhite | 39.11 milyon | 148.4 na milyon | | 82 | Grey partridge | 36.88 milyon | 170.29 na milyon | | 83 | Daga | 31 milyon | 200 milyon | | 84 | Common quail | 22.57 milyon | 117.76 na milyon | | 85 | King quail | 20.52 milyon | 80.48 milyon | | 86 | Diamond dove | 18.21 milyon | 87.88 milyon | | 87 | Daga | 13.7 milyon | 70.9 na milyon | | 88 | Naked mole-rat | 5.31 milyon | 33.62 milyon | ## Mga sistemang AI: mga parameter laban sa mga synapse Lahat ng parameter Ginagamit bawat token All Wika Multimodal Paningin Iba pa (pananalita, biyolohiya, laro, robotics…) Mga trainable na parameter (connection weight), sa logarithmic na iskala: bawat kilalang modelo sa database ng Epoch AI na may bilang ng parameter (kinuha noong Oktubre 3, 2026). Ang solidong bar ay mga bilang na itinuturing ng Epoch na may mataas na kumpiyansa; ang may guhit na bar ay mga bilang na itinuturing nitong malamang o haka-haka. Mga tantiya ang mga bar na may markang \* para sa mga saradong modelo na hindi inilalathala ng mga developer nila ang laki, binuo noong Oktubre 2, 2026 mula sa mga pagsusuri ng ikatlong panig; ipinapakita ng whisker ang saklaw ng tantiya. Gemini 3 at mas bago: walang nailathalang tantiya. Mga bilang ng synapse ang mga guwang na hanay: mas malapit ang parameter sa synapse kaysa sa neuron. 1. Neocortex ng tao 1.6 × 10¹⁴ 2. GPT-5.5 (OpenAI, 2026) \* \~4.7 × 10¹² 3. Claude Fable 5 (Anthropic, 2026) \* \~3.5 × 10¹² 4. Grok 3 (xAI, 2025) \~3 × 10¹² 5. Grok 4 (xAI, 2025) \~3 × 10¹² 6. Gemini 2.5 Pro (Google, 2025) \* \~3 × 10¹² 7. Kimi K3 (Moonshot, 2026) \~2.8 × 10¹² 8. Qwen 3.8 Max (Alibaba, 2026) 2.4 × 10¹² 9. Qwen3.8-2.4T-A95B (Alibaba, 2026) 2.4 × 10¹² 10. Grok 4.7 (xAI, 2026) \* \~2.1 × 10¹² 11. Llama 4 Behemoth (Meta AI, 2025) \~2 × 10¹² 12. GPT-4 (OpenAI, 2023) \~1.8 × 10¹² 13. DeepSeek-V4-Pro (DeepSeek, 2026) \~1.6 × 10¹² 14. LongCat-2.0 (Meituan Inc, 2026) \~1.6 × 10¹² 15. Switch (Google, 2021) 1.6 × 10¹² 16. GLaM (Google, 2021) 1.2 × 10¹² 17. PanGu-Σ (Huawei Noah's Ark Lab, 2023) 1.1 × 10¹² 18. Kimi K2.5 (Moonshot, 2026) \~1 × 10¹² 19. Composer 2 (Cursor, 2026) 1 × 10¹² 20. Kimi K2.6 (Moonshot, 2026) 1 × 10¹² 21. Composer 2.5 (Cursor, 2026) \~1 × 10¹² 22. MiMo-V2-Pro (Xiaomi Corp, 2026) 1 × 10¹² 23. MiMo-V2.5-Pro (Xiaomi Corp, 2026) \~1 × 10¹² 24. M6-T (Alibaba, 2021) \~1 × 10¹² 25. Kimi K2 (Moonshot, 2025) 1 × 10¹² 26. Qwen3-Max (Alibaba, 2025) \~1 × 10¹² 27. Ling-1T (Ant Group, 2025) 1 × 10¹² 28. Kimi K2 Thinking (Moonshot, 2025) \~1 × 10¹² 29. Inkling (Thinking Machines, 2026) \~9.8 × 10¹¹ 30. Tencent Hy4 preview (Tencent, 2026) \~7.7 × 10¹¹ 31. GLM-5.1 (Z.ai (Zhipu AI), 2026) 7.5 × 10¹¹ 32. K-EXAONE 2.0 (LG AI Research, 2026) 7.5 × 10¹¹ 33. DeepSeek V4.1 Flash (DeepSeek, 2026) \~7.5 × 10¹¹ 34. GLM-5 (Z.ai (Zhipu AI), 2026) \~7.4 × 10¹¹ 35. GLM-5.3 (Z.ai (Zhipu AI), 2026) 7.4 × 10¹¹ 36. A.X K2 (SK Telecom, 2026) 6.9 × 10¹¹ 37. DeepSeekMath-V2 (DeepSeek, 2025) 6.8 × 10¹¹ 38. Mistral Large 3 (Mistral AI, 2025) 6.8 × 10¹¹ 39. DeepSeek-V3 (DeepSeek, 2024) 6.7 × 10¹¹ 40. DeepSeek-R1 (DeepSeek, 2025) 6.7 × 10¹¹ Ipakita lahat ng 725 10^4 10^6 10^8 10^10 10^12 10^14 mga parameter o synapse Ipakita ang mga numero | # | System | Lahat ng parameter | Ginagamit bawat token | Taon | Uri | | --- | ------------------------------------------------------------------------------------------- | ------------------ | --------------------- | ---- | --------------------------------------------------------- | | 1 | Neocortex ng tao | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | | mga synapse | | 2 | GPT-5.5 (OpenAI, 2026) \* | \~4.7 × 10¹² | | 2026 | pagtataya | | 3 | Claude Fable 5 (Anthropic, 2026) \* | \~3.5 × 10¹² | | 2026 | pagtataya | | 4 | Grok 3 (xAI, 2025) | \~3 × 10¹² | | 2025 | pagtataya | | 5 | Grok 4 (xAI, 2025) | \~3 × 10¹² | | 2025 | pagtataya | | 6 | Gemini 2.5 Pro (Google, 2025) \* | \~3 × 10¹² | | 2025 | pagtataya | | 7 | Kimi K3 (Moonshot, 2026) | \~2.8 × 10¹² | | 2026 | pagtataya | | 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | hindi tinukoy ang arkitektura | | 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | mixture of experts | | 10 | Grok 4.7 (xAI, 2026) \* | \~2.1 × 10¹² | | 2026 | pagtataya | | 11 | Llama 4 Behemoth (Meta AI, 2025) | \~2 × 10¹² | | 2025 | pagtataya | | 12 | GPT-4 (OpenAI, 2023) | \~1.8 × 10¹² | | 2023 | pagtataya | | 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | \~1.6 × 10¹² | | 2026 | pagtataya | | 14 | LongCat-2.0 (Meituan Inc, 2026) | \~1.6 × 10¹² | | 2026 | pagtataya | | 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | mixture of experts (hindi inilathala ang aktibong bilang) | | 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | mixture of experts (hindi inilathala ang aktibong bilang) | | 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | mixture of experts (hindi inilathala ang aktibong bilang) | | 18 | Kimi K2.5 (Moonshot, 2026) | \~1 × 10¹² | | 2026 | pagtataya | | 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 21 | Composer 2.5 (Cursor, 2026) | \~1 × 10¹² | | 2026 | pagtataya | | 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | hindi tinukoy ang arkitektura | | 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | \~1 × 10¹² | | 2026 | pagtataya | | 24 | M6-T (Alibaba, 2021) | \~1 × 10¹² | | 2021 | pagtataya | | 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 26 | Qwen3-Max (Alibaba, 2025) | \~1 × 10¹² | | 2025 | pagtataya | | 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | mixture of experts | | 28 | Kimi K2 Thinking (Moonshot, 2025) | \~1 × 10¹² | | 2025 | pagtataya | | 29 | Inkling (Thinking Machines, 2026) | \~9.8 × 10¹¹ | | 2026 | pagtataya | | 30 | Tencent Hy4 preview (Tencent, 2026) | \~7.7 × 10¹¹ | | 2026 | pagtataya | | 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | \~7.5 × 10¹¹ | | 2026 | pagtataya | | 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | \~7.4 × 10¹¹ | | 2026 | pagtataya | | 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | mixture of experts | | 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | mixture of experts | | 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | mixture of experts | | 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | mixture of experts | | 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | hindi tinukoy ang arkitektura | | 42 | Step 5 Preview (StepFun, 2026) | \~6 × 10¹¹ | | 2026 | pagtataya | | 43 | PaLM-E (Google, 2023) | \~5.6 × 10¹¹ | | 2023 | pagtataya | | 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 45 | LongCat-Flash (Meituan Inc, 2025) | \~5.6 × 10¹¹ | | 2025 | pagtataya | | 46 | Nemotron 3 Ultra (Nvidia, 2026) | \~5.5 × 10¹¹ | | 2026 | pagtataya | | 47 | PaLM (Google Research, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense | | 48 | Minerva (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense | | 49 | U-PaLM (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense | | 50 | LMSI-Palm (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense | | 51 | Flan-PaLM 540B (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense | | 52 | Megatron-Turing NLG 530B (Microsoft, 2021) | 5.3 × 10¹¹ | 5.3 × 10¹¹ | 2021 | dense | | 53 | MegaScale (ByteDance, 2024) | \~5.3 × 10¹¹ | | 2024 | pagtataya | | 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 55 | Doubao-pro (ByteDance, 2024) | \~5 × 10¹¹ | | 2024 | pagtataya | | 56 | Grok 4.20 (xAI, 2026) | \~5 × 10¹¹ | | 2026 | pagtataya | | 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | mixture of experts | | 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | mixture of experts | | 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | mixture of experts | | 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | dense | | 61 | Llama 4 Maverick (Meta AI, 2025) | \~4 × 10¹¹ | | 2025 | pagtataya | | 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | mixture of experts (hindi inilathala ang aktibong bilang) | | 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | \~4 × 10¹¹ | | 2026 | pagtataya | | 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | mixture of experts (hindi inilathala ang aktibong bilang) | | 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | \~3.6 × 10¹¹ | | 2025 | pagtataya | | 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | \~3.6 × 10¹¹ | | 2025 | pagtataya | | 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | mixture of experts | | 68 | PaLM 2 (Google, 2023) | \~3.4 × 10¹¹ | | 2023 | pagtataya | | 69 | Med-PaLM 2 (Google Research, 2023) | \~3.4 × 10¹¹ | | 2023 | pagtataya | | 70 | Nemotron-4 340B (Nvidia, 2024) | 3.4 × 10¹¹ | 3.4 × 10¹¹ | 2024 | dense | | 71 | GLM-5.3-Flash (Z.ai (Zhipu AI), 2026) | \~3.2 × 10¹¹ | | 2026 | pagtataya | | 72 | Grok-1 (xAI, 2023) | \~3.1 × 10¹¹ | | 2023 | pagtataya | | 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 74 | SB-LM (Google, 2007) | \~3 × 10¹¹ | | 2007 | pagtataya | | 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | \~2.8 × 10¹¹ | | 2026 | pagtataya | | 78 | Gopher (DeepMind, 2021) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2021 | dense | | 79 | TML-Interaction-Small (Thinking Machines, 2026) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2026 | mixture of experts (hindi inilathala ang aktibong bilang) | | 80 | ST-MoE (Google, 2022) | \~2.7 × 10¹¹ | | 2022 | pagtataya | | 81 | ERNIE 3.0 Titan (Baidu, 2021) | 2.6 × 10¹¹ | 2.6 × 10¹¹ | 2021 | dense | | 82 | Solar Open2 250B (Upstage, 2026) | \~2.5 × 10¹¹ | | 2026 | pagtataya | | 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | \~2.5 × 10¹¹ | | 2024 | pagtataya | | 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | hindi tinukoy ang arkitektura | | 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mixture of experts (hindi inilathala ang aktibong bilang) | | 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mixture of experts (hindi inilathala ang aktibong bilang) | | 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | hindi tinukoy ang arkitektura | | 88 | Qwen3-235B-A22B (Alibaba, 2025) | \~2.4 × 10¹¹ | | 2025 | pagtataya | | 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | \~2.4 × 10¹¹ | | 2025 | pagtataya | | 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | \~2.4 × 10¹¹ | | 2025 | pagtataya | | 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | mixture of experts | | 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 94 | HyperCLOVA 204B (NAVER, 2021) | \~2 × 10¹¹ | | 2021 | pagtataya | | 95 | Amazon Titan (Amazon, 2023) | \~2 × 10¹¹ | | 2023 | pagtataya | | 96 | Falcon-180B (Technology Innovation Institute, 2023) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2023 | dense | | 97 | Jurassic-1-Jumbo (AI21 Labs, 2021) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2021 | hindi tinukoy ang arkitektura | | 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense | | 99 | BLUUMI (University of Turku, 2023) | \~1.8 × 10¹¹ | | 2023 | pagtataya | | 100 | InstructGPT 175B (OpenAI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense | | 101 | OPT-175B (Meta AI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense | | 102 | BlenderBot 3 (McGill University, 2022) | \~1.8 × 10¹¹ | | 2022 | pagtataya | | 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | dense | | 104 | Palmyra X 004 (Writer, 2024) | \~1.5 × 10¹¹ | | 2024 | pagtataya | | 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | hindi tinukoy ang arkitektura | | 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | hindi tinukoy ang arkitektura | | 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | hindi tinukoy ang arkitektura | | 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | hindi tinukoy ang arkitektura | | 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | mixture of experts (hindi inilathala ang aktibong bilang) | | 110 | GLM-130B (Tsinghua University, 2022) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2022 | dense | | 111 | Pixtral Large (Mistral AI, 2024) | 1.2 × 10¹¹ | 1.2 × 10¹¹ | 2024 | hindi tinukoy ang arkitektura | | 112 | Mistral Large 2 (Mistral AI, 2024) | \~1.2 × 10¹¹ | | 2024 | pagtataya | | 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | \~1.2 × 10¹¹ | | 2026 | pagtataya | | 114 | Galactica (Meta AI, 2022) | \~1.2 × 10¹¹ | | 2022 | pagtataya | | 115 | Nemotron 3 Super (Nvidia, 2026) | \~1.2 × 10¹¹ | | 2026 | pagtataya | | 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | mixture of experts | | 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | hindi tinukoy ang arkitektura | | 118 | Kaiju Large (Character.ai, 2025) | \~1.1 × 10¹¹ | | 2025 | pagtataya | | 119 | Llama 4 Scout (Meta AI, 2025) | \~1.1 × 10¹¹ | | 2025 | pagtataya | | 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | dense | | 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | hindi tinukoy ang arkitektura | | 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | dense | | 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | hindi tinukoy ang arkitektura | | 124 | Yi-Large (01.AI, 2024) | \~1 × 10¹¹ | | 2024 | pagtataya | | 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 127 | Neocortex ng daga | 8.1 × 10¹⁰ | 8.1 × 10¹⁰ | | mga synapse | | 128 | Flamingo (DeepMind, 2022) | 8 × 10¹⁰ | 8 × 10¹⁰ | 2022 | dense | | 129 | Qwen3-Coder-Next (Alibaba, 2026) | \~8 × 10¹⁰ | | 2026 | pagtataya | | 130 | Qwen2-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense | | 131 | Qwen2.5-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense | | 132 | Qwen2.5 Instruct (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense | | 133 | Qwen-72B (Alibaba, 2023) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2023 | dense | | 134 | Palmyra X 003 (Writer, 2024) | \~7.2 × 10¹⁰ | | 2024 | pagtataya | | 135 | Qwen1.5-72B (Alibaba, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | dense | | 136 | LLaVA-OV-72B (ByteDance, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | dense | | 137 | NVLM-X 72B (Nvidia, 2024) | \~7.2 × 10¹⁰ | | 2024 | pagtataya | | 138 | NVLM-H 72B (Nvidia, 2024) | \~7.2 × 10¹⁰ | | 2024 | pagtataya | | 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 141 | Chinchilla (DeepMind, 2022) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2022 | dense | | 142 | Llama 2-70B (Meta AI, 2023) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2023 | dense | | 143 | PPLX-70B-Online (Perplexity, 2023) | \~7 × 10¹⁰ | | 2023 | pagtataya | | 144 | Llama 3-70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | dense | | 145 | ALLaM adapted 70B (Saudi Data and Artificial Intelligence Authority, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | dense | | 147 | Reka Core (Reka AI, 2024) | \~6.7 × 10¹⁰ | | 2024 | pagtataya | | 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | dense | | 149 | SNM-skip (Google, 2014) | \~6.2 × 10¹⁰ | | 2014 | pagtataya | | 150 | PaLI-X (Google Research, 2023) | \~5.5 × 10¹⁰ | | 2023 | pagtataya | | 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 153 | NLLB (Meta AI, 2022) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2022 | dense | | 154 | BloombergGPT (Bloomberg, 2023) | 5.1 × 10¹⁰ | 5.1 × 10¹⁰ | 2023 | dense | | 155 | M4-50B (Google, 2019) | 5 × 10¹⁰ | 5 × 10¹⁰ | 2019 | mixture of experts (hindi inilathala ang aktibong bilang) | | 156 | Mixtral 8x7B (Mistral AI, 2023) | \~4.7 × 10¹⁰ | | 2023 | pagtataya | | 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 158 | Falcon-40B (Technology Innovation Institute, 2023) | 4 × 10¹⁰ | 4 × 10¹⁰ | 2023 | dense | | 159 | Qwen3-Omni-30B-A3B (Alibaba, 2025) | 3.5 × 10¹⁰ | 3 × 10⁹ | 2025 | mixture of experts | | 160 | Yi-34B (01.AI, 2023) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2023 | dense | | 161 | Cambrian-1-34B (New York University (NYU), 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 163 | Kaiju Medium (Character.ai, 2025) | \~3.4 × 10¹⁰ | | 2025 | pagtataya | | 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | hindi tinukoy ang arkitektura | | 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | dense | | 166 | QwQ-32B (Alibaba, 2025) | \~3.3 × 10¹⁰ | | 2025 | pagtataya | | 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | \~3.2 × 10¹⁰ | | 2025 | pagtataya | | 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 174 | MM1-30B (Apple, 2024) | \~3 × 10¹⁰ | | 2024 | pagtataya | | 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | hindi tinukoy ang arkitektura | | 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | hindi tinukoy ang arkitektura | | 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 181 | KN-LM (Google, 2007) | \~2.1 × 10¹⁰ | | 2007 | pagtataya | | 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 183 | RNN for 1B words (Google, 2013) | \~2 × 10¹⁰ | | 2013 | pagtataya | | 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | dense | | 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 187 | GPT-3.5 Turbo (OpenAI, 2023) | \~2 × 10¹⁰ | | 2023 | pagtataya | | 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | \~2 × 10¹⁰ | | 2023 | pagtataya | | 189 | SPHINX (Shanghai AI Lab, 2023) | \~2 × 10¹⁰ | | 2023 | pagtataya | | 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 191 | CogAgent (Tsinghua University, 2023) | \~1.8 × 10¹⁰ | | 2023 | pagtataya | | 192 | Turing-NLG (Microsoft, 2020) | \~1.7 × 10¹⁰ | | 2020 | pagtataya | | 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 194 | PaLI (Google, 2022) | \~1.7 × 10¹⁰ | | 2022 | pagtataya | | 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 196 | StarCoder (Hugging Face, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | dense | | 197 | ESM2-15B (Meta AI, 2022) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 198 | FunSearch (Google DeepMind, 2023) | \~1.5 × 10¹⁰ | | 2023 | pagtataya | | 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | hindi tinukoy ang arkitektura | | 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 203 | mT5-XXL (Google, 2020) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2020 | dense | | 204 | mT0-13B (Hugging Face, 2022) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2022 | hindi tinukoy ang arkitektura | | 205 | LLaVA (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | dense | | 206 | InstructBLIP (Salesforce Research, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | \~1.3 × 10¹⁰ | | 2023 | pagtataya | | 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 211 | LLaVA 1.5 (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | dense | | 212 | Volcano 13B (Korea University, 2023) | \~1.3 × 10¹⁰ | | 2023 | pagtataya | | 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | \~1.3 × 10¹⁰ | | 2024 | pagtataya | | 214 | Kaiju Small (Character.ai, 2025) | \~1.3 × 10¹⁰ | | 2025 | pagtataya | | 215 | ByT5-XXL (Google, 2021) | \~1.3 × 10¹⁰ | | 2021 | pagtataya | | 216 | CoRe (Tsinghua University, 2023) | \~1.2 × 10¹⁰ | | 2023 | pagtataya | | 217 | DALL-E (OpenAI, 2021) | \~1.2 × 10¹⁰ | | 2021 | pagtataya | | 218 | Codex (OpenAI, 2021) | \~1.2 × 10¹⁰ | | 2021 | pagtataya | | 219 | T5-11B (Google, 2019) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2019 | dense | | 220 | UnifiedQA (Allen Institute for AI, 2020) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2020 | dense | | 221 | Unicorn (Allen Institute for AI, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | hindi tinukoy ang arkitektura | | 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | hindi tinukoy ang arkitektura | | 223 | CoEdiT-xxl (University of Minnesota, 2023) | \~1.1 × 10¹⁰ | | 2023 | pagtataya | | 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | hindi tinukoy ang arkitektura | | 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | hindi tinukoy ang arkitektura | | 226 | Llama 3.2 11B (Meta AI, 2024) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2024 | dense | | 227 | ERNIE 3.0 (Baidu, 2021) | 1 × 10¹⁰ | 1 × 10¹⁰ | 2021 | dense | | 228 | ERNIE-ViLG (Baidu, 2021) | \~1 × 10¹⁰ | | 2021 | pagtataya | | 229 | Qwen-VL (Alibaba, 2023) | \~9.6 × 10⁹ | | 2023 | pagtataya | | 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | mixture of experts (hindi inilathala ang aktibong bilang) | | 232 | CogVideo (Tsinghua University, 2022) | \~9.4 × 10⁹ | | 2022 | pagtataya | | 233 | eDiff-I (Nvidia, 2022) | \~9.1 × 10⁹ | | 2022 | pagtataya | | 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | hindi tinukoy ang arkitektura | | 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | mixture of experts (hindi inilathala ang aktibong bilang) | | 236 | Qwen-Audio-Chat (Alibaba, 2023) | \~8.5 × 10⁹ | | 2023 | pagtataya | | 237 | Megatron-LM (Nvidia, 2019) | \~8.3 × 10⁹ | | 2019 | pagtataya | | 238 | BigSSL (Google, 2021) | \~8 × 10⁹ | | 2021 | pagtataya | | 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | dense | | 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 242 | Imagen (Google Brain, 2022) | \~7.8 × 10⁹ | | 2022 | pagtataya | | 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 246 | mPLUG-Owl2 (Alibaba, 2023) | \~7.1 × 10⁹ | | 2023 | pagtataya | | 247 | Goat-7B (National University of Singapore, 2023) | \~7 × 10⁹ | | 2023 | pagtataya | | 248 | Llama 2-7B (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | dense | | 249 | Llama Guard (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | dense | | 250 | Qwen-VL-Max (Alibaba, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | dense | | 251 | Mathstral (Mistral AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | \~7 × 10⁹ | | 2025 | pagtataya | | 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | hindi tinukoy ang arkitektura | | 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 257 | GPipe (Google, 2018) | \~6 × 10⁹ | | 2018 | pagtataya | | 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | dense | | 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | \~6 × 10⁹ | | 2023 | pagtataya | | 261 | MolmoAct 2 (Allen Institute for AI, 2026) | \~5.5 × 10⁹ | | 2026 | pagtataya | | 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | mixture of experts (hindi inilathala ang aktibong bilang) | | 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 265 | Mesh-TensorFlow Transformer 4.9B (Google Brain, 2018) | 4.9 × 10⁹ | 4.9 × 10⁹ | 2018 | dense | | 266 | W.A.L.T (Stanford University, 2023) | 4.7 × 10⁹ | 4.7 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 267 | CogView (Tsinghua University, 2021) | \~4 × 10⁹ | | 2021 | pagtataya | | 268 | Make-A-Scene (Meta AI, 2022) | \~4 × 10⁹ | | 2022 | pagtataya | | 269 | ONE-PEACE (Alibaba, 2023) | \~4 × 10⁹ | | 2023 | pagtataya | | 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | dense | | 271 | DALL·E 2 (OpenAI, 2022) | \~3.5 × 10⁹ | | 2022 | pagtataya | | 272 | Stable Diffusion XL (Stability AI, 2023) | \~3.4 × 10⁹ | | 2023 | pagtataya | | 273 | MusicGen (Meta AI, 2023) | \~3.4 × 10⁹ | | 2023 | pagtataya | | 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | \~3.2 × 10⁹ | | 2016 | pagtataya | | 278 | BASIC-L (Google, 2021) | \~3.1 × 10⁹ | | 2021 | pagtataya | | 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | \~2.9 × 10⁹ | | 2018 | pagtataya | | 287 | T5-3B (Google, 2019) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2019 | dense | | 288 | DPO on Pythia-2.8B (Stanford University, 2023) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2023 | dense | | 289 | AFM-on-device (Apple, 2024) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | \~2.7 × 10⁹ | | 2022 | pagtataya | | 291 | Hybrid H3-2.7B (Stanford University, 2022) | \~2.7 × 10⁹ | | 2022 | pagtataya | | 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 293 | Meena (Google Brain, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | dense | | 294 | CPM-Large (Tsinghua University, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | dense | | 295 | Nucleotide Transformer (Nvidia, 2023) | \~2.5 × 10⁹ | | 2023 | pagtataya | | 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | dense | | 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 302 | MuseNet (OpenAI, 2019) | \~2 × 10⁹ | | 2019 | pagtataya | | 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | hindi tinukoy ang arkitektura | | 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | dense | | 305 | BEIT-3 (Microsoft, 2022) | \~1.9 × 10⁹ | | 2022 | pagtataya | | 306 | Ankh\_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 309 | Phenaki (University College London (UCL), 2022) | \~1.8 × 10⁹ | | 2022 | pagtataya | | 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 311 | DistBelief Vision (Google, 2012) | \~1.7 × 10⁹ | | 2012 | pagtataya | | 312 | Whisper (OpenAI, 2022) | \~1.6 × 10⁹ | | 2022 | pagtataya | | 313 | GPT-2 (OpenAI, 2019) | \~1.5 × 10⁹ | | 2019 | pagtataya | | 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 316 | AudioLM (Google Research, 2023) | \~1.5 × 10⁹ | | 2023 | pagtataya | | 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 319 | Stable Diffusion (Runway, 2022) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2022 | dense | | 320 | SEER (Facebook AI Research, 2021) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2021 | hindi tinukoy ang arkitektura | | 321 | InstructGPT 1.3B (OpenAI, 2022) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2022 | dense | | 322 | Megatron-LM (Nvidia, 2019) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2019 | dense | | 323 | RDT-1B (Tsinghua University, 2024) | \~1.2 × 10⁹ | | 2024 | pagtataya | | 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | hindi tinukoy ang arkitektura | | 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | hindi tinukoy ang arkitektura | | 329 | Unsupervised High-level Feature Learner (Google, 2012) | \~1 × 10⁹ | | 2012 | pagtataya | | 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | dense | | 331 | HuBERT (Facebook AI Research, 2021) | \~1 × 10⁹ | | 2021 | pagtataya | | 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | dense | | 333 | AudioGen (Meta AI, 2023) | \~1 × 10⁹ | | 2023 | pagtataya | | 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | hindi tinukoy ang arkitektura | | 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | \~9.4 × 10⁸ | | 2013 | pagtataya | | 336 | ImageBind (Meta AI, 2023) | \~9.3 × 10⁸ | | 2023 | pagtataya | | 337 | Big Transfer (Google Brain, 2019) | \~9.3 × 10⁸ | | 2019 | pagtataya | | 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 341 | ResNeXt-101 32x48d (Facebook, 2018) | 8.3 × 10⁸ | 8.3 × 10⁸ | 2018 | dense | | 342 | FixRes ResNeXt-101 WSL (Facebook AI, 2019) | \~8.3 × 10⁸ | | 2019 | pagtataya | | 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 344 | Statement Curriculum Learning (OpenAI, 2022) | \~7.7 × 10⁸ | | 2022 | pagtataya | | 345 | CodeT5-large (Salesforce, 2022) | \~7.7 × 10⁸ | | 2022 | pagtataya | | 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | hindi tinukoy ang arkitektura | | 348 | data2vec (Meta AI, 2022) | \~7.1 × 10⁸ | | 2022 | pagtataya | | 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 350 | Word2Vec (Google, 2013) | \~6.9 × 10⁸ | | 2013 | pagtataya | | 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | \~6.8 × 10⁸ | | 2023 | pagtataya | | 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 354 | XLM (Facebook, 2019) | \~6.7 × 10⁸ | | 2019 | pagtataya | | 355 | ESM1b (Facebook AI Research, 2020) | 6.5 × 10⁸ | 6.5 × 10⁸ | 2020 | dense | | 356 | Segment Anything Model (Meta AI, 2023) | 6.4 × 10⁸ | 6.4 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 357 | ViT-Huge/14 (Google Brain, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | dense | | 358 | Masked Autoencoders ViT-H (Facebook AI Research, 2021) | \~6.3 × 10⁸ | | 2021 | pagtataya | | 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | dense | | 360 | DLDL (University of Oxford, 2016) | \~5.6 × 10⁸ | | 2016 | pagtataya | | 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | dense | | 364 | LUKE (University of Washington, 2020) | \~4.8 × 10⁸ | | 2020 | pagtataya | | 365 | EfficientNet-L2 (Google, 2019) | \~4.8 × 10⁸ | | 2019 | pagtataya | | 366 | Noisy Student (Carnegie Mellon University (CMU), 2019) | 4.8 × 10⁸ | 4.8 × 10⁸ | 2019 | dense | | 367 | Meta Pseudo Labels (Google Brain, 2021) | \~4.8 × 10⁸ | | 2021 | pagtataya | | 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 369 | NMT Transformer 437M (Google, 2019) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2019 | dense | | 370 | Adaptive Inputs + LayerDrop (Facebook AI Research, 2019) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2019 | dense | | 371 | ProtBERT-BFD (Technical University of Munich, 2021) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 372 | OntoProtein (Zhejiang University (ZJU), 2022) | \~4.2 × 10⁸ | | 2022 | pagtataya | | 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 374 | BART-large (Facebook AI, 2019) | \~4.1 × 10⁸ | | 2019 | pagtataya | | 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 376 | BERT-Large-CAS (Amazon, 2019) | \~4 × 10⁸ | | 2019 | pagtataya | | 377 | MoCo (Facebook AI, 2019) | \~3.8 × 10⁸ | | 2019 | pagtataya | | 378 | SimCLR (Google Brain, 2020) | \~3.8 × 10⁸ | | 2020 | pagtataya | | 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 380 | RoBERTa Large (Facebook, 2019) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2019 | dense | | 381 | AbLang (University of Oxford, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | dense | | 383 | VALL-E (Microsoft, 2023) | \~3.5 × 10⁸ | | 2023 | pagtataya | | 384 | BERT-Large (Google, 2018) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2018 | dense | | 385 | XLNet (Carnegie Mellon University (CMU), 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | dense | | 386 | ERNIE-GEN (Baidu, 2020) | \~3.4 × 10⁸ | | 2020 | pagtataya | | 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | dense | | 388 | ELECTRA (Stanford University, 2020) | \~3.4 × 10⁸ | | 2020 | pagtataya | | 389 | German ELECTRA Large (deepset, 2020) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2020 | dense | | 390 | ALM 1.0 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | \~3.4 × 10⁸ | | 2022 | pagtataya | | 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | dense | | 392 | Fusion in Encoder (Samsung, 2022) | \~3.3 × 10⁸ | | 2022 | pagtataya | | 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | dense | | 394 | CPC v2 (DeepMind, 2019) | \~3 × 10⁸ | | 2019 | pagtataya | | 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | dense | | 396 | DDPM-IP (Utrecht University, 2023) | \~3 × 10⁸ | | 2023 | pagtataya | | 397 | GNMT (Google, 2016) | \~2.8 × 10⁸ | | 2016 | pagtataya | | 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 399 | YOLO (University of Washington, 2015) | \~2.7 × 10⁸ | | 2015 | pagtataya | | 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | \~2.6 × 10⁸ | | 2019 | pagtataya | | 401 | Transformer-XL + RMS dynamic eval (University of Edinburgh, 2019) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2019 | dense | | 402 | Transformer-XL Large + Phrase Induction (Massachusetts Institute of Technology (MIT), 2019) | \~2.6 × 10⁸ | | 2019 | pagtataya | | 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | dense | | 407 | S4 (Stanford University, 2021) | \~2.5 × 10⁸ | | 2021 | pagtataya | | 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | \~2.5 × 10⁸ | | 2019 | pagtataya | | 409 | ERNIE-Doc (Baidu, 2020) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2020 | dense | | 410 | Adaptive Input Transformer + RD (Microsoft Research Asia, 2021) | \~2.5 × 10⁸ | | 2021 | pagtataya | | 411 | Transformer (Adaptive Input Embeddings) WT103 (Facebook AI Research, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | dense | | 412 | TaLK Convolution (Carleton University, 2020) | \~2.4 × 10⁸ | | 2020 | pagtataya | | 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | \~2.4 × 10⁸ | | 2020 | pagtataya | | 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | hindi tinukoy ang arkitektura | | 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | hindi tinukoy ang arkitektura | | 417 | MetNet (Google, 2020) | \~2.3 × 10⁸ | | 2020 | pagtataya | | 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | dense | | 419 | CodeT5-base (Salesforce, 2021) | \~2.2 × 10⁸ | | 2021 | pagtataya | | 420 | Transformer (Google Research, 2017) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2017 | dense | | 421 | Sandwich Transformer (Allen Institute for AI, 2019) | \~2.1 × 10⁸ | | 2019 | pagtataya | | 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 423 | Word2Vec (Google, 2013) | \~2.1 × 10⁸ | | 2013 | pagtataya | | 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | dense | | 425 | DreamerV3 (DeepMind, 2023) | \~2 × 10⁸ | | 2023 | pagtataya | | 426 | DL scaling speech (Baidu, 2017) | \~1.9 × 10⁸ | | 2017 | pagtataya | | 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | \~1.9 × 10⁸ | | 2019 | pagtataya | | 428 | (NTT Communication Science Laboratories, 2018) | 1.9 × 10⁸ | 1.9 × 10⁸ | 2018 | dense | | 429 | TrellisNet (Carnegie Mellon University (CMU), 2018) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2018 | dense | | 430 | DL scaling LM (Baidu, 2017) | \~1.8 × 10⁸ | | 2017 | pagtataya | | 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | hindi tinukoy ang arkitektura | | 432 | OpenAI Five Rerun (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | dense | | 433 | OpenAI Five (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | dense | | 434 | 4 layer QRNN (Salesforce Research, 2018) | \~1.5 × 10⁸ | | 2018 | pagtataya | | 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | \~1.5 × 10⁸ | | 2020 | pagtataya | | 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | hindi tinukoy ang arkitektura | | 437 | PermuteFormer (Peking University, 2021) | \~1.5 × 10⁸ | | 2021 | pagtataya | | 438 | Fragment embedding (Stanford University, 2014) | \~1.4 × 10⁸ | | 2014 | pagtataya | | 439 | OverFeat (New York University (NYU), 2013) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2013 | dense | | 440 | VGG19 (University of Oxford, 2014) | \~1.4 × 10⁸ | | 2014 | pagtataya | | 441 | LRCN (UT Austin, 2014) | \~1.4 × 10⁸ | | 2014 | pagtataya | | 442 | Transformer-XL DeFINE (University of Washington, 2019) | \~1.4 × 10⁸ | | 2019 | pagtataya | | 443 | Deep Autoencoders (University of Toronto, 2011) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2011 | dense | | 444 | AlphaStar (DeepMind, 2019) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2019 | dense | | 445 | VGG16 (University of Oxford, 2014) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2014 | dense | | 446 | VGG-Face (University of Oxford, 2015) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2015 | dense | | 447 | SAF R-CNN (Beijing Institute of Technology, 2015) | \~1.4 × 10⁸ | | 2015 | pagtataya | | 448 | Template Adaptation (University of Oxford, 2016) | \~1.4 × 10⁸ | | 2016 | pagtataya | | 449 | LRR-4X (UC Irvine, 2016) | \~1.4 × 10⁸ | | 2016 | pagtataya | | 450 | CMS-RCNN (IEEE, 2016) | \~1.4 × 10⁸ | | 2016 | pagtataya | | 451 | QRNN (Salesforce Research, 2018) | \~1.4 × 10⁸ | | 2018 | pagtataya | | 452 | Multiresolution CNN (Google, 2014) | \~1.3 × 10⁸ | | 2014 | pagtataya | | 453 | FTW (DeepMind, 2018) | \~1.3 × 10⁸ | | 2018 | pagtataya | | 454 | Feedback Transformer (LORIA, 2020) | \~1.3 × 10⁸ | | 2020 | pagtataya | | 455 | KEPLER (Tsinghua University, 2020) | 1.3 × 10⁸ | 1.3 × 10⁸ | 2020 | dense | | 456 | DL scaling Image (Baidu, 2017) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2017 | dense | | 457 | GloVe (Stanford University, 2014) | \~1.2 × 10⁸ | | 2014 | pagtataya | | 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | dense | | 459 | GPT-1 (OpenAI, 2018) | \~1.2 × 10⁸ | | 2018 | pagtataya | | 460 | ContextNet (Google, 2020) | \~1.1 × 10⁸ | | 2020 | pagtataya | | 461 | SciBERT (Allen Institute for AI, 2019) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2019 | dense | | 462 | DNABERT (Northeastern University, 2021) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2021 | hindi tinukoy ang arkitektura | | 463 | Contriever (Meta AI, 2021) | \~1.1 × 10⁸ | | 2021 | pagtataya | | 464 | BERT-RBP (Waseda University, 2022) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2022 | dense | | 465 | RoFormer (Zhuiyi Technology, 2023) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2023 | hindi tinukoy ang arkitektura | | 466 | LSTM LM (RWTH Aachen University, 2012) | \~1 × 10⁸ | | 2012 | pagtataya | | 467 | ReLU-Speech (Google, 2013) | \~1 × 10⁸ | | 2013 | pagtataya | | 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | hindi tinukoy ang arkitektura | | 469 | Support Vector Machines (AT&T, 1995) | \~1 × 10⁸ | | 1995 | pagtataya | | 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | dense | | 471 | MSA Transformer (Facebook AI Research, 2021) | \~1 × 10⁸ | | 2021 | pagtataya | | 472 | YOLOX-X (Megvii Inc, 2021) | \~9.9 × 10⁷ | | 2021 | pagtataya | | 473 | DeLighT (University of Washington, 2020) | \~9.9 × 10⁷ | | 2020 | pagtataya | | 474 | ELMo (University of Washington, 2018) | \~9.4 × 10⁷ | | 2018 | pagtataya | | 475 | AlphaFold 2 (DeepMind, 2020) | \~9.3 × 10⁷ | | 2020 | pagtataya | | 476 | AlphaMissense (Google DeepMind, 2023) | \~9.3 × 10⁷ | | 2023 | pagtataya | | 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | hindi tinukoy ang arkitektura | | 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | \~9.2 × 10⁷ | | 2016 | pagtataya | | 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | hindi tinukoy ang arkitektura | | 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | dense | | 481 | NASNet-A (Google Brain, 2017) | \~8.9 × 10⁷ | | 2017 | pagtataya | | 482 | CLIP (OpenAI, 2021) | \~8.9 × 10⁷ | | 2021 | pagtataya | | 483 | Detic (Meta AI, 2022) | \~8.8 × 10⁷ | | 2022 | pagtataya | | 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | dense | | 485 | BigBiGAN (Google, 2019) | \~8.6 × 10⁷ | | 2019 | pagtataya | | 486 | ViT-Base/32 (Google Brain, 2020) | \~8.6 × 10⁷ | | 2020 | pagtataya | | 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | hindi tinukoy ang arkitektura | | 488 | CaLM (University of Oxford, 2022) | \~8.6 × 10⁷ | | 2022 | pagtataya | | 489 | Tensor-Transformer (University of California (UC) Berkeley, 2020) | 8.5 × 10⁷ | 8.5 × 10⁷ | 2020 | dense | | 490 | ResNeXt-101 (University of California San Diego, 2016) | 8.3 × 10⁷ | 8.3 × 10⁷ | 2016 | dense | | 491 | RBM Image Classifier (University of Toronto, 2009) | \~8 × 10⁷ | | 2009 | pagtataya | | 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | dense | | 493 | Big-Little Net (IBM, 2018) | \~7.7 × 10⁷ | | 2018 | pagtataya | | 494 | EfficientDet (Google Brain, 2020) | \~7.7 × 10⁷ | | 2020 | pagtataya | | 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | hindi tinukoy ang arkitektura | | 496 | RNN LM (Johns Hopkins University, 2010) | \~7 × 10⁷ | | 2010 | pagtataya | | 497 | R-CNN (University of California (UC) Berkeley, 2013) | \~6.9 × 10⁷ | | 2013 | pagtataya | | 498 | Variational (University of Cambridge, 2015) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2015 | dense | | 499 | DistilBERT (Hugging Face, 2019) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2019 | dense | | 500 | Theseus 6/768 (University of California San Diego, 2020) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2020 | dense | | 501 | ResNet-152 (Microsoft, 2015) | 6 × 10⁷ | 6 × 10⁷ | 2015 | dense | | 502 | AlexNet (University of Toronto, 2012) | 6 × 10⁷ | 6 × 10⁷ | 2012 | dense | | 503 | DETR (Facebook, 2020) | 6 × 10⁷ | 6 × 10⁷ | 2020 | dense | | 504 | YOLOv3 (University of Washington, 2018) | \~5.7 × 10⁷ | | 2018 | pagtataya | | 505 | Inception-ResNet-V2 (Google, 2016) | \~5.6 × 10⁷ | | 2016 | pagtataya | | 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | dense | | 507 | Utak ng fruit fly | 5.5 × 10⁷ | 5.5 × 10⁷ | | mga synapse | | 508 | NAS with base 8 and shared embeddings (Google Brain, 2016) | 5.4 × 10⁷ | 5.4 × 10⁷ | 2016 | dense | | 509 | RetinaNet-R101 (Facebook AI Research, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | dense | | 510 | AWD-LSTM+WT+Cache+IOG (NTT Communication Science Laboratories, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | dense | | 511 | Hierarchical Scene Labeling (New York University (NYU), 2013) | 5.2 × 10⁷ | 5.2 × 10⁷ | 2013 | dense | | 512 | VD-LSTM+REAL Large (Salesforce Research, 2016) | 5.1 × 10⁷ | 5.1 × 10⁷ | 2016 | dense | | 513 | YOLOv2 (University of Washington, 2016) | \~5.1 × 10⁷ | | 2016 | pagtataya | | 514 | Dropout-LSTM+Noise (Columbia University, 2018) | \~5.1 × 10⁷ | | 2018 | pagtataya | | 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | \~5 × 10⁷ | | 2017 | pagtataya | | 516 | Dropout (University of Toronto, 2012) | \~4.9 × 10⁷ | | 2012 | pagtataya | | 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | dense | | 518 | DistBelief Speech (Google, 2012) | \~4.7 × 10⁷ | | 2012 | pagtataya | | 519 | AlphaGo Zero (DeepMind, 2017) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2017 | dense | | 520 | Fine-tuned-AWD-LSTM-DOC (Samsung R&D Institute Russia, 2018) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2018 | dense | | 521 | CT-MoS (Google, 2020) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2020 | dense | | 522 | JFT (Google Research, 2017) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2017 | dense | | 523 | ResNet-101 (Microsoft, 2015) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2015 | dense | | 524 | HR-ResNet101 (Carnegie Mellon University (CMU), 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | dense | | 525 | 3DMM-CNN (University of Southern California, 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | dense | | 526 | LaNet-L (Brown University, 2019) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2019 | dense | | 527 | ProBERTa (University of Illinois Urbana-Champaign (UIUC), 2020) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2020 | dense | | 528 | PreTrans-3L-250H (University of Toronto, 2013) | \~4.3 × 10⁷ | | 2013 | pagtataya | | 529 | Inceptionv4 (Google, 2016) | \~4.3 × 10⁷ | | 2016 | pagtataya | | 530 | GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (Ben-Gurion University of the Negev, 2017) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2017 | dense | | 531 | U-Net (University of Freiburg, 2015) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2015 | dense | | 532 | NASv3 (Google Brain, 2016) | \~3.7 × 10⁷ | | 2016 | pagtataya | | 533 | MuZero (DeepMind, 2019) | \~3.7 × 10⁷ | | 2019 | pagtataya | | 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | \~3.5 × 10⁷ | | 2017 | pagtataya | | 535 | AWD-LSTM-MoS+PDR + dynamic evaluation (IBM, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | dense | | 536 | AWD-LSTM-MoS + dynamic evaluation (Peking University, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | dense | | 537 | AWD-LSTM + MoS + Partial Shuffled (University of Texas at Austin, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | dense | | 538 | Mogrifier (d2, MoS2, MC) + dynamic eval (DeepMind, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | dense | | 539 | Mogrifier RLSTM (DeepMind, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | hindi tinukoy ang arkitektura | | 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | hindi tinukoy ang arkitektura | | 541 | DenseNet-264 (Tsinghua University, 2016) | 3.4 × 10⁷ | 3.4 × 10⁷ | 2016 | dense | | 542 | RetinaNet-R50 (Facebook AI Research, 2017) | \~3.4 × 10⁷ | | 2017 | pagtataya | | 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | \~3.4 × 10⁷ | | 2017 | pagtataya | | 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | \~3.4 × 10⁷ | | 2019 | pagtataya | | 545 | AWD-LSTM - 3-layer LSTM (Salesforce Research, 2017) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2017 | dense | | 546 | TCAN (Nanjing University, 2020) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2020 | dense | | 547 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | \~3.2 × 10⁷ | | 2019 | pagtataya | | 548 | Paragraph Vector (Google, 2014) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2014 | dense | | 549 | aLSTM (University of Manchester, 2018) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2018 | dense | | 550 | StyleGAN2 (Nvidia, 2019) | 3 × 10⁷ | 3 × 10⁷ | 2019 | dense | | 551 | UDSMProt (Fraunhofer Heinrich Hertz Institute, 2019) | \~2.8 × 10⁷ | | 2019 | pagtataya | | 552 | SENet (Chinese Academy of Sciences, 2017) | \~2.8 × 10⁷ | | 2017 | pagtataya | | 553 | Perceiver IO (DeepMind, 2020) | 2.8 × 10⁷ | 2.8 × 10⁷ | 2020 | dense | | 554 | Fractional Max-Pooling (University of Warwick, 2014) | \~2.7 × 10⁷ | | 2014 | pagtataya | | 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | hindi tinukoy ang arkitektura | | 556 | RNN-WER (DeepMind, 2014) | \~2.6 × 10⁷ | | 2014 | pagtataya | | 557 | StyleGAN (Nvidia, 2018) | 2.6 × 10⁷ | 2.6 × 10⁷ | 2018 | dense | | 558 | PyramidNet (Korea Advanced Institute of Science and Technology (KAIST), 2017) | \~2.6 × 10⁷ | | 2017 | pagtataya | | 559 | ResNeXt-50 (University of California San Diego, 2016) | \~2.5 × 10⁷ | | 2016 | pagtataya | | 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | dense | | 561 | Inception v3 (Google, 2015) | \~2.4 × 10⁷ | | 2015 | pagtataya | | 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | hindi tinukoy ang arkitektura | | 563 | WeNet (Amazon, 2019) | \~2.3 × 10⁷ | | 2019 | pagtataya | | 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | \~2.3 × 10⁷ | | 2019 | pagtataya | | 565 | NAS+ESS (Northeastern University (China), 2020) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2020 | dense | | 566 | Xception (Google, 2016) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2016 | dense | | 567 | MetaMimic (Google, 2018) | \~2.2 × 10⁷ | | 2018 | pagtataya | | 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | \~2.1 × 10⁷ | | 2019 | pagtataya | | 569 | Pointer Sentinel-LSTM (MetaMind Inc, 2016) | 2.1 × 10⁷ | 2.1 × 10⁷ | 2016 | dense | | 570 | DiffDock (Massachusetts Institute of Technology (MIT), 2022) | \~2 × 10⁷ | | 2022 | pagtataya | | 571 | EI-REHN-1000D (Korea Advanced Institute of Science and Technology (KAIST), 2017) | 1.9 × 10⁷ | 1.9 × 10⁷ | 2017 | dense | | 572 | TC-DNN-BLSTM-DNN (Carnegie Mellon University (CMU), 2015) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2015 | dense | | 573 | OR-WideResNet (Duke University, 2017) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2017 | dense | | 574 | BP-DBN (University of Toronto, 2009) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2009 | dense | | 575 | ALBERT (Toyota Technological Institute at Chicago, 2019) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2019 | dense | | 576 | MedBERT (Peng Cheng Laboratory, 2021) | \~1.7 × 10⁷ | | 2021 | pagtataya | | 577 | LCNP NORB (, 2009) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2009 | dense | | 578 | LF-MMI (Johns Hopkins University, 2016) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2016 | dense | | 579 | AlphaFold (DeepMind, 2020) | \~1.6 × 10⁷ | | 2020 | pagtataya | | 580 | GNoME for crystal discovery (Google DeepMind, 2023) | \~1.6 × 10⁷ | | 2023 | pagtataya | | 581 | ReLU (University of Toronto, 2010) | \~1.6 × 10⁷ | | 2010 | pagtataya | | 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | hindi tinukoy ang arkitektura | | 583 | Eve (Harvard Medical School, 2021) | \~1.5 × 10⁷ | | 2021 | pagtataya | | 584 | LCNP LabelMe (University of Bonn, 2009) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2009 | dense | | 585 | BatchNorm (Google, 2015) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2015 | dense | | 586 | Fold2Seq (IBM, 2021) | \~1.2 × 10⁷ | | 2021 | pagtataya | | 587 | NPLM (University of Montreal / Université de Montréal, 2003) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2003 | dense | | 588 | LCNP MNIST (, 2009) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2009 | dense | | 589 | ISS (Duke University, 2017) | 1.1 × 10⁷ | 1.1 × 10⁷ | 2017 | dense | | 590 | ResNet-1001 (Microsoft, 2016) | \~1 × 10⁷ | | 2016 | pagtataya | | 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | \~9 × 10⁶ | | 2012 | pagtataya | | 592 | Gated HORNN (York University, 2016) | \~9 × 10⁶ | | 2016 | pagtataya | | 593 | AlphaGo Fan (DeepMind, 2015) | \~8.2 × 10⁶ | | 2015 | pagtataya | | 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | \~8 × 10⁶ | | 2015 | pagtataya | | 595 | Once for All (MIT-IBM Watson AI Lab, 2020) | 7.7 × 10⁶ | 7.7 × 10⁶ | 2020 | dense | | 596 | Multi-cell LSTM (University of Hyderabad, 2018) | \~7.2 × 10⁶ | | 2018 | pagtataya | | 597 | Feedforward NN (University of Montreal / Université de Montréal, 2010) | 7.1 × 10⁶ | 7.1 × 10⁶ | 2010 | dense | | 598 | True-Regularization+Finetune+Dynamic-Eval (Mobvoi, 2019) | 7 × 10⁶ | 7 × 10⁶ | 2019 | dense | | 599 | Neural LM (University of Montreal / Université de Montréal, 2000) | 6.9 × 10⁶ | 6.9 × 10⁶ | 2000 | dense | | 600 | GoogLeNet / InceptionV1 (Google, 2014) | 6.8 × 10⁶ | 6.8 × 10⁶ | 2014 | dense | | 601 | HyenaDNA (Stanford University, 2023) | 6.6 × 10⁶ | 6.6 × 10⁶ | 2023 | hindi tinukoy ang arkitektura | | 602 | Dropout (University of Toronto, 2012) | 5.6 × 10⁶ | 5.6 × 10⁶ | 2012 | dense | | 603 | SimpleNet (Sensifai, 2016) | 5.5 × 10⁶ | 5.5 × 10⁶ | 2016 | dense | | 604 | AlphaX-1 (Facebook AI Research, 2019) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2019 | dense | | 605 | 3DDFA (Chinese Academy of Sciences, 2015) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2015 | dense | | 606 | Deep CNN + COTS (IEEE, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | dense | | 607 | DCNN (University of Maryland, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | dense | | 608 | NLP from scratch (NEC Laboratories, 2011) | \~5 × 10⁶ | | 2011 | pagtataya | | 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | \~5 × 10⁶ | | 2014 | pagtataya | | 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | hindi tinukoy ang arkitektura | | 611 | High Performance CNN (IDSIA, 2011) | \~4.9 × 10⁶ | | 2011 | pagtataya | | 612 | MobileNet (Google, 2017) | \~4.2 × 10⁶ | | 2017 | pagtataya | | 613 | NPLM (University of Montreal / Université de Montréal, 2003) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2003 | dense | | 614 | LMICA (, 2004) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2004 | dense | | 615 | PoE MNIST (University College London (UCL), 2000) | 3.9 × 10⁶ | 3.9 × 10⁶ | 2000 | dense | | 616 | Dimensionality Reduction (University of Toronto, 2006) | \~3.8 × 10⁶ | | 2006 | pagtataya | | 617 | GOAT (DeepMind, 2021) | \~3.5 × 10⁶ | | 2021 | pagtataya | | 618 | MobileNetV2 (Google, 2018) | \~3.4 × 10⁶ | | 2018 | pagtataya | | 619 | Big-Little Net (IBM, 2018) | \~3.3 × 10⁶ | | 2018 | pagtataya | | 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | hindi tinukoy ang arkitektura | | 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | dense | | 622 | DeepStack (University of Alberta, 2017) | \~2.5 × 10⁶ | | 2017 | pagtataya | | 623 | KataGo (Jane Street, 2019) | \~2.5 × 10⁶ | | 2019 | pagtataya | | 624 | ShuffleNet v1 (Megvii Inc, 2017) | \~2.4 × 10⁶ | | 2017 | pagtataya | | 625 | ADAM (University of Amsterdam, 2014) | 2.4 × 10⁶ | 2.4 × 10⁶ | 2014 | dense | | 626 | StyleGAN3-T (Nvidia, 2021) | 2.2 × 10⁶ | 2.2 × 10⁶ | 2021 | hindi tinukoy ang arkitektura | | 627 | LSTM+NeuralCache (KU Leuven, 2018) | \~2.1 × 10⁶ | | 2018 | pagtataya | | 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | dense | | 629 | ResNet-110 (Microsoft, 2015) | \~1.7 × 10⁶ | | 2015 | pagtataya | | 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | dense | | 631 | DQN-2015 (Google, 2015) | \~1.7 × 10⁶ | | 2015 | pagtataya | | 632 | Rational DQN Average (TU Darmstadt, 2021) | \~1.7 × 10⁶ | | 2021 | pagtataya | | 633 | IBM-5 (IBM, 1993) | \~1.7 × 10⁶ | | 1993 | pagtataya | | 634 | Deep Belief Nets (University of Toronto, 2006) | \~1.6 × 10⁶ | | 2006 | pagtataya | | 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | dense | | 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | hindi tinukoy ang arkitektura | | 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | \~1.5 × 10⁶ | | 2008 | pagtataya | | 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | dense | | 639 | CNN committee (IDSIA, 2011) | \~1.4 × 10⁶ | | 2011 | pagtataya | | 640 | SqueezeNet (DeepScale, 2016) | \~1.2 × 10⁶ | | 2016 | pagtataya | | 641 | Neocognitron (NHK Broadcasting Science Research Laboratories, 1980) | 1.1 × 10⁶ | 1.1 × 10⁶ | 1980 | dense | | 642 | CURL (University of California (UC) Berkeley, 2020) | \~9.1 × 10⁵ | | 2020 | pagtataya | | 643 | DQN (DeepMind, 2013) | 8.4 × 10⁵ | 8.4 × 10⁵ | 2013 | dense | | 644 | EnhanceNet (Max Planck Institute for Intelligent Systems, 2016) | 8.1 × 10⁵ | 8.1 × 10⁵ | 2016 | dense | | 645 | Image generation (University of Amsterdam, 2013) | 7.8 × 10⁵ | 7.8 × 10⁵ | 2013 | dense | | 646 | Hanabi 4 player (DeepMind, 2019) | 7.6 × 10⁵ | 7.6 × 10⁵ | 2019 | dense | | 647 | TA-CNN (Chinese University of Hong Kong (CUHK), 2014) | \~7.1 × 10⁵ | | 2014 | pagtataya | | 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | hindi tinukoy ang arkitektura | | 649 | LISSOM (University of Texas at Austin, 1995) | \~4.3 × 10⁵ | | 1995 | pagtataya | | 650 | SPIDER2 (Griffith University, 2016) | \~4.1 × 10⁵ | | 2016 | pagtataya | | 651 | Mixture of linear models (, 1994) | \~3.8 × 10⁵ | | 1994 | pagtataya | | 652 | NPD (IEEE, 2014) | 3.1 × 10⁵ | 3.1 × 10⁵ | 2014 | dense | | 653 | Pooling CNN (University of Bonn, 2010) | 2.9 × 10⁵ | 2.9 × 10⁵ | 2010 | dense | | 654 | Pooling CNN (University of Bonn, 2010) | 2.7 × 10⁵ | 2.7 × 10⁵ | 2010 | dense | | 655 | Two Stage Feature Extraction (New York University (NYU), 2009) | 2.6 × 10⁵ | 2.6 × 10⁵ | 2009 | dense | | 656 | Vector Space Model (Stanford University, 2011) | 2.5 × 10⁵ | 2.5 × 10⁵ | 2011 | dense | | 657 | HyperNEAT (University of Texas at Austin, 2014) | \~2.4 × 10⁵ | | 2014 | pagtataya | | 658 | DNN EM segmentation (IDSIA, 2012) | 2.2 × 10⁵ | 2.2 × 10⁵ | 2012 | dense | | 659 | Predictive Coding NN (Technical University of Munich, 1994) | 2.1 × 10⁵ | 2.1 × 10⁵ | 1994 | dense | | 660 | Table Tennis Agent (Google DeepMind, 2024) | \~1.9 × 10⁵ | | 2024 | pagtataya | | 661 | CNN Committee (IDSIA, 2011) | \~1.3 × 10⁵ | | 2011 | pagtataya | | 662 | CNN Committee (IDSIA, 2011) | \~1.2 × 10⁵ | | 2011 | pagtataya | | 663 | RECONTRA-uncategorized (, 1999) | \~1.1 × 10⁵ | | 1999 | pagtataya | | 664 | BLSTM for handwriting (University of Bern, 2007) | \~1 × 10⁵ | | 2007 | pagtataya | | 665 | SVM-CNN (New York University (NYU), 2006) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2006 | dense | | 666 | Invariant CNN (New York University (NYU), 2004) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2004 | dense | | 667 | SexNet compression (, 1990) | 7.3 × 10⁴ | 7.3 × 10⁴ | 1990 | dense | | 668 | NeuroChess (, 1994) | \~7.2 × 10⁴ | | 1994 | pagtataya | | 669 | RECONTRA-categorized (, 1999) | \~6.7 × 10⁴ | | 1999 | pagtataya | | 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | dense | | 671 | Swift (Intel Labs, 2023) | \~5.7 × 10⁴ | | 2023 | pagtataya | | 672 | TCN (Carnegie Mellon University (CMU), 2018) | 4.2 × 10⁴ | 4.2 × 10⁴ | 2018 | dense | | 673 | TRPO (University of California (UC) Berkeley, 2015) | 3.4 × 10⁴ | 3.4 × 10⁴ | 2015 | dense | | 674 | SOM-CNN (, 1997) | 3.2 × 10⁴ | 3.2 × 10⁴ | 1997 | dense | | 675 | NETtalk reimplementation (Oregon State University, 1990) | 2.7 × 10⁴ | 2.7 × 10⁴ | 1990 | dense | | 676 | TD-Gammon (IBM, 1992) | \~2.5 × 10⁴ | | 1992 | pagtataya | | 677 | Cognitron (Biological Cybernetics, 1975) | 2.2 × 10⁴ | 2.2 × 10⁴ | 1975 | dense | | 678 | NetTalk (Princeton University, 1987) | 1.9 × 10⁴ | 1.9 × 10⁴ | 1987 | dense | | 679 | CFSS (SenseTime, 2015) | \~1.7 × 10⁴ | | 2015 | pagtataya | | 680 | MUSIC perceptron (, 1996) | 1.4 × 10⁴ | 1.4 × 10⁴ | 1996 | dense | | 681 | AdaRNN (Beihang University, 2014) | 1.3 × 10⁴ | 1.3 × 10⁴ | 2014 | dense | | 682 | Bidirectional RNN (Advanced Telecommunications Research Institute, 1997) | \~1.3 × 10⁴ | | 1997 | pagtataya | | 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | \~1.2 × 10⁴ | | 2001 | pagtataya | | 684 | LSTM (Technical University of Munich, 1997) | 1.1 × 10⁴ | 1.1 × 10⁴ | 1997 | dense | | 685 | MLN-ASR (McGill University, 1988) | 1 × 10⁴ | 1 × 10⁴ | 1988 | dense | | 686 | Hopfield network (California Institute of Technology, 1982) | \~9.9 × 10³ | | 1982 | pagtataya | | 687 | Zip CNN (AT&T, 1989) | 9.8 × 10³ | 9.8 × 10³ | 1989 | dense | | 688 | Hierarchical Cognitron (NHK Broadcasting Science Research Laboratories, 1984) | \~9.3 × 10³ | | 1984 | pagtataya | | 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | dense | | 690 | Deep Blue (IBM, 1997) | \~8 × 10³ | | 1997 | pagtataya | | 691 | Nematode (C. elegans) | 7 × 10³ | 7 × 10³ | | mga synapse | | 692 | System 11 (Carnegie Mellon University (CMU), 1996) | 6.5 × 10³ | 6.5 × 10³ | 1996 | dense | | 693 | ACF-WIDER (Chinese Academy of Sciences, 2014) | 6.1 × 10³ | 6.1 × 10³ | 2014 | dense | | 694 | RankNet (Microsoft Research, 2005) | 5.7 × 10³ | 5.7 × 10³ | 2005 | dense | | 695 | ANN Eye Tracker (, 1993) | 5.6 × 10³ | 5.6 × 10³ | 1993 | dense | | 696 | JPMAX (, 1994) | \~4.4 × 10³ | | 1994 | pagtataya | | 697 | Kohonen network (Helsinki University of Technology, 1981) | \~4.1 × 10³ | | 1981 | pagtataya | | 698 | Speaker-independent vowel classification (University of Washington, 1989) | 3 × 10³ | 3 × 10³ | 1989 | dense | | 699 | Pattern recognition and reading by machine (Sandia Corporation, 1959) | \~2.6 × 10³ | | 1959 | pagtataya | | 700 | Handwritten digit recognition network (AT&T, 1989) | 2.6 × 10³ | 2.6 × 10³ | 1989 | dense | | 701 | Boosting (Bell Laboratories, 1992) | \~2.6 × 10³ | | 1992 | pagtataya | | 702 | Decision tree adaline (Tokyo Medical and Dental University, 1969) | 2.5 × 10³ | 2.5 × 10³ | 1969 | dense | | 703 | LTE speaker verification system (IBM, 1966) | \~2.1 × 10³ | | 1966 | pagtataya | | 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | \~1.9 × 10³ | | 1994 | pagtataya | | 705 | SexNet classification (, 1990) | \~1.6 × 10³ | | 1990 | pagtataya | | 706 | RAAM (, 1990) | 1.5 × 10³ | 1.5 × 10³ | 1990 | dense | | 707 | Super-vector coding (University of Illinois Urbana-Champaign (UIUC), 2010) | \~1 × 10³ | | 2010 | pagtataya | | 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | \~1 × 10³ | | 1957 | pagtataya | | 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | \~1 × 10³ | | 1960 | pagtataya | | 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | mixture of experts (hindi inilathala ang aktibong bilang) | | 711 | Truck backer-upper (Stanford University, 1989) | 8.1 × 10² | 8.1 × 10² | 1989 | dense | | 712 | Siamese-TDNN (Bell Laboratories, 1993) | \~7.4 × 10² | | 1993 | pagtataya | | 713 | MLP with back-propagation (University of California San Diego, 1986) | 7.2 × 10² | 7.2 × 10² | 1986 | dense | | 714 | Cancer drug mechanism prediction (National Cancer Institute, 1992) | \~5.9 × 10² | | 1992 | pagtataya | | 715 | Distributed representation NN (Carnegie Mellon University (CMU), 1986) | 4.3 × 10² | 4.3 × 10² | 1986 | dense | | 716 | Piecewise linear model (University of Kansas, 1973) | 3.6 × 10² | 3.6 × 10² | 1973 | dense | | 717 | ASE+ACE (University of Massachusetts Amherst, 1983) | \~3.2 × 10² | | 1983 | pagtataya | | 718 | LSTM with forget gates (IDSIA, 1999) | \~2.8 × 10² | | 1999 | pagtataya | | 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | \~2.3 × 10² | | 1955 | pagtataya | | 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | dense | | 721 | SNARC (Harvard University, 1952) | \~4 × 10¹ | | 1952 | pagtataya | | 722 | Bankruptcy-NN (, 1990) | 3.6 × 10¹ | 3.6 × 10¹ | 1990 | dense | | 723 | GNN (University of Siena, 2008) | 3 × 10¹ | 3 × 10¹ | 2008 | dense | | 724 | ADALINE (Stanford University, 1960) | 1.7 × 10¹ | 1.7 × 10¹ | 1960 | dense | | 725 | Samuel Neural Checkers (IBM, 1959) | \~1.6 × 10¹ | | 1959 | pagtataya | ## Mga kolektibo Niraranggo ng mga tsart na ito ang mga lipunan ng hayop, relihiyon, estado, partido at korporasyon sa dalawang paraan. Una ay ayon sa mga miyembro. Ikalawa ay ayon sa neuron-equivalent, na tinitimbang ang bawat miyembro ayon sa bahagi ng oras nilang gising na idinidirekta ng mga tagubilin ng grupo. Pare-pareho ang pagturing sa mga kodigo ng relihiyon, batas, patakaran ng partido at paglalarawan ng trabaho. Pawang hanay ng mga tagubilin ang mga ito, kaya sinusukat natin sila sa iisang patakaran. [Paano tinatantiya ang nakadirektang oras](https://simulation-hypothesis.com/fil/entities/control). ## Mga lipunan ng hayop: mga neuron sa nakaugnay na gawain ng grupo Mga neuron sa grupo Sa gawain ng grupo Mga miyembro ng karaniwang grupo × mga neuron sa cortex o pallium ng bawat miyembro (buong utak para sa mga insekto), sa logarithmic na iskala. Gumagamit ang may guhit na bar ng tinatantiyang laki ng grupo o bilang ng neuron ng kaugnay na uri. 1. Komunidad ng chimpanzee▲1 7.4 × 10¹¹ 2. Kolonya ng leafcutter ant▼1 6.3 × 10¹¹ 3. Komunidad ng bonobo▲2 4.4 × 10¹¹ 4. Kampo ng Hadza (mga tao, bilang sukatan) 4.1 × 10¹¹ 5. Grupo ng Bigg’s orca▼2 2.6 × 10¹¹ 6. Matriline ng resident orca 2.4 × 10¹¹ 7. Kawan ng baboon 8.6 × 10¹⁰ 8. Alyansa ng bottlenose dolphin▲1 7.6 × 10¹⁰ 9. Yunit panlipunan ng sperm whale▲1 6.4 × 10¹⁰ 10. Kolonya ng honeybee▼2 5.8 × 10¹⁰ 11. Pamilya ng elepante 5.6 × 10¹⁰ 12. Pinyon jay flock 5.3 × 10¹⁰ 13. Angkan ng spotted hyena 2.2 × 10¹⁰ 14. Pack ng African wild dog 7.2 × 10⁹ 15. Pack ng lobo 4.2 × 10⁹ 16. Grupo ng meerkat 1.7 × 10⁹ 17. Kolonya ng naked mole-rat 4.1 × 10⁸ 10^8 10^9 10^10 10^11 10^12 neurons Ipakita ang mga numero | # | Grupo | Mga neuron sa grupo | Sa gawain ng grupo | Mga miyembro | Bahagdang idinidirekta | Mga uri ng tawag | | -- | ---------------------------------------- | ------------------- | ------------------ | ------------ | ---------------------- | ---------------- | | 1 | Komunidad ng chimpanzee | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — | | 2 | Kolonya ng leafcutter ant | 6.3 × 10¹¹ | 5 × 10¹¹ | 2.5 milyon | 80% | — | | 3 | Komunidad ng bonobo | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — | | 4 | Kampo ng Hadza (mga tao, bilang sukatan) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — | | 5 | Grupo ng Bigg’s orca | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — | | 6 | Matriline ng resident orca | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 | | 7 | Kawan ng baboon | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 | | 8 | Alyansa ng bottlenose dolphin | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — | | 9 | Yunit panlipunan ng sperm whale | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — | | 10 | Kolonya ng honeybee | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — | | 11 | Pamilya ng elepante | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — | | 12 | Pinyon jay flock | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — | | 13 | Angkan ng spotted hyena | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 | | 14 | Pack ng African wild dog | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 | | 15 | Pack ng lobo | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — | | 16 | Grupo ng meerkat | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 | | 17 | Kolonya ng naked mole-rat | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — | [Paano minamarkahan ang mga grupo](https://simulation-hypothesis.com/fil/entities/control). ## Mga relihiyon: mga miyembro, at mga neuron na sumusunod sa mga tagubilin nito Mga miyembro Sumusunod sa mga tagubilin nito Buong relihiyon Mga sekta at kilusan Mga tagasunod, sa logarithmic na iskala. Hiwalay na ipinapakita ang mga sekta at kilusan sa mga relihiyong kinabibilangan nila. Mga bilang ang solidong bar; mga tantiya ang may guhit na bar. 1. Kristiyanismo▲1 2.27 bilyon 2. Islam▼1 2.02 bilyon 3. Hinduismo 1.18 bilyon 4. Mga katutubong relihiyon 400 milyon 5. Budismo 324.19 na milyon 6. Mga tradisyonal na relihiyong Aprikano▲1 98.73 milyon 7. Shinto▲1 87.92 milyon 8. Sikhismo▲1 25 milyon 9. Hudaismo▼3 14.78 milyon 10. Taoismo▲1 8 milyon 11. Pananampalatayang Baháʼí▲1 8 milyon 12. Falun Gong▲1 7 milyon 13. Jainismo▼3 4.45 milyon 14. Spiritismo 3.8 milyon 15. Cao Đài 2.5 milyon 16. Tenrikyo 1.12 milyon 17. Brahma Kumaris 1 milyon 10^5 10^6 10^7 10^8 10^9 10^10 mga miyembro Ipakita ang mga numero | # | Relihiyon | Mga miyembro | Sumusunod sa mga tagubilin nito | Bahagdang idinidirekta | Oras sa isang linggo | | -- | -------------------------------------- | ---------------- | ------------------------------- | ---------------------- | -------------------- | | 1 | Kristiyanismo | 2.27 bilyon | 1.7 × 10¹⁸ | 0.88% | 0.9 h | | 2 | Islam | 2.02 bilyon | 8.9 × 10¹⁸ | 5.1% | 5.4 h | | 3 | Hinduismo | 1.18 bilyon | 1.6 × 10¹⁸ | 1.6% | 1.6 h | | 4 | Mga katutubong relihiyon | 400 milyon | 2 × 10¹⁷ | 0.59% (proxy) | 0.6 h | | 5 | Budismo | 324.19 na milyon | 7.5 × 10¹⁶ | 0.27% | 0.3 h | | 6 | Mga tradisyonal na relihiyong Aprikano | 98.73 milyon | 5 × 10¹⁶ | 0.59% (proxy) | 0.6 h | | 7 | Shinto | 87.92 milyon | 4.5 × 10¹⁶ | 0.59% (proxy) | 0.6 h | | 8 | Sikhismo | 25 milyon | 3.7 × 10¹⁶ | 1.7% | 1.8 h | | 9 | Hudaismo | 14.78 milyon | 5.3 × 10¹⁶ | 4.2% | 4.4 h | | 10 | Taoismo | 8 milyon | 4.1 × 10¹⁵ | 0.59% (proxy) | 0.6 h | | 11 | Pananampalatayang Baháʼí | 8 milyon | 4.1 × 10¹⁵ | 0.59% (proxy) | 0.6 h | | 12 | Falun Gong | 7 milyon | 3.6 × 10¹⁵ | 0.59% (proxy) | 0.6 h | | 13 | Jainismo | 4.45 milyon | 1.8 × 10¹⁶ | 4.6% | 4.8 h | | 14 | Spiritismo | 3.8 milyon | 1.9 × 10¹⁵ | 0.59% (proxy) | 0.6 h | | 15 | Cao Đài | 2.5 milyon | 1.3 × 10¹⁵ | 0.59% (proxy) | 0.6 h | | 16 | Tenrikyo | 1.12 milyon | 5.7 × 10¹⁴ | 0.59% (proxy) | 0.6 h | | 17 | Brahma Kumaris | 1 milyon | 5.1 × 10¹⁴ | 0.59% (proxy) | 0.6 h | [Bawat gawi, bahagi at pinagmulan](https://simulation-hypothesis.com/fil/entities/control). ## Mga estado: mga residente, at mga neuron na sumusunod sa mga tagubilin nito Mga residente Sumusunod sa mga tagubilin nito Lahat Europa at Gitnang Asya Gitnang Silangan, Hilagang Africa, Afghanistan at Pakistan Hilagang Amerika Latin America at Caribbean Silangang Asya at Pasipiko Sub-Saharan Africa Timog Asya Populasyon, sa logarithmic na iskala. Mga bilang ang solidong bar; mga tantiya ang may guhit na bar. 1. India▲1 1.46 na bilyon 2. China▼1 1.41 bilyon 3. United States 341.78 milyon 4. Indonesia 285.72 milyon 5. Pakistan▲5 255.22 milyon 6. Nigeria▲1 237.53 milyon 7. Brazil▼2 212.81 milyon 8. Bangladesh▲5 175.69 na milyon 9. Russia▼3 143.51 milyon 10. Ethiopia▲7 135.47 milyon 11. Mexico▼2 131.95 milyon 12. Japan▲11 123.37 milyon 13. Egypt▼2 118.37 milyon 14. Philippines 116.79 na milyon 15. DR Congo▼3 112.83 milyon Ipakita lahat ng 196 10^4 10^6 10^8 10^10 mga residente Ipakita ang mga numero | # | Estado | Mga residente | Sumusunod sa mga tagubilin nito | Bahagdang idinidirekta | Oras sa isang linggo | | --- | -------------------------------- | ---------------- | ------------------------------- | ---------------------- | -------------------- | | 1 | India | 1.46 na bilyon | 2.5 × 10¹⁸ | 2% | 2.1 h | | 2 | China | 1.41 bilyon | 5 × 10¹⁸ | 4.1% | 4.3 h | | 3 | United States | 341.78 milyon | 1.6 × 10¹⁸ | 5.6% | 5.8 h | | 4 | Indonesia | 285.72 milyon | 1 × 10¹⁸ | 4.2% | 4.4 h | | 5 | Pakistan | 255.22 milyon | 5.7 × 10¹⁷ | 2.6% | 2.7 h | | 6 | Nigeria | 237.53 milyon | 6.6 × 10¹⁷ | 3.2% | 3.4 h | | 7 | Brazil | 212.81 milyon | 8.7 × 10¹⁷ | 4.8% | 5 h | | 8 | Bangladesh | 175.69 na milyon | 4.6 × 10¹⁷ | 3% | 3.1 h | | 9 | Russia | 143.51 milyon | 7.5 × 10¹⁷ | 6.1% | 6.4 h | | 10 | Ethiopia | 135.47 milyon | 3.9 × 10¹⁷ | 3.4% | 3.5 h | | 11 | Mexico | 131.95 milyon | 5.9 × 10¹⁷ | 5.2% | 5.5 h | | 12 | Japan | 123.37 milyon | 2.8 × 10¹⁷ | 2.7% | 2.8 h | | 13 | Egypt | 118.37 milyon | 5.5 × 10¹⁷ | 5.4% | 5.7 h | | 14 | Philippines | 116.79 na milyon | 4.4 × 10¹⁷ | 4.4% | 4.6 h | | 15 | DR Congo | 112.83 milyon | 4.7 × 10¹⁷ | 4.8% | 5.1 h | | 16 | Vietnam | 101.6 na milyon | 4.2 × 10¹⁷ | 4.8% | 5 h | | 17 | Iran | 92.42 milyon | 3.5 × 10¹⁷ | 4.3% | 4.6 h | | 18 | Turkey | 85.88 milyon | 4.3 × 10¹⁷ | 5.8% | 6 h | | 19 | Germany | 83.49 na milyon | 2.9 × 10¹⁷ | 4% | 4.2 h | | 20 | Thailand | 71.62 milyon | 2.4 × 10¹⁷ | 3.9% | 4.1 h | | 21 | Tanzania | 70.55 milyon | 2.2 × 10¹⁷ | 3.6% | 3.8 h | | 22 | United Kingdom | 69.49 na milyon | 2.8 × 10¹⁷ | 4.6% | 4.8 h | | 23 | France | 68.72 milyon | 3.1 × 10¹⁷ | 5.3% | 5.5 h | | 24 | South Africa | 64.75 milyon | 2.9 × 10¹⁷ | 5.1% | 5.4 h | | 25 | Italy | 58.92 milyon | 2.1 × 10¹⁷ | 4.2% | 4.4 h | | 26 | Kenya | 57.53 milyon | 2.3 × 10¹⁷ | 4.7% | 5 h | | 27 | Myanmar | 54.85 milyon | 1.9 × 10¹⁷ | 4% | 4.2 h | | 28 | Colombia | 53.43 milyon | 1.7 × 10¹⁷ | 3.7% | 3.9 h | | 29 | South Korea | 51.68 milyon | 1.4 × 10¹⁷ | 3% | 3.2 h | | 30 | Sudan | 51.66 na milyon | 1.5 × 10¹⁷ | 3.4% | 3.6 h | | 31 | Uganda | 51.38 milyon | 1.5 × 10¹⁷ | 3.4% | 3.6 h | | 32 | Spain | 49.36 na milyon | 1.8 × 10¹⁷ | 4.2% | 4.3 h | | 33 | Algeria | 47.44 na milyon | 3.2 × 10¹⁷ | 7.9% | 8.3 h | | 34 | Iraq | 47.02 milyon | 1.1 × 10¹⁷ | 2.8% | 2.9 h | | 35 | Argentina | 45.85 milyon | 1.7 × 10¹⁷ | 4.4% | 4.6 h | | 36 | Afghanistan | 43.84 na milyon | 1.8 × 10¹⁷ | 4.8% | 5 h | | 37 | Yemen | 41.77 milyon | 1.3 × 10¹⁷ | 3.7% | 3.9 h | | 38 | Canada | 41.65 milyon | 2 × 10¹⁷ | 5.5% | 5.8 h | | 39 | Angola | 39.04 na milyon | 1.4 × 10¹⁷ | 4.2% | 4.4 h | | 40 | Ukraine | 38.98 milyon | 2.1 × 10¹⁷ | 6.2% | 6.5 h | | 41 | Morocco | 38.43 milyon | 1.5 × 10¹⁷ | 4.5% | 4.7 h | | 42 | Uzbekistan | 37.05 milyon | 2 × 10¹⁷ | 6.3% | 6.6 h | | 43 | Saudi Arabia | 36.97 milyon | 1.4 × 10¹⁷ | 4.3% | 4.6 h | | 44 | Poland | 36.44 na milyon | 2.1 × 10¹⁷ | 6.7% | 7 h | | 45 | Malaysia | 35.98 milyon | 1.8 × 10¹⁷ | 5.8% | 6.1 h | | 46 | Mozambique | 35.63 milyon | 1.5 × 10¹⁷ | 4.8% | 5.1 h | | 47 | Ghana | 35.06 na milyon | 1.2 × 10¹⁷ | 3.9% | 4.1 h | | 48 | Peru | 34.58 milyon | 1.4 × 10¹⁷ | 4.8% | 5 h | | 49 | Madagascar | 32.74 na milyon | 9.4 × 10¹⁶ | 3.3% | 3.5 h | | 50 | Côte d’Ivoire | 32.71 milyon | 9.5 × 10¹⁶ | 3.4% | 3.5 h | | 51 | Cameroon | 29.88 milyon | 9.8 × 10¹⁶ | 3.8% | 4 h | | 52 | Nepal | 29.62 milyon | 1.1 × 10¹⁷ | 4.2% | 4.4 h | | 53 | Venezuela | 28.52 milyon | 1.5 × 10¹⁷ | 6.1% | 6.4 h | | 54 | Niger | 27.92 milyon | 6.4 × 10¹⁶ | 2.6% | 2.8 h | | 55 | Australia | 27.61 milyon | 1.1 × 10¹⁷ | 4.7% | 5 h | | 56 | North Korea | 26.57 milyon | 6.6 × 10¹⁷ | 29% | 30.1 h | | 57 | Syria | 25.62 milyon | 9.8 × 10¹⁶ | 4.4% | 4.6 h | | 58 | Mali | 25.2 milyon | 4.9 × 10¹⁶ | 2.3% | 2.4 h | | 59 | Burkina Faso | 24.07 milyon | 5.7 × 10¹⁶ | 2.8% | 2.9 h | | 60 | Taiwan | 23.31 milyon | 1 × 10¹⁷ | 5% | 5.3 h | | 61 | Malawi | 22.22 milyon | 1 × 10¹⁷ | 5.4% | 5.6 h | | 62 | Zambia | 21.91 milyon | 10 × 10¹⁶ | 5.3% | 5.5 h | | 63 | Sri Lanka | 21.76 na milyon | 1.1 × 10¹⁷ | 6% | 6.3 h | | 64 | Chad | 21 milyon | 4.9 × 10¹⁶ | 2.7% | 2.9 h | | 65 | Kazakhstan | 20.84 na milyon | 1.4 × 10¹⁷ | 7.6% | 7.9 h | | 66 | Chile | 19.86 na milyon | 6.2 × 10¹⁶ | 3.6% | 3.8 h | | 67 | Somalia | 19.65 milyon | 1.5 × 10¹⁶ | 0.88% | 0.9 h | | 68 | Romania | 19.02 milyon | 7.5 × 10¹⁶ | 4.6% | 4.8 h | | 69 | Senegal | 18.93 milyon | 5.9 × 10¹⁶ | 3.6% | 3.8 h | | 70 | Guatemala | 18.69 na milyon | 6 × 10¹⁶ | 3.7% | 3.9 h | | 71 | Ecuador | 18.29 na milyon | 6.6 × 10¹⁶ | 4.2% | 4.4 h | | 72 | Netherlands | 18.09 na milyon | 7.6 × 10¹⁶ | 4.9% | 5.1 h | | 73 | Cambodia | 17.85 milyon | 7.4 × 10¹⁶ | 4.8% | 5.1 h | | 74 | Zimbabwe | 16.95 milyon | 6.1 × 10¹⁶ | 4.2% | 4.4 h | | 75 | Guinea | 15.1 milyon | 3.1 × 10¹⁶ | 2.4% | 2.5 h | | 76 | Benin | 14.81 milyon | 4.4 × 10¹⁶ | 3.4% | 3.6 h | | 77 | Rwanda | 14.57 milyon | 7 × 10¹⁶ | 5.6% | 5.8 h | | 78 | Burundi | 14.39 na milyon | 5.2 × 10¹⁶ | 4.2% | 4.4 h | | 79 | Bolivia | 12.58 milyon | 5.8 × 10¹⁶ | 5.4% | 5.6 h | | 80 | Tunisia | 12.35 milyon | 6.4 × 10¹⁶ | 6% | 6.3 h | | 81 | South Sudan | 12.19 na milyon | 4.4 × 10¹⁶ | 4.2% | 4.4 h | | 82 | Belgium | 11.94 na milyon | 4.5 × 10¹⁶ | 4.3% | 4.5 h | | 83 | Haiti | 11.91 milyon | 1.9 × 10¹⁶ | 1.8% | 1.9 h | | 84 | Jordan | 11.52 milyon | 4.9 × 10¹⁶ | 5% | 5.2 h | | 85 | Dominican Republic | 11.52 milyon | 4.9 × 10¹⁶ | 4.9% | 5.2 h | | 86 | United Arab Emirates | 11.51 milyon | 3.1 × 10¹⁶ | 3.1% | 3.2 h | | 87 | Honduras | 11.01 milyon | 3.2 × 10¹⁶ | 3.3% | 3.5 h | | 88 | Cuba | 10.94 na milyon | 1.6 × 10¹⁷ | 17% | 17.4 h | | 89 | Czechia | 10.89 na milyon | 5.8 × 10¹⁶ | 6.2% | 6.5 h | | 90 | Portugal | 10.8 milyon | 5.1 × 10¹⁶ | 5.5% | 5.7 h | | 91 | Tajikistan | 10.79 na milyon | 5.3 × 10¹⁶ | 5.7% | 6 h | | 92 | Papua New Guinea | 10.76 na milyon | 4.3 × 10¹⁶ | 4.6% | 4.8 h | | 93 | Sweden | 10.6 na milyon | 6.8 × 10¹⁶ | 7.5% | 7.8 h | | 94 | Greece | 10.41 milyon | 4.9 × 10¹⁶ | 5.4% | 5.7 h | | 95 | Azerbaijan | 10.25 milyon | 6.2 × 10¹⁶ | 7% | 7.4 h | | 96 | Israel | 10.12 milyon | 6.4 × 10¹⁶ | 7.3% | 7.7 h | | 97 | Hungary | 9.51 milyon | 5.7 × 10¹⁶ | 6.9% | 7.3 h | | 98 | Austria | 9.21 milyon | 2.5 × 10¹⁶ | 3.2% | 3.4 h | | 99 | Switzerland | 9.09 na milyon | 3.9 × 10¹⁶ | 5% | 5.3 h | | 100 | Belarus | 9.09 na milyon | 7.9 × 10¹⁶ | 10% | 10.6 h | | 101 | Sierra Leone | 8.82 milyon | 4.9 × 10¹⁶ | 6.5% | 6.8 h | | 102 | Togo | 8.59 na milyon | 3 × 10¹⁶ | 4% | 4.2 h | | 103 | Laos | 7.87 milyon | 3.2 × 10¹⁶ | 4.8% | 5 h | | 104 | Turkmenistan | 7.62 milyon | 4.2 × 10¹⁶ | 6.5% | 6.8 h | | 105 | Libya | 7.46 na milyon | 3.9 × 10¹⁶ | 6.1% | 6.4 h | | 106 | Kyrgyzstan | 7.34 na milyon | 4.1 × 10¹⁶ | 6.5% | 6.8 h | | 107 | Paraguay | 7.01 milyon | 2.9 × 10¹⁶ | 4.8% | 5.1 h | | 108 | Nicaragua | 7.01 milyon | 2.8 × 10¹⁶ | 4.6% | 4.8 h | | 109 | Serbia | 6.55 milyon | 4.1 × 10¹⁶ | 7.2% | 7.5 h | | 110 | Republic of the Congo | 6.48 milyon | 1.7 × 10¹⁶ | 3% | 3.1 h | | 111 | Bulgaria | 6.43 milyon | 3.3 × 10¹⁶ | 5.9% | 6.2 h | | 112 | El Salvador | 6.37 milyon | 3.2 × 10¹⁶ | 5.8% | 6.1 h | | 113 | Singapore | 6.11 milyon | 1.9 × 10¹⁶ | 3.6% | 3.7 h | | 114 | Denmark | 6.01 milyon | 3.9 × 10¹⁶ | 7.6% | 8 h | | 115 | Lebanon | 5.85 milyon | 1.5 × 10¹⁶ | 3% | 3.1 h | | 116 | Liberia | 5.73 milyon | 1.5 × 10¹⁶ | 3.1% | 3.3 h | | 117 | Finland | 5.65 milyon | 3.2 × 10¹⁶ | 6.6% | 6.9 h | | 118 | Norway | 5.61 milyon | 3.7 × 10¹⁶ | 7.6% | 8 h | | 119 | Central African Republic | 5.51 milyon | 1.7 × 10¹⁶ | 3.5% | 3.7 h | | 120 | Oman | 5.49 na milyon | 2.9 × 10¹⁶ | 6.2% | 6.5 h | | 121 | Ireland | 5.48 milyon | 3.4 × 10¹⁶ | 7.2% | 7.6 h | | 122 | Slovakia | 5.41 milyon | 3.1 × 10¹⁶ | 6.7% | 7 h | | 123 | Palestine | 5.41 milyon | 2 × 10¹⁶ | 4.3% | 4.5 h | | 124 | New Zealand | 5.32 milyon | 2.7 × 10¹⁶ | 5.9% | 6.2 h | | 125 | Mauritania | 5.32 milyon | 2 × 10¹⁶ | 4.3% | 4.5 h | | 126 | Costa Rica | 5.15 milyon | 2.6 × 10¹⁶ | 5.9% | 6.2 h | | 127 | Kuwait | 4.87 milyon | 2.7 × 10¹⁶ | 6.5% | 6.8 h | | 128 | Panama | 4.57 milyon | 2.3 × 10¹⁶ | 5.9% | 6.2 h | | 129 | Georgia | 3.94 na milyon | 2.1 × 10¹⁶ | 6.1% | 6.4 h | | 130 | Croatia | 3.88 milyon | 2.2 × 10¹⁶ | 6.7% | 7 h | | 131 | Eritrea | 3.61 milyon | 1.5 × 10¹⁶ | 5% | 5.2 h | | 132 | Mongolia | 3.57 milyon | 2.2 × 10¹⁶ | 7% | 7.3 h | | 133 | Uruguay | 3.38 milyon | 1.7 × 10¹⁶ | 5.9% | 6.2 h | | 134 | Bosnia and Herzegovina | 3.14 na milyon | 1.6 × 10¹⁶ | 5.9% | 6.2 h | | 135 | Namibia | 3.09 na milyon | 1.8 × 10¹⁶ | 6.9% | 7.2 h | | 136 | Armenia | 3.09 na milyon | 1.7 × 10¹⁶ | 6.5% | 6.8 h | | 137 | Qatar | 2.97 milyon | 9.6 × 10¹⁵ | 3.8% | 3.9 h | | 138 | Lithuania | 2.89 na milyon | 1.8 × 10¹⁶ | 7.4% | 7.7 h | | 139 | Jamaica | 2.84 na milyon | 1.2 × 10¹⁶ | 5% | 5.2 h | | 140 | Gambia | 2.82 milyon | 1.1 × 10¹⁶ | 4.5% | 4.7 h | | 141 | Gabon | 2.59 na milyon | 9.9 × 10¹⁵ | 4.4% | 4.6 h | | 142 | Botswana | 2.56 na milyon | 1.3 × 10¹⁶ | 5.7% | 6 h | | 143 | Lesotho | 2.36 na milyon | 9.5 × 10¹⁵ | 4.7% | 4.9 h | | 144 | Moldova | 2.36 na milyon | 1.5 × 10¹⁶ | 7.3% | 7.6 h | | 145 | Albania | 2.35 milyon | 1.2 × 10¹⁶ | 5.7% | 6 h | | 146 | Guinea-Bissau | 2.25 milyon | 5.7 × 10¹⁵ | 2.9% | 3.1 h | | 147 | Slovenia | 2.13 milyon | 1.2 × 10¹⁶ | 6.6% | 6.9 h | | 148 | Equatorial Guinea | 1.94 na milyon | 4 × 10¹⁵ | 2.4% | 2.5 h | | 149 | Latvia | 1.85 milyon | 1.2 × 10¹⁶ | 7.6% | 7.9 h | | 150 | North Macedonia | 1.82 milyon | 9.2 × 10¹⁵ | 5.9% | 6.2 h | | 151 | Bahrain | 1.6 na milyon | 5.9 × 10¹⁵ | 4.3% | 4.5 h | | 152 | Kosovo | 1.58 milyon | 8 × 10¹⁵ | 5.9% | 6.2 h | | 153 | Timor-Leste | 1.42 milyon | 8 × 10¹⁵ | 6.6% | 6.9 h | | 154 | Cyprus | 1.37 milyon | 5.5 × 10¹⁵ | 4.7% | 4.9 h | | 155 | Trinidad and Tobago | 1.37 milyon | 9.3 × 10¹⁵ | 7.9% | 8.2 h | | 156 | Estonia | 1.37 milyon | 8.3 × 10¹⁵ | 7% | 7.4 h | | 157 | Eswatini | 1.26 na milyon | 7.5 × 10¹⁵ | 6.9% | 7.2 h | | 158 | Mauritius | 1.24 na milyon | 5.1 × 10¹⁵ | 4.8% | 5 h | | 159 | Djibouti | 1.18 milyon | 4.6 × 10¹⁵ | 4.5% | 4.8 h | | 160 | Fiji | 933,154 | 5.6 × 10¹⁵ | 7% | 7.4 h | | 161 | Comoros | 882,847 | 3.5 × 10¹⁵ | 4.6% | 4.8 h | | 162 | Solomon Islands | 838,645 | 3.2 × 10¹⁵ | 4.4% | 4.6 h | | 163 | Guyana | 835,986 | 4.6 × 10¹⁵ | 6.4% | 6.7 h | | 164 | Bhutan | 796,682 | 5.8 × 10¹⁵ | 8.5% | 8.9 h | | 165 | Luxembourg | 686,970 | 2.4 × 10¹⁵ | 4% | 4.2 h | | 166 | Suriname | 639,850 | 3.7 × 10¹⁵ | 6.7% | 7.1 h | | 167 | Montenegro | 623,129 | 4.3 × 10¹⁵ | 8.1% | 8.5 h | | 168 | Malta | 579,704 | 3.1 × 10¹⁵ | 6.2% | 6.5 h | | 169 | Maldives | 529,676 | 4.7 × 10¹⁵ | 10% | 10.8 h | | 170 | Cape Verde | 527,326 | 3.3 × 10¹⁵ | 7.2% | 7.6 h | | 171 | Brunei | 466,330 | 3.6 × 10¹⁵ | 8.9% | 9.3 h | | 172 | Belize | 422,924 | 1.1 × 10¹⁵ | 2.9% | 3.1 h | | 173 | Bahamas | 403,033 | 2.2 × 10¹⁵ | 6.4% | 6.7 h | | 174 | Iceland | 392,404 | 2.5 × 10¹⁵ | 7.4% | 7.7 h | | 175 | Vanuatu | 335,169 | 2 × 10¹⁵ | 6.9% | 7.3 h | | 176 | Barbados | 282,623 | 1.5 × 10¹⁵ | 6% | 6.3 h | | 177 | São Tomé and Príncipe | 240,254 | 1.1 × 10¹⁵ | 5.4% | 5.7 h | | 178 | Samoa | 219,306 | 1.1 × 10¹⁵ | 5.7% | 5.9 h | | 179 | Saint Lucia | 180,149 | 9.3 × 10¹⁴ | 6% | 6.3 h | | 180 | Kiribati | 136,488 | 7.6 × 10¹⁴ | 6.4% | 6.7 h | | 181 | Seychelles | 122,730 | 8.9 × 10¹⁴ | 8.4% | 8.8 h | | 182 | Grenada | 117,303 | 7.3 × 10¹⁴ | 7.2% | 7.5 h | | 183 | Micronesia | 113,683 | 5.7 × 10¹⁴ | 5.8% | 6.1 h | | 184 | Tonga | 103,742 | 5.9 × 10¹⁴ | 6.6% | 6.9 h | | 185 | Saint Vincent and the Grenadines | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h | | 186 | Antigua and Barbuda | 94,209 | 6.5 × 10¹⁴ | 8% | 8.3 h | | 187 | Andorra | 82,904 | 3.9 × 10¹⁴ | 5.5% | 5.8 h | | 188 | Dominica | 65,871 | 2.3 × 10¹⁴ | 4.1% | 4.3 h | | 189 | Saint Kitts and Nevis | 46,922 | 2.1 × 10¹⁴ | 5.2% | 5.5 h | | 190 | Liechtenstein | 41,024 | 2 × 10¹⁴ | 5.7% | 5.9 h | | 191 | Monaco | 38,341 | 2.3 × 10¹⁴ | 6.9% | 7.3 h | | 192 | Marshall Islands | 36,282 | 3.3 × 10¹⁴ | 10% | 10.9 h | | 193 | San Marino | 34,109 | 1.3 × 10¹⁴ | 4.6% | 4.8 h | | 194 | Palau | 17,663 | 1.6 × 10¹⁴ | 10% | 11 h | | 195 | Nauru | 12,025 | 9.3 × 10¹³ | 9% | 9.4 h | | 196 | Tuvalu | 9,492 | 8 × 10¹³ | 9.8% | 10.2 h | [Ano ang binibilang, at ano ang hindi](https://simulation-hypothesis.com/fil/entities/control). ## Mga partidong pampulitika: mga miyembro, at mga neuron na sumusunod sa mga tagubilin nito Mga miyembro Sumusunod sa mga tagubilin nito Mga miyembro ayon sa ulat ng partido, sa logarithmic na iskala. Mga bilang ang solidong bar; mga tantiya o pahayag ang may guhit na bar. 1. Bharatiya Janata Party (India)▲1 140 milyon 2. Partido Komunista ng China▼1 101.29 na milyon 3. Democratic Party (US)▲13 44.9 na milyon 4. Republican Party (US)▲16 38 milyon 5. Indian National Congress▼2 26 na milyon 6. AIADMK (India)▼2 20.04 na milyon 7. DMK (India)▼2 20 milyon 8. Prosperity Party (Ethiopia)▼1 15.7 milyon 9. TVK (India)▼1 15 milyon 10. Chama Cha Mapinduzi (Tanzania)▼1 13 milyon 11. All Progressives Congress (Nigeria)▼1 12.9 na milyon 12. AK Party (Turkey)▼1 11.71 milyon 13. Morena (Mexico)▼1 11.63 milyon 14. Telugu Desam Party (India)▼1 10.05 milyon 15. Cambodian People’s Party▼1 7 milyon Ipakita lahat ng 59 10^4 10^5 10^6 10^7 10^8 10^9 mga miyembro Ipakita ang mga numero | # | Partido | Mga miyembro | Sumusunod sa mga tagubilin nito | Bahagdang idinidirekta | Oras sa isang linggo | | -- | ------------------------------------------ | ---------------- | ------------------------------- | ---------------------- | -------------------- | | 1 | Bharatiya Janata Party (India) | 140 milyon | 6.3 × 10¹⁶ | 0.52% (proxy) | 0.6 h | | 2 | Partido Komunista ng China | 101.29 na milyon | 8.2 × 10¹⁶ | 0.94% | 1 h | | 3 | Democratic Party (US) | 44.9 na milyon | 2.3 × 10¹⁵ | 0.06% | 0.1 h | | 4 | Republican Party (US) | 38 milyon | 2 × 10¹⁵ | 0.06% | 0.1 h | | 5 | Indian National Congress | 26 na milyon | 1.2 × 10¹⁶ | 0.52% (proxy) | 0.6 h | | 6 | AIADMK (India) | 20.04 na milyon | 9 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 7 | DMK (India) | 20 milyon | 9 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 8 | Prosperity Party (Ethiopia) | 15.7 milyon | 7 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 9 | TVK (India) | 15 milyon | 6.7 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 10 | Chama Cha Mapinduzi (Tanzania) | 13 milyon | 5.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 11 | All Progressives Congress (Nigeria) | 12.9 na milyon | 5.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 12 | AK Party (Turkey) | 11.71 milyon | 5.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 13 | Morena (Mexico) | 11.63 milyon | 5.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 14 | Telugu Desam Party (India) | 10.05 milyon | 4.5 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 15 | Cambodian People’s Party | 7 milyon | 3.1 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 16 | Workers’ Party of Korea | 6.18 milyon | 7.6 × 10¹⁵ | 1.4% | 1.5 h | | 17 | FRELIMO (Mozambique) | 6 na milyon | 2.7 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 18 | Partido Komunista ng Vietnam | 5.6 na milyon | 2.1 × 10¹⁵ | 0.44% | 0.5 h | | 19 | Democratic Party of Korea | 5 milyon | 2.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 20 | People Power Party (South Korea) | 4.44 na milyon | 2 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 21 | PSUV (Venezuela) | 4.24 na milyon | 1.9 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 22 | ZANU-PF (Zimbabwe) | 4.06 na milyon | 1.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 23 | UMNO (Malaysia) | 3.25 milyon | 1.5 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 24 | Justicialist Party (Argentina) | 3.1 milyon | 1.4 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 25 | Colorado Party (Paraguay) | 3 milyon | 1.3 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 26 | United Russia | 2.6 na milyon | 1.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 27 | UzLiDeP (Uzbekistan) | 2.31 milyon | 1 × 10¹⁵ | 0.52% (proxy) | 0.6 h | | 28 | PLD (Dominican Republic) | 2.18 milyon | 9.7 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 29 | MDB (Brazil) | 2.02 milyon | 9 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 30 | CHP (Turkey) | 1.88 milyon | 8.4 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 31 | Radical Civic Union (Argentina) | 1.79 na milyon | 8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 32 | Workers’ Party, PT (Brazil) | 1.67 milyon | 7.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 33 | PRM (Dominican Republic) | 1.55 milyon | 6.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 34 | Authentic Radical Liberal Party (Paraguay) | 1.55 milyon | 6.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 35 | Partido Federal ng Pilipinas | 1.5 milyon | 6.7 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 36 | PAS (Malaysia) | 1.3 milyon | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 37 | PRD (Brazil) | 1.29 na milyon | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 38 | Progressistas (Brazil) | 1.29 na milyon | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 39 | PSDB (Brazil) | 1.26 na milyon | 5.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 40 | PKR (Malaysia) | 1.15 milyon | 5.2 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 41 | Economic Freedom Fighters (South Africa) | 1.09 na milyon | 4.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 42 | PDT (Brazil) | 1.07 milyon | 4.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 43 | União Brasil | 1.07 milyon | 4.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 44 | CPI(M) (India) | 1.02 milyon | 4.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 45 | Liberal Democratic Party (Japan) | 1 milyon | 4.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 46 | FLN (Algeria) | 1 milyon | 4.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 47 | Liberal Party, PL (Brazil) | 952,881 | 4.3 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 48 | Partido Komunista ng Cuba | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h | | 49 | African National Congress | 688,454 | 3.1 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 50 | Conservative Party of Canada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h | | 51 | CDU (Germany) | 364,202 | 1.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 52 | SPD (Germany) | 357,116 | 1.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h | | 53 | Labour Party (UK) | 333,235 | 1.6 × 10¹⁴ | 0.55% | 0.6 h | | 54 | Liberal Party of Canada | 163,836 | 6.5 × 10¹³ | 0.46% | 0.5 h | | 55 | Greens (Germany) | 155,296 | 7 × 10¹³ | 0.52% (proxy) | 0.6 h | | 56 | CSU (Germany) | 125,153 | 5.6 × 10¹³ | 0.52% (proxy) | 0.6 h | | 57 | Conservative Party (UK) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h | | 58 | New Democratic Party (Canada) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h | | 59 | AfD (Germany) | 51,335 | 2.3 × 10¹³ | 0.52% (proxy) | 0.6 h | [Mga pulong, survey at pinagmulan](https://simulation-hypothesis.com/fil/entities/control). ## Mga korporasyon: mga empleyado, at mga neuron na sumusunod sa mga tagubilin nito Mga empleyado Sumusunod sa mga tagubilin nito Mga empleyado sa pinakahuling taunang ulat o listahan, sa linear na iskala. 1. Walmart 2.1 milyon 2. Amazon▲1 1.58 milyon 3. State Grid▼1 1.36 na milyon 4. China National Petroleum 1.03 milyon 5. JD.com 951,420 6. BYD▲1 869,622 7. Accenture▼1 779,000 8. Allied Universal▲3 770,000 9. China Post Group▼1 719,448 10. Russian Railways (RZD)▲2 677,363 11. Volkswagen Group▲6 662,942 12. Foxconn (Hon Hai)▼2 633,167 13. Schwarz Group▲16 604,000 14. Compass Group▲18 590,000 15. Tata Consultancy Services▼6 584,510 Ipakita lahat ng 71 0 0.5M 1M 1.5M 2M 2.5M employees Ipakita ang mga numero | # | Korporasyon | Mga empleyado | Sumusunod sa mga tagubilin nito | Oras sa isang linggo | Bahagdang idinidirekta | | -- | ------------------------------------ | -------------- | ------------------------------- | ------------------------ | ---------------------- | | 1 | Walmart | 2.1 milyon | 6.3 × 10¹⁶ | 36.3 h (United States) | 35% | | 2 | Amazon | 1.58 milyon | 4.7 × 10¹⁶ | 36.3 h (United States) | 35% | | 3 | State Grid | 1.36 na milyon | 5.5 × 10¹⁶ | 49 h (China) | 47% | | 4 | China National Petroleum | 1.03 milyon | 4.1 × 10¹⁶ | 49 h (China) | 47% | | 5 | JD.com | 951,420 | 3.8 × 10¹⁶ | 49 h (China) | 47% | | 6 | BYD | 869,622 | 3.5 × 10¹⁶ | 49 h (China) | 47% | | 7 | Accenture | 779,000 | 3.7 × 10¹⁶ | 57.58 h (India) | 55% | | 8 | Allied Universal | 770,000 | 2.3 × 10¹⁶ | 36.52 h (United States) | 35% | | 9 | China Post Group | 719,448 | 2.9 × 10¹⁶ | 49 h (China) | 47% | | 10 | Russian Railways (RZD) | 677,363 | 2.2 × 10¹⁶ | 39.96 h (Russia) | 38% | | 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (Germany) | 34% | | 12 | Foxconn (Hon Hai) | 633,167 | 2.5 × 10¹⁶ | 49 h (China) | 47% | | 13 | Schwarz Group | 604,000 | 1.6 × 10¹⁶ | 31.49 h (Germany) | 30% | | 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (United States) | 30% | | 15 | Tata Consultancy Services | 584,510 | 2.8 × 10¹⁶ | 58.23 h (India) | 56% | | 16 | DHL Group | 583,998 | 1.7 × 10¹⁶ | 35.81 h (Germany) | 34% | | 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (China) | 47% | | 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (Russia) | 39% | | 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (United States) | 38% | | 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (United States) | 35% | | 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (China) | 47% | | 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (United States) | 38% | | 23 | Agricultural Bank of China | 457,835 | 1.8 × 10¹⁶ | 49 h (China) | 47% | | 24 | Concentrix | 455,000 | 1.7 × 10¹⁶ | 46.71 h (Philippines) | 45% | | 25 | Teleperformance | 446,716 | 2.2 × 10¹⁶ | 58.89 h (India) | 56% | | 26 | Jardine Matheson | 443,000 | 1.5 × 10¹⁶ | 41.93 h (Indonesia) | 40% | | 27 | Sodexo | 426,464 | 1.1 × 10¹⁶ | 31.7 h (United States) | 30% | | 28 | Capgemini | 423,400 | 2 × 10¹⁶ | 58.23 h (India) | 56% | | 29 | Reliance Industries | 419,911 | 2.1 × 10¹⁶ | 59.57 h (India) | 57% | | 30 | Edeka | 417,500 | 1.1 × 10¹⁶ | 31.49 h (Germany) | 30% | | 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (China) | 47% | | 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (United States) | 35% | | 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (United States) | 30% | | 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (Germany) | 34% | | 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (China) | 47% | | 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (United States) | 35% | | 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (China) | 47% | | 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (Russia) | 38% | | 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (Japan) | 38% | | 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (United States) | 35% | | 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (Russia) | 38% | | 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (United States) | 36% | | 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (United States) | 35% | | 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (United States) | 30% | | 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (Germany) | 30% | | 48 | China Construction Bank | 378,344 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 49 | TJX Companies | 377,000 | 1.1 × 10¹⁶ | 36.3 h (United States) | 35% | | 50 | China State Construction Engineering | 370,272 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 51 | FEMSA | 368,776 | 1.4 × 10¹⁶ | 47.09 h (Mexico) | 45% | | 52 | Japan Post Holdings | 360,000 | 1.2 × 10¹⁶ | 41.6 h (Japan) | 40% | | 53 | Tesco | 354,744 | 9.3 × 10¹⁵ | 31.81 h (United Kingdom) | 30% | | 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (India) | 56% | | 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (Japan) | 38% | | 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (United States) | 35% | | 57 | Infosys | 328,594 | 1.6 × 10¹⁶ | 58.23 h (India) | 56% | | 58 | ISS | 325,000 | 1.6 × 10¹⁶ | 58.89 h (India) | 56% | | 59 | Securitas | 322,000 | 9.7 × 10¹⁵ | 36.52 h (United States) | 35% | | 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (United States) | 35% | | 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (Russia) | 39% | | 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (United States) | 38% | | 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (Germany) | 34% | | 64 | Bank of China | 313,746 | 1.3 × 10¹⁶ | 49 h (China) | 47% | | 65 | Walgreens Boots Alliance | 312,000 | 9.3 × 10¹⁵ | 36.3 h (United States) | 35% | | 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (United States) | 39% | | 67 | China Railway Construction (CRCC) | 304,927 | 1.2 × 10¹⁶ | 49 h (China) | 47% | | 68 | CHN Energy (China Energy Investment) | 303,418 | 1.2 × 10¹⁶ | 49 h (China) | 47% | | 69 | China Railway Group (CREC) | 302,527 | 1.2 × 10¹⁶ | 49 h (China) | 47% | | 70 | CK Hutchison | 300,000 | 8.6 × 10¹⁵ | 35.08 h (United Kingdom) | 33% | | 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (United States) | 35% | [Mga oras at pinagmulan](https://simulation-hypothesis.com/fil/entities/control). ## Mga hayop at iba pang organismo Mula sa mga solong selulang walang neuron hanggang sa utak ng tao. Nasusukat ang mga bilang ng neuron kung saan mayroon. ### Walang neuron * [Escherichia coli](https://simulation-hypothesis.com/fil/entities/ecoli)Escherichia coli 0 nasukat Isang selulang walang neuron. Gumagalaw ito papunta sa pagkain at may kemikal na alaalang tumatagal ng ilang segundo. * [Slime mould](https://simulation-hypothesis.com/fil/entities/physarum)Physarum polycephalum 0 nasukat Isang higanteng selulang walang neuron. Nilulutas nito ang mga maze at nagpapalago ng mahuhusay na network ng transportasyon. * [Sensitive plant](https://simulation-hypothesis.com/fil/entities/mimosa)Mimosa pudica 0 nasukat Isang halamang sumasara ang mga dahon kapag nahipo. May isang pag-aaral na nag-ulat na natututo itong huwag pansinin ang mga hipong hindi nakapipinsala. Pinagtalunan ito ng isang muling pagsusuri. * [Espongha](https://simulation-hypothesis.com/fil/entities/sponge)Porifera (e.g. Amphimedon queenslandica) 0 nasukat Isang hayop na walang neuron o synapse pero nagkukuntrata pa rin ang buong katawan nang koordinado. ### Mga nerve net, walang utak * [Hydra](https://simulation-hypothesis.com/fil/entities/hydra)Hydra vulgaris mga 3,000 hanggang 5,000 pagtataya Isang maliit na cnidarian sa tubig-tabang. Kinokontrol ito ng nakakalat na nerve net at wala itong utak. * [Box jellyfish](https://simulation-hypothesis.com/fil/entities/jellyfish)Tripedalia cystophora Walang nailathalang bilang Walang sentral na utak ang box jellyfish, pero nagpakita ito ng associative learning. ### Mga invertebrate na may utak * [Roundworm](https://simulation-hypothesis.com/fil/entities/celegans)Caenorhabditis elegans 302 nasukat Isang uod na may haba na mga isang milimetro at eksaktong 302 neuron. Ito ang unang hayop na nabuo ang kumpletong diagram ng mga koneksyon. * [Langaw ng prutas](https://simulation-hypothesis.com/fil/entities/drosophila)Drosophila melanogaster 139,255 nasukat Ang pinakamalaking kumpletong diagram ng koneksyon ng utak na nabuo hanggang ngayon, na sumasaklaw sa mga 139,000 neuron. * [Honeybee](https://simulation-hypothesis.com/fil/entities/honeybee)Apis mellifera mga 1 milyon pagtataya Mga isang milyong neuron, at natututo ang mga bubuyog ng mga abstraktong konsepto tulad ng pareho at magkaiba. * [Langgam (Camponotus / Formica)](https://simulation-hypothesis.com/fil/entities/ant)Camponotus spp. Walang nailathalang bilang Isang maliit na utak na humahawak ng malayuang nabigasyon sa pagbilang ng mga hakbang (path integration) at pag-alala sa mga palatandaan. * [Portia jumping spider](https://simulation-hypothesis.com/fil/entities/portia)Portia fimbriata / labiata Walang nailathalang bilang Isang jumping spider na nagpaplano ng mga likong daan. Dumadaan ito sa mga ruta na papalayo sa biktima nito at wala sa tanaw. * [Shore crab / crayfish](https://simulation-hypothesis.com/fil/entities/decapod)Carcinus maenas; Procambarus / Astacus spp. Walang nailathalang bilang Mga alimango at crayfish. Isang pagsusuri ng gobyerno noong 2021 ang nakakita ng matibay na ebidensyang sentient ang mga tunay na alimango. * [Karaniwang pugita](https://simulation-hypothesis.com/fil/entities/octopus)Octopus vulgaris 500 milyon pagtataya Karamihan ng neuron nito ay nasa mga galamay. Nakita ng isang pagsusuri noong 2021 ang napakatibay na ebidensyang sentient ito. ### Mga isda at amphibian * [Zebrafish](https://simulation-hypothesis.com/fil/entities/zebrafish)Danio rerio Walang nailathalang bilang Isang maliit na isdang malawakang ginagamit sa neuroscience. Hindi pa ganap na nabibilang ang mga neuron nito. May mga 10 milyong selula ang utak ng nasa hustong gulang. * [Cleaner wrasse](https://simulation-hypothesis.com/fil/entities/wrasse)Labroides dimidiatus Walang nailathalang bilang Isang isdang-bahura na pumasa sa isang bersyon ng mirror mark test. Hindi magkasundo ang mga tao kung ano ang ipinapakita nito. * [Palaka](https://simulation-hypothesis.com/fil/entities/frog)Anura (e.g. Xenopus / Rana) Walang nailathalang bilang Sinasabi ng New York Declaration noong 2024 na may makatotohanang posibilidad ng mulat na karanasan sa mga amphibian. ### Mga ibon * [Kalapati](https://simulation-hypothesis.com/fil/entities/pigeon)Columba livia 310 milyon nasukat Daan-daang milyong neuron sa isang maliit na utak. Naibubukod ng mga kalapati ang mga komplikadong larawan sa mga kategorya. * [Karaniwang uwak](https://simulation-hypothesis.com/fil/entities/raven)Corvus corax 2.17 bilyon nasukat Mahigit dalawang bilyong neuron, masikip ang pagkakapuno. Naaalala ng mga uwak ang mga indibidwal na kakumpitensya. * [New Caledonian crow](https://simulation-hypothesis.com/fil/entities/nccrow)Corvus moneduloides Walang nailathalang bilang Gumagawa ito ng mga kagamitang may kawit mula sa sanga at alambre. Hindi pa nabibilang ang mga neuron nito. * [African grey parrot](https://simulation-hypothesis.com/fil/entities/greyparrot)Psittacus erithacus 1.57 bilyon nasukat Mga 1.6 bilyong neuron. Mga dekada ng pag-aaral sa laboratoryo ng numerosity at pagkakategorya. ### Mammals * [Daga](https://simulation-hypothesis.com/fil/entities/mouse)Mus musculus 70.9 milyon nasukat Mga 71 milyong neuron. Nagpapakita ang mga daga ng ekspresyon ng mukha ng sakit na nasusukat. * [Daga](https://simulation-hypothesis.com/fil/entities/rat)Rattus norvegicus 200 milyon nasukat Mga 200 milyong neuron. Pinalalaya ng mga daga ang mga kasamang nakakulong. * [Alagang pusa](https://simulation-hypothesis.com/fil/entities/cat)Felis catus 1.22 bilyon nasukat Mga 250 milyong neuron sa cerebral cortex. * [Alagang aso](https://simulation-hypothesis.com/fil/entities/dog)Canis familiaris 1.8 hanggang 2.6 bilyon, depende sa aso nasukat Hanggang mga 600 milyong neuron sa cortex, depende sa aso. Mas marami iyan kaysa sa pusa. * [Alagang baboy](https://simulation-hypothesis.com/fil/entities/pig)Sus scrofa domesticus 2.22 bilyon nasukat Natututo ang mga baboy kung ano ang ipinapakita ng salamin at ginagamit ito para humanap ng pagkain. * [Bottlenose dolphin](https://simulation-hypothesis.com/fil/entities/dolphin)Tursiops truncatus Walang nailathalang bilang Pumapasa sa mga pagsubok ng pagkilala sa sarili sa salamin. Walang nasukat na bilang ng neuron para sa bottlenose dolphin. * [Elepanteng Aprikano](https://simulation-hypothesis.com/fil/entities/elephant)Loxodonta africana 257 bilyon nasukat 257 bilyong neuron, tatlong beses ang dami kaysa sa tao, halos lahat ay nasa cerebellum. * [Chimpanzee](https://simulation-hypothesis.com/fil/entities/chimp)Pan troglodytes mga 28 bilyon (hinulaan mula sa mga tuntunin ng pag-scale, hindi binilang) pagtataya Isa sa dalawa nating pinakamalapit na buhay na kamag-anak (kasama ang bonobo). Pumapasa ito sa mirror test. * [Tao](https://simulation-hypothesis.com/fil/entities/human)Homo sapiens 86.1 bilyon nasukat 86 bilyong neuron, 16 bilyon nito ay nasa cerebral cortex. Tayo lang ang uring ang mga miyembro ay kayang magbigay ng berbal na ulat ng karanasan nila. ## Mga kolektibong sistema Mga grupong gumagawa ng mga pasyang hindi kayang gawin ng iisang miyembro. Pinag-aaralan sila bilang collective intelligence, hindi bilang mga isip. * [Mga kolonya ng langgam](https://simulation-hypothesis.com/fil/entities/c-ants)Pinipili ng mga kolonya ang mas mabuting pugad dahil sumusunod ang bawat langgam sa isang quorum threshold. Walang langgam na naghahambing sa mga lugar sa kabuuan. * [Mga swarm ng honeybee](https://simulation-hypothesis.com/fil/entities/c-bees)Pinipili ng mga swarm ang pinakamahusay na lungga sa pamamagitan ng kumpetisyon ng mga scout at ng quorum. Nagbibigay ang mga hudyat ng paghinto ng cross-inhibition, na bumabasag sa mga deadlock. * [Mga kawan ng starling](https://simulation-hypothesis.com/fil/entities/c-starlings)Sinusubaybayan ng bawat ibon ang takdang bilang ng kapitbahay (mga 6-7), hindi ang bawat ibon sa loob ng takdang distansya. Tinatawag itong topolohikal na interaksyon, hindi metriko. * [Mga kawan ng isda](https://simulation-hypothesis.com/fil/entities/c-fish)Gumagamit ang mga kawan ng mga tuntunin ng quorum para magpasya. Nakadarama ang malalaking grupo ng mga gradient sa kapaligirang hindi kayang madama ng iisang isda. ## Mga artipisyal na sistema Mga uri ng machine intelligence, pinagsunud-sunod ayon sa paraan ng paggana. Walang kasalukuyang sistemang itinuturing na matibay na kandidato para sa kamalayan. * [Mga expert system na nakabatay sa tuntunin](https://simulation-hypothesis.com/fil/entities/ai-expert)Mga programang nag-eencode ng kaalaman ng isang dalubhasa bilang tahasang tuntuning if-then. Sunud-sunod na inilalapat ng isang inference engine ang mga tuntunin para makarating sa mga konklusyon, karaniwang may paliwanag ng pangangatwiran nito. * [Mga game-tree search engine (chess)](https://simulation-hypothesis.com/fil/entities/ai-chess)Mga programang naglilista ng posibleng pagkakasunod-sunod ng galaw hanggang sa takdang lalim at nagbibigay ng marka sa mga resultang posisyon gamit ang hand-built na evaluation function. Pinuputol nila ang mga sangang hindi makapagbabago ng pagpili (minimax na may alpha-beta pruning). * [Mga deep reinforcement learning agent](https://simulation-hypothesis.com/fil/entities/ai-drl)Mga neural network na sinanay sa pagsubok at pagkakamali para pataasin ang signal ng gantimpala. Natututo sila ng policy o value function sa pakikipag-ugnayan sa isang kapaligiran, madalas na simulado tulad ng isang laro. * [Mga large language model](https://simulation-hypothesis.com/fil/entities/ai-llm)Mga transformer neural network na sinanay para hulaan ang susunod na token sa napakalalaking koleksyon ng teksto, saka madalas na inaayos gamit ang feedback ng tao. * [Mga embodied na robot](https://simulation-hypothesis.com/fil/entities/ai-robots)Mga makinang may sensor at actuator na kumikilos sa pisikal na mundo. Madalas silang gumagamit ng patong-patong na reaktibong kontrol sa halip na sentral na modelo ng mundo, kaya lumilitaw ang asal mula sa ikot sa pagitan ng katawan at kapaligiran. * [Neuromorphic hardware](https://simulation-hypothesis.com/fil/entities/ai-neuromorphic)Mga chip na ginagaya ng mga circuit ang mga neuron na nagpapadala ng spike at ang mga synapse. Pinapagana sila ng mga pangyayari at kaunti ang kuryenteng ginagamit, hindi tulad ng karaniwang disenyong processor-at-memorya. ## Mga haka-hakang isip Mga isip na iminungkahi sa siyentipiko at pilosopikong literatura. Wala pang nakakita ng isa. * [Emulasyon ng buong utak](https://simulation-hypothesis.com/fil/entities/h-wbe)hindi pa naoobserbahan Pag-scan sa isang utak nang pinong-pino at pagpapatakbo ng software na modelo nito. * [Mga simuladong isip sa mga simulasyon ng mga ninuno](https://simulation-hypothesis.com/fil/entities/h-ancestor)hindi pa naoobserbahan Trilemma ni Bostrom: halos walang sibilisasyong umaabot sa posthuman na yugto, o halos walang nagpapatakbo ng simulasyon ng mga ninuno, o halos tiyak na nabubuhay tayo sa isa. * [Mga Boltzmann brain](https://simulation-hypothesis.com/fil/entities/h-boltzmann)hindi pa naoobserbahan Mga tagamasid na tulad ng utak na lumilitaw mula sa bihirang thermal o quantum na pagbabagu-bago. * [Pag-upload ng isip sa pamamagitan ng unti-unting pagpapalit](https://simulation-hypothesis.com/fil/entities/h-uploading)hindi pa naoobserbahan Pinapalitan ang mga neuron nang isa-isa ng mga artipisyal na bahaging kapareho ang tungkulin. * [Superintelligence](https://simulation-hypothesis.com/fil/entities/h-superintelligence)hindi pa naoobserbahan Isang haka-hakang talino na higit na lampas sa kakayahan ng tao sa halos lahat ng larangan. Sinusuri ng aklat ni Bostrom ang mga daan patungo rito (emulasyon ng buong utak, biyolohikal na pag-iisip, AI), ang mga anyo nito at ang mga suliranin ng pagkontrol. Bersyon ng datos 2.0.0\. [I-download (JSON)](https://simulation-hypothesis.com/entities/minds.json).