--- title: "Viumbe · akili na akili zinazowezekana, zilizopangwa kwa ushahidi · Dhana ya Uigaji" url: https://simulation-hypothesis.com/sw/entities description: "Mwongozo wenye vyanzo wa akili: wanyama wenye hesabu za niuroni zilizopimwa, mifumo ya pamoja na bandia, na majaribio yaliyochapishwa ya kuhisi na fahamu." license: https://creativecommons.org/licenses/by/4.0/ credit: "Ross Allan Dahl, Dhana ya Uigaji (simulation-hypothesis.com)" --- # Viumbe · akili na akili zinazowezekana, zilizopangwa kwa ushahidi Kama dunia ni uigaji, akili zinaweza kuwa miongoni mwa vitu vigumu zaidi kukokotoa ndani yake. Mwongozo huu unaorodhesha visa vinavyojulikana, kutoka seli moja hadi binadamu. Pia unahusu mifumo bandia iliyojengwa hadi sasa, na akili ambazo wanasayansi na wanafalsafa wamependekeza lakini hakuna aliyeziona. Kila nambari ina chanzo na lebo ya uhakika. imepimwa ni hesabu ya moja kwa moja, makadirio imekokotolewa kutoka data nyingine, na haijahesabiwa inamaanisha hakuna aliyechapisha hesabu. Kama mfumo wowote kati ya hii una uzoefu ni swali tofauti. Ushahidi wake uko chini ya [vigezo vya uwezo wa kuhisi](https://simulation-hypothesis.com/sw/entities/sentience) na [viashiria vya fahamu](https://simulation-hypothesis.com/sw/entities/indicators). ## Akili za mtu mmoja mmoja ## Wanyama: niuroni katika gamba na ubongo mzima Gamba au pallium Ubongo mzima Vyote Mamalia Ndege Niuroni katika gamba la ubongo (mamalia) au pallium (ndege), kiashiria cha anatomia kinachotumika sana cha uwezo wa kiakili. Kipimo cha logarithmiki; nguzo imara ni hesabu, nguzo zenye michirizi ni makadirio au hesabu zinazobishaniwa (†). Reptilia na wanyama wasio na uti wa mgongo hawaonyeshwi, kwa kuwa hakuna hesabu inayolinganishwa. 1. Orca † bilioni 43.1 2. Nyangumi rubani mwenye mapezi marefu † bilioni 37.2 3. [Binadamu](https://simulation-hypothesis.com/sw/entities/human)▲1 bilioni 16.3 4. Pomboo-bandari † bilioni 14.9 5. [Pomboo mwenye pua ya chupa †](https://simulation-hypothesis.com/sw/entities/dolphin) bilioni 12.7 6. Nyangumi mwenye mdomo wa Cuvier bilioni 9.1 7. Sokwe mkubwa bilioni 8.5 8. Orangutan bilioni 8.5 9. [Sokwe mtu](https://simulation-hypothesis.com/sw/entities/chimp) bilioni 7.4 10. Pomboo wa kawaida bilioni 6.7 11. Sili wa kinubi bilioni 6.1 12. [Tembo wa Afrika](https://simulation-hypothesis.com/sw/entities/elephant)▼1 bilioni 5.6 13. Nyangumi mkubwa wa fin bilioni 5.2 14. Nyangumi wa buluu bilioni 5.1 15. Nyangumi minke bilioni 3.2 Onyesha zote 88 10^6 10^7 10^8 10^9 10^10 10^11 neurons Onyesha nambari | # | Mnyama | Gamba au pallium | Ubongo mzima | | -- | -------------------------------------- | ---------------- | -------------- | | 1 | Orca † | bilioni 43.1 | | | 2 | Nyangumi rubani mwenye mapezi marefu † | bilioni 37.2 | | | 3 | Binadamu | bilioni 16.3 | bilioni 86.1 | | 4 | Pomboo-bandari † | bilioni 14.9 | | | 5 | Pomboo mwenye pua ya chupa † | bilioni 12.7 | | | 6 | Nyangumi mwenye mdomo wa Cuvier | bilioni 9.1 | | | 7 | Sokwe mkubwa | bilioni 8.5 | | | 8 | Orangutan | bilioni 8.5 | | | 9 | Sokwe mtu | bilioni 7.4 | | | 10 | Pomboo wa kawaida | bilioni 6.7 | | | 11 | Sili wa kinubi | bilioni 6.1 | | | 12 | Tembo wa Afrika | bilioni 5.6 | bilioni 257 | | 13 | Nyangumi mkubwa wa fin | bilioni 5.2 | | | 14 | Nyangumi wa buluu | bilioni 5.1 | | | 15 | Nyangumi minke | bilioni 3.2 | bilioni 57.4 | | 16 | Nyani wa mzeituni | bilioni 2.88 | bilioni 10.95 | | 17 | Nyangumi mweupe | bilioni 2.7 | | | 18 | Nyangumi mbegu mdogo | bilioni 2.02 | | | 19 | Makao wa buluu na manjano | bilioni 1.92 | bilioni 3.14 | | 20 | Makaka rhesus | bilioni 1.71 | bilioni 6.39 | | 21 | Twiga | bilioni 1.67 | bilioni 11.21 | | 22 | Kima-kindi | bilioni 1.34 | bilioni 3.25 | | 23 | Bundi mkubwa wa Eurasia | bilioni 1.33 | bilioni 1.9 | | 24 | Kea | bilioni 1.28 | bilioni 2.15 | | 25 | Bundi theluji | bilioni 1.27 | bilioni 1.81 | | 26 | Kunguru mkubwa | bilioni 1.2 | bilioni 2.17 | | 27 | Bundi wa kahawia-njano | bilioni 1.15 | bilioni 1.58 | | 28 | Kima wa kapuchini | bilioni 1.14 | bilioni 3.69 | | 29 | Kokatu mwenye kishungi cha salfa | bilioni 1.14 | bilioni 2.12 | | 30 | Kasuku wa kijivu wa Afrika | milioni 850 | bilioni 1.57 | | 31 | Kunguru-mshindo | milioni 820 | bilioni 1.51 | | 32 | Kudu mkubwa | milioni 793.07 | bilioni 6.09 | | 33 | Bundi mwenye masikio marefu | milioni 673 | milioni 991.31 | | 34 | Kokatu wa Goffin | milioni 599 | bilioni 1.16 | | 35 | Simba | milioni 545.24 | bilioni 4.67 | | 36 | Tai mwenye mkia mweupe | milioni 542.93 | bilioni 1.65 | | 37 | Kunguru wa Ulaya | milioni 529 | bilioni 1.09 | | 38 | Mbwa wa nyumbani | milioni 526 | | | 39 | Fisi mwenye mistari | milioni 495.28 | bilioni 3.88 | | 40 | Kunguru-mdogo wa magharibi | milioni 492 | milioni 967.99 | | 41 | Mbuni wa kawaida | milioni 479.41 | bilioni 1.63 | | 42 | Magpie wa Eurasia | milioni 443 | milioni 897.27 | | 43 | Falcon saker | milioni 442.95 | bilioni 1.2 | | 44 | Emu | milioni 439 | bilioni 1.34 | | 45 | Rakuni | milioni 437.94 | bilioni 2.15 | | 46 | Bundi wa ghalani | milioni 437 | milioni 689.54 | | 47 | Bundi mdogo | milioni 400.82 | milioni 585.92 | | 48 | Tai mweusi | milioni 398.9 | bilioni 1.01 | | 49 | Bundi mdogo wa Eurasia | milioni 364 | milioni 533.26 | | 50 | Kipanga wa kawaida | milioni 351.7 | bilioni 1 | | 51 | Rhea mkubwa | milioni 330.34 | bilioni 1.03 | | 52 | Swani bubu | milioni 323.66 | bilioni 1 | | 53 | Nguruwe wa nyumbani | milioni 293 | bilioni 2.22 | | 54 | Bata-bukini wa kijivu | milioni 258.65 | milioni 738.23 | | 55 | Dubu wa kahawia | milioni 250.97 | bilioni 9.59 | | 56 | Paka wa nyumbani | milioni 250 | bilioni 1.22 | | 57 | Marmoseti wa kawaida | milioni 245 | milioni 638 | | 58 | Kestrel wa kawaida | milioni 237.9 | milioni 624.58 | | 59 | Bata mdogo wa Eurasia | milioni 167.29 | milioni 375.41 | | 60 | Kasuku-nyasi | milioni 149 | milioni 321.82 | | 61 | Bata-bukini mwenye kifua chekundu | milioni 148.62 | milioni 482.71 | | 62 | Bata wa mbao | milioni 138.21 | milioni 305.82 | | 63 | Kipanga mdogo | milioni 131.9 | milioni 350.37 | | 64 | Bata wa marumaru | milioni 130.14 | milioni 386.92 | | 65 | Tausi wa India | milioni 129.62 | milioni 570.93 | | 66 | Kurasow mwenye kishungi cha manjano | milioni 124.62 | milioni 652.99 | | 67 | Njiwa wa Victoria mwenye kishungi | milioni 118.45 | milioni 578.7 | | 68 | Bata-mwitu | milioni 112.26 | milioni 367.47 | | 69 | Bata mzinga wa porini | milioni 105.65 | milioni 492.87 | | 70 | Kipanga wa Eurasia | milioni 87.83 | milioni 401.75 | | 71 | Kozi wa Reeves | milioni 80.69 | milioni 387.17 | | 72 | Njiwa wa mwamba | milioni 72 | milioni 310 | | 73 | Sungura wa Ulaya | milioni 71.5 | milioni 513 | | 74 | Kuku-mwitu mwekundu | milioni 60.7 | milioni 220.84 | | 75 | Tinamu wa Chile | milioni 59.13 | milioni 241.37 | | 76 | Finchi pundamilia | milioni 55.2 | milioni 135.98 | | 77 | Tinamu mwenye kishungi mzuri | milioni 51.38 | milioni 217.77 | | 78 | Njiwa-miti wa kawaida | milioni 51.33 | milioni 258.68 | | 79 | Tinamu mwenye kishungi wa quebracho | milioni 48.29 | milioni 235.67 | | 80 | Hua wa Kiafrika mwenye kola | milioni 40 | milioni 139.27 | | 81 | Kware wa kaskazini | milioni 39.11 | milioni 148.4 | | 82 | Kwale wa kijivu | milioni 36.88 | milioni 170.29 | | 83 | Panya | milioni 31 | milioni 200 | | 84 | Kware wa kawaida | milioni 22.57 | milioni 117.76 | | 85 | Kware mfalme | milioni 20.52 | milioni 80.48 | | 86 | Njiwa wa almasi | milioni 18.21 | milioni 87.88 | | 87 | Panya | milioni 13.7 | milioni 70.9 | | 88 | Panya-fuko asiye na manyoya | milioni 5.31 | milioni 33.62 | ## Mifumo ya AI: vigezo dhidi ya sinapsi Vigezo vyote Vinavyotumika kwa tokeni All Lugha Multimodali Maono Nyingine (usemi, baiolojia, michezo, roboti…) Vigezo vinavyofunzika (uzito wa miunganisho), kwa kipimo cha logarithmiki: kila modeli mashuhuri katika hifadhidata ya Epoch AI yenye idadi ya vigezo (imepatikana 3 Oktoba 2026). Nguzo imara ni hesabu ambazo Epoch inaziona za uhakika; nguzo zenye michirizi ni hesabu inazoziona zinazowezekana au za kubahatisha. Nguzo zenye \* ni makadirio ya modeli zilizofungwa ambazo wasanidi wake hawachapishi ukubwa wake, yaliyokusanywa 2 Oktoba 2026 kutoka uchambuzi wa watu wengine; kijiti kinaonyesha masafa ya kadirio. Gemini 3 na baadaye: hakuna kadirio lililochapishwa. Safu tupu ni idadi za sinapsi: kigezo kiko karibu na sinapsi kuliko niuroni. 1. Neokotekisi ya binadamu 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 Onyesha zote 725 10^4 10^6 10^8 10^10 10^12 10^14 vigezo au sinapsi Onyesha nambari | # | Mfumo | Vigezo vyote | Vinavyotumika kwa tokeni | Mwaka | Aina | | --- | ------------------------------------------------------------------------------------------- | ------------ | ------------------------ | ----- | ---------------------------------------------------- | | 1 | Neokotekisi ya binadamu | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | | sinapsi | | 2 | GPT-5.5 (OpenAI, 2026) \* | \~4.7 × 10¹² | | 2026 | makadirio | | 3 | Claude Fable 5 (Anthropic, 2026) \* | \~3.5 × 10¹² | | 2026 | makadirio | | 4 | Grok 3 (xAI, 2025) | \~3 × 10¹² | | 2025 | makadirio | | 5 | Grok 4 (xAI, 2025) | \~3 × 10¹² | | 2025 | makadirio | | 6 | Gemini 2.5 Pro (Google, 2025) \* | \~3 × 10¹² | | 2025 | makadirio | | 7 | Kimi K3 (Moonshot, 2026) | \~2.8 × 10¹² | | 2026 | makadirio | | 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | usanifu haujatajwa | | 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | mchanganyiko wa wataalamu | | 10 | Grok 4.7 (xAI, 2026) \* | \~2.1 × 10¹² | | 2026 | makadirio | | 11 | Llama 4 Behemoth (Meta AI, 2025) | \~2 × 10¹² | | 2025 | makadirio | | 12 | GPT-4 (OpenAI, 2023) | \~1.8 × 10¹² | | 2023 | makadirio | | 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | \~1.6 × 10¹² | | 2026 | makadirio | | 14 | LongCat-2.0 (Meituan Inc, 2026) | \~1.6 × 10¹² | | 2026 | makadirio | | 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 18 | Kimi K2.5 (Moonshot, 2026) | \~1 × 10¹² | | 2026 | makadirio | | 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 21 | Composer 2.5 (Cursor, 2026) | \~1 × 10¹² | | 2026 | makadirio | | 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | usanifu haujatajwa | | 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | \~1 × 10¹² | | 2026 | makadirio | | 24 | M6-T (Alibaba, 2021) | \~1 × 10¹² | | 2021 | makadirio | | 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 26 | Qwen3-Max (Alibaba, 2025) | \~1 × 10¹² | | 2025 | makadirio | | 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 28 | Kimi K2 Thinking (Moonshot, 2025) | \~1 × 10¹² | | 2025 | makadirio | | 29 | Inkling (Thinking Machines, 2026) | \~9.8 × 10¹¹ | | 2026 | makadirio | | 30 | Tencent Hy4 preview (Tencent, 2026) | \~7.7 × 10¹¹ | | 2026 | makadirio | | 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | \~7.5 × 10¹¹ | | 2026 | makadirio | | 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | \~7.4 × 10¹¹ | | 2026 | makadirio | | 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | mchanganyiko wa wataalamu | | 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | mchanganyiko wa wataalamu | | 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | usanifu haujatajwa | | 42 | Step 5 Preview (StepFun, 2026) | \~6 × 10¹¹ | | 2026 | makadirio | | 43 | PaLM-E (Google, 2023) | \~5.6 × 10¹¹ | | 2023 | makadirio | | 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 45 | LongCat-Flash (Meituan Inc, 2025) | \~5.6 × 10¹¹ | | 2025 | makadirio | | 46 | Nemotron 3 Ultra (Nvidia, 2026) | \~5.5 × 10¹¹ | | 2026 | makadirio | | 47 | PaLM (Google Research, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | mnene | | 48 | Minerva (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | mnene | | 49 | U-PaLM (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | mnene | | 50 | LMSI-Palm (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | mnene | | 51 | Flan-PaLM 540B (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | mnene | | 52 | Megatron-Turing NLG 530B (Microsoft, 2021) | 5.3 × 10¹¹ | 5.3 × 10¹¹ | 2021 | mnene | | 53 | MegaScale (ByteDance, 2024) | \~5.3 × 10¹¹ | | 2024 | makadirio | | 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 55 | Doubao-pro (ByteDance, 2024) | \~5 × 10¹¹ | | 2024 | makadirio | | 56 | Grok 4.20 (xAI, 2026) | \~5 × 10¹¹ | | 2026 | makadirio | | 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | mchanganyiko wa wataalamu | | 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | mnene | | 61 | Llama 4 Maverick (Meta AI, 2025) | \~4 × 10¹¹ | | 2025 | makadirio | | 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | \~4 × 10¹¹ | | 2026 | makadirio | | 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | \~3.6 × 10¹¹ | | 2025 | makadirio | | 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | \~3.6 × 10¹¹ | | 2025 | makadirio | | 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 68 | PaLM 2 (Google, 2023) | \~3.4 × 10¹¹ | | 2023 | makadirio | | 69 | Med-PaLM 2 (Google Research, 2023) | \~3.4 × 10¹¹ | | 2023 | makadirio | | 70 | Nemotron-4 340B (Nvidia, 2024) | 3.4 × 10¹¹ | 3.4 × 10¹¹ | 2024 | mnene | | 71 | GLM-5.3-Flash (Z.ai (Zhipu AI), 2026) | \~3.2 × 10¹¹ | | 2026 | makadirio | | 72 | Grok-1 (xAI, 2023) | \~3.1 × 10¹¹ | | 2023 | makadirio | | 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 74 | SB-LM (Google, 2007) | \~3 × 10¹¹ | | 2007 | makadirio | | 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | \~2.8 × 10¹¹ | | 2026 | makadirio | | 78 | Gopher (DeepMind, 2021) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2021 | mnene | | 79 | TML-Interaction-Small (Thinking Machines, 2026) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2026 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 80 | ST-MoE (Google, 2022) | \~2.7 × 10¹¹ | | 2022 | makadirio | | 81 | ERNIE 3.0 Titan (Baidu, 2021) | 2.6 × 10¹¹ | 2.6 × 10¹¹ | 2021 | mnene | | 82 | Solar Open2 250B (Upstage, 2026) | \~2.5 × 10¹¹ | | 2026 | makadirio | | 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | \~2.5 × 10¹¹ | | 2024 | makadirio | | 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | usanifu haujatajwa | | 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | usanifu haujatajwa | | 88 | Qwen3-235B-A22B (Alibaba, 2025) | \~2.4 × 10¹¹ | | 2025 | makadirio | | 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | \~2.4 × 10¹¹ | | 2025 | makadirio | | 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | \~2.4 × 10¹¹ | | 2025 | makadirio | | 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu | | 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 94 | HyperCLOVA 204B (NAVER, 2021) | \~2 × 10¹¹ | | 2021 | makadirio | | 95 | Amazon Titan (Amazon, 2023) | \~2 × 10¹¹ | | 2023 | makadirio | | 96 | Falcon-180B (Technology Innovation Institute, 2023) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2023 | mnene | | 97 | Jurassic-1-Jumbo (AI21 Labs, 2021) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2021 | usanifu haujatajwa | | 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | mnene | | 99 | BLUUMI (University of Turku, 2023) | \~1.8 × 10¹¹ | | 2023 | makadirio | | 100 | InstructGPT 175B (OpenAI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | mnene | | 101 | OPT-175B (Meta AI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | mnene | | 102 | BlenderBot 3 (McGill University, 2022) | \~1.8 × 10¹¹ | | 2022 | makadirio | | 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | mnene | | 104 | Palmyra X 004 (Writer, 2024) | \~1.5 × 10¹¹ | | 2024 | makadirio | | 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | usanifu haujatajwa | | 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | usanifu haujatajwa | | 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | usanifu haujatajwa | | 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | usanifu haujatajwa | | 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 110 | GLM-130B (Tsinghua University, 2022) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2022 | mnene | | 111 | Pixtral Large (Mistral AI, 2024) | 1.2 × 10¹¹ | 1.2 × 10¹¹ | 2024 | usanifu haujatajwa | | 112 | Mistral Large 2 (Mistral AI, 2024) | \~1.2 × 10¹¹ | | 2024 | makadirio | | 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | \~1.2 × 10¹¹ | | 2026 | makadirio | | 114 | Galactica (Meta AI, 2022) | \~1.2 × 10¹¹ | | 2022 | makadirio | | 115 | Nemotron 3 Super (Nvidia, 2026) | \~1.2 × 10¹¹ | | 2026 | makadirio | | 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | mchanganyiko wa wataalamu | | 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | usanifu haujatajwa | | 118 | Kaiju Large (Character.ai, 2025) | \~1.1 × 10¹¹ | | 2025 | makadirio | | 119 | Llama 4 Scout (Meta AI, 2025) | \~1.1 × 10¹¹ | | 2025 | makadirio | | 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | mnene | | 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | usanifu haujatajwa | | 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | mnene | | 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | usanifu haujatajwa | | 124 | Yi-Large (01.AI, 2024) | \~1 × 10¹¹ | | 2024 | makadirio | | 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | usanifu haujatajwa | | 127 | Neokotekisi ya panya | 8.1 × 10¹⁰ | 8.1 × 10¹⁰ | | sinapsi | | 128 | Flamingo (DeepMind, 2022) | 8 × 10¹⁰ | 8 × 10¹⁰ | 2022 | mnene | | 129 | Qwen3-Coder-Next (Alibaba, 2026) | \~8 × 10¹⁰ | | 2026 | makadirio | | 130 | Qwen2-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | mnene | | 131 | Qwen2.5-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | mnene | | 132 | Qwen2.5 Instruct (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | mnene | | 133 | Qwen-72B (Alibaba, 2023) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2023 | mnene | | 134 | Palmyra X 003 (Writer, 2024) | \~7.2 × 10¹⁰ | | 2024 | makadirio | | 135 | Qwen1.5-72B (Alibaba, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | mnene | | 136 | LLaVA-OV-72B (ByteDance, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | mnene | | 137 | NVLM-X 72B (Nvidia, 2024) | \~7.2 × 10¹⁰ | | 2024 | makadirio | | 138 | NVLM-H 72B (Nvidia, 2024) | \~7.2 × 10¹⁰ | | 2024 | makadirio | | 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | usanifu haujatajwa | | 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | usanifu haujatajwa | | 141 | Chinchilla (DeepMind, 2022) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2022 | mnene | | 142 | Llama 2-70B (Meta AI, 2023) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2023 | mnene | | 143 | PPLX-70B-Online (Perplexity, 2023) | \~7 × 10¹⁰ | | 2023 | makadirio | | 144 | Llama 3-70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | mnene | | 145 | ALLaM adapted 70B (Saudi Data and Artificial Intelligence Authority, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | usanifu haujatajwa | | 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | mnene | | 147 | Reka Core (Reka AI, 2024) | \~6.7 × 10¹⁰ | | 2024 | makadirio | | 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | mnene | | 149 | SNM-skip (Google, 2014) | \~6.2 × 10¹⁰ | | 2014 | makadirio | | 150 | PaLI-X (Google Research, 2023) | \~5.5 × 10¹⁰ | | 2023 | makadirio | | 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | usanifu haujatajwa | | 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | usanifu haujatajwa | | 153 | NLLB (Meta AI, 2022) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2022 | mnene | | 154 | BloombergGPT (Bloomberg, 2023) | 5.1 × 10¹⁰ | 5.1 × 10¹⁰ | 2023 | mnene | | 155 | M4-50B (Google, 2019) | 5 × 10¹⁰ | 5 × 10¹⁰ | 2019 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 156 | Mixtral 8x7B (Mistral AI, 2023) | \~4.7 × 10¹⁰ | | 2023 | makadirio | | 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | usanifu haujatajwa | | 158 | Falcon-40B (Technology Innovation Institute, 2023) | 4 × 10¹⁰ | 4 × 10¹⁰ | 2023 | mnene | | 159 | Qwen3-Omni-30B-A3B (Alibaba, 2025) | 3.5 × 10¹⁰ | 3 × 10⁹ | 2025 | mchanganyiko wa wataalamu | | 160 | Yi-34B (01.AI, 2023) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2023 | mnene | | 161 | Cambrian-1-34B (New York University (NYU), 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | usanifu haujatajwa | | 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | usanifu haujatajwa | | 163 | Kaiju Medium (Character.ai, 2025) | \~3.4 × 10¹⁰ | | 2025 | makadirio | | 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | usanifu haujatajwa | | 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | mnene | | 166 | QwQ-32B (Alibaba, 2025) | \~3.3 × 10¹⁰ | | 2025 | makadirio | | 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | usanifu haujatajwa | | 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | usanifu haujatajwa | | 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | usanifu haujatajwa | | 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | usanifu haujatajwa | | 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | usanifu haujatajwa | | 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | \~3.2 × 10¹⁰ | | 2025 | makadirio | | 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | usanifu haujatajwa | | 174 | MM1-30B (Apple, 2024) | \~3 × 10¹⁰ | | 2024 | makadirio | | 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | usanifu haujatajwa | | 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | usanifu haujatajwa | | 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | usanifu haujatajwa | | 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | usanifu haujatajwa | | 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | usanifu haujatajwa | | 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | usanifu haujatajwa | | 181 | KN-LM (Google, 2007) | \~2.1 × 10¹⁰ | | 2007 | makadirio | | 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 183 | RNN for 1B words (Google, 2013) | \~2 × 10¹⁰ | | 2013 | makadirio | | 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | mnene | | 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | usanifu haujatajwa | | 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | usanifu haujatajwa | | 187 | GPT-3.5 Turbo (OpenAI, 2023) | \~2 × 10¹⁰ | | 2023 | makadirio | | 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | \~2 × 10¹⁰ | | 2023 | makadirio | | 189 | SPHINX (Shanghai AI Lab, 2023) | \~2 × 10¹⁰ | | 2023 | makadirio | | 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | usanifu haujatajwa | | 191 | CogAgent (Tsinghua University, 2023) | \~1.8 × 10¹⁰ | | 2023 | makadirio | | 192 | Turing-NLG (Microsoft, 2020) | \~1.7 × 10¹⁰ | | 2020 | makadirio | | 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | usanifu haujatajwa | | 194 | PaLI (Google, 2022) | \~1.7 × 10¹⁰ | | 2022 | makadirio | | 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | usanifu haujatajwa | | 196 | StarCoder (Hugging Face, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | mnene | | 197 | ESM2-15B (Meta AI, 2022) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2022 | usanifu haujatajwa | | 198 | FunSearch (Google DeepMind, 2023) | \~1.5 × 10¹⁰ | | 2023 | makadirio | | 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | usanifu haujatajwa | | 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | usanifu haujatajwa | | 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | usanifu haujatajwa | | 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | usanifu haujatajwa | | 203 | mT5-XXL (Google, 2020) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2020 | mnene | | 204 | mT0-13B (Hugging Face, 2022) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2022 | usanifu haujatajwa | | 205 | LLaVA (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | mnene | | 206 | InstructBLIP (Salesforce Research, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | usanifu haujatajwa | | 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | usanifu haujatajwa | | 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | \~1.3 × 10¹⁰ | | 2023 | makadirio | | 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | usanifu haujatajwa | | 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | usanifu haujatajwa | | 211 | LLaVA 1.5 (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | mnene | | 212 | Volcano 13B (Korea University, 2023) | \~1.3 × 10¹⁰ | | 2023 | makadirio | | 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | \~1.3 × 10¹⁰ | | 2024 | makadirio | | 214 | Kaiju Small (Character.ai, 2025) | \~1.3 × 10¹⁰ | | 2025 | makadirio | | 215 | ByT5-XXL (Google, 2021) | \~1.3 × 10¹⁰ | | 2021 | makadirio | | 216 | CoRe (Tsinghua University, 2023) | \~1.2 × 10¹⁰ | | 2023 | makadirio | | 217 | DALL-E (OpenAI, 2021) | \~1.2 × 10¹⁰ | | 2021 | makadirio | | 218 | Codex (OpenAI, 2021) | \~1.2 × 10¹⁰ | | 2021 | makadirio | | 219 | T5-11B (Google, 2019) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2019 | mnene | | 220 | UnifiedQA (Allen Institute for AI, 2020) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2020 | mnene | | 221 | Unicorn (Allen Institute for AI, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | usanifu haujatajwa | | 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | usanifu haujatajwa | | 223 | CoEdiT-xxl (University of Minnesota, 2023) | \~1.1 × 10¹⁰ | | 2023 | makadirio | | 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | usanifu haujatajwa | | 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | usanifu haujatajwa | | 226 | Llama 3.2 11B (Meta AI, 2024) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2024 | mnene | | 227 | ERNIE 3.0 (Baidu, 2021) | 1 × 10¹⁰ | 1 × 10¹⁰ | 2021 | mnene | | 228 | ERNIE-ViLG (Baidu, 2021) | \~1 × 10¹⁰ | | 2021 | makadirio | | 229 | Qwen-VL (Alibaba, 2023) | \~9.6 × 10⁹ | | 2023 | makadirio | | 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | usanifu haujatajwa | | 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 232 | CogVideo (Tsinghua University, 2022) | \~9.4 × 10⁹ | | 2022 | makadirio | | 233 | eDiff-I (Nvidia, 2022) | \~9.1 × 10⁹ | | 2022 | makadirio | | 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | usanifu haujatajwa | | 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 236 | Qwen-Audio-Chat (Alibaba, 2023) | \~8.5 × 10⁹ | | 2023 | makadirio | | 237 | Megatron-LM (Nvidia, 2019) | \~8.3 × 10⁹ | | 2019 | makadirio | | 238 | BigSSL (Google, 2021) | \~8 × 10⁹ | | 2021 | makadirio | | 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | usanifu haujatajwa | | 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | mnene | | 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 242 | Imagen (Google Brain, 2022) | \~7.8 × 10⁹ | | 2022 | makadirio | | 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | usanifu haujatajwa | | 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | usanifu haujatajwa | | 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | usanifu haujatajwa | | 246 | mPLUG-Owl2 (Alibaba, 2023) | \~7.1 × 10⁹ | | 2023 | makadirio | | 247 | Goat-7B (National University of Singapore, 2023) | \~7 × 10⁹ | | 2023 | makadirio | | 248 | Llama 2-7B (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | mnene | | 249 | Llama Guard (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | mnene | | 250 | Qwen-VL-Max (Alibaba, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | mnene | | 251 | Mathstral (Mistral AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | usanifu haujatajwa | | 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | usanifu haujatajwa | | 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | \~7 × 10⁹ | | 2025 | makadirio | | 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | usanifu haujatajwa | | 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | usanifu haujatajwa | | 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | usanifu haujatajwa | | 257 | GPipe (Google, 2018) | \~6 × 10⁹ | | 2018 | makadirio | | 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | mnene | | 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | usanifu haujatajwa | | 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | \~6 × 10⁹ | | 2023 | makadirio | | 261 | MolmoAct 2 (Allen Institute for AI, 2026) | \~5.5 × 10⁹ | | 2026 | makadirio | | 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | usanifu haujatajwa | | 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | usanifu haujatajwa | | 265 | Mesh-TensorFlow Transformer 4.9B (Google Brain, 2018) | 4.9 × 10⁹ | 4.9 × 10⁹ | 2018 | mnene | | 266 | W.A.L.T (Stanford University, 2023) | 4.7 × 10⁹ | 4.7 × 10⁹ | 2023 | usanifu haujatajwa | | 267 | CogView (Tsinghua University, 2021) | \~4 × 10⁹ | | 2021 | makadirio | | 268 | Make-A-Scene (Meta AI, 2022) | \~4 × 10⁹ | | 2022 | makadirio | | 269 | ONE-PEACE (Alibaba, 2023) | \~4 × 10⁹ | | 2023 | makadirio | | 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | mnene | | 271 | DALL·E 2 (OpenAI, 2022) | \~3.5 × 10⁹ | | 2022 | makadirio | | 272 | Stable Diffusion XL (Stability AI, 2023) | \~3.4 × 10⁹ | | 2023 | makadirio | | 273 | MusicGen (Meta AI, 2023) | \~3.4 × 10⁹ | | 2023 | makadirio | | 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | usanifu haujatajwa | | 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | usanifu haujatajwa | | 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | usanifu haujatajwa | | 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | \~3.2 × 10⁹ | | 2016 | makadirio | | 278 | BASIC-L (Google, 2021) | \~3.1 × 10⁹ | | 2021 | makadirio | | 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | usanifu haujatajwa | | 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | usanifu haujatajwa | | 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | usanifu haujatajwa | | 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | usanifu haujatajwa | | 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | usanifu haujatajwa | | 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | usanifu haujatajwa | | 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | usanifu haujatajwa | | 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | \~2.9 × 10⁹ | | 2018 | makadirio | | 287 | T5-3B (Google, 2019) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2019 | mnene | | 288 | DPO on Pythia-2.8B (Stanford University, 2023) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2023 | mnene | | 289 | AFM-on-device (Apple, 2024) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2024 | usanifu haujatajwa | | 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | \~2.7 × 10⁹ | | 2022 | makadirio | | 291 | Hybrid H3-2.7B (Stanford University, 2022) | \~2.7 × 10⁹ | | 2022 | makadirio | | 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | usanifu haujatajwa | | 293 | Meena (Google Brain, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | mnene | | 294 | CPM-Large (Tsinghua University, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | mnene | | 295 | Nucleotide Transformer (Nvidia, 2023) | \~2.5 × 10⁹ | | 2023 | makadirio | | 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | usanifu haujatajwa | | 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | usanifu haujatajwa | | 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | mnene | | 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | usanifu haujatajwa | | 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | usanifu haujatajwa | | 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | usanifu haujatajwa | | 302 | MuseNet (OpenAI, 2019) | \~2 × 10⁹ | | 2019 | makadirio | | 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | usanifu haujatajwa | | 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | mnene | | 305 | BEIT-3 (Microsoft, 2022) | \~1.9 × 10⁹ | | 2022 | makadirio | | 306 | Ankh\_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | usanifu haujatajwa | | 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | usanifu haujatajwa | | 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | usanifu haujatajwa | | 309 | Phenaki (University College London (UCL), 2022) | \~1.8 × 10⁹ | | 2022 | makadirio | | 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | usanifu haujatajwa | | 311 | DistBelief Vision (Google, 2012) | \~1.7 × 10⁹ | | 2012 | makadirio | | 312 | Whisper (OpenAI, 2022) | \~1.6 × 10⁹ | | 2022 | makadirio | | 313 | GPT-2 (OpenAI, 2019) | \~1.5 × 10⁹ | | 2019 | makadirio | | 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | usanifu haujatajwa | | 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | usanifu haujatajwa | | 316 | AudioLM (Google Research, 2023) | \~1.5 × 10⁹ | | 2023 | makadirio | | 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | usanifu haujatajwa | | 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | usanifu haujatajwa | | 319 | Stable Diffusion (Runway, 2022) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2022 | mnene | | 320 | SEER (Facebook AI Research, 2021) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2021 | usanifu haujatajwa | | 321 | InstructGPT 1.3B (OpenAI, 2022) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2022 | mnene | | 322 | Megatron-LM (Nvidia, 2019) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2019 | mnene | | 323 | RDT-1B (Tsinghua University, 2024) | \~1.2 × 10⁹ | | 2024 | makadirio | | 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | usanifu haujatajwa | | 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | usanifu haujatajwa | | 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | usanifu haujatajwa | | 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | usanifu haujatajwa | | 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | usanifu haujatajwa | | 329 | Unsupervised High-level Feature Learner (Google, 2012) | \~1 × 10⁹ | | 2012 | makadirio | | 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | mnene | | 331 | HuBERT (Facebook AI Research, 2021) | \~1 × 10⁹ | | 2021 | makadirio | | 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | mnene | | 333 | AudioGen (Meta AI, 2023) | \~1 × 10⁹ | | 2023 | makadirio | | 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | usanifu haujatajwa | | 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | \~9.4 × 10⁸ | | 2013 | makadirio | | 336 | ImageBind (Meta AI, 2023) | \~9.3 × 10⁸ | | 2023 | makadirio | | 337 | Big Transfer (Google Brain, 2019) | \~9.3 × 10⁸ | | 2019 | makadirio | | 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | usanifu haujatajwa | | 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | usanifu haujatajwa | | 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | usanifu haujatajwa | | 341 | ResNeXt-101 32x48d (Facebook, 2018) | 8.3 × 10⁸ | 8.3 × 10⁸ | 2018 | mnene | | 342 | FixRes ResNeXt-101 WSL (Facebook AI, 2019) | \~8.3 × 10⁸ | | 2019 | makadirio | | 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | usanifu haujatajwa | | 344 | Statement Curriculum Learning (OpenAI, 2022) | \~7.7 × 10⁸ | | 2022 | makadirio | | 345 | CodeT5-large (Salesforce, 2022) | \~7.7 × 10⁸ | | 2022 | makadirio | | 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | usanifu haujatajwa | | 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | usanifu haujatajwa | | 348 | data2vec (Meta AI, 2022) | \~7.1 × 10⁸ | | 2022 | makadirio | | 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | usanifu haujatajwa | | 350 | Word2Vec (Google, 2013) | \~6.9 × 10⁸ | | 2013 | makadirio | | 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | usanifu haujatajwa | | 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | \~6.8 × 10⁸ | | 2023 | makadirio | | 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | usanifu haujatajwa | | 354 | XLM (Facebook, 2019) | \~6.7 × 10⁸ | | 2019 | makadirio | | 355 | ESM1b (Facebook AI Research, 2020) | 6.5 × 10⁸ | 6.5 × 10⁸ | 2020 | mnene | | 356 | Segment Anything Model (Meta AI, 2023) | 6.4 × 10⁸ | 6.4 × 10⁸ | 2023 | usanifu haujatajwa | | 357 | ViT-Huge/14 (Google Brain, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | mnene | | 358 | Masked Autoencoders ViT-H (Facebook AI Research, 2021) | \~6.3 × 10⁸ | | 2021 | makadirio | | 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | mnene | | 360 | DLDL (University of Oxford, 2016) | \~5.6 × 10⁸ | | 2016 | makadirio | | 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | usanifu haujatajwa | | 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | usanifu haujatajwa | | 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | mnene | | 364 | LUKE (University of Washington, 2020) | \~4.8 × 10⁸ | | 2020 | makadirio | | 365 | EfficientNet-L2 (Google, 2019) | \~4.8 × 10⁸ | | 2019 | makadirio | | 366 | Noisy Student (Carnegie Mellon University (CMU), 2019) | 4.8 × 10⁸ | 4.8 × 10⁸ | 2019 | mnene | | 367 | Meta Pseudo Labels (Google Brain, 2021) | \~4.8 × 10⁸ | | 2021 | makadirio | | 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | usanifu haujatajwa | | 369 | NMT Transformer 437M (Google, 2019) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2019 | mnene | | 370 | Adaptive Inputs + LayerDrop (Facebook AI Research, 2019) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2019 | mnene | | 371 | ProtBERT-BFD (Technical University of Munich, 2021) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2021 | usanifu haujatajwa | | 372 | OntoProtein (Zhejiang University (ZJU), 2022) | \~4.2 × 10⁸ | | 2022 | makadirio | | 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | usanifu haujatajwa | | 374 | BART-large (Facebook AI, 2019) | \~4.1 × 10⁸ | | 2019 | makadirio | | 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | usanifu haujatajwa | | 376 | BERT-Large-CAS (Amazon, 2019) | \~4 × 10⁸ | | 2019 | makadirio | | 377 | MoCo (Facebook AI, 2019) | \~3.8 × 10⁸ | | 2019 | makadirio | | 378 | SimCLR (Google Brain, 2020) | \~3.8 × 10⁸ | | 2020 | makadirio | | 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | usanifu haujatajwa | | 380 | RoBERTa Large (Facebook, 2019) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2019 | mnene | | 381 | AbLang (University of Oxford, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | usanifu haujatajwa | | 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | mnene | | 383 | VALL-E (Microsoft, 2023) | \~3.5 × 10⁸ | | 2023 | makadirio | | 384 | BERT-Large (Google, 2018) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2018 | mnene | | 385 | XLNet (Carnegie Mellon University (CMU), 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | mnene | | 386 | ERNIE-GEN (Baidu, 2020) | \~3.4 × 10⁸ | | 2020 | makadirio | | 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | mnene | | 388 | ELECTRA (Stanford University, 2020) | \~3.4 × 10⁸ | | 2020 | makadirio | | 389 | German ELECTRA Large (deepset, 2020) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2020 | mnene | | 390 | ALM 1.0 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | \~3.4 × 10⁸ | | 2022 | makadirio | | 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | mnene | | 392 | Fusion in Encoder (Samsung, 2022) | \~3.3 × 10⁸ | | 2022 | makadirio | | 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | mnene | | 394 | CPC v2 (DeepMind, 2019) | \~3 × 10⁸ | | 2019 | makadirio | | 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | mnene | | 396 | DDPM-IP (Utrecht University, 2023) | \~3 × 10⁸ | | 2023 | makadirio | | 397 | GNMT (Google, 2016) | \~2.8 × 10⁸ | | 2016 | makadirio | | 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | usanifu haujatajwa | | 399 | YOLO (University of Washington, 2015) | \~2.7 × 10⁸ | | 2015 | makadirio | | 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | \~2.6 × 10⁸ | | 2019 | makadirio | | 401 | Transformer-XL + RMS dynamic eval (University of Edinburgh, 2019) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2019 | mnene | | 402 | Transformer-XL Large + Phrase Induction (Massachusetts Institute of Technology (MIT), 2019) | \~2.6 × 10⁸ | | 2019 | makadirio | | 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | usanifu haujatajwa | | 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | usanifu haujatajwa | | 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | usanifu haujatajwa | | 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | mnene | | 407 | S4 (Stanford University, 2021) | \~2.5 × 10⁸ | | 2021 | makadirio | | 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | \~2.5 × 10⁸ | | 2019 | makadirio | | 409 | ERNIE-Doc (Baidu, 2020) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2020 | mnene | | 410 | Adaptive Input Transformer + RD (Microsoft Research Asia, 2021) | \~2.5 × 10⁸ | | 2021 | makadirio | | 411 | Transformer (Adaptive Input Embeddings) WT103 (Facebook AI Research, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | mnene | | 412 | TaLK Convolution (Carleton University, 2020) | \~2.4 × 10⁸ | | 2020 | makadirio | | 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | \~2.4 × 10⁸ | | 2020 | makadirio | | 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | usanifu haujatajwa | | 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | usanifu haujatajwa | | 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | usanifu haujatajwa | | 417 | MetNet (Google, 2020) | \~2.3 × 10⁸ | | 2020 | makadirio | | 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | mnene | | 419 | CodeT5-base (Salesforce, 2021) | \~2.2 × 10⁸ | | 2021 | makadirio | | 420 | Transformer (Google Research, 2017) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2017 | mnene | | 421 | Sandwich Transformer (Allen Institute for AI, 2019) | \~2.1 × 10⁸ | | 2019 | makadirio | | 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | usanifu haujatajwa | | 423 | Word2Vec (Google, 2013) | \~2.1 × 10⁸ | | 2013 | makadirio | | 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | mnene | | 425 | DreamerV3 (DeepMind, 2023) | \~2 × 10⁸ | | 2023 | makadirio | | 426 | DL scaling speech (Baidu, 2017) | \~1.9 × 10⁸ | | 2017 | makadirio | | 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | \~1.9 × 10⁸ | | 2019 | makadirio | | 428 | (NTT Communication Science Laboratories, 2018) | 1.9 × 10⁸ | 1.9 × 10⁸ | 2018 | mnene | | 429 | TrellisNet (Carnegie Mellon University (CMU), 2018) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2018 | mnene | | 430 | DL scaling LM (Baidu, 2017) | \~1.8 × 10⁸ | | 2017 | makadirio | | 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | usanifu haujatajwa | | 432 | OpenAI Five Rerun (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | mnene | | 433 | OpenAI Five (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | mnene | | 434 | 4 layer QRNN (Salesforce Research, 2018) | \~1.5 × 10⁸ | | 2018 | makadirio | | 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | \~1.5 × 10⁸ | | 2020 | makadirio | | 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | usanifu haujatajwa | | 437 | PermuteFormer (Peking University, 2021) | \~1.5 × 10⁸ | | 2021 | makadirio | | 438 | Fragment embedding (Stanford University, 2014) | \~1.4 × 10⁸ | | 2014 | makadirio | | 439 | OverFeat (New York University (NYU), 2013) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2013 | mnene | | 440 | VGG19 (University of Oxford, 2014) | \~1.4 × 10⁸ | | 2014 | makadirio | | 441 | LRCN (UT Austin, 2014) | \~1.4 × 10⁸ | | 2014 | makadirio | | 442 | Transformer-XL DeFINE (University of Washington, 2019) | \~1.4 × 10⁸ | | 2019 | makadirio | | 443 | Deep Autoencoders (University of Toronto, 2011) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2011 | mnene | | 444 | AlphaStar (DeepMind, 2019) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2019 | mnene | | 445 | VGG16 (University of Oxford, 2014) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2014 | mnene | | 446 | VGG-Face (University of Oxford, 2015) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2015 | mnene | | 447 | SAF R-CNN (Beijing Institute of Technology, 2015) | \~1.4 × 10⁸ | | 2015 | makadirio | | 448 | Template Adaptation (University of Oxford, 2016) | \~1.4 × 10⁸ | | 2016 | makadirio | | 449 | LRR-4X (UC Irvine, 2016) | \~1.4 × 10⁸ | | 2016 | makadirio | | 450 | CMS-RCNN (IEEE, 2016) | \~1.4 × 10⁸ | | 2016 | makadirio | | 451 | QRNN (Salesforce Research, 2018) | \~1.4 × 10⁸ | | 2018 | makadirio | | 452 | Multiresolution CNN (Google, 2014) | \~1.3 × 10⁸ | | 2014 | makadirio | | 453 | FTW (DeepMind, 2018) | \~1.3 × 10⁸ | | 2018 | makadirio | | 454 | Feedback Transformer (LORIA, 2020) | \~1.3 × 10⁸ | | 2020 | makadirio | | 455 | KEPLER (Tsinghua University, 2020) | 1.3 × 10⁸ | 1.3 × 10⁸ | 2020 | mnene | | 456 | DL scaling Image (Baidu, 2017) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2017 | mnene | | 457 | GloVe (Stanford University, 2014) | \~1.2 × 10⁸ | | 2014 | makadirio | | 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | mnene | | 459 | GPT-1 (OpenAI, 2018) | \~1.2 × 10⁸ | | 2018 | makadirio | | 460 | ContextNet (Google, 2020) | \~1.1 × 10⁸ | | 2020 | makadirio | | 461 | SciBERT (Allen Institute for AI, 2019) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2019 | mnene | | 462 | DNABERT (Northeastern University, 2021) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2021 | usanifu haujatajwa | | 463 | Contriever (Meta AI, 2021) | \~1.1 × 10⁸ | | 2021 | makadirio | | 464 | BERT-RBP (Waseda University, 2022) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2022 | mnene | | 465 | RoFormer (Zhuiyi Technology, 2023) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2023 | usanifu haujatajwa | | 466 | LSTM LM (RWTH Aachen University, 2012) | \~1 × 10⁸ | | 2012 | makadirio | | 467 | ReLU-Speech (Google, 2013) | \~1 × 10⁸ | | 2013 | makadirio | | 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | usanifu haujatajwa | | 469 | Support Vector Machines (AT&T, 1995) | \~1 × 10⁸ | | 1995 | makadirio | | 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | mnene | | 471 | MSA Transformer (Facebook AI Research, 2021) | \~1 × 10⁸ | | 2021 | makadirio | | 472 | YOLOX-X (Megvii Inc, 2021) | \~9.9 × 10⁷ | | 2021 | makadirio | | 473 | DeLighT (University of Washington, 2020) | \~9.9 × 10⁷ | | 2020 | makadirio | | 474 | ELMo (University of Washington, 2018) | \~9.4 × 10⁷ | | 2018 | makadirio | | 475 | AlphaFold 2 (DeepMind, 2020) | \~9.3 × 10⁷ | | 2020 | makadirio | | 476 | AlphaMissense (Google DeepMind, 2023) | \~9.3 × 10⁷ | | 2023 | makadirio | | 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | usanifu haujatajwa | | 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | \~9.2 × 10⁷ | | 2016 | makadirio | | 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | usanifu haujatajwa | | 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | mnene | | 481 | NASNet-A (Google Brain, 2017) | \~8.9 × 10⁷ | | 2017 | makadirio | | 482 | CLIP (OpenAI, 2021) | \~8.9 × 10⁷ | | 2021 | makadirio | | 483 | Detic (Meta AI, 2022) | \~8.8 × 10⁷ | | 2022 | makadirio | | 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | mnene | | 485 | BigBiGAN (Google, 2019) | \~8.6 × 10⁷ | | 2019 | makadirio | | 486 | ViT-Base/32 (Google Brain, 2020) | \~8.6 × 10⁷ | | 2020 | makadirio | | 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | usanifu haujatajwa | | 488 | CaLM (University of Oxford, 2022) | \~8.6 × 10⁷ | | 2022 | makadirio | | 489 | Tensor-Transformer (University of California (UC) Berkeley, 2020) | 8.5 × 10⁷ | 8.5 × 10⁷ | 2020 | mnene | | 490 | ResNeXt-101 (University of California San Diego, 2016) | 8.3 × 10⁷ | 8.3 × 10⁷ | 2016 | mnene | | 491 | RBM Image Classifier (University of Toronto, 2009) | \~8 × 10⁷ | | 2009 | makadirio | | 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | mnene | | 493 | Big-Little Net (IBM, 2018) | \~7.7 × 10⁷ | | 2018 | makadirio | | 494 | EfficientDet (Google Brain, 2020) | \~7.7 × 10⁷ | | 2020 | makadirio | | 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | usanifu haujatajwa | | 496 | RNN LM (Johns Hopkins University, 2010) | \~7 × 10⁷ | | 2010 | makadirio | | 497 | R-CNN (University of California (UC) Berkeley, 2013) | \~6.9 × 10⁷ | | 2013 | makadirio | | 498 | Variational (University of Cambridge, 2015) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2015 | mnene | | 499 | DistilBERT (Hugging Face, 2019) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2019 | mnene | | 500 | Theseus 6/768 (University of California San Diego, 2020) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2020 | mnene | | 501 | ResNet-152 (Microsoft, 2015) | 6 × 10⁷ | 6 × 10⁷ | 2015 | mnene | | 502 | AlexNet (University of Toronto, 2012) | 6 × 10⁷ | 6 × 10⁷ | 2012 | mnene | | 503 | DETR (Facebook, 2020) | 6 × 10⁷ | 6 × 10⁷ | 2020 | mnene | | 504 | YOLOv3 (University of Washington, 2018) | \~5.7 × 10⁷ | | 2018 | makadirio | | 505 | Inception-ResNet-V2 (Google, 2016) | \~5.6 × 10⁷ | | 2016 | makadirio | | 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | mnene | | 507 | Ubongo wa nzi wa matunda | 5.5 × 10⁷ | 5.5 × 10⁷ | | sinapsi | | 508 | NAS with base 8 and shared embeddings (Google Brain, 2016) | 5.4 × 10⁷ | 5.4 × 10⁷ | 2016 | mnene | | 509 | RetinaNet-R101 (Facebook AI Research, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | mnene | | 510 | AWD-LSTM+WT+Cache+IOG (NTT Communication Science Laboratories, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | mnene | | 511 | Hierarchical Scene Labeling (New York University (NYU), 2013) | 5.2 × 10⁷ | 5.2 × 10⁷ | 2013 | mnene | | 512 | VD-LSTM+REAL Large (Salesforce Research, 2016) | 5.1 × 10⁷ | 5.1 × 10⁷ | 2016 | mnene | | 513 | YOLOv2 (University of Washington, 2016) | \~5.1 × 10⁷ | | 2016 | makadirio | | 514 | Dropout-LSTM+Noise (Columbia University, 2018) | \~5.1 × 10⁷ | | 2018 | makadirio | | 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | \~5 × 10⁷ | | 2017 | makadirio | | 516 | Dropout (University of Toronto, 2012) | \~4.9 × 10⁷ | | 2012 | makadirio | | 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | mnene | | 518 | DistBelief Speech (Google, 2012) | \~4.7 × 10⁷ | | 2012 | makadirio | | 519 | AlphaGo Zero (DeepMind, 2017) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2017 | mnene | | 520 | Fine-tuned-AWD-LSTM-DOC (Samsung R&D Institute Russia, 2018) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2018 | mnene | | 521 | CT-MoS (Google, 2020) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2020 | mnene | | 522 | JFT (Google Research, 2017) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2017 | mnene | | 523 | ResNet-101 (Microsoft, 2015) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2015 | mnene | | 524 | HR-ResNet101 (Carnegie Mellon University (CMU), 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | mnene | | 525 | 3DMM-CNN (University of Southern California, 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | mnene | | 526 | LaNet-L (Brown University, 2019) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2019 | mnene | | 527 | ProBERTa (University of Illinois Urbana-Champaign (UIUC), 2020) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2020 | mnene | | 528 | PreTrans-3L-250H (University of Toronto, 2013) | \~4.3 × 10⁷ | | 2013 | makadirio | | 529 | Inceptionv4 (Google, 2016) | \~4.3 × 10⁷ | | 2016 | makadirio | | 530 | GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (Ben-Gurion University of the Negev, 2017) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2017 | mnene | | 531 | U-Net (University of Freiburg, 2015) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2015 | mnene | | 532 | NASv3 (Google Brain, 2016) | \~3.7 × 10⁷ | | 2016 | makadirio | | 533 | MuZero (DeepMind, 2019) | \~3.7 × 10⁷ | | 2019 | makadirio | | 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | \~3.5 × 10⁷ | | 2017 | makadirio | | 535 | AWD-LSTM-MoS+PDR + dynamic evaluation (IBM, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | mnene | | 536 | AWD-LSTM-MoS + dynamic evaluation (Peking University, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | mnene | | 537 | AWD-LSTM + MoS + Partial Shuffled (University of Texas at Austin, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | mnene | | 538 | Mogrifier (d2, MoS2, MC) + dynamic eval (DeepMind, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | mnene | | 539 | Mogrifier RLSTM (DeepMind, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | usanifu haujatajwa | | 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | usanifu haujatajwa | | 541 | DenseNet-264 (Tsinghua University, 2016) | 3.4 × 10⁷ | 3.4 × 10⁷ | 2016 | mnene | | 542 | RetinaNet-R50 (Facebook AI Research, 2017) | \~3.4 × 10⁷ | | 2017 | makadirio | | 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | \~3.4 × 10⁷ | | 2017 | makadirio | | 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | \~3.4 × 10⁷ | | 2019 | makadirio | | 545 | AWD-LSTM - 3-layer LSTM (Salesforce Research, 2017) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2017 | mnene | | 546 | TCAN (Nanjing University, 2020) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2020 | mnene | | 547 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | \~3.2 × 10⁷ | | 2019 | makadirio | | 548 | Paragraph Vector (Google, 2014) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2014 | mnene | | 549 | aLSTM (University of Manchester, 2018) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2018 | mnene | | 550 | StyleGAN2 (Nvidia, 2019) | 3 × 10⁷ | 3 × 10⁷ | 2019 | mnene | | 551 | UDSMProt (Fraunhofer Heinrich Hertz Institute, 2019) | \~2.8 × 10⁷ | | 2019 | makadirio | | 552 | SENet (Chinese Academy of Sciences, 2017) | \~2.8 × 10⁷ | | 2017 | makadirio | | 553 | Perceiver IO (DeepMind, 2020) | 2.8 × 10⁷ | 2.8 × 10⁷ | 2020 | mnene | | 554 | Fractional Max-Pooling (University of Warwick, 2014) | \~2.7 × 10⁷ | | 2014 | makadirio | | 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | usanifu haujatajwa | | 556 | RNN-WER (DeepMind, 2014) | \~2.6 × 10⁷ | | 2014 | makadirio | | 557 | StyleGAN (Nvidia, 2018) | 2.6 × 10⁷ | 2.6 × 10⁷ | 2018 | mnene | | 558 | PyramidNet (Korea Advanced Institute of Science and Technology (KAIST), 2017) | \~2.6 × 10⁷ | | 2017 | makadirio | | 559 | ResNeXt-50 (University of California San Diego, 2016) | \~2.5 × 10⁷ | | 2016 | makadirio | | 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | mnene | | 561 | Inception v3 (Google, 2015) | \~2.4 × 10⁷ | | 2015 | makadirio | | 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | usanifu haujatajwa | | 563 | WeNet (Amazon, 2019) | \~2.3 × 10⁷ | | 2019 | makadirio | | 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | \~2.3 × 10⁷ | | 2019 | makadirio | | 565 | NAS+ESS (Northeastern University (China), 2020) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2020 | mnene | | 566 | Xception (Google, 2016) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2016 | mnene | | 567 | MetaMimic (Google, 2018) | \~2.2 × 10⁷ | | 2018 | makadirio | | 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | \~2.1 × 10⁷ | | 2019 | makadirio | | 569 | Pointer Sentinel-LSTM (MetaMind Inc, 2016) | 2.1 × 10⁷ | 2.1 × 10⁷ | 2016 | mnene | | 570 | DiffDock (Massachusetts Institute of Technology (MIT), 2022) | \~2 × 10⁷ | | 2022 | makadirio | | 571 | EI-REHN-1000D (Korea Advanced Institute of Science and Technology (KAIST), 2017) | 1.9 × 10⁷ | 1.9 × 10⁷ | 2017 | mnene | | 572 | TC-DNN-BLSTM-DNN (Carnegie Mellon University (CMU), 2015) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2015 | mnene | | 573 | OR-WideResNet (Duke University, 2017) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2017 | mnene | | 574 | BP-DBN (University of Toronto, 2009) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2009 | mnene | | 575 | ALBERT (Toyota Technological Institute at Chicago, 2019) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2019 | mnene | | 576 | MedBERT (Peng Cheng Laboratory, 2021) | \~1.7 × 10⁷ | | 2021 | makadirio | | 577 | LCNP NORB (, 2009) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2009 | mnene | | 578 | LF-MMI (Johns Hopkins University, 2016) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2016 | mnene | | 579 | AlphaFold (DeepMind, 2020) | \~1.6 × 10⁷ | | 2020 | makadirio | | 580 | GNoME for crystal discovery (Google DeepMind, 2023) | \~1.6 × 10⁷ | | 2023 | makadirio | | 581 | ReLU (University of Toronto, 2010) | \~1.6 × 10⁷ | | 2010 | makadirio | | 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | usanifu haujatajwa | | 583 | Eve (Harvard Medical School, 2021) | \~1.5 × 10⁷ | | 2021 | makadirio | | 584 | LCNP LabelMe (University of Bonn, 2009) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2009 | mnene | | 585 | BatchNorm (Google, 2015) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2015 | mnene | | 586 | Fold2Seq (IBM, 2021) | \~1.2 × 10⁷ | | 2021 | makadirio | | 587 | NPLM (University of Montreal / Université de Montréal, 2003) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2003 | mnene | | 588 | LCNP MNIST (, 2009) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2009 | mnene | | 589 | ISS (Duke University, 2017) | 1.1 × 10⁷ | 1.1 × 10⁷ | 2017 | mnene | | 590 | ResNet-1001 (Microsoft, 2016) | \~1 × 10⁷ | | 2016 | makadirio | | 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | \~9 × 10⁶ | | 2012 | makadirio | | 592 | Gated HORNN (York University, 2016) | \~9 × 10⁶ | | 2016 | makadirio | | 593 | AlphaGo Fan (DeepMind, 2015) | \~8.2 × 10⁶ | | 2015 | makadirio | | 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | \~8 × 10⁶ | | 2015 | makadirio | | 595 | Once for All (MIT-IBM Watson AI Lab, 2020) | 7.7 × 10⁶ | 7.7 × 10⁶ | 2020 | mnene | | 596 | Multi-cell LSTM (University of Hyderabad, 2018) | \~7.2 × 10⁶ | | 2018 | makadirio | | 597 | Feedforward NN (University of Montreal / Université de Montréal, 2010) | 7.1 × 10⁶ | 7.1 × 10⁶ | 2010 | mnene | | 598 | True-Regularization+Finetune+Dynamic-Eval (Mobvoi, 2019) | 7 × 10⁶ | 7 × 10⁶ | 2019 | mnene | | 599 | Neural LM (University of Montreal / Université de Montréal, 2000) | 6.9 × 10⁶ | 6.9 × 10⁶ | 2000 | mnene | | 600 | GoogLeNet / InceptionV1 (Google, 2014) | 6.8 × 10⁶ | 6.8 × 10⁶ | 2014 | mnene | | 601 | HyenaDNA (Stanford University, 2023) | 6.6 × 10⁶ | 6.6 × 10⁶ | 2023 | usanifu haujatajwa | | 602 | Dropout (University of Toronto, 2012) | 5.6 × 10⁶ | 5.6 × 10⁶ | 2012 | mnene | | 603 | SimpleNet (Sensifai, 2016) | 5.5 × 10⁶ | 5.5 × 10⁶ | 2016 | mnene | | 604 | AlphaX-1 (Facebook AI Research, 2019) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2019 | mnene | | 605 | 3DDFA (Chinese Academy of Sciences, 2015) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2015 | mnene | | 606 | Deep CNN + COTS (IEEE, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | mnene | | 607 | DCNN (University of Maryland, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | mnene | | 608 | NLP from scratch (NEC Laboratories, 2011) | \~5 × 10⁶ | | 2011 | makadirio | | 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | \~5 × 10⁶ | | 2014 | makadirio | | 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | usanifu haujatajwa | | 611 | High Performance CNN (IDSIA, 2011) | \~4.9 × 10⁶ | | 2011 | makadirio | | 612 | MobileNet (Google, 2017) | \~4.2 × 10⁶ | | 2017 | makadirio | | 613 | NPLM (University of Montreal / Université de Montréal, 2003) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2003 | mnene | | 614 | LMICA (, 2004) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2004 | mnene | | 615 | PoE MNIST (University College London (UCL), 2000) | 3.9 × 10⁶ | 3.9 × 10⁶ | 2000 | mnene | | 616 | Dimensionality Reduction (University of Toronto, 2006) | \~3.8 × 10⁶ | | 2006 | makadirio | | 617 | GOAT (DeepMind, 2021) | \~3.5 × 10⁶ | | 2021 | makadirio | | 618 | MobileNetV2 (Google, 2018) | \~3.4 × 10⁶ | | 2018 | makadirio | | 619 | Big-Little Net (IBM, 2018) | \~3.3 × 10⁶ | | 2018 | makadirio | | 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | usanifu haujatajwa | | 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | mnene | | 622 | DeepStack (University of Alberta, 2017) | \~2.5 × 10⁶ | | 2017 | makadirio | | 623 | KataGo (Jane Street, 2019) | \~2.5 × 10⁶ | | 2019 | makadirio | | 624 | ShuffleNet v1 (Megvii Inc, 2017) | \~2.4 × 10⁶ | | 2017 | makadirio | | 625 | ADAM (University of Amsterdam, 2014) | 2.4 × 10⁶ | 2.4 × 10⁶ | 2014 | mnene | | 626 | StyleGAN3-T (Nvidia, 2021) | 2.2 × 10⁶ | 2.2 × 10⁶ | 2021 | usanifu haujatajwa | | 627 | LSTM+NeuralCache (KU Leuven, 2018) | \~2.1 × 10⁶ | | 2018 | makadirio | | 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | mnene | | 629 | ResNet-110 (Microsoft, 2015) | \~1.7 × 10⁶ | | 2015 | makadirio | | 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | mnene | | 631 | DQN-2015 (Google, 2015) | \~1.7 × 10⁶ | | 2015 | makadirio | | 632 | Rational DQN Average (TU Darmstadt, 2021) | \~1.7 × 10⁶ | | 2021 | makadirio | | 633 | IBM-5 (IBM, 1993) | \~1.7 × 10⁶ | | 1993 | makadirio | | 634 | Deep Belief Nets (University of Toronto, 2006) | \~1.6 × 10⁶ | | 2006 | makadirio | | 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | mnene | | 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | usanifu haujatajwa | | 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | \~1.5 × 10⁶ | | 2008 | makadirio | | 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | mnene | | 639 | CNN committee (IDSIA, 2011) | \~1.4 × 10⁶ | | 2011 | makadirio | | 640 | SqueezeNet (DeepScale, 2016) | \~1.2 × 10⁶ | | 2016 | makadirio | | 641 | Neocognitron (NHK Broadcasting Science Research Laboratories, 1980) | 1.1 × 10⁶ | 1.1 × 10⁶ | 1980 | mnene | | 642 | CURL (University of California (UC) Berkeley, 2020) | \~9.1 × 10⁵ | | 2020 | makadirio | | 643 | DQN (DeepMind, 2013) | 8.4 × 10⁵ | 8.4 × 10⁵ | 2013 | mnene | | 644 | EnhanceNet (Max Planck Institute for Intelligent Systems, 2016) | 8.1 × 10⁵ | 8.1 × 10⁵ | 2016 | mnene | | 645 | Image generation (University of Amsterdam, 2013) | 7.8 × 10⁵ | 7.8 × 10⁵ | 2013 | mnene | | 646 | Hanabi 4 player (DeepMind, 2019) | 7.6 × 10⁵ | 7.6 × 10⁵ | 2019 | mnene | | 647 | TA-CNN (Chinese University of Hong Kong (CUHK), 2014) | \~7.1 × 10⁵ | | 2014 | makadirio | | 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | usanifu haujatajwa | | 649 | LISSOM (University of Texas at Austin, 1995) | \~4.3 × 10⁵ | | 1995 | makadirio | | 650 | SPIDER2 (Griffith University, 2016) | \~4.1 × 10⁵ | | 2016 | makadirio | | 651 | Mixture of linear models (, 1994) | \~3.8 × 10⁵ | | 1994 | makadirio | | 652 | NPD (IEEE, 2014) | 3.1 × 10⁵ | 3.1 × 10⁵ | 2014 | mnene | | 653 | Pooling CNN (University of Bonn, 2010) | 2.9 × 10⁵ | 2.9 × 10⁵ | 2010 | mnene | | 654 | Pooling CNN (University of Bonn, 2010) | 2.7 × 10⁵ | 2.7 × 10⁵ | 2010 | mnene | | 655 | Two Stage Feature Extraction (New York University (NYU), 2009) | 2.6 × 10⁵ | 2.6 × 10⁵ | 2009 | mnene | | 656 | Vector Space Model (Stanford University, 2011) | 2.5 × 10⁵ | 2.5 × 10⁵ | 2011 | mnene | | 657 | HyperNEAT (University of Texas at Austin, 2014) | \~2.4 × 10⁵ | | 2014 | makadirio | | 658 | DNN EM segmentation (IDSIA, 2012) | 2.2 × 10⁵ | 2.2 × 10⁵ | 2012 | mnene | | 659 | Predictive Coding NN (Technical University of Munich, 1994) | 2.1 × 10⁵ | 2.1 × 10⁵ | 1994 | mnene | | 660 | Table Tennis Agent (Google DeepMind, 2024) | \~1.9 × 10⁵ | | 2024 | makadirio | | 661 | CNN Committee (IDSIA, 2011) | \~1.3 × 10⁵ | | 2011 | makadirio | | 662 | CNN Committee (IDSIA, 2011) | \~1.2 × 10⁵ | | 2011 | makadirio | | 663 | RECONTRA-uncategorized (, 1999) | \~1.1 × 10⁵ | | 1999 | makadirio | | 664 | BLSTM for handwriting (University of Bern, 2007) | \~1 × 10⁵ | | 2007 | makadirio | | 665 | SVM-CNN (New York University (NYU), 2006) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2006 | mnene | | 666 | Invariant CNN (New York University (NYU), 2004) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2004 | mnene | | 667 | SexNet compression (, 1990) | 7.3 × 10⁴ | 7.3 × 10⁴ | 1990 | mnene | | 668 | NeuroChess (, 1994) | \~7.2 × 10⁴ | | 1994 | makadirio | | 669 | RECONTRA-categorized (, 1999) | \~6.7 × 10⁴ | | 1999 | makadirio | | 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | mnene | | 671 | Swift (Intel Labs, 2023) | \~5.7 × 10⁴ | | 2023 | makadirio | | 672 | TCN (Carnegie Mellon University (CMU), 2018) | 4.2 × 10⁴ | 4.2 × 10⁴ | 2018 | mnene | | 673 | TRPO (University of California (UC) Berkeley, 2015) | 3.4 × 10⁴ | 3.4 × 10⁴ | 2015 | mnene | | 674 | SOM-CNN (, 1997) | 3.2 × 10⁴ | 3.2 × 10⁴ | 1997 | mnene | | 675 | NETtalk reimplementation (Oregon State University, 1990) | 2.7 × 10⁴ | 2.7 × 10⁴ | 1990 | mnene | | 676 | TD-Gammon (IBM, 1992) | \~2.5 × 10⁴ | | 1992 | makadirio | | 677 | Cognitron (Biological Cybernetics, 1975) | 2.2 × 10⁴ | 2.2 × 10⁴ | 1975 | mnene | | 678 | NetTalk (Princeton University, 1987) | 1.9 × 10⁴ | 1.9 × 10⁴ | 1987 | mnene | | 679 | CFSS (SenseTime, 2015) | \~1.7 × 10⁴ | | 2015 | makadirio | | 680 | MUSIC perceptron (, 1996) | 1.4 × 10⁴ | 1.4 × 10⁴ | 1996 | mnene | | 681 | AdaRNN (Beihang University, 2014) | 1.3 × 10⁴ | 1.3 × 10⁴ | 2014 | mnene | | 682 | Bidirectional RNN (Advanced Telecommunications Research Institute, 1997) | \~1.3 × 10⁴ | | 1997 | makadirio | | 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | \~1.2 × 10⁴ | | 2001 | makadirio | | 684 | LSTM (Technical University of Munich, 1997) | 1.1 × 10⁴ | 1.1 × 10⁴ | 1997 | mnene | | 685 | MLN-ASR (McGill University, 1988) | 1 × 10⁴ | 1 × 10⁴ | 1988 | mnene | | 686 | Hopfield network (California Institute of Technology, 1982) | \~9.9 × 10³ | | 1982 | makadirio | | 687 | Zip CNN (AT&T, 1989) | 9.8 × 10³ | 9.8 × 10³ | 1989 | mnene | | 688 | Hierarchical Cognitron (NHK Broadcasting Science Research Laboratories, 1984) | \~9.3 × 10³ | | 1984 | makadirio | | 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | mnene | | 690 | Deep Blue (IBM, 1997) | \~8 × 10³ | | 1997 | makadirio | | 691 | Nematodi (C. elegans) | 7 × 10³ | 7 × 10³ | | sinapsi | | 692 | System 11 (Carnegie Mellon University (CMU), 1996) | 6.5 × 10³ | 6.5 × 10³ | 1996 | mnene | | 693 | ACF-WIDER (Chinese Academy of Sciences, 2014) | 6.1 × 10³ | 6.1 × 10³ | 2014 | mnene | | 694 | RankNet (Microsoft Research, 2005) | 5.7 × 10³ | 5.7 × 10³ | 2005 | mnene | | 695 | ANN Eye Tracker (, 1993) | 5.6 × 10³ | 5.6 × 10³ | 1993 | mnene | | 696 | JPMAX (, 1994) | \~4.4 × 10³ | | 1994 | makadirio | | 697 | Kohonen network (Helsinki University of Technology, 1981) | \~4.1 × 10³ | | 1981 | makadirio | | 698 | Speaker-independent vowel classification (University of Washington, 1989) | 3 × 10³ | 3 × 10³ | 1989 | mnene | | 699 | Pattern recognition and reading by machine (Sandia Corporation, 1959) | \~2.6 × 10³ | | 1959 | makadirio | | 700 | Handwritten digit recognition network (AT&T, 1989) | 2.6 × 10³ | 2.6 × 10³ | 1989 | mnene | | 701 | Boosting (Bell Laboratories, 1992) | \~2.6 × 10³ | | 1992 | makadirio | | 702 | Decision tree adaline (Tokyo Medical and Dental University, 1969) | 2.5 × 10³ | 2.5 × 10³ | 1969 | mnene | | 703 | LTE speaker verification system (IBM, 1966) | \~2.1 × 10³ | | 1966 | makadirio | | 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | \~1.9 × 10³ | | 1994 | makadirio | | 705 | SexNet classification (, 1990) | \~1.6 × 10³ | | 1990 | makadirio | | 706 | RAAM (, 1990) | 1.5 × 10³ | 1.5 × 10³ | 1990 | mnene | | 707 | Super-vector coding (University of Illinois Urbana-Champaign (UIUC), 2010) | \~1 × 10³ | | 2010 | makadirio | | 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | \~1 × 10³ | | 1957 | makadirio | | 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | \~1 × 10³ | | 1960 | makadirio | | 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | mchanganyiko wa wataalamu (idadi hai haijachapishwa) | | 711 | Truck backer-upper (Stanford University, 1989) | 8.1 × 10² | 8.1 × 10² | 1989 | mnene | | 712 | Siamese-TDNN (Bell Laboratories, 1993) | \~7.4 × 10² | | 1993 | makadirio | | 713 | MLP with back-propagation (University of California San Diego, 1986) | 7.2 × 10² | 7.2 × 10² | 1986 | mnene | | 714 | Cancer drug mechanism prediction (National Cancer Institute, 1992) | \~5.9 × 10² | | 1992 | makadirio | | 715 | Distributed representation NN (Carnegie Mellon University (CMU), 1986) | 4.3 × 10² | 4.3 × 10² | 1986 | mnene | | 716 | Piecewise linear model (University of Kansas, 1973) | 3.6 × 10² | 3.6 × 10² | 1973 | mnene | | 717 | ASE+ACE (University of Massachusetts Amherst, 1983) | \~3.2 × 10² | | 1983 | makadirio | | 718 | LSTM with forget gates (IDSIA, 1999) | \~2.8 × 10² | | 1999 | makadirio | | 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | \~2.3 × 10² | | 1955 | makadirio | | 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | mnene | | 721 | SNARC (Harvard University, 1952) | \~4 × 10¹ | | 1952 | makadirio | | 722 | Bankruptcy-NN (, 1990) | 3.6 × 10¹ | 3.6 × 10¹ | 1990 | mnene | | 723 | GNN (University of Siena, 2008) | 3 × 10¹ | 3 × 10¹ | 2008 | mnene | | 724 | ADALINE (Stanford University, 1960) | 1.7 × 10¹ | 1.7 × 10¹ | 1960 | mnene | | 725 | Samuel Neural Checkers (IBM, 1959) | \~1.6 × 10¹ | | 1959 | makadirio | ## Makundi ya pamoja Chati hizi zinapanga jamii za wanyama, dini, mataifa, vyama na mashirika kwa njia mbili. Moja ni kwa wanachama. Nyingine ni kwa niuroni sawia, ambazo zinampa kila mwanachama uzito kulingana na sehemu ya muda wake wa kuamka ambao maagizo ya kundi yanauelekeza. Kanuni za kidini, sheria, kanuni za vyama na maelezo ya kazi vyote vinahesabiwa vivyo hivyo. Vyote ni seti za maagizo, kwa hiyo tunavipima kwa kanuni ileile. [Jinsi muda unaoelekezwa unavyokadiriwa](https://simulation-hypothesis.com/sw/entities/control). ## Jamii za wanyama: niuroni katika shughuli ya kikundi iliyoratibiwa Niuroni katika kundi Katika shughuli ya kikundi Wanachama wa kundi la kawaida × niuroni katika gamba au pallium ya kila mwanachama (ubongo mzima kwa wadudu), kwa kipimo cha logarithmiki. Nguzo zenye michirizi hutumia ukubwa wa kundi uliokadiriwa au idadi ya niuroni ya spishi jamaa. 1. Jamii ya sokwe mtu▲1 7.4 × 10¹¹ 2. Koloni la chungu wakata-majani▼1 6.3 × 10¹¹ 3. Jamii ya bonobo▲2 4.4 × 10¹¹ 4. Kambi ya Wahadza (binadamu, kwa kulinganisha ukubwa) 4.1 × 10¹¹ 5. Kundi la orca wa Bigg▼2 2.6 × 10¹¹ 6. Ukoo wa kike wa orca wakazi 2.4 × 10¹¹ 7. Kundi la nyani wa savanna 8.6 × 10¹⁰ 8. Muungano wa pomboo wenye pua ya chupa▲1 7.6 × 10¹⁰ 9. Kitengo cha kijamii cha nyangumi mbegu▲1 6.4 × 10¹⁰ 10. Koloni la nyuki wa asali▼2 5.8 × 10¹⁰ 11. Familia ya tembo 5.6 × 10¹⁰ 12. Kundi la kunguru wa pinyon 5.3 × 10¹⁰ 13. Ukoo wa fisi madoa 2.2 × 10¹⁰ 14. Kundi la mbwa mwitu wa Afrika 7.2 × 10⁹ 15. Kundi la mbwa mwitu 4.2 × 10⁹ 16. Kundi la suricati 1.7 × 10⁹ 17. Koloni la panya-fuko wasio na manyoya 4.1 × 10⁸ 10^8 10^9 10^10 10^11 10^12 neurons Onyesha nambari | # | Kundi | Niuroni katika kundi | Katika shughuli ya kikundi | Wanachama | Sehemu inayoelekezwa | Aina za miito | | -- | ---------------------------------------------------- | -------------------- | -------------------------- | ----------- | -------------------- | ------------- | | 1 | Jamii ya sokwe mtu | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — | | 2 | Koloni la chungu wakata-majani | 6.3 × 10¹¹ | 5 × 10¹¹ | milioni 2.5 | 80% | — | | 3 | Jamii ya bonobo | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — | | 4 | Kambi ya Wahadza (binadamu, kwa kulinganisha ukubwa) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — | | 5 | Kundi la orca wa Bigg | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — | | 6 | Ukoo wa kike wa orca wakazi | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 | | 7 | Kundi la nyani wa savanna | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 | | 8 | Muungano wa pomboo wenye pua ya chupa | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — | | 9 | Kitengo cha kijamii cha nyangumi mbegu | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — | | 10 | Koloni la nyuki wa asali | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — | | 11 | Familia ya tembo | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — | | 12 | Kundi la kunguru wa pinyon | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — | | 13 | Ukoo wa fisi madoa | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 | | 14 | Kundi la mbwa mwitu wa Afrika | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 | | 15 | Kundi la mbwa mwitu | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — | | 16 | Kundi la suricati | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 | | 17 | Koloni la panya-fuko wasio na manyoya | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — | [Jinsi makundi yanavyopewa alama](https://simulation-hypothesis.com/sw/entities/control). ## Dini: waumini, na niuroni zinazofuata maagizo yao Wanachama Kufuata maagizo yake Dini nzima Madhehebu na harakati Wafuasi, kwa kipimo cha logarithmiki. Madhehebu na harakati zinaonyeshwa tofauti na dini zinazozijumuisha. Nguzo imara ni hesabu; nguzo zenye michirizi ni makadirio. 1. Ukristo▲1 bilioni 2.27 2. Uislamu▼1 bilioni 2.02 3. Uhindu bilioni 1.18 4. Dini za kienyeji milioni 400 5. Ubuddha milioni 324.19 6. Dini za jadi za Kiafrika▲1 milioni 98.73 7. Shinto▲1 milioni 87.92 8. Usikhi▲1 milioni 25 9. Uyahudi▼3 milioni 14.78 10. Utao▲1 milioni 8 11. Imani ya Baháʼí▲1 milioni 8 12. Falun Gong▲1 milioni 7 13. Ujaini▼3 milioni 4.45 14. Uspiritisti milioni 3.8 15. Cao Đài milioni 2.5 16. Tenrikyo milioni 1.12 17. Brahma Kumaris milioni 1 10^5 10^6 10^7 10^8 10^9 10^10 wanachama Onyesha nambari | # | Dini | Wanachama | Kufuata maagizo yake | Sehemu inayoelekezwa | Saa kwa wiki | | -- | ------------------------ | -------------- | -------------------- | -------------------- | ------------ | | 1 | Ukristo | bilioni 2.27 | 1.7 × 10¹⁸ | 0.88% | 0.9 h | | 2 | Uislamu | bilioni 2.02 | 8.9 × 10¹⁸ | 5.1% | 5.4 h | | 3 | Uhindu | bilioni 1.18 | 1.6 × 10¹⁸ | 1.6% | 1.6 h | | 4 | Dini za kienyeji | milioni 400 | 2 × 10¹⁷ | 0.59% (mbadala) | 0.6 h | | 5 | Ubuddha | milioni 324.19 | 7.5 × 10¹⁶ | 0.27% | 0.3 h | | 6 | Dini za jadi za Kiafrika | milioni 98.73 | 5 × 10¹⁶ | 0.59% (mbadala) | 0.6 h | | 7 | Shinto | milioni 87.92 | 4.5 × 10¹⁶ | 0.59% (mbadala) | 0.6 h | | 8 | Usikhi | milioni 25 | 3.7 × 10¹⁶ | 1.7% | 1.8 h | | 9 | Uyahudi | milioni 14.78 | 5.3 × 10¹⁶ | 4.2% | 4.4 h | | 10 | Utao | milioni 8 | 4.1 × 10¹⁵ | 0.59% (mbadala) | 0.6 h | | 11 | Imani ya Baháʼí | milioni 8 | 4.1 × 10¹⁵ | 0.59% (mbadala) | 0.6 h | | 12 | Falun Gong | milioni 7 | 3.6 × 10¹⁵ | 0.59% (mbadala) | 0.6 h | | 13 | Ujaini | milioni 4.45 | 1.8 × 10¹⁶ | 4.6% | 4.8 h | | 14 | Uspiritisti | milioni 3.8 | 1.9 × 10¹⁵ | 0.59% (mbadala) | 0.6 h | | 15 | Cao Đài | milioni 2.5 | 1.3 × 10¹⁵ | 0.59% (mbadala) | 0.6 h | | 16 | Tenrikyo | milioni 1.12 | 5.7 × 10¹⁴ | 0.59% (mbadala) | 0.6 h | | 17 | Brahma Kumaris | milioni 1 | 5.1 × 10¹⁴ | 0.59% (mbadala) | 0.6 h | [Kila desturi, sehemu na chanzo](https://simulation-hypothesis.com/sw/entities/control). ## Mataifa: wakazi, na niuroni zinazofuata maagizo yake Wakazi Kufuata maagizo yake Vyote Afrika Kusini mwa Jangwa la Sahara Amerika Kaskazini Amerika ya Kusini na Karibea Asia Kusini Asia Mashariki na Pasifiki Mashariki ya Kati, Afrika Kaskazini, Afghanistan na Pakistan Ulaya na Asia ya Kati Idadi ya watu, kwa kipimo cha logarithmiki. Nguzo imara ni hesabu; nguzo zenye michirizi ni makadirio. 1. India▲1 bilioni 1.46 2. China▼1 bilioni 1.41 3. Marekani milioni 341.78 4. Indonesia milioni 285.72 5. Pakistan▲5 milioni 255.22 6. Nigeria▲1 milioni 237.53 7. Brazili▼2 milioni 212.81 8. Bangladesh▲5 milioni 175.69 9. Urusi▼3 milioni 143.51 10. Ethiopia▲7 milioni 135.47 11. Meksiko▼2 milioni 131.95 12. Japani▲11 milioni 123.37 13. Misri▼2 milioni 118.37 14. Ufilipino milioni 116.79 15. DR Kongo▼3 milioni 112.83 Onyesha zote 196 10^4 10^6 10^8 10^10 wakazi Onyesha nambari | # | Taifa | Wakazi | Kufuata maagizo yake | Sehemu inayoelekezwa | Saa kwa wiki | | --- | --------------------------- | -------------- | -------------------- | -------------------- | ------------ | | 1 | India | bilioni 1.46 | 2.5 × 10¹⁸ | 2% | 2.1 h | | 2 | China | bilioni 1.41 | 5 × 10¹⁸ | 4.1% | 4.3 h | | 3 | Marekani | milioni 341.78 | 1.6 × 10¹⁸ | 5.6% | 5.8 h | | 4 | Indonesia | milioni 285.72 | 1 × 10¹⁸ | 4.2% | 4.4 h | | 5 | Pakistan | milioni 255.22 | 5.7 × 10¹⁷ | 2.6% | 2.7 h | | 6 | Nigeria | milioni 237.53 | 6.6 × 10¹⁷ | 3.2% | 3.4 h | | 7 | Brazili | milioni 212.81 | 8.7 × 10¹⁷ | 4.8% | 5 h | | 8 | Bangladesh | milioni 175.69 | 4.6 × 10¹⁷ | 3% | 3.1 h | | 9 | Urusi | milioni 143.51 | 7.5 × 10¹⁷ | 6.1% | 6.4 h | | 10 | Ethiopia | milioni 135.47 | 3.9 × 10¹⁷ | 3.4% | 3.5 h | | 11 | Meksiko | milioni 131.95 | 5.9 × 10¹⁷ | 5.2% | 5.5 h | | 12 | Japani | milioni 123.37 | 2.8 × 10¹⁷ | 2.7% | 2.8 h | | 13 | Misri | milioni 118.37 | 5.5 × 10¹⁷ | 5.4% | 5.7 h | | 14 | Ufilipino | milioni 116.79 | 4.4 × 10¹⁷ | 4.4% | 4.6 h | | 15 | DR Kongo | milioni 112.83 | 4.7 × 10¹⁷ | 4.8% | 5.1 h | | 16 | Vietnam | milioni 101.6 | 4.2 × 10¹⁷ | 4.8% | 5 h | | 17 | Iran | milioni 92.42 | 3.5 × 10¹⁷ | 4.3% | 4.6 h | | 18 | Uturuki | milioni 85.88 | 4.3 × 10¹⁷ | 5.8% | 6 h | | 19 | Ujerumani | milioni 83.49 | 2.9 × 10¹⁷ | 4% | 4.2 h | | 20 | Thailand | milioni 71.62 | 2.4 × 10¹⁷ | 3.9% | 4.1 h | | 21 | Tanzania | milioni 70.55 | 2.2 × 10¹⁷ | 3.6% | 3.8 h | | 22 | Uingereza | milioni 69.49 | 2.8 × 10¹⁷ | 4.6% | 4.8 h | | 23 | Ufaransa | milioni 68.72 | 3.1 × 10¹⁷ | 5.3% | 5.5 h | | 24 | Afrika Kusini | milioni 64.75 | 2.9 × 10¹⁷ | 5.1% | 5.4 h | | 25 | Italia | milioni 58.92 | 2.1 × 10¹⁷ | 4.2% | 4.4 h | | 26 | Kenya | milioni 57.53 | 2.3 × 10¹⁷ | 4.7% | 5 h | | 27 | Myanmar | milioni 54.85 | 1.9 × 10¹⁷ | 4% | 4.2 h | | 28 | Kolombia | milioni 53.43 | 1.7 × 10¹⁷ | 3.7% | 3.9 h | | 29 | Korea Kusini | milioni 51.68 | 1.4 × 10¹⁷ | 3% | 3.2 h | | 30 | Sudan | milioni 51.66 | 1.5 × 10¹⁷ | 3.4% | 3.6 h | | 31 | Uganda | milioni 51.38 | 1.5 × 10¹⁷ | 3.4% | 3.6 h | | 32 | Uhispania | milioni 49.36 | 1.8 × 10¹⁷ | 4.2% | 4.3 h | | 33 | Aljeria | milioni 47.44 | 3.2 × 10¹⁷ | 7.9% | 8.3 h | | 34 | Iraki | milioni 47.02 | 1.1 × 10¹⁷ | 2.8% | 2.9 h | | 35 | Ajentina | milioni 45.85 | 1.7 × 10¹⁷ | 4.4% | 4.6 h | | 36 | Afghanistan | milioni 43.84 | 1.8 × 10¹⁷ | 4.8% | 5 h | | 37 | Yemen | milioni 41.77 | 1.3 × 10¹⁷ | 3.7% | 3.9 h | | 38 | Kanada | milioni 41.65 | 2 × 10¹⁷ | 5.5% | 5.8 h | | 39 | Angola | milioni 39.04 | 1.4 × 10¹⁷ | 4.2% | 4.4 h | | 40 | Ukraine | milioni 38.98 | 2.1 × 10¹⁷ | 6.2% | 6.5 h | | 41 | Moroko | milioni 38.43 | 1.5 × 10¹⁷ | 4.5% | 4.7 h | | 42 | Uzbekistan | milioni 37.05 | 2 × 10¹⁷ | 6.3% | 6.6 h | | 43 | Saudi Arabia | milioni 36.97 | 1.4 × 10¹⁷ | 4.3% | 4.6 h | | 44 | Poland | milioni 36.44 | 2.1 × 10¹⁷ | 6.7% | 7 h | | 45 | Malaysia | milioni 35.98 | 1.8 × 10¹⁷ | 5.8% | 6.1 h | | 46 | Msumbiji | milioni 35.63 | 1.5 × 10¹⁷ | 4.8% | 5.1 h | | 47 | Ghana | milioni 35.06 | 1.2 × 10¹⁷ | 3.9% | 4.1 h | | 48 | Peru | milioni 34.58 | 1.4 × 10¹⁷ | 4.8% | 5 h | | 49 | Madagaska | milioni 32.74 | 9.4 × 10¹⁶ | 3.3% | 3.5 h | | 50 | Côte d’Ivoire | milioni 32.71 | 9.5 × 10¹⁶ | 3.4% | 3.5 h | | 51 | Kamerun | milioni 29.88 | 9.8 × 10¹⁶ | 3.8% | 4 h | | 52 | Nepal | milioni 29.62 | 1.1 × 10¹⁷ | 4.2% | 4.4 h | | 53 | Venezuela | milioni 28.52 | 1.5 × 10¹⁷ | 6.1% | 6.4 h | | 54 | Niger | milioni 27.92 | 6.4 × 10¹⁶ | 2.6% | 2.8 h | | 55 | Australia | milioni 27.61 | 1.1 × 10¹⁷ | 4.7% | 5 h | | 56 | Korea Kaskazini | milioni 26.57 | 6.6 × 10¹⁷ | 29% | 30.1 h | | 57 | Syria | milioni 25.62 | 9.8 × 10¹⁶ | 4.4% | 4.6 h | | 58 | Mali | milioni 25.2 | 4.9 × 10¹⁶ | 2.3% | 2.4 h | | 59 | Burkina Faso | milioni 24.07 | 5.7 × 10¹⁶ | 2.8% | 2.9 h | | 60 | Taiwan | milioni 23.31 | 1 × 10¹⁷ | 5% | 5.3 h | | 61 | Malawi | milioni 22.22 | 1 × 10¹⁷ | 5.4% | 5.6 h | | 62 | Zambia | milioni 21.91 | 10 × 10¹⁶ | 5.3% | 5.5 h | | 63 | Sri Lanka | milioni 21.76 | 1.1 × 10¹⁷ | 6% | 6.3 h | | 64 | Chadi | milioni 21 | 4.9 × 10¹⁶ | 2.7% | 2.9 h | | 65 | Kazakhstan | milioni 20.84 | 1.4 × 10¹⁷ | 7.6% | 7.9 h | | 66 | Chile | milioni 19.86 | 6.2 × 10¹⁶ | 3.6% | 3.8 h | | 67 | Somalia | milioni 19.65 | 1.5 × 10¹⁶ | 0.88% | 0.9 h | | 68 | Romania | milioni 19.02 | 7.5 × 10¹⁶ | 4.6% | 4.8 h | | 69 | Senegal | milioni 18.93 | 5.9 × 10¹⁶ | 3.6% | 3.8 h | | 70 | Guatemala | milioni 18.69 | 6 × 10¹⁶ | 3.7% | 3.9 h | | 71 | Ekuado | milioni 18.29 | 6.6 × 10¹⁶ | 4.2% | 4.4 h | | 72 | Uholanzi | milioni 18.09 | 7.6 × 10¹⁶ | 4.9% | 5.1 h | | 73 | Kambodia | milioni 17.85 | 7.4 × 10¹⁶ | 4.8% | 5.1 h | | 74 | Zimbabwe | milioni 16.95 | 6.1 × 10¹⁶ | 4.2% | 4.4 h | | 75 | Guinea | milioni 15.1 | 3.1 × 10¹⁶ | 2.4% | 2.5 h | | 76 | Benin | milioni 14.81 | 4.4 × 10¹⁶ | 3.4% | 3.6 h | | 77 | Rwanda | milioni 14.57 | 7 × 10¹⁶ | 5.6% | 5.8 h | | 78 | Burundi | milioni 14.39 | 5.2 × 10¹⁶ | 4.2% | 4.4 h | | 79 | Bolivia | milioni 12.58 | 5.8 × 10¹⁶ | 5.4% | 5.6 h | | 80 | Tunisia | milioni 12.35 | 6.4 × 10¹⁶ | 6% | 6.3 h | | 81 | Sudan Kusini | milioni 12.19 | 4.4 × 10¹⁶ | 4.2% | 4.4 h | | 82 | Ubelgiji | milioni 11.94 | 4.5 × 10¹⁶ | 4.3% | 4.5 h | | 83 | Haiti | milioni 11.91 | 1.9 × 10¹⁶ | 1.8% | 1.9 h | | 84 | Yordani | milioni 11.52 | 4.9 × 10¹⁶ | 5% | 5.2 h | | 85 | Jamhuri ya Dominika | milioni 11.52 | 4.9 × 10¹⁶ | 4.9% | 5.2 h | | 86 | Falme za Kiarabu | milioni 11.51 | 3.1 × 10¹⁶ | 3.1% | 3.2 h | | 87 | Honduras | milioni 11.01 | 3.2 × 10¹⁶ | 3.3% | 3.5 h | | 88 | Cuba | milioni 10.94 | 1.6 × 10¹⁷ | 17% | 17.4 h | | 89 | Chekia | milioni 10.89 | 5.8 × 10¹⁶ | 6.2% | 6.5 h | | 90 | Ureno | milioni 10.8 | 5.1 × 10¹⁶ | 5.5% | 5.7 h | | 91 | Tajikistan | milioni 10.79 | 5.3 × 10¹⁶ | 5.7% | 6 h | | 92 | Papua New Guinea | milioni 10.76 | 4.3 × 10¹⁶ | 4.6% | 4.8 h | | 93 | Uswidi | milioni 10.6 | 6.8 × 10¹⁶ | 7.5% | 7.8 h | | 94 | Ugiriki | milioni 10.41 | 4.9 × 10¹⁶ | 5.4% | 5.7 h | | 95 | Azerbaijan | milioni 10.25 | 6.2 × 10¹⁶ | 7% | 7.4 h | | 96 | Israeli | milioni 10.12 | 6.4 × 10¹⁶ | 7.3% | 7.7 h | | 97 | Hungaria | milioni 9.51 | 5.7 × 10¹⁶ | 6.9% | 7.3 h | | 98 | Austria | milioni 9.21 | 2.5 × 10¹⁶ | 3.2% | 3.4 h | | 99 | Uswisi | milioni 9.09 | 3.9 × 10¹⁶ | 5% | 5.3 h | | 100 | Belarus | milioni 9.09 | 7.9 × 10¹⁶ | 10% | 10.6 h | | 101 | Sierra Leone | milioni 8.82 | 4.9 × 10¹⁶ | 6.5% | 6.8 h | | 102 | Togo | milioni 8.59 | 3 × 10¹⁶ | 4% | 4.2 h | | 103 | Laos | milioni 7.87 | 3.2 × 10¹⁶ | 4.8% | 5 h | | 104 | Turkmenistan | milioni 7.62 | 4.2 × 10¹⁶ | 6.5% | 6.8 h | | 105 | Libya | milioni 7.46 | 3.9 × 10¹⁶ | 6.1% | 6.4 h | | 106 | Kyrgyzstan | milioni 7.34 | 4.1 × 10¹⁶ | 6.5% | 6.8 h | | 107 | Paraguay | milioni 7.01 | 2.9 × 10¹⁶ | 4.8% | 5.1 h | | 108 | Nikaragua | milioni 7.01 | 2.8 × 10¹⁶ | 4.6% | 4.8 h | | 109 | Serbia | milioni 6.55 | 4.1 × 10¹⁶ | 7.2% | 7.5 h | | 110 | Jamhuri ya Kongo | milioni 6.48 | 1.7 × 10¹⁶ | 3% | 3.1 h | | 111 | Bulgaria | milioni 6.43 | 3.3 × 10¹⁶ | 5.9% | 6.2 h | | 112 | El Salvador | milioni 6.37 | 3.2 × 10¹⁶ | 5.8% | 6.1 h | | 113 | Singapore | milioni 6.11 | 1.9 × 10¹⁶ | 3.6% | 3.7 h | | 114 | Denmark | milioni 6.01 | 3.9 × 10¹⁶ | 7.6% | 8 h | | 115 | Lebanon | milioni 5.85 | 1.5 × 10¹⁶ | 3% | 3.1 h | | 116 | Liberia | milioni 5.73 | 1.5 × 10¹⁶ | 3.1% | 3.3 h | | 117 | Ufini | milioni 5.65 | 3.2 × 10¹⁶ | 6.6% | 6.9 h | | 118 | Norwe | milioni 5.61 | 3.7 × 10¹⁶ | 7.6% | 8 h | | 119 | Jamhuri ya Afrika ya Kati | milioni 5.51 | 1.7 × 10¹⁶ | 3.5% | 3.7 h | | 120 | Omani | milioni 5.49 | 2.9 × 10¹⁶ | 6.2% | 6.5 h | | 121 | Ayalandi | milioni 5.48 | 3.4 × 10¹⁶ | 7.2% | 7.6 h | | 122 | Slovakia | milioni 5.41 | 3.1 × 10¹⁶ | 6.7% | 7 h | | 123 | Palestina | milioni 5.41 | 2 × 10¹⁶ | 4.3% | 4.5 h | | 124 | New Zealand | milioni 5.32 | 2.7 × 10¹⁶ | 5.9% | 6.2 h | | 125 | Mauritania | milioni 5.32 | 2 × 10¹⁶ | 4.3% | 4.5 h | | 126 | Kosta Rika | milioni 5.15 | 2.6 × 10¹⁶ | 5.9% | 6.2 h | | 127 | Kuwait | milioni 4.87 | 2.7 × 10¹⁶ | 6.5% | 6.8 h | | 128 | Panama | milioni 4.57 | 2.3 × 10¹⁶ | 5.9% | 6.2 h | | 129 | Georgia | milioni 3.94 | 2.1 × 10¹⁶ | 6.1% | 6.4 h | | 130 | Kroatia | milioni 3.88 | 2.2 × 10¹⁶ | 6.7% | 7 h | | 131 | Eritrea | milioni 3.61 | 1.5 × 10¹⁶ | 5% | 5.2 h | | 132 | Mongolia | milioni 3.57 | 2.2 × 10¹⁶ | 7% | 7.3 h | | 133 | Uruguay | milioni 3.38 | 1.7 × 10¹⁶ | 5.9% | 6.2 h | | 134 | Bosnia na Herzegovina | milioni 3.14 | 1.6 × 10¹⁶ | 5.9% | 6.2 h | | 135 | Namibia | milioni 3.09 | 1.8 × 10¹⁶ | 6.9% | 7.2 h | | 136 | Armenia | milioni 3.09 | 1.7 × 10¹⁶ | 6.5% | 6.8 h | | 137 | Qatar | milioni 2.97 | 9.6 × 10¹⁵ | 3.8% | 3.9 h | | 138 | Lithuania | milioni 2.89 | 1.8 × 10¹⁶ | 7.4% | 7.7 h | | 139 | Jamaica | milioni 2.84 | 1.2 × 10¹⁶ | 5% | 5.2 h | | 140 | Gambia | milioni 2.82 | 1.1 × 10¹⁶ | 4.5% | 4.7 h | | 141 | Gabon | milioni 2.59 | 9.9 × 10¹⁵ | 4.4% | 4.6 h | | 142 | Botswana | milioni 2.56 | 1.3 × 10¹⁶ | 5.7% | 6 h | | 143 | Lesotho | milioni 2.36 | 9.5 × 10¹⁵ | 4.7% | 4.9 h | | 144 | Moldova | milioni 2.36 | 1.5 × 10¹⁶ | 7.3% | 7.6 h | | 145 | Albania | milioni 2.35 | 1.2 × 10¹⁶ | 5.7% | 6 h | | 146 | Guinea-Bissau | milioni 2.25 | 5.7 × 10¹⁵ | 2.9% | 3.1 h | | 147 | Slovenia | milioni 2.13 | 1.2 × 10¹⁶ | 6.6% | 6.9 h | | 148 | Guinea ya Ikweta | milioni 1.94 | 4 × 10¹⁵ | 2.4% | 2.5 h | | 149 | Latvia | milioni 1.85 | 1.2 × 10¹⁶ | 7.6% | 7.9 h | | 150 | Makedonia Kaskazini | milioni 1.82 | 9.2 × 10¹⁵ | 5.9% | 6.2 h | | 151 | Bahareni | milioni 1.6 | 5.9 × 10¹⁵ | 4.3% | 4.5 h | | 152 | Kosovo | milioni 1.58 | 8 × 10¹⁵ | 5.9% | 6.2 h | | 153 | Timor-Leste | milioni 1.42 | 8 × 10¹⁵ | 6.6% | 6.9 h | | 154 | Kupro | milioni 1.37 | 5.5 × 10¹⁵ | 4.7% | 4.9 h | | 155 | Trinidad na Tobago | milioni 1.37 | 9.3 × 10¹⁵ | 7.9% | 8.2 h | | 156 | Estonia | milioni 1.37 | 8.3 × 10¹⁵ | 7% | 7.4 h | | 157 | Eswatini | milioni 1.26 | 7.5 × 10¹⁵ | 6.9% | 7.2 h | | 158 | Mauritius | milioni 1.24 | 5.1 × 10¹⁵ | 4.8% | 5 h | | 159 | Djibouti | milioni 1.18 | 4.6 × 10¹⁵ | 4.5% | 4.8 h | | 160 | Fiji | 933,154 | 5.6 × 10¹⁵ | 7% | 7.4 h | | 161 | Komoro | 882,847 | 3.5 × 10¹⁵ | 4.6% | 4.8 h | | 162 | Visiwa vya Solomon | 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 | Bahama | 403,033 | 2.2 × 10¹⁵ | 6.4% | 6.7 h | | 174 | Aisilandi | 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 na 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 | Shelisheli | 122,730 | 8.9 × 10¹⁴ | 8.4% | 8.8 h | | 182 | Grenada | 117,303 | 7.3 × 10¹⁴ | 7.2% | 7.5 h | | 183 | Mikronesia | 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 na Grenadines | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h | | 186 | Antigua na Barbuda | 94,209 | 6.5 × 10¹⁴ | 8% | 8.3 h | | 187 | Andorra | 82,904 | 3.9 × 10¹⁴ | 5.5% | 5.8 h | | 188 | Dominika | 65,871 | 2.3 × 10¹⁴ | 4.1% | 4.3 h | | 189 | Saint Kitts na 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 | Visiwa vya Marshall | 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 | [Kinachohesabiwa, na kisichohesabiwa](https://simulation-hypothesis.com/sw/entities/control). ## Vyama vya siasa: wanachama, na niuroni zinazofuata maagizo yao Wanachama Kufuata maagizo yake Wanachama kama chama kinavyoripoti, kwa kipimo cha logarithmiki. Nguzo imara ni hesabu; nguzo zenye michirizi ni makadirio au madai. 1. Bharatiya Janata Party (India)▲1 milioni 140 2. Chama cha Kikomunisti cha China▼1 milioni 101.29 3. Democratic Party (Marekani)▲13 milioni 44.9 4. Republican Party (Marekani)▲16 milioni 38 5. Indian National Congress▼2 milioni 26 6. AIADMK (India)▼2 milioni 20.04 7. DMK (India)▼2 milioni 20 8. Prosperity Party (Ethiopia)▼1 milioni 15.7 9. TVK (India)▼1 milioni 15 10. Chama Cha Mapinduzi (Tanzania)▼1 milioni 13 11. All Progressives Congress (Nigeria)▼1 milioni 12.9 12. AK Party (Uturuki)▼1 milioni 11.71 13. Morena (Meksiko)▼1 milioni 11.63 14. Telugu Desam Party (India)▼1 milioni 10.05 15. Cambodian People’s Party▼1 milioni 7 Onyesha zote 59 10^4 10^5 10^6 10^7 10^8 10^9 wanachama Onyesha nambari | # | Chama | Wanachama | Kufuata maagizo yake | Sehemu inayoelekezwa | Saa kwa wiki | | -- | ------------------------------------------ | -------------- | -------------------- | -------------------- | ------------ | | 1 | Bharatiya Janata Party (India) | milioni 140 | 6.3 × 10¹⁶ | 0.52% (mbadala) | 0.6 h | | 2 | Chama cha Kikomunisti cha China | milioni 101.29 | 8.2 × 10¹⁶ | 0.94% | 1 h | | 3 | Democratic Party (Marekani) | milioni 44.9 | 2.3 × 10¹⁵ | 0.06% | 0.1 h | | 4 | Republican Party (Marekani) | milioni 38 | 2 × 10¹⁵ | 0.06% | 0.1 h | | 5 | Indian National Congress | milioni 26 | 1.2 × 10¹⁶ | 0.52% (mbadala) | 0.6 h | | 6 | AIADMK (India) | milioni 20.04 | 9 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 7 | DMK (India) | milioni 20 | 9 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 8 | Prosperity Party (Ethiopia) | milioni 15.7 | 7 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 9 | TVK (India) | milioni 15 | 6.7 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 10 | Chama Cha Mapinduzi (Tanzania) | milioni 13 | 5.8 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 11 | All Progressives Congress (Nigeria) | milioni 12.9 | 5.8 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 12 | AK Party (Uturuki) | milioni 11.71 | 5.2 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 13 | Morena (Meksiko) | milioni 11.63 | 5.2 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 14 | Telugu Desam Party (India) | milioni 10.05 | 4.5 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 15 | Cambodian People’s Party | milioni 7 | 3.1 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 16 | Workers’ Party of Korea | milioni 6.18 | 7.6 × 10¹⁵ | 1.4% | 1.5 h | | 17 | FRELIMO (Msumbiji) | milioni 6 | 2.7 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 18 | Chama cha Kikomunisti cha Vietnam | milioni 5.6 | 2.1 × 10¹⁵ | 0.44% | 0.5 h | | 19 | Democratic Party of Korea | milioni 5 | 2.2 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 20 | People Power Party (Korea Kusini) | milioni 4.44 | 2 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 21 | PSUV (Venezuela) | milioni 4.24 | 1.9 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 22 | ZANU-PF (Zimbabwe) | milioni 4.06 | 1.8 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 23 | UMNO (Malaysia) | milioni 3.25 | 1.5 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 24 | Justicialist Party (Ajentina) | milioni 3.1 | 1.4 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 25 | Colorado Party (Paraguay) | milioni 3 | 1.3 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 26 | United Russia | milioni 2.6 | 1.2 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 27 | UzLiDeP (Uzbekistan) | milioni 2.31 | 1 × 10¹⁵ | 0.52% (mbadala) | 0.6 h | | 28 | PLD (Jamhuri ya Dominika) | milioni 2.18 | 9.7 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 29 | MDB (Brazili) | milioni 2.02 | 9 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 30 | CHP (Uturuki) | milioni 1.88 | 8.4 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 31 | Radical Civic Union (Ajentina) | milioni 1.79 | 8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 32 | Workers’ Party, PT (Brazili) | milioni 1.67 | 7.5 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 33 | PRM (Jamhuri ya Dominika) | milioni 1.55 | 6.9 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 34 | Authentic Radical Liberal Party (Paraguay) | milioni 1.55 | 6.9 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 35 | Partido Federal ng Pilipinas | milioni 1.5 | 6.7 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 36 | PAS (Malaysia) | milioni 1.3 | 5.8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 37 | PRD (Brazili) | milioni 1.29 | 5.8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 38 | Progressistas (Brazili) | milioni 1.29 | 5.8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 39 | PSDB (Brazili) | milioni 1.26 | 5.6 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 40 | PKR (Malaysia) | milioni 1.15 | 5.2 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 41 | Economic Freedom Fighters (Afrika Kusini) | milioni 1.09 | 4.9 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 42 | PDT (Brazili) | milioni 1.07 | 4.8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 43 | União Brasil | milioni 1.07 | 4.8 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 44 | CPI(M) (India) | milioni 1.02 | 4.6 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 45 | Liberal Democratic Party (Japani) | milioni 1 | 4.5 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 46 | FLN (Aljeria) | milioni 1 | 4.5 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 47 | Liberal Party, PL (Brazili) | 952,881 | 4.3 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 48 | Chama cha Kikomunisti cha Cuba | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h | | 49 | African National Congress | 688,454 | 3.1 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 50 | Conservative Party of Canada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h | | 51 | CDU (Ujerumani) | 364,202 | 1.6 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 52 | SPD (Ujerumani) | 357,116 | 1.6 × 10¹⁴ | 0.52% (mbadala) | 0.6 h | | 53 | Labour Party (Uingereza) | 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 (Ujerumani) | 155,296 | 7 × 10¹³ | 0.52% (mbadala) | 0.6 h | | 56 | CSU (Ujerumani) | 125,153 | 5.6 × 10¹³ | 0.52% (mbadala) | 0.6 h | | 57 | Conservative Party (Uingereza) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h | | 58 | New Democratic Party (Kanada) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h | | 59 | AfD (Ujerumani) | 51,335 | 2.3 × 10¹³ | 0.52% (mbadala) | 0.6 h | [Mikutano, tafiti na vyanzo](https://simulation-hypothesis.com/sw/entities/control). ## Mashirika: wafanyakazi, na niuroni zinazofuata maagizo yao Wafanyakazi Kufuata maagizo yake Wafanyakazi katika ripoti ya mwaka au orodha ya karibuni zaidi, kwa kipimo cha mstari. 1. Walmart milioni 2.1 2. Amazon▲1 milioni 1.58 3. State Grid▼1 milioni 1.36 4. China National Petroleum milioni 1.03 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. Reli za Urusi (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 Onyesha zote 71 0 0.5M 1M 1.5M 2M 2.5M employees Onyesha nambari | # | Shirika | Wafanyakazi | Kufuata maagizo yake | Saa kwa wiki | Sehemu inayoelekezwa | | -- | ------------------------------------ | ------------ | -------------------- | ------------------- | -------------------- | | 1 | Walmart | milioni 2.1 | 6.3 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 2 | Amazon | milioni 1.58 | 4.7 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 3 | State Grid | milioni 1.36 | 5.5 × 10¹⁶ | 49 h (China) | 47% | | 4 | China National Petroleum | milioni 1.03 | 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 (Marekani) | 35% | | 9 | China Post Group | 719,448 | 2.9 × 10¹⁶ | 49 h (China) | 47% | | 10 | Reli za Urusi (RZD) | 677,363 | 2.2 × 10¹⁶ | 39.96 h (Urusi) | 38% | | 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (Ujerumani) | 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 (Ujerumani) | 30% | | 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (Marekani) | 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 (Ujerumani) | 34% | | 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (China) | 47% | | 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (Urusi) | 39% | | 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (Marekani) | 38% | | 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (China) | 47% | | 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (Marekani) | 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 (Ufilipino) | 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 (Marekani) | 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 (Ujerumani) | 30% | | 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (China) | 47% | | 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (Marekani) | 30% | | 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (Ujerumani) | 34% | | 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (China) | 47% | | 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (China) | 47% | | 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (Urusi) | 38% | | 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (Japani) | 38% | | 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (Marekani) | 35% | | 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (Urusi) | 38% | | 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (Marekani) | 36% | | 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (Marekani) | 35% | | 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (China) | 47% | | 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (Marekani) | 30% | | 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (Ujerumani) | 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 (Marekani) | 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 (Meksiko) | 45% | | 52 | Japan Post Holdings | 360,000 | 1.2 × 10¹⁶ | 41.6 h (Japani) | 40% | | 53 | Tesco | 354,744 | 9.3 × 10¹⁵ | 31.81 h (Uingereza) | 30% | | 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (India) | 56% | | 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (Japani) | 38% | | 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (Marekani) | 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 (Marekani) | 35% | | 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (Marekani) | 35% | | 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (Urusi) | 39% | | 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (Marekani) | 38% | | 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (Ujerumani) | 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 (Marekani) | 35% | | 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (Marekani) | 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 (Uingereza) | 33% | | 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (Marekani) | 35% | [Saa na vyanzo](https://simulation-hypothesis.com/sw/entities/control). ## Wanyama na viumbe wengine Kutoka seli moja isiyo na niuroni hadi ubongo wa binadamu. Idadi za niuroni zimepimwa pale zinapopatikana. ### Hakuna niuroni * [Escherichia coli](https://simulation-hypothesis.com/sw/entities/ecoli)Escherichia coli 0 imepimwa Seli moja isiyo na niuroni. Huelekea kwenye chakula na ina kumbukumbu ya kikemikali inayodumu sekunde chache. * [Ukungu wa ute](https://simulation-hypothesis.com/sw/entities/physarum)Physarum polycephalum 0 imepimwa Seli moja kubwa isiyo na niuroni. Hutatua labirinti na hukuza mitandao bora ya usafirishaji. * [Mmea nyeti](https://simulation-hypothesis.com/sw/entities/mimosa)Mimosa pudica 0 imepimwa Mmea ambao majani yake hujikunja yakiguswa. Utafiti mmoja uliripoti kwamba hujifunza kupuuza miguso isiyo na madhara. Uchambuzi wa baadaye ulipinga hilo. * [Sponji](https://simulation-hypothesis.com/sw/entities/sponge)Porifera (k.m. Amphimedon queenslandica) 0 imepimwa Mnyama asiye na niuroni wala sinapsi ambaye bado hujikunja mwili mzima kwa njia iliyoratibiwa. ### Mitandao ya neva, bila ubongo * [Hydra](https://simulation-hypothesis.com/sw/entities/hydra)Hydra vulgaris takriban 3,000 hadi 5,000 makadirio Kiumbe mdogo wa maji baridi wa kundi la cnidaria. Anadhibitiwa na mtandao wa neva uliotawanyika na hana ubongo. * [Jellyfish wa boksi](https://simulation-hypothesis.com/sw/entities/jellyfish)Tripedalia cystophora Hakuna hesabu iliyochapishwa Jellyfish wa boksi hana ubongo wa kati, lakini alionyesha kujifunza kwa kuhusisha. ### Wanyama wasio na uti wa mgongo wenye ubongo * [Mnyoo duara](https://simulation-hypothesis.com/sw/entities/celegans)Caenorhabditis elegans 302 imepimwa Mnyoo wa urefu wa takriban milimita moja mwenye niuroni 302 kamili. Ndiye mnyama wa kwanza ambaye mchoro kamili wa nyaya zake ulichorwa. * [Inzi wa matunda](https://simulation-hypothesis.com/sw/entities/drosophila)Drosophila melanogaster 139,255 imepimwa Mchoro kamili mkubwa zaidi wa nyaya za ubongo uliochorwa hadi sasa, unaojumuisha takriban niuroni 139,000. * [Nyuki wa asali](https://simulation-hypothesis.com/sw/entities/honeybee)Apis mellifera takriban milioni 1 makadirio Takriban niuroni milioni moja, na nyuki wanaweza kujifunza dhana dhahania kama sawa na tofauti. * [Chungu (Camponotus / Formica)](https://simulation-hypothesis.com/sw/entities/ant)Camponotus spp. Hakuna hesabu iliyochapishwa Ubongo mdogo unaoshughulikia uelekezaji wa umbali mrefu kwa kuhesabu hatua (ujumlishaji wa njia) na kukumbuka alama za mahali. * [Buibui anayeruka wa Portia](https://simulation-hypothesis.com/sw/entities/portia)Portia fimbriata / labiata Hakuna hesabu iliyochapishwa Buibui anayeruka anayepanga njia za kuzunguka. Huchukua njia zinazomwongoza mbali na windo lake na nje ya macho yake. * [Kaa wa pwani / kamba-maji](https://simulation-hypothesis.com/sw/entities/decapod)Carcinus maenas; Procambarus / Astacus spp. Hakuna hesabu iliyochapishwa Kaa na kamba-maji. Mapitio ya serikali ya 2021 yalipata ushahidi mkubwa kwamba kaa halisi wana uwezo wa kuhisi. * [Pweza wa kawaida](https://simulation-hypothesis.com/sw/entities/octopus)Octopus vulgaris milioni 500 makadirio Niuroni zake nyingi ziko katika mikono yake. Mapitio ya 2021 yalipata ushahidi wenye nguvu sana kwamba ana uwezo wa kuhisi. ### Samaki na amfibia * [Zebrafish](https://simulation-hypothesis.com/sw/entities/zebrafish)Danio rerio Hakuna hesabu iliyochapishwa Samaki mdogo anayetumika sana katika neurosayansi. Niuroni zake hazijahesabiwa kikamilifu. Ubongo wa mtu mzima una takriban seli milioni 10. * [Samaki msafishaji](https://simulation-hypothesis.com/sw/entities/wrasse)Labroides dimidiatus Hakuna hesabu iliyochapishwa Samaki wa miamba aliyefaulu toleo la jaribio la alama ya kioo. Watu hawakubaliani kuhusu kinachoonyeshwa na hilo. * [Chura](https://simulation-hypothesis.com/sw/entities/frog)Anura (k.m. Xenopus / Rana) Hakuna hesabu iliyochapishwa Azimio la New York la 2024 linasema kuna uwezekano halisi wa uzoefu wa fahamu kwa amfibia. ### Ndege * [Njiwa wa mwamba](https://simulation-hypothesis.com/sw/entities/pigeon)Columba livia milioni 310 imepimwa Mamia ya mamilioni ya niuroni katika ubongo mdogo. Njiwa wanaweza kupanga picha tata katika makundi. * [Kunguru mkubwa](https://simulation-hypothesis.com/sw/entities/raven)Corvus corax bilioni 2.17 imepimwa Zaidi ya niuroni bilioni mbili, zilizosongamana. Kunguru hukumbuka washindani mmojammoja. * [Kunguru wa New Caledonia](https://simulation-hypothesis.com/sw/entities/nccrow)Corvus moneduloides Hakuna hesabu iliyochapishwa Hutengeneza zana zenye ndoana kutoka vijiti na waya. Niuroni zake hazijahesabiwa. * [Kasuku wa kijivu wa Afrika](https://simulation-hypothesis.com/sw/entities/greyparrot)Psittacus erithacus bilioni 1.57 imepimwa Takriban niuroni bilioni 1.6\. Miongo ya utafiti wa maabara kuhusu hisia ya idadi na uainishaji. ### Mammals * [Panya](https://simulation-hypothesis.com/sw/entities/mouse)Mus musculus milioni 70.9 imepimwa Takriban niuroni milioni 71\. Panya huonyesha sura za uso za maumivu zinazoweza kupimwa. * [Panya](https://simulation-hypothesis.com/sw/entities/rat)Rattus norvegicus milioni 200 imepimwa Takriban niuroni milioni 200\. Panya huwaachilia wenzao waliofungiwa. * [Paka wa nyumbani](https://simulation-hypothesis.com/sw/entities/cat)Felis catus bilioni 1.22 imepimwa Takriban niuroni milioni 250 katika gamba la ubongo. * [Mbwa wa nyumbani](https://simulation-hypothesis.com/sw/entities/dog)Canis familiaris bilioni 1.8 hadi 2.6, kutegemea mbwa imepimwa Hadi takriban niuroni milioni 600 za gamba, kutegemea mbwa. Hiyo ni zaidi ya paka. * [Nguruwe wa nyumbani](https://simulation-hypothesis.com/sw/entities/pig)Sus scrofa domesticus bilioni 2.22 imepimwa Nguruwe hujifunza kioo kinaonyesha nini na kukitumia kupata chakula. * [Pomboo mwenye pua ya chupa](https://simulation-hypothesis.com/sw/entities/dolphin)Tursiops truncatus Hakuna hesabu iliyochapishwa Hufaulu majaribio ya kujitambua kwenye kioo. Hakuna idadi ya niuroni iliyopimwa kwa pomboo mwenye pua ya chupa. * [Tembo wa Afrika](https://simulation-hypothesis.com/sw/entities/elephant)Loxodonta africana bilioni 257 imepimwa Niuroni bilioni 257, mara tatu ya binadamu, karibu zote katika cerebellum. * [Sokwe mtu](https://simulation-hypothesis.com/sw/entities/chimp)Pan troglodytes takriban bilioni 28 (imetabiriwa kutoka kanuni za kuongeza ukubwa, haijahesabiwa) makadirio Mmoja wa jamaa zetu wawili walio hai walio karibu zaidi (pamoja na bonobo). Anafaulu jaribio la kioo. * [Binadamu](https://simulation-hypothesis.com/sw/entities/human)Homo sapiens bilioni 86.1 imepimwa Niuroni bilioni 86, bilioni 16 kati yake katika gamba la ubongo. Sisi ndio spishi pekee ambayo wanachama wake wanaweza kutoa ripoti za maneno kuhusu uzoefu wao. ## Mifumo ya pamoja Vikundi vinavyofanya maamuzi ambayo mwanachama mmoja hawezi kuyafanya peke yake. Husomwa kama akili ya pamoja, si kama akili. * [Makoloni ya chungu](https://simulation-hypothesis.com/sw/entities/c-ants)Makoloni huchagua kiota bora kwa sababu kila chungu hufuata kizingiti cha akidi. Hakuna chungu kinacholinganisha maeneo kwa ujumla. * [Makundi ya nyuki wa asali](https://simulation-hypothesis.com/sw/entities/c-bees)Makundi huchagua pango bora kupitia ushindani kati ya wapelelezi na akidi. Ishara za kusimamisha hutoa ukandamizaji wa pande zote, unaovunja mkwamo. * [Makundi ya shorwe](https://simulation-hypothesis.com/sw/entities/c-starlings)Kila ndege hufuatilia idadi maalum ya jirani (takriban 6-7), si kila ndege aliye ndani ya umbali maalum. Hii inaitwa mwingiliano wa kitopolojia, si wa kimetriki. * [Makundi ya samaki](https://simulation-hypothesis.com/sw/entities/c-fish)Makundi hutumia kanuni za akidi kuamua. Makundi makubwa yanaweza kuhisi miteremko ya kimazingira ambayo samaki mmoja hawezi kuihisi. ## Mifumo bandia Aina za akili za mashine, zilizopangwa kwa jinsi zinavyofanya kazi. Hakuna mfumo wa sasa unaochukuliwa kuwa mgombea madhubuti wa fahamu. * [Mifumo ya wataalamu inayotegemea kanuni](https://simulation-hypothesis.com/sw/entities/ai-expert)Programu zinazoweka maarifa ya mtaalamu wa fani kama kanuni dhahiri za kama-basi. Injini ya makisio hutumia kanuni hizo kwa mfuatano kufikia hitimisho, kwa kawaida pamoja na maelezo ya hoja yake. * [Injini za utafutaji wa mti wa mchezo (chesi)](https://simulation-hypothesis.com/sw/entities/ai-chess)Programu zinazohesabu mfuatano wa hatua zinazowezekana hadi kina kisichobadilika na kutoa alama kwa nafasi zinazotokea kwa kutumia kitendakazi cha tathmini kilichotengenezwa kwa mkono. Hukata matawi yasiyoweza kubadilisha uchaguzi (minimax pamoja na alpha-beta pruning). * [Mawakala wa kujifunza kwa uimarishaji wa kina](https://simulation-hypothesis.com/sw/entities/ai-drl)Mitandao ya neva iliyofunzwa kwa kujaribu na kukosea ili kuongeza ishara ya zawadi. Hujifunza sera au kitendakazi cha thamani kwa kuingiliana na mazingira, mara nyingi yaliyoigwa kama mchezo. * [Modeli kubwa za lugha](https://simulation-hypothesis.com/sw/entities/ai-llm)Mitandao ya neva ya Transformer iliyofunzwa kutabiri tokeni inayofuata katika mkusanyiko mkubwa sana wa maandishi, kisha mara nyingi hurekebishwa kwa maoni ya binadamu. * [Roboti zenye miili](https://simulation-hypothesis.com/sw/entities/ai-robots)Mashine zenye vihisi na viendeshaji zinazotenda katika ulimwengu wa kimwili. Mara nyingi hutumia udhibiti wa kuitikia wa tabaka badala ya modeli kuu ya dunia, kwa hiyo tabia hujitokeza kutoka mzunguko kati ya mwili na mazingira. * [Maunzi ya neuromorphic](https://simulation-hypothesis.com/sw/entities/ai-neuromorphic)Chipu ambazo saketi zake huiga niuroni na sinapsi zinazotoa mipigo. Zinaendeshwa na matukio na hutumia nguvu kidogo, tofauti na muundo wa kawaida wa kichakataji na kumbukumbu. ## Akili za kudhania Akili zilizopendekezwa katika maandiko ya kisayansi na kifalsafa. Hakuna aliyewahi kuona hata moja. * [Uigaji wa ubongo mzima](https://simulation-hypothesis.com/sw/entities/h-wbe)haijaonekana Kuchanganua ubongo kwa undani mdogo na kuendesha modeli yake ya programu. * [Akili zilizoigwa katika uigaji wa mababu](https://simulation-hypothesis.com/sw/entities/h-ancestor)haijaonekana Mkanganyiko wa pande tatu wa Bostrom: ama karibu hakuna ustaarabu unaofikia hatua ya baada ya binadamu, au karibu hakuna unaoendesha uigaji wa mababu, au karibu hakika tunaishi ndani ya mmoja. * [Ubongo wa Boltzmann](https://simulation-hypothesis.com/sw/entities/h-boltzmann)haijaonekana Waangalizi wanaofanana na ubongo wanaotokea kutokana na mabadiliko nadra ya joto au ya kwanta. * [Kupakia akili kwa kubadilisha taratibu](https://simulation-hypothesis.com/sw/entities/h-uploading)haijaonekana Kubadilisha niuroni moja baada ya nyingine kwa sehemu bandia zenye kazi inayofanana. * [Akili kuu](https://simulation-hypothesis.com/sw/entities/h-superintelligence)haijaonekana Akili ya kudhania inayozidi sana utendaji wa binadamu karibu katika nyanja zote. Kitabu cha Bostrom kinachambua njia za kuifikia (uigaji wa ubongo mzima, utambuzi wa kibiolojia, AI), aina zake na matatizo ya udhibiti. Toleo la data 2.0.0\. [Pakua (JSON)](https://simulation-hypothesis.com/entities/minds.json).