አካላት · በማስረጃ የተደረደሩ አእምሮዎችና ሊሆኑ የሚችሉ አእምሮዎች
ዓለም ማስመሰል ከሆነ፣ አእምሮዎች በውስጡ ለማስላት ከባዱ ነገሮች ሊሆኑ ይችላሉ። ይህ መመሪያ ከነጠላ ሕዋሳት እስከ ሰዎች የታወቁ ጉዳዮችን ይዘረዝራል። እስካሁን የተገነቡ ሰው ሠራሽ ሥርዓቶችን፣ እንዲሁም ሳይንቲስቶችና ፈላስፎች ያቀረቧቸውን ግን ማንም ያላየውን አእምሮዎችም ይሸፍናል።
እያንዳንዱ አኃዝ ምንጭና የእምነት መለያ አለው። የተለካ ቀጥተኛ ቆጠራ ነው፣ ግምት ከሌላ መረጃ የተሰላ ነው፣ አልተቆጠረም ደግሞ ማንም ቆጠራ አላተመም ማለት ነው። ከእነዚህ ሥርዓቶች አንዳቸው ልምድ አላቸው ወይ የሚለው የተለየ ጥያቄ ነው። ለዚያ ማስረጃው በየስሜት መቻል መስፈርቶች እና የንቃተ ህሊና አመልካቾች ስር ነው።
ነጠላ አእምሮዎች
- ኦርካ †43.1 ቢሊዮን
- ረጅም ክንፍ ፓይሎት ዓሣ ነባሪ †37.2 ቢሊዮን
- ሰው▲116.3 ቢሊዮን
- ወደብ ፖርፖይዝ †14.9 ቢሊዮን
- ቦትልኖዝ ዶልፊን †12.7 ቢሊዮን
- የኩቪየር ምንቃር ዓሣ ነባሪ9.1 ቢሊዮን
- ጎሪላ8.5 ቢሊዮን
- ኦራንጉታን8.5 ቢሊዮን
- ቺምፓንዚ7.4 ቢሊዮን
- የተለመደ ዶልፊን6.7 ቢሊዮን
- ሃርፕ ማኅተም6.1 ቢሊዮን
- የአፍሪካ ዝሆን▼15.6 ቢሊዮን
- ፊን ዓሣ ነባሪ5.2 ቢሊዮን
- ሰማያዊ ዓሣ ነባሪ5.1 ቢሊዮን
- ሚንኪ ዓሣ ነባሪ3.2 ቢሊዮን
ቁጥሮቹን አሳይ
| # | እንስሳ | የአንጎል ቅርፊት ወይም ፓሊየም | መላ አንጎል |
|---|---|---|---|
| 1 | ኦርካ † | 43.1 ቢሊዮን | |
| 2 | ረጅም ክንፍ ፓይሎት ዓሣ ነባሪ † | 37.2 ቢሊዮን | |
| 3 | ሰው | 16.3 ቢሊዮን | 86.1 ቢሊዮን |
| 4 | ወደብ ፖርፖይዝ † | 14.9 ቢሊዮን | |
| 5 | ቦትልኖዝ ዶልፊን † | 12.7 ቢሊዮን | |
| 6 | የኩቪየር ምንቃር ዓሣ ነባሪ | 9.1 ቢሊዮን | |
| 7 | ጎሪላ | 8.5 ቢሊዮን | |
| 8 | ኦራንጉታን | 8.5 ቢሊዮን | |
| 9 | ቺምፓንዚ | 7.4 ቢሊዮን | |
| 10 | የተለመደ ዶልፊን | 6.7 ቢሊዮን | |
| 11 | ሃርፕ ማኅተም | 6.1 ቢሊዮን | |
| 12 | የአፍሪካ ዝሆን | 5.6 ቢሊዮን | 257 ቢሊዮን |
| 13 | ፊን ዓሣ ነባሪ | 5.2 ቢሊዮን | |
| 14 | ሰማያዊ ዓሣ ነባሪ | 5.1 ቢሊዮን | |
| 15 | ሚንኪ ዓሣ ነባሪ | 3.2 ቢሊዮን | 57.4 ቢሊዮን |
| 16 | የወይራ ዝንጀሮ (ባቦን) | 2.88 ቢሊዮን | 10.95 ቢሊዮን |
| 17 | ቤሉጋ | 2.7 ቢሊዮን | |
| 18 | ድንክ ስፐርም ዓሣ ነባሪ | 2.02 ቢሊዮን | |
| 19 | ሰማያዊና ቢጫ ማካው | 1.92 ቢሊዮን | 3.14 ቢሊዮን |
| 20 | ሬሰስ ማካክ | 1.71 ቢሊዮን | 6.39 ቢሊዮን |
| 21 | ቀጭኔ | 1.67 ቢሊዮን | 11.21 ቢሊዮን |
| 22 | ሽኮኮ ዝንጀሮ | 1.34 ቢሊዮን | 3.25 ቢሊዮን |
| 23 | ዩራሺያን ንስር ጉጉት | 1.33 ቢሊዮን | 1.9 ቢሊዮን |
| 24 | ኬአ | 1.28 ቢሊዮን | 2.15 ቢሊዮን |
| 25 | የበረዶ ጉጉት | 1.27 ቢሊዮን | 1.81 ቢሊዮን |
| 26 | የተለመደ ቁራ | 1.2 ቢሊዮን | 2.17 ቢሊዮን |
| 27 | ቡናማ ጉጉት | 1.15 ቢሊዮን | 1.58 ቢሊዮን |
| 28 | ካፑቺን ዝንጀሮ | 1.14 ቢሊዮን | 3.69 ቢሊዮን |
| 29 | ሰልፈር ክሬስትድ ኮካቱ | 1.14 ቢሊዮን | 2.12 ቢሊዮን |
| 30 | የአፍሪካ ግሬይ በቀቀን | 850 ሚሊዮን | 1.57 ቢሊዮን |
| 31 | ሩክ | 820 ሚሊዮን | 1.51 ቢሊዮን |
| 32 | ታላቅ ኩዱ | 793.07 ሚሊዮን | 6.09 ቢሊዮን |
| 33 | ረጅም ጆሮ ጉጉት | 673 ሚሊዮን | 991.31 ሚሊዮን |
| 34 | ጎፊን ኮካቱ | 599 ሚሊዮን | 1.16 ቢሊዮን |
| 35 | አንበሳ | 545.24 ሚሊዮን | 4.67 ቢሊዮን |
| 36 | ነጭ ጭራ ንስር | 542.93 ሚሊዮን | 1.65 ቢሊዮን |
| 37 | ዩራሺያን ጄይ | 529 ሚሊዮን | 1.09 ቢሊዮን |
| 38 | የቤት ውሻ | 526 ሚሊዮን | |
| 39 | ባለ መስመር ጅብ | 495.28 ሚሊዮን | 3.88 ቢሊዮን |
| 40 | ምዕራባዊ ጃክዳው | 492 ሚሊዮን | 967.99 ሚሊዮን |
| 41 | የተለመደ ሰጎን | 479.41 ሚሊዮን | 1.63 ቢሊዮን |
| 42 | ዩራሺያን ማግፓይ | 443 ሚሊዮን | 897.27 ሚሊዮን |
| 43 | ሳከር ጭልፊት | 442.95 ሚሊዮን | 1.2 ቢሊዮን |
| 44 | Emu | 439 ሚሊዮን | 1.34 ቢሊዮን |
| 45 | ራኩን | 437.94 ሚሊዮን | 2.15 ቢሊዮን |
| 46 | ባርን ጉጉት | 437 ሚሊዮን | 689.54 ሚሊዮን |
| 47 | ትንሽ ጉጉት | 400.82 ሚሊዮን | 585.92 ሚሊዮን |
| 48 | ጥቁር ጥንብ አንሳ | 398.9 ሚሊዮን | 1.01 ቢሊዮን |
| 49 | ዩራሺያን ትንሽ ጉጉት | 364 ሚሊዮን | 533.26 ሚሊዮን |
| 50 | የተለመደ ባዛርድ | 351.7 ሚሊዮን | 1 ቢሊዮን |
| 51 | ታላቅ ሪያ | 330.34 ሚሊዮን | 1.03 ቢሊዮን |
| 52 | ድምፅ አልባ ስዋን | 323.66 ሚሊዮን | 1 ቢሊዮን |
| 53 | የቤት አሳማ | 293 ሚሊዮን | 2.22 ቢሊዮን |
| 54 | ግሬይላግ ዝይ | 258.65 ሚሊዮን | 738.23 ሚሊዮን |
| 55 | ቡናማ ድብ | 250.97 ሚሊዮን | 9.59 ቢሊዮን |
| 56 | የቤት ድመት | 250 ሚሊዮን | 1.22 ቢሊዮን |
| 57 | የተለመደ ማርሞሴት | 245 ሚሊዮን | 638 ሚሊዮን |
| 58 | የተለመደ ኬስትሬል | 237.9 ሚሊዮን | 624.58 ሚሊዮን |
| 59 | ዩራሺያን ቲል | 167.29 ሚሊዮን | 375.41 ሚሊዮን |
| 60 | ቡጂሪጋር | 149 ሚሊዮን | 321.82 ሚሊዮን |
| 61 | ቀይ ደረት ዝይ | 148.62 ሚሊዮን | 482.71 ሚሊዮን |
| 62 | የእንጨት ዳክዬ | 138.21 ሚሊዮን | 305.82 ሚሊዮን |
| 63 | ድንክ ጭልፊት | 131.9 ሚሊዮን | 350.37 ሚሊዮን |
| 64 | ዕብነ በረድ ዳክዬ | 130.14 ሚሊዮን | 386.92 ሚሊዮን |
| 65 | የሕንድ ፒኮክ | 129.62 ሚሊዮን | 570.93 ሚሊዮን |
| 66 | ቢጫ ጉብጉብ ኩራሶው | 124.62 ሚሊዮን | 652.99 ሚሊዮን |
| 67 | ቪክቶሪያ ክረስትድ ርግብ | 118.45 ሚሊዮን | 578.7 ሚሊዮን |
| 68 | ማላርድ | 112.26 ሚሊዮን | 367.47 ሚሊዮን |
| 69 | የዱር ቱርክ | 105.65 ሚሊዮን | 492.87 ሚሊዮን |
| 70 | ዩራሺያን ድንቢጥ ጭልፊት | 87.83 ሚሊዮን | 401.75 ሚሊዮን |
| 71 | የሪቭስ ፌዛንት | 80.69 ሚሊዮን | 387.17 ሚሊዮን |
| 72 | የዓለት ርግብ | 72 ሚሊዮን | 310 ሚሊዮን |
| 73 | የአውሮፓ ጥንቸል | 71.5 ሚሊዮን | 513 ሚሊዮን |
| 74 | ቀይ ጫካ ዶሮ | 60.7 ሚሊዮን | 220.84 ሚሊዮን |
| 75 | የቺሊ ቲናሙ | 59.13 ሚሊዮን | 241.37 ሚሊዮን |
| 76 | የሜዳ አህያ ፊንች | 55.2 ሚሊዮን | 135.98 ሚሊዮን |
| 77 | ቆንጆ ክረስትድ ቲናሙ | 51.38 ሚሊዮን | 217.77 ሚሊዮን |
| 78 | የተለመደ የእንጨት ርግብ | 51.33 ሚሊዮን | 258.68 ሚሊዮን |
| 79 | ክዌብራኮ ክረስትድ ቲናሙ | 48.29 ሚሊዮን | 235.67 ሚሊዮን |
| 80 | የአፍሪካ አንገተ ቀለበት ርግብ | 40 ሚሊዮን | 139.27 ሚሊዮን |
| 81 | ሰሜናዊ ቦብዋይት | 39.11 ሚሊዮን | 148.4 ሚሊዮን |
| 82 | ግራጫ ጅግራ | 36.88 ሚሊዮን | 170.29 ሚሊዮን |
| 83 | አይጥ | 31 ሚሊዮን | 200 ሚሊዮን |
| 84 | የተለመደ ድርጭት | 22.57 ሚሊዮን | 117.76 ሚሊዮን |
| 85 | ኪንግ ድርጭት | 20.52 ሚሊዮን | 80.48 ሚሊዮን |
| 86 | የአልማዝ ርግብ | 18.21 ሚሊዮን | 87.88 ሚሊዮን |
| 87 | አይጥ | 13.7 ሚሊዮን | 70.9 ሚሊዮን |
| 88 | ራቁት ሞል-አይጥ | 5.31 ሚሊዮን | 33.62 ሚሊዮን |
- የሰው ኒዮኮርቴክስ1.6 × 10¹⁴
- GPT-5.5 (OpenAI, 2026) *~4.7 × 10¹²
- Claude Fable 5 (Anthropic, 2026) *~3.5 × 10¹²
- Grok 3 (xAI, 2025)~3 × 10¹²
- Grok 4 (xAI, 2025)~3 × 10¹²
- Gemini 2.5 Pro (Google, 2025) *~3 × 10¹²
- Kimi K3 (Moonshot, 2026)~2.8 × 10¹²
- Qwen 3.8 Max (Alibaba, 2026)2.4 × 10¹²
- Qwen3.8-2.4T-A95B (Alibaba, 2026)2.4 × 10¹²
- Grok 4.7 (xAI, 2026) *~2.1 × 10¹²
- Llama 4 Behemoth (Meta AI, 2025)~2 × 10¹²
- GPT-4 (OpenAI, 2023)~1.8 × 10¹²
- DeepSeek-V4-Pro (DeepSeek, 2026)~1.6 × 10¹²
- LongCat-2.0 (Meituan Inc, 2026)~1.6 × 10¹²
- Switch (Google, 2021)1.6 × 10¹²
- GLaM (Google, 2021)1.2 × 10¹²
- PanGu-Σ (Huawei Noah's Ark Lab, 2023)1.1 × 10¹²
- Kimi K2.5 (Moonshot, 2026)~1 × 10¹²
- Composer 2 (Cursor, 2026)1 × 10¹²
- Kimi K2.6 (Moonshot, 2026)1 × 10¹²
- Composer 2.5 (Cursor, 2026)~1 × 10¹²
- MiMo-V2-Pro (Xiaomi Corp, 2026)1 × 10¹²
- MiMo-V2.5-Pro (Xiaomi Corp, 2026)~1 × 10¹²
- M6-T (Alibaba, 2021)~1 × 10¹²
- Kimi K2 (Moonshot, 2025)1 × 10¹²
- Qwen3-Max (Alibaba, 2025)~1 × 10¹²
- Ling-1T (Ant Group, 2025)1 × 10¹²
- Kimi K2 Thinking (Moonshot, 2025)~1 × 10¹²
- Inkling (Thinking Machines, 2026)~9.8 × 10¹¹
- Tencent Hy4 preview (Tencent, 2026)~7.7 × 10¹¹
- GLM-5.1 (Z.ai (Zhipu AI), 2026)7.5 × 10¹¹
- K-EXAONE 2.0 (LG AI Research, 2026)7.5 × 10¹¹
- DeepSeek V4.1 Flash (DeepSeek, 2026)~7.5 × 10¹¹
- GLM-5 (Z.ai (Zhipu AI), 2026)~7.4 × 10¹¹
- GLM-5.3 (Z.ai (Zhipu AI), 2026)7.4 × 10¹¹
- A.X K2 (SK Telecom, 2026)6.9 × 10¹¹
- DeepSeekMath-V2 (DeepSeek, 2025)6.8 × 10¹¹
- Mistral Large 3 (Mistral AI, 2025)6.8 × 10¹¹
- DeepSeek-V3 (DeepSeek, 2024)6.7 × 10¹¹
- DeepSeek-R1 (DeepSeek, 2025)6.7 × 10¹¹
ቁጥሮቹን አሳይ
| # | ሥርዓት | ሁሉም ፓራሜትሮች | በአንድ ቶከን የሚያገለግል | ዓመት | ዓይነት |
|---|---|---|---|---|---|
| 1 | የሰው ኒዮኮርቴክስ | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | ሲናፕሶች | |
| 2 | GPT-5.5 (OpenAI, 2026) * | ~4.7 × 10¹² | 2026 | ግምት | |
| 3 | Claude Fable 5 (Anthropic, 2026) * | ~3.5 × 10¹² | 2026 | ግምት | |
| 4 | Grok 3 (xAI, 2025) | ~3 × 10¹² | 2025 | ግምት | |
| 5 | Grok 4 (xAI, 2025) | ~3 × 10¹² | 2025 | ግምት | |
| 6 | Gemini 2.5 Pro (Google, 2025) * | ~3 × 10¹² | 2025 | ግምት | |
| 7 | Kimi K3 (Moonshot, 2026) | ~2.8 × 10¹² | 2026 | ግምት | |
| 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | አርክቴክቸር አልተገለጸም |
| 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | የባለሙያዎች ድብልቅ |
| 10 | Grok 4.7 (xAI, 2026) * | ~2.1 × 10¹² | 2026 | ግምት | |
| 11 | Llama 4 Behemoth (Meta AI, 2025) | ~2 × 10¹² | 2025 | ግምት | |
| 12 | GPT-4 (OpenAI, 2023) | ~1.8 × 10¹² | 2023 | ግምት | |
| 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | ~1.6 × 10¹² | 2026 | ግምት | |
| 14 | LongCat-2.0 (Meituan Inc, 2026) | ~1.6 × 10¹² | 2026 | ግምት | |
| 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 18 | Kimi K2.5 (Moonshot, 2026) | ~1 × 10¹² | 2026 | ግምት | |
| 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 21 | Composer 2.5 (Cursor, 2026) | ~1 × 10¹² | 2026 | ግምት | |
| 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | አርክቴክቸር አልተገለጸም |
| 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | ~1 × 10¹² | 2026 | ግምት | |
| 24 | M6-T (Alibaba, 2021) | ~1 × 10¹² | 2021 | ግምት | |
| 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 26 | Qwen3-Max (Alibaba, 2025) | ~1 × 10¹² | 2025 | ግምት | |
| 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 28 | Kimi K2 Thinking (Moonshot, 2025) | ~1 × 10¹² | 2025 | ግምት | |
| 29 | Inkling (Thinking Machines, 2026) | ~9.8 × 10¹¹ | 2026 | ግምት | |
| 30 | Tencent Hy4 preview (Tencent, 2026) | ~7.7 × 10¹¹ | 2026 | ግምት | |
| 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | ~7.5 × 10¹¹ | 2026 | ግምት | |
| 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | ~7.4 × 10¹¹ | 2026 | ግምት | |
| 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | የባለሙያዎች ድብልቅ |
| 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | የባለሙያዎች ድብልቅ |
| 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | አርክቴክቸር አልተገለጸም |
| 42 | Step 5 Preview (StepFun, 2026) | ~6 × 10¹¹ | 2026 | ግምት | |
| 43 | PaLM-E (Google, 2023) | ~5.6 × 10¹¹ | 2023 | ግምት | |
| 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 45 | LongCat-Flash (Meituan Inc, 2025) | ~5.6 × 10¹¹ | 2025 | ግምት | |
| 46 | Nemotron 3 Ultra (Nvidia, 2026) | ~5.5 × 10¹¹ | 2026 | ግምት | |
| 47 | PaLM (Google Research, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | ጥቅጥቅ |
| 48 | Minerva (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | ጥቅጥቅ |
| 49 | U-PaLM (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | ጥቅጥቅ |
| 50 | LMSI-Palm (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | ጥቅጥቅ |
| 51 | Flan-PaLM 540B (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | ጥቅጥቅ |
| 52 | Megatron-Turing NLG 530B (Microsoft, 2021) | 5.3 × 10¹¹ | 5.3 × 10¹¹ | 2021 | ጥቅጥቅ |
| 53 | MegaScale (ByteDance, 2024) | ~5.3 × 10¹¹ | 2024 | ግምት | |
| 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 55 | Doubao-pro (ByteDance, 2024) | ~5 × 10¹¹ | 2024 | ግምት | |
| 56 | Grok 4.20 (xAI, 2026) | ~5 × 10¹¹ | 2026 | ግምት | |
| 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | የባለሙያዎች ድብልቅ |
| 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | ጥቅጥቅ |
| 61 | Llama 4 Maverick (Meta AI, 2025) | ~4 × 10¹¹ | 2025 | ግምት | |
| 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | ~4 × 10¹¹ | 2026 | ግምት | |
| 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | ግምት | |
| 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | ግምት | |
| 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 68 | PaLM 2 (Google, 2023) | ~3.4 × 10¹¹ | 2023 | ግምት | |
| 69 | Med-PaLM 2 (Google Research, 2023) | ~3.4 × 10¹¹ | 2023 | ግምት | |
| 70 | Nemotron-4 340B (Nvidia, 2024) | 3.4 × 10¹¹ | 3.4 × 10¹¹ | 2024 | ጥቅጥቅ |
| 71 | GLM-5.3-Flash (Z.ai (Zhipu AI), 2026) | ~3.2 × 10¹¹ | 2026 | ግምት | |
| 72 | Grok-1 (xAI, 2023) | ~3.1 × 10¹¹ | 2023 | ግምት | |
| 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 74 | SB-LM (Google, 2007) | ~3 × 10¹¹ | 2007 | ግምት | |
| 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | ~2.8 × 10¹¹ | 2026 | ግምት | |
| 78 | Gopher (DeepMind, 2021) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2021 | ጥቅጥቅ |
| 79 | TML-Interaction-Small (Thinking Machines, 2026) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2026 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 80 | ST-MoE (Google, 2022) | ~2.7 × 10¹¹ | 2022 | ግምት | |
| 81 | ERNIE 3.0 Titan (Baidu, 2021) | 2.6 × 10¹¹ | 2.6 × 10¹¹ | 2021 | ጥቅጥቅ |
| 82 | Solar Open2 250B (Upstage, 2026) | ~2.5 × 10¹¹ | 2026 | ግምት | |
| 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | ~2.5 × 10¹¹ | 2024 | ግምት | |
| 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | አርክቴክቸር አልተገለጸም |
| 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | አርክቴክቸር አልተገለጸም |
| 88 | Qwen3-235B-A22B (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | ግምት | |
| 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | ግምት | |
| 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | ~2.4 × 10¹¹ | 2025 | ግምት | |
| 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ |
| 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 94 | HyperCLOVA 204B (NAVER, 2021) | ~2 × 10¹¹ | 2021 | ግምት | |
| 95 | Amazon Titan (Amazon, 2023) | ~2 × 10¹¹ | 2023 | ግምት | |
| 96 | Falcon-180B (Technology Innovation Institute, 2023) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2023 | ጥቅጥቅ |
| 97 | Jurassic-1-Jumbo (AI21 Labs, 2021) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2021 | አርክቴክቸር አልተገለጸም |
| 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | ጥቅጥቅ |
| 99 | BLUUMI (University of Turku, 2023) | ~1.8 × 10¹¹ | 2023 | ግምት | |
| 100 | InstructGPT 175B (OpenAI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | ጥቅጥቅ |
| 101 | OPT-175B (Meta AI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | ጥቅጥቅ |
| 102 | BlenderBot 3 (McGill University, 2022) | ~1.8 × 10¹¹ | 2022 | ግምት | |
| 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | ጥቅጥቅ |
| 104 | Palmyra X 004 (Writer, 2024) | ~1.5 × 10¹¹ | 2024 | ግምት | |
| 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | አርክቴክቸር አልተገለጸም |
| 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | አርክቴክቸር አልተገለጸም |
| 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | አርክቴክቸር አልተገለጸም |
| 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | አርክቴክቸር አልተገለጸም |
| 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 110 | GLM-130B (Tsinghua University, 2022) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2022 | ጥቅጥቅ |
| 111 | Pixtral Large (Mistral AI, 2024) | 1.2 × 10¹¹ | 1.2 × 10¹¹ | 2024 | አርክቴክቸር አልተገለጸም |
| 112 | Mistral Large 2 (Mistral AI, 2024) | ~1.2 × 10¹¹ | 2024 | ግምት | |
| 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | ~1.2 × 10¹¹ | 2026 | ግምት | |
| 114 | Galactica (Meta AI, 2022) | ~1.2 × 10¹¹ | 2022 | ግምት | |
| 115 | Nemotron 3 Super (Nvidia, 2026) | ~1.2 × 10¹¹ | 2026 | ግምት | |
| 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | የባለሙያዎች ድብልቅ |
| 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | አርክቴክቸር አልተገለጸም |
| 118 | Kaiju Large (Character.ai, 2025) | ~1.1 × 10¹¹ | 2025 | ግምት | |
| 119 | Llama 4 Scout (Meta AI, 2025) | ~1.1 × 10¹¹ | 2025 | ግምት | |
| 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | ጥቅጥቅ |
| 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | አርክቴክቸር አልተገለጸም |
| 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | ጥቅጥቅ |
| 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | አርክቴክቸር አልተገለጸም |
| 124 | Yi-Large (01.AI, 2024) | ~1 × 10¹¹ | 2024 | ግምት | |
| 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 127 | የአይጥ ኒዮኮርቴክስ | 8.1 × 10¹⁰ | 8.1 × 10¹⁰ | ሲናፕሶች | |
| 128 | Flamingo (DeepMind, 2022) | 8 × 10¹⁰ | 8 × 10¹⁰ | 2022 | ጥቅጥቅ |
| 129 | Qwen3-Coder-Next (Alibaba, 2026) | ~8 × 10¹⁰ | 2026 | ግምት | |
| 130 | Qwen2-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 131 | Qwen2.5-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 132 | Qwen2.5 Instruct (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 133 | Qwen-72B (Alibaba, 2023) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 134 | Palmyra X 003 (Writer, 2024) | ~7.2 × 10¹⁰ | 2024 | ግምት | |
| 135 | Qwen1.5-72B (Alibaba, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 136 | LLaVA-OV-72B (ByteDance, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 137 | NVLM-X 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | ግምት | |
| 138 | NVLM-H 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | ግምት | |
| 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 141 | Chinchilla (DeepMind, 2022) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2022 | ጥቅጥቅ |
| 142 | Llama 2-70B (Meta AI, 2023) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 143 | PPLX-70B-Online (Perplexity, 2023) | ~7 × 10¹⁰ | 2023 | ግምት | |
| 144 | Llama 3-70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 145 | ALLaM adapted 70B (Saudi Data and Artificial Intelligence Authority, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 147 | Reka Core (Reka AI, 2024) | ~6.7 × 10¹⁰ | 2024 | ግምት | |
| 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 149 | SNM-skip (Google, 2014) | ~6.2 × 10¹⁰ | 2014 | ግምት | |
| 150 | PaLI-X (Google Research, 2023) | ~5.5 × 10¹⁰ | 2023 | ግምት | |
| 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 153 | NLLB (Meta AI, 2022) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2022 | ጥቅጥቅ |
| 154 | BloombergGPT (Bloomberg, 2023) | 5.1 × 10¹⁰ | 5.1 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 155 | M4-50B (Google, 2019) | 5 × 10¹⁰ | 5 × 10¹⁰ | 2019 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 156 | Mixtral 8x7B (Mistral AI, 2023) | ~4.7 × 10¹⁰ | 2023 | ግምት | |
| 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 158 | Falcon-40B (Technology Innovation Institute, 2023) | 4 × 10¹⁰ | 4 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 159 | Qwen3-Omni-30B-A3B (Alibaba, 2025) | 3.5 × 10¹⁰ | 3 × 10⁹ | 2025 | የባለሙያዎች ድብልቅ |
| 160 | Yi-34B (01.AI, 2023) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 161 | Cambrian-1-34B (New York University (NYU), 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 163 | Kaiju Medium (Character.ai, 2025) | ~3.4 × 10¹⁰ | 2025 | ግምት | |
| 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | አርክቴክቸር አልተገለጸም |
| 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 166 | QwQ-32B (Alibaba, 2025) | ~3.3 × 10¹⁰ | 2025 | ግምት | |
| 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | ~3.2 × 10¹⁰ | 2025 | ግምት | |
| 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 174 | MM1-30B (Apple, 2024) | ~3 × 10¹⁰ | 2024 | ግምት | |
| 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | አርክቴክቸር አልተገለጸም |
| 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | አርክቴክቸር አልተገለጸም |
| 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 181 | KN-LM (Google, 2007) | ~2.1 × 10¹⁰ | 2007 | ግምት | |
| 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 183 | RNN for 1B words (Google, 2013) | ~2 × 10¹⁰ | 2013 | ግምት | |
| 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | ጥቅጥቅ |
| 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 187 | GPT-3.5 Turbo (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | ግምት | |
| 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | ግምት | |
| 189 | SPHINX (Shanghai AI Lab, 2023) | ~2 × 10¹⁰ | 2023 | ግምት | |
| 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 191 | CogAgent (Tsinghua University, 2023) | ~1.8 × 10¹⁰ | 2023 | ግምት | |
| 192 | Turing-NLG (Microsoft, 2020) | ~1.7 × 10¹⁰ | 2020 | ግምት | |
| 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 194 | PaLI (Google, 2022) | ~1.7 × 10¹⁰ | 2022 | ግምት | |
| 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 196 | StarCoder (Hugging Face, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 197 | ESM2-15B (Meta AI, 2022) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 198 | FunSearch (Google DeepMind, 2023) | ~1.5 × 10¹⁰ | 2023 | ግምት | |
| 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | አርክቴክቸር አልተገለጸም |
| 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 203 | mT5-XXL (Google, 2020) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2020 | ጥቅጥቅ |
| 204 | mT0-13B (Hugging Face, 2022) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2022 | አርክቴክቸር አልተገለጸም |
| 205 | LLaVA (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 206 | InstructBLIP (Salesforce Research, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | ~1.3 × 10¹⁰ | 2023 | ግምት | |
| 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 211 | LLaVA 1.5 (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ጥቅጥቅ |
| 212 | Volcano 13B (Korea University, 2023) | ~1.3 × 10¹⁰ | 2023 | ግምት | |
| 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | ~1.3 × 10¹⁰ | 2024 | ግምት | |
| 214 | Kaiju Small (Character.ai, 2025) | ~1.3 × 10¹⁰ | 2025 | ግምት | |
| 215 | ByT5-XXL (Google, 2021) | ~1.3 × 10¹⁰ | 2021 | ግምት | |
| 216 | CoRe (Tsinghua University, 2023) | ~1.2 × 10¹⁰ | 2023 | ግምት | |
| 217 | DALL-E (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | ግምት | |
| 218 | Codex (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | ግምት | |
| 219 | T5-11B (Google, 2019) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2019 | ጥቅጥቅ |
| 220 | UnifiedQA (Allen Institute for AI, 2020) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2020 | ጥቅጥቅ |
| 221 | Unicorn (Allen Institute for AI, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | አርክቴክቸር አልተገለጸም |
| 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | አርክቴክቸር አልተገለጸም |
| 223 | CoEdiT-xxl (University of Minnesota, 2023) | ~1.1 × 10¹⁰ | 2023 | ግምት | |
| 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | አርክቴክቸር አልተገለጸም |
| 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | አርክቴክቸር አልተገለጸም |
| 226 | Llama 3.2 11B (Meta AI, 2024) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2024 | ጥቅጥቅ |
| 227 | ERNIE 3.0 (Baidu, 2021) | 1 × 10¹⁰ | 1 × 10¹⁰ | 2021 | ጥቅጥቅ |
| 228 | ERNIE-ViLG (Baidu, 2021) | ~1 × 10¹⁰ | 2021 | ግምት | |
| 229 | Qwen-VL (Alibaba, 2023) | ~9.6 × 10⁹ | 2023 | ግምት | |
| 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 232 | CogVideo (Tsinghua University, 2022) | ~9.4 × 10⁹ | 2022 | ግምት | |
| 233 | eDiff-I (Nvidia, 2022) | ~9.1 × 10⁹ | 2022 | ግምት | |
| 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | አርክቴክቸር አልተገለጸም |
| 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 236 | Qwen-Audio-Chat (Alibaba, 2023) | ~8.5 × 10⁹ | 2023 | ግምት | |
| 237 | Megatron-LM (Nvidia, 2019) | ~8.3 × 10⁹ | 2019 | ግምት | |
| 238 | BigSSL (Google, 2021) | ~8 × 10⁹ | 2021 | ግምት | |
| 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | ጥቅጥቅ |
| 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 242 | Imagen (Google Brain, 2022) | ~7.8 × 10⁹ | 2022 | ግምት | |
| 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 246 | mPLUG-Owl2 (Alibaba, 2023) | ~7.1 × 10⁹ | 2023 | ግምት | |
| 247 | Goat-7B (National University of Singapore, 2023) | ~7 × 10⁹ | 2023 | ግምት | |
| 248 | Llama 2-7B (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | ጥቅጥቅ |
| 249 | Llama Guard (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | ጥቅጥቅ |
| 250 | Qwen-VL-Max (Alibaba, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | ጥቅጥቅ |
| 251 | Mathstral (Mistral AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | ~7 × 10⁹ | 2025 | ግምት | |
| 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | አርክቴክቸር አልተገለጸም |
| 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 257 | GPipe (Google, 2018) | ~6 × 10⁹ | 2018 | ግምት | |
| 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | ጥቅጥቅ |
| 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | ~6 × 10⁹ | 2023 | ግምት | |
| 261 | MolmoAct 2 (Allen Institute for AI, 2026) | ~5.5 × 10⁹ | 2026 | ግምት | |
| 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 265 | Mesh-TensorFlow Transformer 4.9B (Google Brain, 2018) | 4.9 × 10⁹ | 4.9 × 10⁹ | 2018 | ጥቅጥቅ |
| 266 | W.A.L.T (Stanford University, 2023) | 4.7 × 10⁹ | 4.7 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 267 | CogView (Tsinghua University, 2021) | ~4 × 10⁹ | 2021 | ግምት | |
| 268 | Make-A-Scene (Meta AI, 2022) | ~4 × 10⁹ | 2022 | ግምት | |
| 269 | ONE-PEACE (Alibaba, 2023) | ~4 × 10⁹ | 2023 | ግምት | |
| 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | ጥቅጥቅ |
| 271 | DALL·E 2 (OpenAI, 2022) | ~3.5 × 10⁹ | 2022 | ግምት | |
| 272 | Stable Diffusion XL (Stability AI, 2023) | ~3.4 × 10⁹ | 2023 | ግምት | |
| 273 | MusicGen (Meta AI, 2023) | ~3.4 × 10⁹ | 2023 | ግምት | |
| 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | ~3.2 × 10⁹ | 2016 | ግምት | |
| 278 | BASIC-L (Google, 2021) | ~3.1 × 10⁹ | 2021 | ግምት | |
| 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | ~2.9 × 10⁹ | 2018 | ግምት | |
| 287 | T5-3B (Google, 2019) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2019 | ጥቅጥቅ |
| 288 | DPO on Pythia-2.8B (Stanford University, 2023) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2023 | ጥቅጥቅ |
| 289 | AFM-on-device (Apple, 2024) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | ~2.7 × 10⁹ | 2022 | ግምት | |
| 291 | Hybrid H3-2.7B (Stanford University, 2022) | ~2.7 × 10⁹ | 2022 | ግምት | |
| 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 293 | Meena (Google Brain, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | ጥቅጥቅ |
| 294 | CPM-Large (Tsinghua University, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | ጥቅጥቅ |
| 295 | Nucleotide Transformer (Nvidia, 2023) | ~2.5 × 10⁹ | 2023 | ግምት | |
| 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | ጥቅጥቅ |
| 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 302 | MuseNet (OpenAI, 2019) | ~2 × 10⁹ | 2019 | ግምት | |
| 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | አርክቴክቸር አልተገለጸም |
| 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | ጥቅጥቅ |
| 305 | BEIT-3 (Microsoft, 2022) | ~1.9 × 10⁹ | 2022 | ግምት | |
| 306 | Ankh_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 309 | Phenaki (University College London (UCL), 2022) | ~1.8 × 10⁹ | 2022 | ግምት | |
| 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 311 | DistBelief Vision (Google, 2012) | ~1.7 × 10⁹ | 2012 | ግምት | |
| 312 | Whisper (OpenAI, 2022) | ~1.6 × 10⁹ | 2022 | ግምት | |
| 313 | GPT-2 (OpenAI, 2019) | ~1.5 × 10⁹ | 2019 | ግምት | |
| 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 316 | AudioLM (Google Research, 2023) | ~1.5 × 10⁹ | 2023 | ግምት | |
| 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 319 | Stable Diffusion (Runway, 2022) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2022 | ጥቅጥቅ |
| 320 | SEER (Facebook AI Research, 2021) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2021 | አርክቴክቸር አልተገለጸም |
| 321 | InstructGPT 1.3B (OpenAI, 2022) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2022 | ጥቅጥቅ |
| 322 | Megatron-LM (Nvidia, 2019) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2019 | ጥቅጥቅ |
| 323 | RDT-1B (Tsinghua University, 2024) | ~1.2 × 10⁹ | 2024 | ግምት | |
| 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | አርክቴክቸር አልተገለጸም |
| 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | አርክቴክቸር አልተገለጸም |
| 329 | Unsupervised High-level Feature Learner (Google, 2012) | ~1 × 10⁹ | 2012 | ግምት | |
| 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | ጥቅጥቅ |
| 331 | HuBERT (Facebook AI Research, 2021) | ~1 × 10⁹ | 2021 | ግምት | |
| 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | ጥቅጥቅ |
| 333 | AudioGen (Meta AI, 2023) | ~1 × 10⁹ | 2023 | ግምት | |
| 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | አርክቴክቸር አልተገለጸም |
| 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | ~9.4 × 10⁸ | 2013 | ግምት | |
| 336 | ImageBind (Meta AI, 2023) | ~9.3 × 10⁸ | 2023 | ግምት | |
| 337 | Big Transfer (Google Brain, 2019) | ~9.3 × 10⁸ | 2019 | ግምት | |
| 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 341 | ResNeXt-101 32x48d (Facebook, 2018) | 8.3 × 10⁸ | 8.3 × 10⁸ | 2018 | ጥቅጥቅ |
| 342 | FixRes ResNeXt-101 WSL (Facebook AI, 2019) | ~8.3 × 10⁸ | 2019 | ግምት | |
| 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 344 | Statement Curriculum Learning (OpenAI, 2022) | ~7.7 × 10⁸ | 2022 | ግምት | |
| 345 | CodeT5-large (Salesforce, 2022) | ~7.7 × 10⁸ | 2022 | ግምት | |
| 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | አርክቴክቸር አልተገለጸም |
| 348 | data2vec (Meta AI, 2022) | ~7.1 × 10⁸ | 2022 | ግምት | |
| 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 350 | Word2Vec (Google, 2013) | ~6.9 × 10⁸ | 2013 | ግምት | |
| 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | ~6.8 × 10⁸ | 2023 | ግምት | |
| 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 354 | XLM (Facebook, 2019) | ~6.7 × 10⁸ | 2019 | ግምት | |
| 355 | ESM1b (Facebook AI Research, 2020) | 6.5 × 10⁸ | 6.5 × 10⁸ | 2020 | ጥቅጥቅ |
| 356 | Segment Anything Model (Meta AI, 2023) | 6.4 × 10⁸ | 6.4 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 357 | ViT-Huge/14 (Google Brain, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | ጥቅጥቅ |
| 358 | Masked Autoencoders ViT-H (Facebook AI Research, 2021) | ~6.3 × 10⁸ | 2021 | ግምት | |
| 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | ጥቅጥቅ |
| 360 | DLDL (University of Oxford, 2016) | ~5.6 × 10⁸ | 2016 | ግምት | |
| 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | ጥቅጥቅ |
| 364 | LUKE (University of Washington, 2020) | ~4.8 × 10⁸ | 2020 | ግምት | |
| 365 | EfficientNet-L2 (Google, 2019) | ~4.8 × 10⁸ | 2019 | ግምት | |
| 366 | Noisy Student (Carnegie Mellon University (CMU), 2019) | 4.8 × 10⁸ | 4.8 × 10⁸ | 2019 | ጥቅጥቅ |
| 367 | Meta Pseudo Labels (Google Brain, 2021) | ~4.8 × 10⁸ | 2021 | ግምት | |
| 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 369 | NMT Transformer 437M (Google, 2019) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2019 | ጥቅጥቅ |
| 370 | Adaptive Inputs + LayerDrop (Facebook AI Research, 2019) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2019 | ጥቅጥቅ |
| 371 | ProtBERT-BFD (Technical University of Munich, 2021) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 372 | OntoProtein (Zhejiang University (ZJU), 2022) | ~4.2 × 10⁸ | 2022 | ግምት | |
| 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 374 | BART-large (Facebook AI, 2019) | ~4.1 × 10⁸ | 2019 | ግምት | |
| 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 376 | BERT-Large-CAS (Amazon, 2019) | ~4 × 10⁸ | 2019 | ግምት | |
| 377 | MoCo (Facebook AI, 2019) | ~3.8 × 10⁸ | 2019 | ግምት | |
| 378 | SimCLR (Google Brain, 2020) | ~3.8 × 10⁸ | 2020 | ግምት | |
| 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 380 | RoBERTa Large (Facebook, 2019) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2019 | ጥቅጥቅ |
| 381 | AbLang (University of Oxford, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | ጥቅጥቅ |
| 383 | VALL-E (Microsoft, 2023) | ~3.5 × 10⁸ | 2023 | ግምት | |
| 384 | BERT-Large (Google, 2018) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2018 | ጥቅጥቅ |
| 385 | XLNet (Carnegie Mellon University (CMU), 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | ጥቅጥቅ |
| 386 | ERNIE-GEN (Baidu, 2020) | ~3.4 × 10⁸ | 2020 | ግምት | |
| 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | ጥቅጥቅ |
| 388 | ELECTRA (Stanford University, 2020) | ~3.4 × 10⁸ | 2020 | ግምት | |
| 389 | German ELECTRA Large (deepset, 2020) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2020 | ጥቅጥቅ |
| 390 | ALM 1.0 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | ~3.4 × 10⁸ | 2022 | ግምት | |
| 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | ጥቅጥቅ |
| 392 | Fusion in Encoder (Samsung, 2022) | ~3.3 × 10⁸ | 2022 | ግምት | |
| 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | ጥቅጥቅ |
| 394 | CPC v2 (DeepMind, 2019) | ~3 × 10⁸ | 2019 | ግምት | |
| 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | ጥቅጥቅ |
| 396 | DDPM-IP (Utrecht University, 2023) | ~3 × 10⁸ | 2023 | ግምት | |
| 397 | GNMT (Google, 2016) | ~2.8 × 10⁸ | 2016 | ግምት | |
| 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 399 | YOLO (University of Washington, 2015) | ~2.7 × 10⁸ | 2015 | ግምት | |
| 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | ~2.6 × 10⁸ | 2019 | ግምት | |
| 401 | Transformer-XL + RMS dynamic eval (University of Edinburgh, 2019) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2019 | ጥቅጥቅ |
| 402 | Transformer-XL Large + Phrase Induction (Massachusetts Institute of Technology (MIT), 2019) | ~2.6 × 10⁸ | 2019 | ግምት | |
| 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | ጥቅጥቅ |
| 407 | S4 (Stanford University, 2021) | ~2.5 × 10⁸ | 2021 | ግምት | |
| 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | ~2.5 × 10⁸ | 2019 | ግምት | |
| 409 | ERNIE-Doc (Baidu, 2020) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2020 | ጥቅጥቅ |
| 410 | Adaptive Input Transformer + RD (Microsoft Research Asia, 2021) | ~2.5 × 10⁸ | 2021 | ግምት | |
| 411 | Transformer (Adaptive Input Embeddings) WT103 (Facebook AI Research, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | ጥቅጥቅ |
| 412 | TaLK Convolution (Carleton University, 2020) | ~2.4 × 10⁸ | 2020 | ግምት | |
| 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | ~2.4 × 10⁸ | 2020 | ግምት | |
| 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | አርክቴክቸር አልተገለጸም |
| 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | አርክቴክቸር አልተገለጸም |
| 417 | MetNet (Google, 2020) | ~2.3 × 10⁸ | 2020 | ግምት | |
| 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | ጥቅጥቅ |
| 419 | CodeT5-base (Salesforce, 2021) | ~2.2 × 10⁸ | 2021 | ግምት | |
| 420 | Transformer (Google Research, 2017) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2017 | ጥቅጥቅ |
| 421 | Sandwich Transformer (Allen Institute for AI, 2019) | ~2.1 × 10⁸ | 2019 | ግምት | |
| 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 423 | Word2Vec (Google, 2013) | ~2.1 × 10⁸ | 2013 | ግምት | |
| 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | ጥቅጥቅ |
| 425 | DreamerV3 (DeepMind, 2023) | ~2 × 10⁸ | 2023 | ግምት | |
| 426 | DL scaling speech (Baidu, 2017) | ~1.9 × 10⁸ | 2017 | ግምት | |
| 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | ~1.9 × 10⁸ | 2019 | ግምት | |
| 428 | (NTT Communication Science Laboratories, 2018) | 1.9 × 10⁸ | 1.9 × 10⁸ | 2018 | ጥቅጥቅ |
| 429 | TrellisNet (Carnegie Mellon University (CMU), 2018) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2018 | ጥቅጥቅ |
| 430 | DL scaling LM (Baidu, 2017) | ~1.8 × 10⁸ | 2017 | ግምት | |
| 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | አርክቴክቸር አልተገለጸም |
| 432 | OpenAI Five Rerun (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | ጥቅጥቅ |
| 433 | OpenAI Five (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | ጥቅጥቅ |
| 434 | 4 layer QRNN (Salesforce Research, 2018) | ~1.5 × 10⁸ | 2018 | ግምት | |
| 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | ~1.5 × 10⁸ | 2020 | ግምት | |
| 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | አርክቴክቸር አልተገለጸም |
| 437 | PermuteFormer (Peking University, 2021) | ~1.5 × 10⁸ | 2021 | ግምት | |
| 438 | Fragment embedding (Stanford University, 2014) | ~1.4 × 10⁸ | 2014 | ግምት | |
| 439 | OverFeat (New York University (NYU), 2013) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2013 | ጥቅጥቅ |
| 440 | VGG19 (University of Oxford, 2014) | ~1.4 × 10⁸ | 2014 | ግምት | |
| 441 | LRCN (UT Austin, 2014) | ~1.4 × 10⁸ | 2014 | ግምት | |
| 442 | Transformer-XL DeFINE (University of Washington, 2019) | ~1.4 × 10⁸ | 2019 | ግምት | |
| 443 | Deep Autoencoders (University of Toronto, 2011) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2011 | ጥቅጥቅ |
| 444 | AlphaStar (DeepMind, 2019) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2019 | ጥቅጥቅ |
| 445 | VGG16 (University of Oxford, 2014) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2014 | ጥቅጥቅ |
| 446 | VGG-Face (University of Oxford, 2015) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2015 | ጥቅጥቅ |
| 447 | SAF R-CNN (Beijing Institute of Technology, 2015) | ~1.4 × 10⁸ | 2015 | ግምት | |
| 448 | Template Adaptation (University of Oxford, 2016) | ~1.4 × 10⁸ | 2016 | ግምት | |
| 449 | LRR-4X (UC Irvine, 2016) | ~1.4 × 10⁸ | 2016 | ግምት | |
| 450 | CMS-RCNN (IEEE, 2016) | ~1.4 × 10⁸ | 2016 | ግምት | |
| 451 | QRNN (Salesforce Research, 2018) | ~1.4 × 10⁸ | 2018 | ግምት | |
| 452 | Multiresolution CNN (Google, 2014) | ~1.3 × 10⁸ | 2014 | ግምት | |
| 453 | FTW (DeepMind, 2018) | ~1.3 × 10⁸ | 2018 | ግምት | |
| 454 | Feedback Transformer (LORIA, 2020) | ~1.3 × 10⁸ | 2020 | ግምት | |
| 455 | KEPLER (Tsinghua University, 2020) | 1.3 × 10⁸ | 1.3 × 10⁸ | 2020 | ጥቅጥቅ |
| 456 | DL scaling Image (Baidu, 2017) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2017 | ጥቅጥቅ |
| 457 | GloVe (Stanford University, 2014) | ~1.2 × 10⁸ | 2014 | ግምት | |
| 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | ጥቅጥቅ |
| 459 | GPT-1 (OpenAI, 2018) | ~1.2 × 10⁸ | 2018 | ግምት | |
| 460 | ContextNet (Google, 2020) | ~1.1 × 10⁸ | 2020 | ግምት | |
| 461 | SciBERT (Allen Institute for AI, 2019) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2019 | ጥቅጥቅ |
| 462 | DNABERT (Northeastern University, 2021) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2021 | አርክቴክቸር አልተገለጸም |
| 463 | Contriever (Meta AI, 2021) | ~1.1 × 10⁸ | 2021 | ግምት | |
| 464 | BERT-RBP (Waseda University, 2022) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2022 | ጥቅጥቅ |
| 465 | RoFormer (Zhuiyi Technology, 2023) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2023 | አርክቴክቸር አልተገለጸም |
| 466 | LSTM LM (RWTH Aachen University, 2012) | ~1 × 10⁸ | 2012 | ግምት | |
| 467 | ReLU-Speech (Google, 2013) | ~1 × 10⁸ | 2013 | ግምት | |
| 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | አርክቴክቸር አልተገለጸም |
| 469 | Support Vector Machines (AT&T, 1995) | ~1 × 10⁸ | 1995 | ግምት | |
| 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | ጥቅጥቅ |
| 471 | MSA Transformer (Facebook AI Research, 2021) | ~1 × 10⁸ | 2021 | ግምት | |
| 472 | YOLOX-X (Megvii Inc, 2021) | ~9.9 × 10⁷ | 2021 | ግምት | |
| 473 | DeLighT (University of Washington, 2020) | ~9.9 × 10⁷ | 2020 | ግምት | |
| 474 | ELMo (University of Washington, 2018) | ~9.4 × 10⁷ | 2018 | ግምት | |
| 475 | AlphaFold 2 (DeepMind, 2020) | ~9.3 × 10⁷ | 2020 | ግምት | |
| 476 | AlphaMissense (Google DeepMind, 2023) | ~9.3 × 10⁷ | 2023 | ግምት | |
| 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | አርክቴክቸር አልተገለጸም |
| 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | ~9.2 × 10⁷ | 2016 | ግምት | |
| 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | አርክቴክቸር አልተገለጸም |
| 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | ጥቅጥቅ |
| 481 | NASNet-A (Google Brain, 2017) | ~8.9 × 10⁷ | 2017 | ግምት | |
| 482 | CLIP (OpenAI, 2021) | ~8.9 × 10⁷ | 2021 | ግምት | |
| 483 | Detic (Meta AI, 2022) | ~8.8 × 10⁷ | 2022 | ግምት | |
| 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | ጥቅጥቅ |
| 485 | BigBiGAN (Google, 2019) | ~8.6 × 10⁷ | 2019 | ግምት | |
| 486 | ViT-Base/32 (Google Brain, 2020) | ~8.6 × 10⁷ | 2020 | ግምት | |
| 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | አርክቴክቸር አልተገለጸም |
| 488 | CaLM (University of Oxford, 2022) | ~8.6 × 10⁷ | 2022 | ግምት | |
| 489 | Tensor-Transformer (University of California (UC) Berkeley, 2020) | 8.5 × 10⁷ | 8.5 × 10⁷ | 2020 | ጥቅጥቅ |
| 490 | ResNeXt-101 (University of California San Diego, 2016) | 8.3 × 10⁷ | 8.3 × 10⁷ | 2016 | ጥቅጥቅ |
| 491 | RBM Image Classifier (University of Toronto, 2009) | ~8 × 10⁷ | 2009 | ግምት | |
| 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | ጥቅጥቅ |
| 493 | Big-Little Net (IBM, 2018) | ~7.7 × 10⁷ | 2018 | ግምት | |
| 494 | EfficientDet (Google Brain, 2020) | ~7.7 × 10⁷ | 2020 | ግምት | |
| 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | አርክቴክቸር አልተገለጸም |
| 496 | RNN LM (Johns Hopkins University, 2010) | ~7 × 10⁷ | 2010 | ግምት | |
| 497 | R-CNN (University of California (UC) Berkeley, 2013) | ~6.9 × 10⁷ | 2013 | ግምት | |
| 498 | Variational (University of Cambridge, 2015) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2015 | ጥቅጥቅ |
| 499 | DistilBERT (Hugging Face, 2019) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2019 | ጥቅጥቅ |
| 500 | Theseus 6/768 (University of California San Diego, 2020) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2020 | ጥቅጥቅ |
| 501 | ResNet-152 (Microsoft, 2015) | 6 × 10⁷ | 6 × 10⁷ | 2015 | ጥቅጥቅ |
| 502 | AlexNet (University of Toronto, 2012) | 6 × 10⁷ | 6 × 10⁷ | 2012 | ጥቅጥቅ |
| 503 | DETR (Facebook, 2020) | 6 × 10⁷ | 6 × 10⁷ | 2020 | ጥቅጥቅ |
| 504 | YOLOv3 (University of Washington, 2018) | ~5.7 × 10⁷ | 2018 | ግምት | |
| 505 | Inception-ResNet-V2 (Google, 2016) | ~5.6 × 10⁷ | 2016 | ግምት | |
| 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | ጥቅጥቅ |
| 507 | የዝንብ አንጎል | 5.5 × 10⁷ | 5.5 × 10⁷ | ሲናፕሶች | |
| 508 | NAS with base 8 and shared embeddings (Google Brain, 2016) | 5.4 × 10⁷ | 5.4 × 10⁷ | 2016 | ጥቅጥቅ |
| 509 | RetinaNet-R101 (Facebook AI Research, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | ጥቅጥቅ |
| 510 | AWD-LSTM+WT+Cache+IOG (NTT Communication Science Laboratories, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | ጥቅጥቅ |
| 511 | Hierarchical Scene Labeling (New York University (NYU), 2013) | 5.2 × 10⁷ | 5.2 × 10⁷ | 2013 | ጥቅጥቅ |
| 512 | VD-LSTM+REAL Large (Salesforce Research, 2016) | 5.1 × 10⁷ | 5.1 × 10⁷ | 2016 | ጥቅጥቅ |
| 513 | YOLOv2 (University of Washington, 2016) | ~5.1 × 10⁷ | 2016 | ግምት | |
| 514 | Dropout-LSTM+Noise (Columbia University, 2018) | ~5.1 × 10⁷ | 2018 | ግምት | |
| 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | ~5 × 10⁷ | 2017 | ግምት | |
| 516 | Dropout (University of Toronto, 2012) | ~4.9 × 10⁷ | 2012 | ግምት | |
| 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | ጥቅጥቅ |
| 518 | DistBelief Speech (Google, 2012) | ~4.7 × 10⁷ | 2012 | ግምት | |
| 519 | AlphaGo Zero (DeepMind, 2017) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2017 | ጥቅጥቅ |
| 520 | Fine-tuned-AWD-LSTM-DOC (Samsung R&D Institute Russia, 2018) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2018 | ጥቅጥቅ |
| 521 | CT-MoS (Google, 2020) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2020 | ጥቅጥቅ |
| 522 | JFT (Google Research, 2017) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2017 | ጥቅጥቅ |
| 523 | ResNet-101 (Microsoft, 2015) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2015 | ጥቅጥቅ |
| 524 | HR-ResNet101 (Carnegie Mellon University (CMU), 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | ጥቅጥቅ |
| 525 | 3DMM-CNN (University of Southern California, 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | ጥቅጥቅ |
| 526 | LaNet-L (Brown University, 2019) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2019 | ጥቅጥቅ |
| 527 | ProBERTa (University of Illinois Urbana-Champaign (UIUC), 2020) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2020 | ጥቅጥቅ |
| 528 | PreTrans-3L-250H (University of Toronto, 2013) | ~4.3 × 10⁷ | 2013 | ግምት | |
| 529 | Inceptionv4 (Google, 2016) | ~4.3 × 10⁷ | 2016 | ግምት | |
| 530 | GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (Ben-Gurion University of the Negev, 2017) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2017 | ጥቅጥቅ |
| 531 | U-Net (University of Freiburg, 2015) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2015 | ጥቅጥቅ |
| 532 | NASv3 (Google Brain, 2016) | ~3.7 × 10⁷ | 2016 | ግምት | |
| 533 | MuZero (DeepMind, 2019) | ~3.7 × 10⁷ | 2019 | ግምት | |
| 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | ~3.5 × 10⁷ | 2017 | ግምት | |
| 535 | AWD-LSTM-MoS+PDR + dynamic evaluation (IBM, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | ጥቅጥቅ |
| 536 | AWD-LSTM-MoS + dynamic evaluation (Peking University, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | ጥቅጥቅ |
| 537 | AWD-LSTM + MoS + Partial Shuffled (University of Texas at Austin, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | ጥቅጥቅ |
| 538 | Mogrifier (d2, MoS2, MC) + dynamic eval (DeepMind, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | ጥቅጥቅ |
| 539 | Mogrifier RLSTM (DeepMind, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | አርክቴክቸር አልተገለጸም |
| 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | አርክቴክቸር አልተገለጸም |
| 541 | DenseNet-264 (Tsinghua University, 2016) | 3.4 × 10⁷ | 3.4 × 10⁷ | 2016 | ጥቅጥቅ |
| 542 | RetinaNet-R50 (Facebook AI Research, 2017) | ~3.4 × 10⁷ | 2017 | ግምት | |
| 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | ~3.4 × 10⁷ | 2017 | ግምት | |
| 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | ~3.4 × 10⁷ | 2019 | ግምት | |
| 545 | AWD-LSTM - 3-layer LSTM (Salesforce Research, 2017) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2017 | ጥቅጥቅ |
| 546 | TCAN (Nanjing University, 2020) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2020 | ጥቅጥቅ |
| 547 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~3.2 × 10⁷ | 2019 | ግምት | |
| 548 | Paragraph Vector (Google, 2014) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2014 | ጥቅጥቅ |
| 549 | aLSTM (University of Manchester, 2018) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2018 | ጥቅጥቅ |
| 550 | StyleGAN2 (Nvidia, 2019) | 3 × 10⁷ | 3 × 10⁷ | 2019 | ጥቅጥቅ |
| 551 | UDSMProt (Fraunhofer Heinrich Hertz Institute, 2019) | ~2.8 × 10⁷ | 2019 | ግምት | |
| 552 | SENet (Chinese Academy of Sciences, 2017) | ~2.8 × 10⁷ | 2017 | ግምት | |
| 553 | Perceiver IO (DeepMind, 2020) | 2.8 × 10⁷ | 2.8 × 10⁷ | 2020 | ጥቅጥቅ |
| 554 | Fractional Max-Pooling (University of Warwick, 2014) | ~2.7 × 10⁷ | 2014 | ግምት | |
| 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | አርክቴክቸር አልተገለጸም |
| 556 | RNN-WER (DeepMind, 2014) | ~2.6 × 10⁷ | 2014 | ግምት | |
| 557 | StyleGAN (Nvidia, 2018) | 2.6 × 10⁷ | 2.6 × 10⁷ | 2018 | ጥቅጥቅ |
| 558 | PyramidNet (Korea Advanced Institute of Science and Technology (KAIST), 2017) | ~2.6 × 10⁷ | 2017 | ግምት | |
| 559 | ResNeXt-50 (University of California San Diego, 2016) | ~2.5 × 10⁷ | 2016 | ግምት | |
| 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | ጥቅጥቅ |
| 561 | Inception v3 (Google, 2015) | ~2.4 × 10⁷ | 2015 | ግምት | |
| 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | አርክቴክቸር አልተገለጸም |
| 563 | WeNet (Amazon, 2019) | ~2.3 × 10⁷ | 2019 | ግምት | |
| 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | ~2.3 × 10⁷ | 2019 | ግምት | |
| 565 | NAS+ESS (Northeastern University (China), 2020) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2020 | ጥቅጥቅ |
| 566 | Xception (Google, 2016) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2016 | ጥቅጥቅ |
| 567 | MetaMimic (Google, 2018) | ~2.2 × 10⁷ | 2018 | ግምት | |
| 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~2.1 × 10⁷ | 2019 | ግምት | |
| 569 | Pointer Sentinel-LSTM (MetaMind Inc, 2016) | 2.1 × 10⁷ | 2.1 × 10⁷ | 2016 | ጥቅጥቅ |
| 570 | DiffDock (Massachusetts Institute of Technology (MIT), 2022) | ~2 × 10⁷ | 2022 | ግምት | |
| 571 | EI-REHN-1000D (Korea Advanced Institute of Science and Technology (KAIST), 2017) | 1.9 × 10⁷ | 1.9 × 10⁷ | 2017 | ጥቅጥቅ |
| 572 | TC-DNN-BLSTM-DNN (Carnegie Mellon University (CMU), 2015) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2015 | ጥቅጥቅ |
| 573 | OR-WideResNet (Duke University, 2017) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2017 | ጥቅጥቅ |
| 574 | BP-DBN (University of Toronto, 2009) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2009 | ጥቅጥቅ |
| 575 | ALBERT (Toyota Technological Institute at Chicago, 2019) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2019 | ጥቅጥቅ |
| 576 | MedBERT (Peng Cheng Laboratory, 2021) | ~1.7 × 10⁷ | 2021 | ግምት | |
| 577 | LCNP NORB (, 2009) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2009 | ጥቅጥቅ |
| 578 | LF-MMI (Johns Hopkins University, 2016) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2016 | ጥቅጥቅ |
| 579 | AlphaFold (DeepMind, 2020) | ~1.6 × 10⁷ | 2020 | ግምት | |
| 580 | GNoME for crystal discovery (Google DeepMind, 2023) | ~1.6 × 10⁷ | 2023 | ግምት | |
| 581 | ReLU (University of Toronto, 2010) | ~1.6 × 10⁷ | 2010 | ግምት | |
| 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | አርክቴክቸር አልተገለጸም |
| 583 | Eve (Harvard Medical School, 2021) | ~1.5 × 10⁷ | 2021 | ግምት | |
| 584 | LCNP LabelMe (University of Bonn, 2009) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2009 | ጥቅጥቅ |
| 585 | BatchNorm (Google, 2015) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2015 | ጥቅጥቅ |
| 586 | Fold2Seq (IBM, 2021) | ~1.2 × 10⁷ | 2021 | ግምት | |
| 587 | NPLM (University of Montreal / Université de Montréal, 2003) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2003 | ጥቅጥቅ |
| 588 | LCNP MNIST (, 2009) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2009 | ጥቅጥቅ |
| 589 | ISS (Duke University, 2017) | 1.1 × 10⁷ | 1.1 × 10⁷ | 2017 | ጥቅጥቅ |
| 590 | ResNet-1001 (Microsoft, 2016) | ~1 × 10⁷ | 2016 | ግምት | |
| 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | ~9 × 10⁶ | 2012 | ግምት | |
| 592 | Gated HORNN (York University, 2016) | ~9 × 10⁶ | 2016 | ግምት | |
| 593 | AlphaGo Fan (DeepMind, 2015) | ~8.2 × 10⁶ | 2015 | ግምት | |
| 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | ~8 × 10⁶ | 2015 | ግምት | |
| 595 | Once for All (MIT-IBM Watson AI Lab, 2020) | 7.7 × 10⁶ | 7.7 × 10⁶ | 2020 | ጥቅጥቅ |
| 596 | Multi-cell LSTM (University of Hyderabad, 2018) | ~7.2 × 10⁶ | 2018 | ግምት | |
| 597 | Feedforward NN (University of Montreal / Université de Montréal, 2010) | 7.1 × 10⁶ | 7.1 × 10⁶ | 2010 | ጥቅጥቅ |
| 598 | True-Regularization+Finetune+Dynamic-Eval (Mobvoi, 2019) | 7 × 10⁶ | 7 × 10⁶ | 2019 | ጥቅጥቅ |
| 599 | Neural LM (University of Montreal / Université de Montréal, 2000) | 6.9 × 10⁶ | 6.9 × 10⁶ | 2000 | ጥቅጥቅ |
| 600 | GoogLeNet / InceptionV1 (Google, 2014) | 6.8 × 10⁶ | 6.8 × 10⁶ | 2014 | ጥቅጥቅ |
| 601 | HyenaDNA (Stanford University, 2023) | 6.6 × 10⁶ | 6.6 × 10⁶ | 2023 | አርክቴክቸር አልተገለጸም |
| 602 | Dropout (University of Toronto, 2012) | 5.6 × 10⁶ | 5.6 × 10⁶ | 2012 | ጥቅጥቅ |
| 603 | SimpleNet (Sensifai, 2016) | 5.5 × 10⁶ | 5.5 × 10⁶ | 2016 | ጥቅጥቅ |
| 604 | AlphaX-1 (Facebook AI Research, 2019) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2019 | ጥቅጥቅ |
| 605 | 3DDFA (Chinese Academy of Sciences, 2015) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2015 | ጥቅጥቅ |
| 606 | Deep CNN + COTS (IEEE, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | ጥቅጥቅ |
| 607 | DCNN (University of Maryland, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | ጥቅጥቅ |
| 608 | NLP from scratch (NEC Laboratories, 2011) | ~5 × 10⁶ | 2011 | ግምት | |
| 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | ~5 × 10⁶ | 2014 | ግምት | |
| 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | አርክቴክቸር አልተገለጸም |
| 611 | High Performance CNN (IDSIA, 2011) | ~4.9 × 10⁶ | 2011 | ግምት | |
| 612 | MobileNet (Google, 2017) | ~4.2 × 10⁶ | 2017 | ግምት | |
| 613 | NPLM (University of Montreal / Université de Montréal, 2003) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2003 | ጥቅጥቅ |
| 614 | LMICA (, 2004) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2004 | ጥቅጥቅ |
| 615 | PoE MNIST (University College London (UCL), 2000) | 3.9 × 10⁶ | 3.9 × 10⁶ | 2000 | ጥቅጥቅ |
| 616 | Dimensionality Reduction (University of Toronto, 2006) | ~3.8 × 10⁶ | 2006 | ግምት | |
| 617 | GOAT (DeepMind, 2021) | ~3.5 × 10⁶ | 2021 | ግምት | |
| 618 | MobileNetV2 (Google, 2018) | ~3.4 × 10⁶ | 2018 | ግምት | |
| 619 | Big-Little Net (IBM, 2018) | ~3.3 × 10⁶ | 2018 | ግምት | |
| 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | አርክቴክቸር አልተገለጸም |
| 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | ጥቅጥቅ |
| 622 | DeepStack (University of Alberta, 2017) | ~2.5 × 10⁶ | 2017 | ግምት | |
| 623 | KataGo (Jane Street, 2019) | ~2.5 × 10⁶ | 2019 | ግምት | |
| 624 | ShuffleNet v1 (Megvii Inc, 2017) | ~2.4 × 10⁶ | 2017 | ግምት | |
| 625 | ADAM (University of Amsterdam, 2014) | 2.4 × 10⁶ | 2.4 × 10⁶ | 2014 | ጥቅጥቅ |
| 626 | StyleGAN3-T (Nvidia, 2021) | 2.2 × 10⁶ | 2.2 × 10⁶ | 2021 | አርክቴክቸር አልተገለጸም |
| 627 | LSTM+NeuralCache (KU Leuven, 2018) | ~2.1 × 10⁶ | 2018 | ግምት | |
| 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | ጥቅጥቅ |
| 629 | ResNet-110 (Microsoft, 2015) | ~1.7 × 10⁶ | 2015 | ግምት | |
| 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | ጥቅጥቅ |
| 631 | DQN-2015 (Google, 2015) | ~1.7 × 10⁶ | 2015 | ግምት | |
| 632 | Rational DQN Average (TU Darmstadt, 2021) | ~1.7 × 10⁶ | 2021 | ግምት | |
| 633 | IBM-5 (IBM, 1993) | ~1.7 × 10⁶ | 1993 | ግምት | |
| 634 | Deep Belief Nets (University of Toronto, 2006) | ~1.6 × 10⁶ | 2006 | ግምት | |
| 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | ጥቅጥቅ |
| 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | አርክቴክቸር አልተገለጸም |
| 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | ~1.5 × 10⁶ | 2008 | ግምት | |
| 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | ጥቅጥቅ |
| 639 | CNN committee (IDSIA, 2011) | ~1.4 × 10⁶ | 2011 | ግምት | |
| 640 | SqueezeNet (DeepScale, 2016) | ~1.2 × 10⁶ | 2016 | ግምት | |
| 641 | Neocognitron (NHK Broadcasting Science Research Laboratories, 1980) | 1.1 × 10⁶ | 1.1 × 10⁶ | 1980 | ጥቅጥቅ |
| 642 | CURL (University of California (UC) Berkeley, 2020) | ~9.1 × 10⁵ | 2020 | ግምት | |
| 643 | DQN (DeepMind, 2013) | 8.4 × 10⁵ | 8.4 × 10⁵ | 2013 | ጥቅጥቅ |
| 644 | EnhanceNet (Max Planck Institute for Intelligent Systems, 2016) | 8.1 × 10⁵ | 8.1 × 10⁵ | 2016 | ጥቅጥቅ |
| 645 | Image generation (University of Amsterdam, 2013) | 7.8 × 10⁵ | 7.8 × 10⁵ | 2013 | ጥቅጥቅ |
| 646 | Hanabi 4 player (DeepMind, 2019) | 7.6 × 10⁵ | 7.6 × 10⁵ | 2019 | ጥቅጥቅ |
| 647 | TA-CNN (Chinese University of Hong Kong (CUHK), 2014) | ~7.1 × 10⁵ | 2014 | ግምት | |
| 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | አርክቴክቸር አልተገለጸም |
| 649 | LISSOM (University of Texas at Austin, 1995) | ~4.3 × 10⁵ | 1995 | ግምት | |
| 650 | SPIDER2 (Griffith University, 2016) | ~4.1 × 10⁵ | 2016 | ግምት | |
| 651 | Mixture of linear models (, 1994) | ~3.8 × 10⁵ | 1994 | ግምት | |
| 652 | NPD (IEEE, 2014) | 3.1 × 10⁵ | 3.1 × 10⁵ | 2014 | ጥቅጥቅ |
| 653 | Pooling CNN (University of Bonn, 2010) | 2.9 × 10⁵ | 2.9 × 10⁵ | 2010 | ጥቅጥቅ |
| 654 | Pooling CNN (University of Bonn, 2010) | 2.7 × 10⁵ | 2.7 × 10⁵ | 2010 | ጥቅጥቅ |
| 655 | Two Stage Feature Extraction (New York University (NYU), 2009) | 2.6 × 10⁵ | 2.6 × 10⁵ | 2009 | ጥቅጥቅ |
| 656 | Vector Space Model (Stanford University, 2011) | 2.5 × 10⁵ | 2.5 × 10⁵ | 2011 | ጥቅጥቅ |
| 657 | HyperNEAT (University of Texas at Austin, 2014) | ~2.4 × 10⁵ | 2014 | ግምት | |
| 658 | DNN EM segmentation (IDSIA, 2012) | 2.2 × 10⁵ | 2.2 × 10⁵ | 2012 | ጥቅጥቅ |
| 659 | Predictive Coding NN (Technical University of Munich, 1994) | 2.1 × 10⁵ | 2.1 × 10⁵ | 1994 | ጥቅጥቅ |
| 660 | Table Tennis Agent (Google DeepMind, 2024) | ~1.9 × 10⁵ | 2024 | ግምት | |
| 661 | CNN Committee (IDSIA, 2011) | ~1.3 × 10⁵ | 2011 | ግምት | |
| 662 | CNN Committee (IDSIA, 2011) | ~1.2 × 10⁵ | 2011 | ግምት | |
| 663 | RECONTRA-uncategorized (, 1999) | ~1.1 × 10⁵ | 1999 | ግምት | |
| 664 | BLSTM for handwriting (University of Bern, 2007) | ~1 × 10⁵ | 2007 | ግምት | |
| 665 | SVM-CNN (New York University (NYU), 2006) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2006 | ጥቅጥቅ |
| 666 | Invariant CNN (New York University (NYU), 2004) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2004 | ጥቅጥቅ |
| 667 | SexNet compression (, 1990) | 7.3 × 10⁴ | 7.3 × 10⁴ | 1990 | ጥቅጥቅ |
| 668 | NeuroChess (, 1994) | ~7.2 × 10⁴ | 1994 | ግምት | |
| 669 | RECONTRA-categorized (, 1999) | ~6.7 × 10⁴ | 1999 | ግምት | |
| 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | ጥቅጥቅ |
| 671 | Swift (Intel Labs, 2023) | ~5.7 × 10⁴ | 2023 | ግምት | |
| 672 | TCN (Carnegie Mellon University (CMU), 2018) | 4.2 × 10⁴ | 4.2 × 10⁴ | 2018 | ጥቅጥቅ |
| 673 | TRPO (University of California (UC) Berkeley, 2015) | 3.4 × 10⁴ | 3.4 × 10⁴ | 2015 | ጥቅጥቅ |
| 674 | SOM-CNN (, 1997) | 3.2 × 10⁴ | 3.2 × 10⁴ | 1997 | ጥቅጥቅ |
| 675 | NETtalk reimplementation (Oregon State University, 1990) | 2.7 × 10⁴ | 2.7 × 10⁴ | 1990 | ጥቅጥቅ |
| 676 | TD-Gammon (IBM, 1992) | ~2.5 × 10⁴ | 1992 | ግምት | |
| 677 | Cognitron (Biological Cybernetics, 1975) | 2.2 × 10⁴ | 2.2 × 10⁴ | 1975 | ጥቅጥቅ |
| 678 | NetTalk (Princeton University, 1987) | 1.9 × 10⁴ | 1.9 × 10⁴ | 1987 | ጥቅጥቅ |
| 679 | CFSS (SenseTime, 2015) | ~1.7 × 10⁴ | 2015 | ግምት | |
| 680 | MUSIC perceptron (, 1996) | 1.4 × 10⁴ | 1.4 × 10⁴ | 1996 | ጥቅጥቅ |
| 681 | AdaRNN (Beihang University, 2014) | 1.3 × 10⁴ | 1.3 × 10⁴ | 2014 | ጥቅጥቅ |
| 682 | Bidirectional RNN (Advanced Telecommunications Research Institute, 1997) | ~1.3 × 10⁴ | 1997 | ግምት | |
| 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | ~1.2 × 10⁴ | 2001 | ግምት | |
| 684 | LSTM (Technical University of Munich, 1997) | 1.1 × 10⁴ | 1.1 × 10⁴ | 1997 | ጥቅጥቅ |
| 685 | MLN-ASR (McGill University, 1988) | 1 × 10⁴ | 1 × 10⁴ | 1988 | ጥቅጥቅ |
| 686 | Hopfield network (California Institute of Technology, 1982) | ~9.9 × 10³ | 1982 | ግምት | |
| 687 | Zip CNN (AT&T, 1989) | 9.8 × 10³ | 9.8 × 10³ | 1989 | ጥቅጥቅ |
| 688 | Hierarchical Cognitron (NHK Broadcasting Science Research Laboratories, 1984) | ~9.3 × 10³ | 1984 | ግምት | |
| 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | ጥቅጥቅ |
| 690 | Deep Blue (IBM, 1997) | ~8 × 10³ | 1997 | ግምት | |
| 691 | ኔማቶድ (C. elegans) | 7 × 10³ | 7 × 10³ | ሲናፕሶች | |
| 692 | System 11 (Carnegie Mellon University (CMU), 1996) | 6.5 × 10³ | 6.5 × 10³ | 1996 | ጥቅጥቅ |
| 693 | ACF-WIDER (Chinese Academy of Sciences, 2014) | 6.1 × 10³ | 6.1 × 10³ | 2014 | ጥቅጥቅ |
| 694 | RankNet (Microsoft Research, 2005) | 5.7 × 10³ | 5.7 × 10³ | 2005 | ጥቅጥቅ |
| 695 | ANN Eye Tracker (, 1993) | 5.6 × 10³ | 5.6 × 10³ | 1993 | ጥቅጥቅ |
| 696 | JPMAX (, 1994) | ~4.4 × 10³ | 1994 | ግምት | |
| 697 | Kohonen network (Helsinki University of Technology, 1981) | ~4.1 × 10³ | 1981 | ግምት | |
| 698 | Speaker-independent vowel classification (University of Washington, 1989) | 3 × 10³ | 3 × 10³ | 1989 | ጥቅጥቅ |
| 699 | Pattern recognition and reading by machine (Sandia Corporation, 1959) | ~2.6 × 10³ | 1959 | ግምት | |
| 700 | Handwritten digit recognition network (AT&T, 1989) | 2.6 × 10³ | 2.6 × 10³ | 1989 | ጥቅጥቅ |
| 701 | Boosting (Bell Laboratories, 1992) | ~2.6 × 10³ | 1992 | ግምት | |
| 702 | Decision tree adaline (Tokyo Medical and Dental University, 1969) | 2.5 × 10³ | 2.5 × 10³ | 1969 | ጥቅጥቅ |
| 703 | LTE speaker verification system (IBM, 1966) | ~2.1 × 10³ | 1966 | ግምት | |
| 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | ~1.9 × 10³ | 1994 | ግምት | |
| 705 | SexNet classification (, 1990) | ~1.6 × 10³ | 1990 | ግምት | |
| 706 | RAAM (, 1990) | 1.5 × 10³ | 1.5 × 10³ | 1990 | ጥቅጥቅ |
| 707 | Super-vector coding (University of Illinois Urbana-Champaign (UIUC), 2010) | ~1 × 10³ | 2010 | ግምት | |
| 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | ~1 × 10³ | 1957 | ግምት | |
| 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | ~1 × 10³ | 1960 | ግምት | |
| 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | የባለሙያዎች ድብልቅ (ንቁ ቆጠራ ያልታተመ) |
| 711 | Truck backer-upper (Stanford University, 1989) | 8.1 × 10² | 8.1 × 10² | 1989 | ጥቅጥቅ |
| 712 | Siamese-TDNN (Bell Laboratories, 1993) | ~7.4 × 10² | 1993 | ግምት | |
| 713 | MLP with back-propagation (University of California San Diego, 1986) | 7.2 × 10² | 7.2 × 10² | 1986 | ጥቅጥቅ |
| 714 | Cancer drug mechanism prediction (National Cancer Institute, 1992) | ~5.9 × 10² | 1992 | ግምት | |
| 715 | Distributed representation NN (Carnegie Mellon University (CMU), 1986) | 4.3 × 10² | 4.3 × 10² | 1986 | ጥቅጥቅ |
| 716 | Piecewise linear model (University of Kansas, 1973) | 3.6 × 10² | 3.6 × 10² | 1973 | ጥቅጥቅ |
| 717 | ASE+ACE (University of Massachusetts Amherst, 1983) | ~3.2 × 10² | 1983 | ግምት | |
| 718 | LSTM with forget gates (IDSIA, 1999) | ~2.8 × 10² | 1999 | ግምት | |
| 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | ~2.3 × 10² | 1955 | ግምት | |
| 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | ጥቅጥቅ |
| 721 | SNARC (Harvard University, 1952) | ~4 × 10¹ | 1952 | ግምት | |
| 722 | Bankruptcy-NN (, 1990) | 3.6 × 10¹ | 3.6 × 10¹ | 1990 | ጥቅጥቅ |
| 723 | GNN (University of Siena, 2008) | 3 × 10¹ | 3 × 10¹ | 2008 | ጥቅጥቅ |
| 724 | ADALINE (Stanford University, 1960) | 1.7 × 10¹ | 1.7 × 10¹ | 1960 | ጥቅጥቅ |
| 725 | Samuel Neural Checkers (IBM, 1959) | ~1.6 × 10¹ | 1959 | ግምት |
ስብስቦች
እነዚህ ሠንጠረዦች የእንስሳት ማኅበረሰቦችን፣ ሃይማኖቶችን፣ መንግሥታትን፣ ፓርቲዎችንና ኮርፖሬሽኖችን በሁለት መንገድ ይደረድራሉ። አንዱ በአባላት ነው። ሌላው በነርቭ ተመጣጣኞች ነው፣ ይህም እያንዳንዱን አባል የቡድኑ መመሪያ በሚመራው የንቃት ጊዜው ድርሻ ይመዝናል። የሃይማኖት ሥርዓተ ሕግ፣ የመንግሥት ሕጎች፣ የፓርቲ ደንቦችና የሥራ መግለጫዎች ሁሉም በተመሳሳይ ይቆጠራሉ። ሁሉም የመመሪያዎች ስብስቦች ናቸው፤ ስለዚህ በተመሳሳይ ደንብ እንለካቸዋለን። የተመራ ጊዜ እንዴት እንደሚገመት።
- የቺምፓንዚ ማኅበረሰብ▲17.4 × 10¹¹
- የቅጠል ቆራጭ ጉንዳን ማኅበረሰብ▼16.3 × 10¹¹
- የቦኖቦ ማኅበረሰብ▲24.4 × 10¹¹
- የሃድዛ ሰፈር (ሰዎች፣ ለሚዛን)4.1 × 10¹¹
- የBigg’s ኦርካ ቡድን▼22.6 × 10¹¹
- የነዋሪ ኦርካ እናት መስመር2.4 × 10¹¹
- የዝንጀሮ ቡድን8.6 × 10¹⁰
- የቦትልኖዝ ዶልፊን ጥምረት▲17.6 × 10¹⁰
- የስፐርም ዓሣ ነባሪ ማኅበራዊ ክፍል▲16.4 × 10¹⁰
- የማር ንብ ማኅበረሰብ▼25.8 × 10¹⁰
- የዝሆን ቤተሰብ5.6 × 10¹⁰
- የፒኞን ጄይ መንጋ5.3 × 10¹⁰
- የነጠብጣብ ጅብ ጎሳ2.2 × 10¹⁰
- የአፍሪካ የዱር ውሻ መንጋ7.2 × 10⁹
- የተኩላ መንጋ4.2 × 10⁹
- የሚርካት ቡድን1.7 × 10⁹
- የራቁት ሞል-አይጥ ማኅበረሰብ4.1 × 10⁸
ቁጥሮቹን አሳይ
| # | ቡድን | በቡድኑ ውስጥ ያሉ ነርቮች | በቡድን እንቅስቃሴ | አባላት | የተመራ ድርሻ | የጥሪ ዓይነቶች |
|---|---|---|---|---|---|---|
| 1 | የቺምፓንዚ ማኅበረሰብ | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — |
| 2 | የቅጠል ቆራጭ ጉንዳን ማኅበረሰብ | 6.3 × 10¹¹ | 5 × 10¹¹ | 2.5 ሚሊዮን | 80% | — |
| 3 | የቦኖቦ ማኅበረሰብ | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — |
| 4 | የሃድዛ ሰፈር (ሰዎች፣ ለሚዛን) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — |
| 5 | የBigg’s ኦርካ ቡድን | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — |
| 6 | የነዋሪ ኦርካ እናት መስመር | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 |
| 7 | የዝንጀሮ ቡድን | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 |
| 8 | የቦትልኖዝ ዶልፊን ጥምረት | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — |
| 9 | የስፐርም ዓሣ ነባሪ ማኅበራዊ ክፍል | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — |
| 10 | የማር ንብ ማኅበረሰብ | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — |
| 11 | የዝሆን ቤተሰብ | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — |
| 12 | የፒኞን ጄይ መንጋ | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — |
| 13 | የነጠብጣብ ጅብ ጎሳ | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 |
| 14 | የአፍሪካ የዱር ውሻ መንጋ | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 |
| 15 | የተኩላ መንጋ | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — |
| 16 | የሚርካት ቡድን | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 |
| 17 | የራቁት ሞል-አይጥ ማኅበረሰብ | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — |
- ክርስትና▲12.27 ቢሊዮን
- እስልምና▼12.02 ቢሊዮን
- ሂንዱይዝም1.18 ቢሊዮን
- ሕዝባዊ ሃይማኖቶች400 ሚሊዮን
- ቡድሂዝም324.19 ሚሊዮን
- የአፍሪካ ባሕላዊ ሃይማኖቶች▲198.73 ሚሊዮን
- ሺንቶ▲187.92 ሚሊዮን
- ሲክሂዝም▲125 ሚሊዮን
- ይሁዲነት▼314.78 ሚሊዮን
- ታኦይዝም▲18 ሚሊዮን
- ባሃኢ እምነት▲18 ሚሊዮን
- ፋሉን ጎንግ▲17 ሚሊዮን
- ጃይኒዝም▼34.45 ሚሊዮን
- ስፒሪቲዝም3.8 ሚሊዮን
- Cao Đài2.5 ሚሊዮን
- ቴንሪኪዮ1.12 ሚሊዮን
- Brahma Kumaris1 ሚሊዮን
ቁጥሮቹን አሳይ
| # | ሃይማኖት | አባላት | መመሪያውን መከተል | የተመራ ድርሻ | በሳምንት ሰዓታት |
|---|---|---|---|---|---|
| 1 | ክርስትና | 2.27 ቢሊዮን | 1.7 × 10¹⁸ | 0.88% | 0.9 h |
| 2 | እስልምና | 2.02 ቢሊዮን | 8.9 × 10¹⁸ | 5.1% | 5.4 h |
| 3 | ሂንዱይዝም | 1.18 ቢሊዮን | 1.6 × 10¹⁸ | 1.6% | 1.6 h |
| 4 | ሕዝባዊ ሃይማኖቶች | 400 ሚሊዮን | 2 × 10¹⁷ | 0.59% (ተወካይ) | 0.6 h |
| 5 | ቡድሂዝም | 324.19 ሚሊዮን | 7.5 × 10¹⁶ | 0.27% | 0.3 h |
| 6 | የአፍሪካ ባሕላዊ ሃይማኖቶች | 98.73 ሚሊዮን | 5 × 10¹⁶ | 0.59% (ተወካይ) | 0.6 h |
| 7 | ሺንቶ | 87.92 ሚሊዮን | 4.5 × 10¹⁶ | 0.59% (ተወካይ) | 0.6 h |
| 8 | ሲክሂዝም | 25 ሚሊዮን | 3.7 × 10¹⁶ | 1.7% | 1.8 h |
| 9 | ይሁዲነት | 14.78 ሚሊዮን | 5.3 × 10¹⁶ | 4.2% | 4.4 h |
| 10 | ታኦይዝም | 8 ሚሊዮን | 4.1 × 10¹⁵ | 0.59% (ተወካይ) | 0.6 h |
| 11 | ባሃኢ እምነት | 8 ሚሊዮን | 4.1 × 10¹⁵ | 0.59% (ተወካይ) | 0.6 h |
| 12 | ፋሉን ጎንግ | 7 ሚሊዮን | 3.6 × 10¹⁵ | 0.59% (ተወካይ) | 0.6 h |
| 13 | ጃይኒዝም | 4.45 ሚሊዮን | 1.8 × 10¹⁶ | 4.6% | 4.8 h |
| 14 | ስፒሪቲዝም | 3.8 ሚሊዮን | 1.9 × 10¹⁵ | 0.59% (ተወካይ) | 0.6 h |
| 15 | Cao Đài | 2.5 ሚሊዮን | 1.3 × 10¹⁵ | 0.59% (ተወካይ) | 0.6 h |
| 16 | ቴንሪኪዮ | 1.12 ሚሊዮን | 5.7 × 10¹⁴ | 0.59% (ተወካይ) | 0.6 h |
| 17 | Brahma Kumaris | 1 ሚሊዮን | 5.1 × 10¹⁴ | 0.59% (ተወካይ) | 0.6 h |
- ሕንድ▲11.46 ቢሊዮን
- ቻይና▼11.41 ቢሊዮን
- አሜሪካ341.78 ሚሊዮን
- ኢንዶኔዥያ285.72 ሚሊዮን
- ፓኪስታን▲5255.22 ሚሊዮን
- ናይጄሪያ▲1237.53 ሚሊዮን
- ብራዚል▼2212.81 ሚሊዮን
- ባንግላዴሽ▲5175.69 ሚሊዮን
- ሩሲያ▼3143.51 ሚሊዮን
- ኢትዮጵያ▲7135.47 ሚሊዮን
- ሜክሲኮ▼2131.95 ሚሊዮን
- ጃፓን▲11123.37 ሚሊዮን
- ግብፅ▼2118.37 ሚሊዮን
- ፊሊፒንስ116.79 ሚሊዮን
- DR Congo▼3112.83 ሚሊዮን
ቁጥሮቹን አሳይ
| # | መንግሥት | ነዋሪዎች | መመሪያውን መከተል | የተመራ ድርሻ | በሳምንት ሰዓታት |
|---|---|---|---|---|---|
| 1 | ሕንድ | 1.46 ቢሊዮን | 2.5 × 10¹⁸ | 2% | 2.1 h |
| 2 | ቻይና | 1.41 ቢሊዮን | 5 × 10¹⁸ | 4.1% | 4.3 h |
| 3 | አሜሪካ | 341.78 ሚሊዮን | 1.6 × 10¹⁸ | 5.6% | 5.8 h |
| 4 | ኢንዶኔዥያ | 285.72 ሚሊዮን | 1 × 10¹⁸ | 4.2% | 4.4 h |
| 5 | ፓኪስታን | 255.22 ሚሊዮን | 5.7 × 10¹⁷ | 2.6% | 2.7 h |
| 6 | ናይጄሪያ | 237.53 ሚሊዮን | 6.6 × 10¹⁷ | 3.2% | 3.4 h |
| 7 | ብራዚል | 212.81 ሚሊዮን | 8.7 × 10¹⁷ | 4.8% | 5 h |
| 8 | ባንግላዴሽ | 175.69 ሚሊዮን | 4.6 × 10¹⁷ | 3% | 3.1 h |
| 9 | ሩሲያ | 143.51 ሚሊዮን | 7.5 × 10¹⁷ | 6.1% | 6.4 h |
| 10 | ኢትዮጵያ | 135.47 ሚሊዮን | 3.9 × 10¹⁷ | 3.4% | 3.5 h |
| 11 | ሜክሲኮ | 131.95 ሚሊዮን | 5.9 × 10¹⁷ | 5.2% | 5.5 h |
| 12 | ጃፓን | 123.37 ሚሊዮን | 2.8 × 10¹⁷ | 2.7% | 2.8 h |
| 13 | ግብፅ | 118.37 ሚሊዮን | 5.5 × 10¹⁷ | 5.4% | 5.7 h |
| 14 | ፊሊፒንስ | 116.79 ሚሊዮን | 4.4 × 10¹⁷ | 4.4% | 4.6 h |
| 15 | DR Congo | 112.83 ሚሊዮን | 4.7 × 10¹⁷ | 4.8% | 5.1 h |
| 16 | ቬትናም | 101.6 ሚሊዮን | 4.2 × 10¹⁷ | 4.8% | 5 h |
| 17 | ኢራን | 92.42 ሚሊዮን | 3.5 × 10¹⁷ | 4.3% | 4.6 h |
| 18 | ቱርክ | 85.88 ሚሊዮን | 4.3 × 10¹⁷ | 5.8% | 6 h |
| 19 | ጀርመን | 83.49 ሚሊዮን | 2.9 × 10¹⁷ | 4% | 4.2 h |
| 20 | ታይላንድ | 71.62 ሚሊዮን | 2.4 × 10¹⁷ | 3.9% | 4.1 h |
| 21 | ታንዛኒያ | 70.55 ሚሊዮን | 2.2 × 10¹⁷ | 3.6% | 3.8 h |
| 22 | ዩናይትድ ኪንግደም | 69.49 ሚሊዮን | 2.8 × 10¹⁷ | 4.6% | 4.8 h |
| 23 | ፈረንሳይ | 68.72 ሚሊዮን | 3.1 × 10¹⁷ | 5.3% | 5.5 h |
| 24 | ደቡብ አፍሪካ | 64.75 ሚሊዮን | 2.9 × 10¹⁷ | 5.1% | 5.4 h |
| 25 | ጣሊያን | 58.92 ሚሊዮን | 2.1 × 10¹⁷ | 4.2% | 4.4 h |
| 26 | ኬንያ | 57.53 ሚሊዮን | 2.3 × 10¹⁷ | 4.7% | 5 h |
| 27 | ሚያንማር | 54.85 ሚሊዮን | 1.9 × 10¹⁷ | 4% | 4.2 h |
| 28 | ኮሎምቢያ | 53.43 ሚሊዮን | 1.7 × 10¹⁷ | 3.7% | 3.9 h |
| 29 | ደቡብ ኮሪያ | 51.68 ሚሊዮን | 1.4 × 10¹⁷ | 3% | 3.2 h |
| 30 | ሱዳን | 51.66 ሚሊዮን | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 31 | ኡጋንዳ | 51.38 ሚሊዮን | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 32 | ስፔን | 49.36 ሚሊዮን | 1.8 × 10¹⁷ | 4.2% | 4.3 h |
| 33 | አልጄሪያ | 47.44 ሚሊዮን | 3.2 × 10¹⁷ | 7.9% | 8.3 h |
| 34 | ኢራቅ | 47.02 ሚሊዮን | 1.1 × 10¹⁷ | 2.8% | 2.9 h |
| 35 | አርጀንቲና | 45.85 ሚሊዮን | 1.7 × 10¹⁷ | 4.4% | 4.6 h |
| 36 | አፍጋኒስታን | 43.84 ሚሊዮን | 1.8 × 10¹⁷ | 4.8% | 5 h |
| 37 | የመን | 41.77 ሚሊዮን | 1.3 × 10¹⁷ | 3.7% | 3.9 h |
| 38 | ካናዳ | 41.65 ሚሊዮን | 2 × 10¹⁷ | 5.5% | 5.8 h |
| 39 | አንጎላ | 39.04 ሚሊዮን | 1.4 × 10¹⁷ | 4.2% | 4.4 h |
| 40 | ዩክሬን | 38.98 ሚሊዮን | 2.1 × 10¹⁷ | 6.2% | 6.5 h |
| 41 | ሞሮኮ | 38.43 ሚሊዮን | 1.5 × 10¹⁷ | 4.5% | 4.7 h |
| 42 | ኡዝቤኪስታን | 37.05 ሚሊዮን | 2 × 10¹⁷ | 6.3% | 6.6 h |
| 43 | ሳውዲ ዓረቢያ | 36.97 ሚሊዮን | 1.4 × 10¹⁷ | 4.3% | 4.6 h |
| 44 | ፖላንድ | 36.44 ሚሊዮን | 2.1 × 10¹⁷ | 6.7% | 7 h |
| 45 | ማሌዥያ | 35.98 ሚሊዮን | 1.8 × 10¹⁷ | 5.8% | 6.1 h |
| 46 | ሞዛምቢክ | 35.63 ሚሊዮን | 1.5 × 10¹⁷ | 4.8% | 5.1 h |
| 47 | ጋና | 35.06 ሚሊዮን | 1.2 × 10¹⁷ | 3.9% | 4.1 h |
| 48 | ፔሩ | 34.58 ሚሊዮን | 1.4 × 10¹⁷ | 4.8% | 5 h |
| 49 | ማዳጋስካር | 32.74 ሚሊዮን | 9.4 × 10¹⁶ | 3.3% | 3.5 h |
| 50 | ኮት ዲቯር | 32.71 ሚሊዮን | 9.5 × 10¹⁶ | 3.4% | 3.5 h |
| 51 | ካሜሩን | 29.88 ሚሊዮን | 9.8 × 10¹⁶ | 3.8% | 4 h |
| 52 | ኔፓል | 29.62 ሚሊዮን | 1.1 × 10¹⁷ | 4.2% | 4.4 h |
| 53 | ቬንዙዌላ | 28.52 ሚሊዮን | 1.5 × 10¹⁷ | 6.1% | 6.4 h |
| 54 | ኒጀር | 27.92 ሚሊዮን | 6.4 × 10¹⁶ | 2.6% | 2.8 h |
| 55 | አውስትራሊያ | 27.61 ሚሊዮን | 1.1 × 10¹⁷ | 4.7% | 5 h |
| 56 | ሰሜን ኮሪያ | 26.57 ሚሊዮን | 6.6 × 10¹⁷ | 29% | 30.1 h |
| 57 | ሶሪያ | 25.62 ሚሊዮን | 9.8 × 10¹⁶ | 4.4% | 4.6 h |
| 58 | ማሊ | 25.2 ሚሊዮን | 4.9 × 10¹⁶ | 2.3% | 2.4 h |
| 59 | ቡርኪና ፋሶ | 24.07 ሚሊዮን | 5.7 × 10¹⁶ | 2.8% | 2.9 h |
| 60 | ታይዋን | 23.31 ሚሊዮን | 1 × 10¹⁷ | 5% | 5.3 h |
| 61 | ማላዊ | 22.22 ሚሊዮን | 1 × 10¹⁷ | 5.4% | 5.6 h |
| 62 | ዛምቢያ | 21.91 ሚሊዮን | 10 × 10¹⁶ | 5.3% | 5.5 h |
| 63 | ስሪ ላንካ | 21.76 ሚሊዮን | 1.1 × 10¹⁷ | 6% | 6.3 h |
| 64 | ቻድ | 21 ሚሊዮን | 4.9 × 10¹⁶ | 2.7% | 2.9 h |
| 65 | ካዛኪስታን | 20.84 ሚሊዮን | 1.4 × 10¹⁷ | 7.6% | 7.9 h |
| 66 | ቺሊ | 19.86 ሚሊዮን | 6.2 × 10¹⁶ | 3.6% | 3.8 h |
| 67 | ሶማሊያ | 19.65 ሚሊዮን | 1.5 × 10¹⁶ | 0.88% | 0.9 h |
| 68 | ሮማኒያ | 19.02 ሚሊዮን | 7.5 × 10¹⁶ | 4.6% | 4.8 h |
| 69 | ሴኔጋል | 18.93 ሚሊዮን | 5.9 × 10¹⁶ | 3.6% | 3.8 h |
| 70 | ጓቲማላ | 18.69 ሚሊዮን | 6 × 10¹⁶ | 3.7% | 3.9 h |
| 71 | ኢኳዶር | 18.29 ሚሊዮን | 6.6 × 10¹⁶ | 4.2% | 4.4 h |
| 72 | ኔዘርላንድስ | 18.09 ሚሊዮን | 7.6 × 10¹⁶ | 4.9% | 5.1 h |
| 73 | ካምቦዲያ | 17.85 ሚሊዮን | 7.4 × 10¹⁶ | 4.8% | 5.1 h |
| 74 | ዚምባብዌ | 16.95 ሚሊዮን | 6.1 × 10¹⁶ | 4.2% | 4.4 h |
| 75 | ጊኒ | 15.1 ሚሊዮን | 3.1 × 10¹⁶ | 2.4% | 2.5 h |
| 76 | ቤኒን | 14.81 ሚሊዮን | 4.4 × 10¹⁶ | 3.4% | 3.6 h |
| 77 | ሩዋንዳ | 14.57 ሚሊዮን | 7 × 10¹⁶ | 5.6% | 5.8 h |
| 78 | ቡሩንዲ | 14.39 ሚሊዮን | 5.2 × 10¹⁶ | 4.2% | 4.4 h |
| 79 | ቦሊቪያ | 12.58 ሚሊዮን | 5.8 × 10¹⁶ | 5.4% | 5.6 h |
| 80 | ቱኒዚያ | 12.35 ሚሊዮን | 6.4 × 10¹⁶ | 6% | 6.3 h |
| 81 | ደቡብ ሱዳን | 12.19 ሚሊዮን | 4.4 × 10¹⁶ | 4.2% | 4.4 h |
| 82 | ቤልጂየም | 11.94 ሚሊዮን | 4.5 × 10¹⁶ | 4.3% | 4.5 h |
| 83 | ሄይቲ | 11.91 ሚሊዮን | 1.9 × 10¹⁶ | 1.8% | 1.9 h |
| 84 | ዮርዳኖስ | 11.52 ሚሊዮን | 4.9 × 10¹⁶ | 5% | 5.2 h |
| 85 | ዶሚኒካን ሪፐብሊክ | 11.52 ሚሊዮን | 4.9 × 10¹⁶ | 4.9% | 5.2 h |
| 86 | የተባበሩት ዓረብ ኤምሬትስ | 11.51 ሚሊዮን | 3.1 × 10¹⁶ | 3.1% | 3.2 h |
| 87 | ሆንዱራስ | 11.01 ሚሊዮን | 3.2 × 10¹⁶ | 3.3% | 3.5 h |
| 88 | ኩባ | 10.94 ሚሊዮን | 1.6 × 10¹⁷ | 17% | 17.4 h |
| 89 | ቼክያ | 10.89 ሚሊዮን | 5.8 × 10¹⁶ | 6.2% | 6.5 h |
| 90 | ፖርቹጋል | 10.8 ሚሊዮን | 5.1 × 10¹⁶ | 5.5% | 5.7 h |
| 91 | ታጂኪስታን | 10.79 ሚሊዮን | 5.3 × 10¹⁶ | 5.7% | 6 h |
| 92 | ፓፑዋ ኒው ጊኒ | 10.76 ሚሊዮን | 4.3 × 10¹⁶ | 4.6% | 4.8 h |
| 93 | ስዊድን | 10.6 ሚሊዮን | 6.8 × 10¹⁶ | 7.5% | 7.8 h |
| 94 | ግሪክ | 10.41 ሚሊዮን | 4.9 × 10¹⁶ | 5.4% | 5.7 h |
| 95 | አዘርባጃን | 10.25 ሚሊዮን | 6.2 × 10¹⁶ | 7% | 7.4 h |
| 96 | እስራኤል | 10.12 ሚሊዮን | 6.4 × 10¹⁶ | 7.3% | 7.7 h |
| 97 | ሃንጋሪ | 9.51 ሚሊዮን | 5.7 × 10¹⁶ | 6.9% | 7.3 h |
| 98 | ኦስትሪያ | 9.21 ሚሊዮን | 2.5 × 10¹⁶ | 3.2% | 3.4 h |
| 99 | ስዊዘርላንድ | 9.09 ሚሊዮን | 3.9 × 10¹⁶ | 5% | 5.3 h |
| 100 | ቤላሩስ | 9.09 ሚሊዮን | 7.9 × 10¹⁶ | 10% | 10.6 h |
| 101 | ሴራ ሊዮን | 8.82 ሚሊዮን | 4.9 × 10¹⁶ | 6.5% | 6.8 h |
| 102 | ቶጎ | 8.59 ሚሊዮን | 3 × 10¹⁶ | 4% | 4.2 h |
| 103 | ላኦስ | 7.87 ሚሊዮን | 3.2 × 10¹⁶ | 4.8% | 5 h |
| 104 | ቱርክሜኒስታን | 7.62 ሚሊዮን | 4.2 × 10¹⁶ | 6.5% | 6.8 h |
| 105 | ሊቢያ | 7.46 ሚሊዮን | 3.9 × 10¹⁶ | 6.1% | 6.4 h |
| 106 | ኪርጊስታን | 7.34 ሚሊዮን | 4.1 × 10¹⁶ | 6.5% | 6.8 h |
| 107 | ፓራጓይ | 7.01 ሚሊዮን | 2.9 × 10¹⁶ | 4.8% | 5.1 h |
| 108 | ኒካራጓ | 7.01 ሚሊዮን | 2.8 × 10¹⁶ | 4.6% | 4.8 h |
| 109 | ሰርቢያ | 6.55 ሚሊዮን | 4.1 × 10¹⁶ | 7.2% | 7.5 h |
| 110 | የኮንጎ ሪፐብሊክ | 6.48 ሚሊዮን | 1.7 × 10¹⁶ | 3% | 3.1 h |
| 111 | ቡልጋሪያ | 6.43 ሚሊዮን | 3.3 × 10¹⁶ | 5.9% | 6.2 h |
| 112 | ኤል ሳልቫዶር | 6.37 ሚሊዮን | 3.2 × 10¹⁶ | 5.8% | 6.1 h |
| 113 | ሲንጋፖር | 6.11 ሚሊዮን | 1.9 × 10¹⁶ | 3.6% | 3.7 h |
| 114 | ዴንማርክ | 6.01 ሚሊዮን | 3.9 × 10¹⁶ | 7.6% | 8 h |
| 115 | ሊባኖስ | 5.85 ሚሊዮን | 1.5 × 10¹⁶ | 3% | 3.1 h |
| 116 | ላይቤሪያ | 5.73 ሚሊዮን | 1.5 × 10¹⁶ | 3.1% | 3.3 h |
| 117 | ፊንላንድ | 5.65 ሚሊዮን | 3.2 × 10¹⁶ | 6.6% | 6.9 h |
| 118 | ኖርዌይ | 5.61 ሚሊዮን | 3.7 × 10¹⁶ | 7.6% | 8 h |
| 119 | ማዕከላዊ አፍሪካ ሪፐብሊክ | 5.51 ሚሊዮን | 1.7 × 10¹⁶ | 3.5% | 3.7 h |
| 120 | ኦማን | 5.49 ሚሊዮን | 2.9 × 10¹⁶ | 6.2% | 6.5 h |
| 121 | አየርላንድ | 5.48 ሚሊዮን | 3.4 × 10¹⁶ | 7.2% | 7.6 h |
| 122 | ስሎቫኪያ | 5.41 ሚሊዮን | 3.1 × 10¹⁶ | 6.7% | 7 h |
| 123 | ፍልስጤም | 5.41 ሚሊዮን | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 124 | ኒው ዚላንድ | 5.32 ሚሊዮን | 2.7 × 10¹⁶ | 5.9% | 6.2 h |
| 125 | ሞሪታኒያ | 5.32 ሚሊዮን | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 126 | ኮስታ ሪካ | 5.15 ሚሊዮን | 2.6 × 10¹⁶ | 5.9% | 6.2 h |
| 127 | ኩዌት | 4.87 ሚሊዮን | 2.7 × 10¹⁶ | 6.5% | 6.8 h |
| 128 | ፓናማ | 4.57 ሚሊዮን | 2.3 × 10¹⁶ | 5.9% | 6.2 h |
| 129 | ጆርጂያ | 3.94 ሚሊዮን | 2.1 × 10¹⁶ | 6.1% | 6.4 h |
| 130 | ክሮኤሺያ | 3.88 ሚሊዮን | 2.2 × 10¹⁶ | 6.7% | 7 h |
| 131 | ኤርትራ | 3.61 ሚሊዮን | 1.5 × 10¹⁶ | 5% | 5.2 h |
| 132 | ሞንጎሊያ | 3.57 ሚሊዮን | 2.2 × 10¹⁶ | 7% | 7.3 h |
| 133 | ኡራጓይ | 3.38 ሚሊዮን | 1.7 × 10¹⁶ | 5.9% | 6.2 h |
| 134 | ቦስኒያና ሄርዞጎቪና | 3.14 ሚሊዮን | 1.6 × 10¹⁶ | 5.9% | 6.2 h |
| 135 | ናሚቢያ | 3.09 ሚሊዮን | 1.8 × 10¹⁶ | 6.9% | 7.2 h |
| 136 | አርሜኒያ | 3.09 ሚሊዮን | 1.7 × 10¹⁶ | 6.5% | 6.8 h |
| 137 | ኳታር | 2.97 ሚሊዮን | 9.6 × 10¹⁵ | 3.8% | 3.9 h |
| 138 | ሊትዌኒያ | 2.89 ሚሊዮን | 1.8 × 10¹⁶ | 7.4% | 7.7 h |
| 139 | ጃማይካ | 2.84 ሚሊዮን | 1.2 × 10¹⁶ | 5% | 5.2 h |
| 140 | ጋምቢያ | 2.82 ሚሊዮን | 1.1 × 10¹⁶ | 4.5% | 4.7 h |
| 141 | ጋቦን | 2.59 ሚሊዮን | 9.9 × 10¹⁵ | 4.4% | 4.6 h |
| 142 | ቦትስዋና | 2.56 ሚሊዮን | 1.3 × 10¹⁶ | 5.7% | 6 h |
| 143 | ሌሶቶ | 2.36 ሚሊዮን | 9.5 × 10¹⁵ | 4.7% | 4.9 h |
| 144 | ሞልዶቫ | 2.36 ሚሊዮን | 1.5 × 10¹⁶ | 7.3% | 7.6 h |
| 145 | አልባኒያ | 2.35 ሚሊዮን | 1.2 × 10¹⁶ | 5.7% | 6 h |
| 146 | ጊኒ ቢሳው | 2.25 ሚሊዮን | 5.7 × 10¹⁵ | 2.9% | 3.1 h |
| 147 | ስሎቬኒያ | 2.13 ሚሊዮን | 1.2 × 10¹⁶ | 6.6% | 6.9 h |
| 148 | ኢኳቶሪያል ጊኒ | 1.94 ሚሊዮን | 4 × 10¹⁵ | 2.4% | 2.5 h |
| 149 | ላትቪያ | 1.85 ሚሊዮን | 1.2 × 10¹⁶ | 7.6% | 7.9 h |
| 150 | ሰሜን መቄዶንያ | 1.82 ሚሊዮን | 9.2 × 10¹⁵ | 5.9% | 6.2 h |
| 151 | ባሕሬን | 1.6 ሚሊዮን | 5.9 × 10¹⁵ | 4.3% | 4.5 h |
| 152 | ኮሶቮ | 1.58 ሚሊዮን | 8 × 10¹⁵ | 5.9% | 6.2 h |
| 153 | ቲሞር-ሌስቴ | 1.42 ሚሊዮን | 8 × 10¹⁵ | 6.6% | 6.9 h |
| 154 | ቆጵሮስ | 1.37 ሚሊዮን | 5.5 × 10¹⁵ | 4.7% | 4.9 h |
| 155 | ትሪኒዳድና ቶባጎ | 1.37 ሚሊዮን | 9.3 × 10¹⁵ | 7.9% | 8.2 h |
| 156 | ኢስቶኒያ | 1.37 ሚሊዮን | 8.3 × 10¹⁵ | 7% | 7.4 h |
| 157 | ኤስዋቲኒ | 1.26 ሚሊዮን | 7.5 × 10¹⁵ | 6.9% | 7.2 h |
| 158 | ሞሪሺየስ | 1.24 ሚሊዮን | 5.1 × 10¹⁵ | 4.8% | 5 h |
| 159 | ጅቡቲ | 1.18 ሚሊዮን | 4.6 × 10¹⁵ | 4.5% | 4.8 h |
| 160 | ፊጂ | 933,154 | 5.6 × 10¹⁵ | 7% | 7.4 h |
| 161 | ኮሞሮስ | 882,847 | 3.5 × 10¹⁵ | 4.6% | 4.8 h |
| 162 | ሰለሞን ደሴቶች | 838,645 | 3.2 × 10¹⁵ | 4.4% | 4.6 h |
| 163 | ጋያና | 835,986 | 4.6 × 10¹⁵ | 6.4% | 6.7 h |
| 164 | ቡታን | 796,682 | 5.8 × 10¹⁵ | 8.5% | 8.9 h |
| 165 | ሉክሰምበርግ | 686,970 | 2.4 × 10¹⁵ | 4% | 4.2 h |
| 166 | ሱሪናም | 639,850 | 3.7 × 10¹⁵ | 6.7% | 7.1 h |
| 167 | ሞንቴኔግሮ | 623,129 | 4.3 × 10¹⁵ | 8.1% | 8.5 h |
| 168 | Malta | 579,704 | 3.1 × 10¹⁵ | 6.2% | 6.5 h |
| 169 | ማልዲቭስ | 529,676 | 4.7 × 10¹⁵ | 10% | 10.8 h |
| 170 | ኬፕ ቨርድ | 527,326 | 3.3 × 10¹⁵ | 7.2% | 7.6 h |
| 171 | ብሩኒ | 466,330 | 3.6 × 10¹⁵ | 8.9% | 9.3 h |
| 172 | ቤሊዝ | 422,924 | 1.1 × 10¹⁵ | 2.9% | 3.1 h |
| 173 | ባሃማስ | 403,033 | 2.2 × 10¹⁵ | 6.4% | 6.7 h |
| 174 | አይስላንድ | 392,404 | 2.5 × 10¹⁵ | 7.4% | 7.7 h |
| 175 | ቫኑአቱ | 335,169 | 2 × 10¹⁵ | 6.9% | 7.3 h |
| 176 | ባርቤዶስ | 282,623 | 1.5 × 10¹⁵ | 6% | 6.3 h |
| 177 | ሳኦ ቶሜና ፕሪንሲፔ | 240,254 | 1.1 × 10¹⁵ | 5.4% | 5.7 h |
| 178 | ሳሞአ | 219,306 | 1.1 × 10¹⁵ | 5.7% | 5.9 h |
| 179 | ሴንት ሉሲያ | 180,149 | 9.3 × 10¹⁴ | 6% | 6.3 h |
| 180 | ኪሪባቲ | 136,488 | 7.6 × 10¹⁴ | 6.4% | 6.7 h |
| 181 | ሲሸልስ | 122,730 | 8.9 × 10¹⁴ | 8.4% | 8.8 h |
| 182 | ግሬናዳ | 117,303 | 7.3 × 10¹⁴ | 7.2% | 7.5 h |
| 183 | ማይክሮኔዥያ | 113,683 | 5.7 × 10¹⁴ | 5.8% | 6.1 h |
| 184 | ቶንጋ | 103,742 | 5.9 × 10¹⁴ | 6.6% | 6.9 h |
| 185 | ሴንት ቪንሰንትና ግሬናዲንስ | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h |
| 186 | አንቲጓና ባርቡዳ | 94,209 | 6.5 × 10¹⁴ | 8% | 8.3 h |
| 187 | አንዶራ | 82,904 | 3.9 × 10¹⁴ | 5.5% | 5.8 h |
| 188 | ዶሚኒካ | 65,871 | 2.3 × 10¹⁴ | 4.1% | 4.3 h |
| 189 | ሴንት ኪትስና ኔቪስ | 46,922 | 2.1 × 10¹⁴ | 5.2% | 5.5 h |
| 190 | ሊችተንስታይን | 41,024 | 2 × 10¹⁴ | 5.7% | 5.9 h |
| 191 | ሞናኮ | 38,341 | 2.3 × 10¹⁴ | 6.9% | 7.3 h |
| 192 | ማርሻል ደሴቶች | 36,282 | 3.3 × 10¹⁴ | 10% | 10.9 h |
| 193 | ሳን ማሪኖ | 34,109 | 1.3 × 10¹⁴ | 4.6% | 4.8 h |
| 194 | ፓላው | 17,663 | 1.6 × 10¹⁴ | 10% | 11 h |
| 195 | ናኡሩ | 12,025 | 9.3 × 10¹³ | 9% | 9.4 h |
| 196 | ቱቫሉ | 9,492 | 8 × 10¹³ | 9.8% | 10.2 h |
- Bharatiya Janata Party (ሕንድ)▲1140 ሚሊዮን
- የቻይና ኮሚኒስት ፓርቲ▼1101.29 ሚሊዮን
- Democratic Party (አሜሪካ)▲1344.9 ሚሊዮን
- Republican Party (አሜሪካ)▲1638 ሚሊዮን
- የሕንድ ብሔራዊ ኮንግረስ▼226 ሚሊዮን
- AIADMK (ሕንድ)▼220.04 ሚሊዮን
- DMK (ሕንድ)▼220 ሚሊዮን
- የብልጽግና ፓርቲ (ኢትዮጵያ)▼115.7 ሚሊዮን
- TVK (ሕንድ)▼115 ሚሊዮን
- Chama Cha Mapinduzi (ታንዛኒያ)▼113 ሚሊዮን
- All Progressives Congress (ናይጄሪያ)▼112.9 ሚሊዮን
- AK Party (ቱርክ)▼111.71 ሚሊዮን
- Morena (ሜክሲኮ)▼111.63 ሚሊዮን
- Telugu Desam Party (ሕንድ)▼110.05 ሚሊዮን
- የካምቦዲያ ሕዝብ ፓርቲ▼17 ሚሊዮን
ቁጥሮቹን አሳይ
| # | ፓርቲ | አባላት | መመሪያውን መከተል | የተመራ ድርሻ | በሳምንት ሰዓታት |
|---|---|---|---|---|---|
| 1 | Bharatiya Janata Party (ሕንድ) | 140 ሚሊዮን | 6.3 × 10¹⁶ | 0.52% (ተወካይ) | 0.6 h |
| 2 | የቻይና ኮሚኒስት ፓርቲ | 101.29 ሚሊዮን | 8.2 × 10¹⁶ | 0.94% | 1 h |
| 3 | Democratic Party (አሜሪካ) | 44.9 ሚሊዮን | 2.3 × 10¹⁵ | 0.06% | 0.1 h |
| 4 | Republican Party (አሜሪካ) | 38 ሚሊዮን | 2 × 10¹⁵ | 0.06% | 0.1 h |
| 5 | የሕንድ ብሔራዊ ኮንግረስ | 26 ሚሊዮን | 1.2 × 10¹⁶ | 0.52% (ተወካይ) | 0.6 h |
| 6 | AIADMK (ሕንድ) | 20.04 ሚሊዮን | 9 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 7 | DMK (ሕንድ) | 20 ሚሊዮን | 9 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 8 | የብልጽግና ፓርቲ (ኢትዮጵያ) | 15.7 ሚሊዮን | 7 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 9 | TVK (ሕንድ) | 15 ሚሊዮን | 6.7 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 10 | Chama Cha Mapinduzi (ታንዛኒያ) | 13 ሚሊዮን | 5.8 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 11 | All Progressives Congress (ናይጄሪያ) | 12.9 ሚሊዮን | 5.8 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 12 | AK Party (ቱርክ) | 11.71 ሚሊዮን | 5.2 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 13 | Morena (ሜክሲኮ) | 11.63 ሚሊዮን | 5.2 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 14 | Telugu Desam Party (ሕንድ) | 10.05 ሚሊዮን | 4.5 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 15 | የካምቦዲያ ሕዝብ ፓርቲ | 7 ሚሊዮን | 3.1 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 16 | የኮሪያ ሠራተኞች ፓርቲ | 6.18 ሚሊዮን | 7.6 × 10¹⁵ | 1.4% | 1.5 h |
| 17 | FRELIMO (ሞዛምቢክ) | 6 ሚሊዮን | 2.7 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 18 | የቬትናም ኮሚኒስት ፓርቲ | 5.6 ሚሊዮን | 2.1 × 10¹⁵ | 0.44% | 0.5 h |
| 19 | የኮሪያ ዴሞክራቲክ ፓርቲ | 5 ሚሊዮን | 2.2 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 20 | People Power Party (ደቡብ ኮሪያ) | 4.44 ሚሊዮን | 2 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 21 | PSUV (ቬንዙዌላ) | 4.24 ሚሊዮን | 1.9 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 22 | ZANU-PF (ዚምባብዌ) | 4.06 ሚሊዮን | 1.8 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 23 | UMNO (ማሌዥያ) | 3.25 ሚሊዮን | 1.5 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 24 | Justicialist Party (አርጀንቲና) | 3.1 ሚሊዮን | 1.4 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 25 | Colorado Party (ፓራጓይ) | 3 ሚሊዮን | 1.3 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 26 | የተባበረች ሩሲያ | 2.6 ሚሊዮን | 1.2 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 27 | UzLiDeP (ኡዝቤኪስታን) | 2.31 ሚሊዮን | 1 × 10¹⁵ | 0.52% (ተወካይ) | 0.6 h |
| 28 | PLD (ዶሚኒካን ሪፐብሊክ) | 2.18 ሚሊዮን | 9.7 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 29 | MDB (ብራዚል) | 2.02 ሚሊዮን | 9 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 30 | CHP (ቱርክ) | 1.88 ሚሊዮን | 8.4 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 31 | Radical Civic Union (አርጀንቲና) | 1.79 ሚሊዮን | 8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 32 | Workers’ Party፣ PT (ብራዚል) | 1.67 ሚሊዮን | 7.5 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 33 | PRM (ዶሚኒካን ሪፐብሊክ) | 1.55 ሚሊዮን | 6.9 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 34 | Authentic Radical Liberal Party (ፓራጓይ) | 1.55 ሚሊዮን | 6.9 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 35 | Partido Federal ng Pilipinas | 1.5 ሚሊዮን | 6.7 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 36 | PAS (ማሌዥያ) | 1.3 ሚሊዮን | 5.8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 37 | PRD (ብራዚል) | 1.29 ሚሊዮን | 5.8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 38 | Progressistas (ብራዚል) | 1.29 ሚሊዮን | 5.8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 39 | PSDB (ብራዚል) | 1.26 ሚሊዮን | 5.6 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 40 | PKR (ማሌዥያ) | 1.15 ሚሊዮን | 5.2 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 41 | Economic Freedom Fighters (ደቡብ አፍሪካ) | 1.09 ሚሊዮን | 4.9 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 42 | PDT (ብራዚል) | 1.07 ሚሊዮን | 4.8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 43 | União Brasil | 1.07 ሚሊዮን | 4.8 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 44 | CPI(M) (ሕንድ) | 1.02 ሚሊዮን | 4.6 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 45 | Liberal Democratic Party (ጃፓን) | 1 ሚሊዮን | 4.5 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 46 | FLN (አልጄሪያ) | 1 ሚሊዮን | 4.5 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 47 | Liberal Party፣ PL (ብራዚል) | 952,881 | 4.3 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 48 | የኩባ ኮሚኒስት ፓርቲ | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h |
| 49 | የአፍሪካ ብሔራዊ ኮንግረስ | 688,454 | 3.1 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 50 | Conservative Party of Canada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h |
| 51 | CDU (ጀርመን) | 364,202 | 1.6 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 52 | SPD (ጀርመን) | 357,116 | 1.6 × 10¹⁴ | 0.52% (ተወካይ) | 0.6 h |
| 53 | Labour Party (ዩኬ) | 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 (ጀርመን) | 155,296 | 7 × 10¹³ | 0.52% (ተወካይ) | 0.6 h |
| 56 | CSU (ጀርመን) | 125,153 | 5.6 × 10¹³ | 0.52% (ተወካይ) | 0.6 h |
| 57 | Conservative Party (ዩኬ) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h |
| 58 | New Democratic Party (ካናዳ) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h |
| 59 | AfD (ጀርመን) | 51,335 | 2.3 × 10¹³ | 0.52% (ተወካይ) | 0.6 h |
- Walmart2.1 ሚሊዮን
- Amazon▲11.58 ሚሊዮን
- State Grid▼11.36 ሚሊዮን
- China National Petroleum1.03 ሚሊዮን
- JD.com951,420
- BYD▲1869,622
- Accenture▼1779,000
- Allied Universal▲3770,000
- China Post Group▼1719,448
- የሩሲያ የባቡር ሐዲዶች (RZD)▲2677,363
- Volkswagen Group▲6662,942
- Foxconn (Hon Hai)▼2633,167
- Schwarz Group▲16604,000
- Compass Group▲18590,000
- Tata Consultancy Services▼6584,510
ቁጥሮቹን አሳይ
| # | ኮርፖሬሽን | ሠራተኞች | መመሪያውን መከተል | በሳምንት ሰዓታት | የተመራ ድርሻ |
|---|---|---|---|---|---|
| 1 | Walmart | 2.1 ሚሊዮን | 6.3 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 2 | Amazon | 1.58 ሚሊዮን | 4.7 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 3 | State Grid | 1.36 ሚሊዮን | 5.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 4 | China National Petroleum | 1.03 ሚሊዮን | 4.1 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 5 | JD.com | 951,420 | 3.8 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 6 | BYD | 869,622 | 3.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 7 | Accenture | 779,000 | 3.7 × 10¹⁶ | 57.58 h (ሕንድ) | 55% |
| 8 | Allied Universal | 770,000 | 2.3 × 10¹⁶ | 36.52 h (አሜሪካ) | 35% |
| 9 | China Post Group | 719,448 | 2.9 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 10 | የሩሲያ የባቡር ሐዲዶች (RZD) | 677,363 | 2.2 × 10¹⁶ | 39.96 h (ሩሲያ) | 38% |
| 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (ጀርመን) | 34% |
| 12 | Foxconn (Hon Hai) | 633,167 | 2.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 13 | Schwarz Group | 604,000 | 1.6 × 10¹⁶ | 31.49 h (ጀርመን) | 30% |
| 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (አሜሪካ) | 30% |
| 15 | Tata Consultancy Services | 584,510 | 2.8 × 10¹⁶ | 58.23 h (ሕንድ) | 56% |
| 16 | DHL Group | 583,998 | 1.7 × 10¹⁶ | 35.81 h (ጀርመን) | 34% |
| 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (ሩሲያ) | 39% |
| 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (አሜሪካ) | 38% |
| 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (አሜሪካ) | 38% |
| 23 | Agricultural Bank of China | 457,835 | 1.8 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 24 | Concentrix | 455,000 | 1.7 × 10¹⁶ | 46.71 h (ፊሊፒንስ) | 45% |
| 25 | Teleperformance | 446,716 | 2.2 × 10¹⁶ | 58.89 h (ሕንድ) | 56% |
| 26 | Jardine Matheson | 443,000 | 1.5 × 10¹⁶ | 41.93 h (ኢንዶኔዥያ) | 40% |
| 27 | Sodexo | 426,464 | 1.1 × 10¹⁶ | 31.7 h (አሜሪካ) | 30% |
| 28 | Capgemini | 423,400 | 2 × 10¹⁶ | 58.23 h (ሕንድ) | 56% |
| 29 | Reliance Industries | 419,911 | 2.1 × 10¹⁶ | 59.57 h (ሕንድ) | 57% |
| 30 | Edeka | 417,500 | 1.1 × 10¹⁶ | 31.49 h (ጀርመን) | 30% |
| 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (አሜሪካ) | 30% |
| 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (ጀርመን) | 34% |
| 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (ሩሲያ) | 38% |
| 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (ጃፓን) | 38% |
| 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (አሜሪካ) | 35% |
| 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (ሩሲያ) | 38% |
| 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (አሜሪካ) | 36% |
| 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (አሜሪካ) | 30% |
| 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (ጀርመን) | 30% |
| 48 | China Construction Bank | 378,344 | 1.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 49 | TJX Companies | 377,000 | 1.1 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 50 | China State Construction Engineering | 370,272 | 1.5 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 51 | FEMSA | 368,776 | 1.4 × 10¹⁶ | 47.09 h (ሜክሲኮ) | 45% |
| 52 | Japan Post Holdings | 360,000 | 1.2 × 10¹⁶ | 41.6 h (ጃፓን) | 40% |
| 53 | Tesco | 354,744 | 9.3 × 10¹⁵ | 31.81 h (ዩናይትድ ኪንግደም) | 30% |
| 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (ሕንድ) | 56% |
| 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (ጃፓን) | 38% |
| 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (አሜሪካ) | 35% |
| 57 | Infosys | 328,594 | 1.6 × 10¹⁶ | 58.23 h (ሕንድ) | 56% |
| 58 | ISS | 325,000 | 1.6 × 10¹⁶ | 58.89 h (ሕንድ) | 56% |
| 59 | Securitas | 322,000 | 9.7 × 10¹⁵ | 36.52 h (አሜሪካ) | 35% |
| 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (አሜሪካ) | 35% |
| 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (ሩሲያ) | 39% |
| 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (አሜሪካ) | 38% |
| 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (ጀርመን) | 34% |
| 64 | Bank of China | 313,746 | 1.3 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 65 | Walgreens Boots Alliance | 312,000 | 9.3 × 10¹⁵ | 36.3 h (አሜሪካ) | 35% |
| 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (አሜሪካ) | 39% |
| 67 | China Railway Construction (CRCC) | 304,927 | 1.2 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 68 | CHN Energy (China Energy Investment) | 303,418 | 1.2 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 69 | China Railway Group (CREC) | 302,527 | 1.2 × 10¹⁶ | 49 h (ቻይና) | 47% |
| 70 | CK Hutchison | 300,000 | 8.6 × 10¹⁵ | 35.08 h (ዩናይትድ ኪንግደም) | 33% |
| 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (አሜሪካ) | 35% |
እንስሳትና ሌሎች ፍጥረታት
ነርቮች ከሌሉ ነጠላ ሕዋሳት እስከ የሰው አንጎል። የነርቭ ቆጠራዎች ባሉበት ቦታ ተለክተዋል።
ነርቮች የሉም
- Escherichia coliEscherichia coli0 የተለካነርቮች የሌሉት ነጠላ ሕዋስ። አቅጣጫውን ወደ ምግብ ያዞራል፣ ጥቂት ሰከንዶች የሚቆይ ኬሚካላዊ ትውስታም አለው።
- ዝቃጭ ሻጋታPhysarum polycephalum0 የተለካነርቮች የሌሉት ነጠላ ግዙፍ ሕዋስ። ማዝ ይፈታል፣ ቀልጣፋ የትራንስፖርት መረቦችንም ያበቅላል።
- ስሜታዊ ተክልMimosa pudica0 የተለካሲነኩት ቅጠሎቹ የሚታጠፉ ተክል። አንድ ጥናት ጉዳት የሌላቸውን ንክኪዎች ችላ ማለት እንደሚማር ዘገበ። ከጊዜ በኋላ የተደረገ ትንተና ተከራከረው።
- ስፖንጅPorifera (e.g. Amphimedon queenslandica)0 የተለካነርቮችም ሲናፕሶችም የሌሉት እንስሳ፣ ግን አሁንም መላ ሰውነቱ በተቀናጀ መንገድ ይኮማተራል።
የነርቭ መረቦች፣ አንጎል የለም
አንጎል ያላቸው አከርካሪ የሌላቸው እንስሳት
- ክብ ትልCaenorhabditis elegans302 የተለካወደ አንድ ሚሊሜትር ርዝመት ያለው ትል፣ በትክክል 302 ነርቮች ያሉት። ሙሉ የሽቦ ዲያግራሙ የተሳለ የመጀመሪያው እንስሳ ነበር።
- የፍራፍሬ ዝንብDrosophila melanogaster139,255 የተለካእስካሁን የተሳለው ትልቁ ሙሉ የአንጎል የሽቦ ዲያግራም፣ ወደ 139,000 ነርቮችን የሚሸፍን።
- የማር ንብApis melliferaወደ 1 ሚሊዮን ግምትወደ አንድ ሚሊዮን ነርቮች፣ ንቦችም እንደ ተመሳሳይና የተለየ ያሉ ረቂቅ ሐሳቦችን መማር ይችላሉ።
- ጉንዳን (Camponotus / Formica)Camponotus spp.የታተመ ቆጠራ የለምእርምጃዎችን በመቁጠር (የመንገድ ውህደት) እና ምልክቶችን በማስታወስ የረጅም ርቀት አቅጣጫ ፍለጋን የሚያስተዳድር ትንሽ አንጎል።
- ፖርቲያ የሚዘል ሸረሪትPortia fimbriata / labiataየታተመ ቆጠራ የለምዙሪያ ጥምጥም መንገድ የሚያቅድ የሚዘል ሸረሪት። ከምርኮው የሚያርቁና ከዓይን የሚሰውሩ መንገዶችን ይወስዳል።
- የባሕር ዳር ሸርጣን / ክሬይፊሽCarcinus maenas; Procambarus / Astacus spp.የታተመ ቆጠራ የለምሸርጣኖችና ክሬይፊሽ። የ2021 የመንግሥት ግምገማ እውነተኛ ሸርጣኖች ስሜት ያላቸው መሆናቸውን ጠንካራ ማስረጃ አገኘ።
- የተለመደ ኦክቶፐስOctopus vulgaris500 ሚሊዮን ግምትአብዛኞቹ ነርቮቹ በክንዶቹ ውስጥ ናቸው። የ2021 ግምገማ ስሜት ያለው መሆኑን እጅግ ጠንካራ ማስረጃ አገኘ።
ዓሣና አምፊቢያን
- ዘብራፊሽDanio rerioየታተመ ቆጠራ የለምበኒውሮሳይንስ በሰፊው የሚያገለግል ትንሽ ዓሣ። ነርቮቹ ሙሉ በሙሉ አልተቆጠሩም። የጎልማሳው አንጎል ወደ 10 ሚሊዮን ሕዋሳት አለው።
- ንጹህ ዓሣ ዎራስLabroides dimidiatusየታተመ ቆጠራ የለምየመስታወት ምልክት ፈተና አንድ ስሪት ያለፈ የሪፍ ዓሣ። ሰዎች ያ ምን እንደሚያሳይ አይስማሙም።
- እንቁራሪትAnura (e.g. Xenopus / Rana)የታተመ ቆጠራ የለምየ2024 የኒው ዮርክ መግለጫ በአምፊቢያኖች ውስጥ የንቃተ ህሊና ልምድ ተጨባጭ ዕድል እንዳለ ይናገራል።
ወፎች
- የዓለት ርግብColumba livia310 ሚሊዮን የተለካበትንሽ አንጎል ውስጥ በመቶ ሚሊዮኖች የሚቆጠሩ ነርቮች። ርግቦች ውስብስብ ምስሎችን በምድብ መከፋፈል ይችላሉ።
- የተለመደ ቁራCorvus corax2.17 ቢሊዮን የተለካከሁለት ቢሊዮን በላይ ነርቮች፣ በጥብቅ የታሸጉ። ቁራዎች ተፎካካሪዎችን በግል ያስታውሳሉ።
- የኒው ካሌዶኒያ ቁራCorvus moneduloidesየታተመ ቆጠራ የለምከቅርንጫፍና ከሽቦ የተጠመዘዙ መሣሪያዎችን ይሠራል። ነርቮቹ አልተቆጠሩም።
- የአፍሪካ ግሬይ በቀቀንPsittacus erithacus1.57 ቢሊዮን የተለካወደ 1.6 ቢሊዮን ነርቮች። የቁጥር ማስተዋልና የምድብ ክፍፍል የአሥርተ ዓመታት የላቦራቶሪ ጥናት።
Mammals
- አይጥMus musculus70.9 ሚሊዮን የተለካወደ 71 ሚሊዮን ነርቮች። አይጦች ሊለካ የሚችል የሕመም የፊት ገጽታ ያሳያሉ።
- አይጥRattus norvegicus200 ሚሊዮን የተለካወደ 200 ሚሊዮን ነርቮች። አይጦች የታሰሩ የጓዳ ጓደኞቻቸውን ይለቃሉ።
- የቤት ድመትFelis catus1.22 ቢሊዮን የተለካበአንጎል ቅርፊት ውስጥ ወደ 250 ሚሊዮን ነርቮች።
- የቤት ውሻCanis familiarisከ1.8 እስከ 2.6 ቢሊዮን፣ እንደ ውሻው የተለካእንደ ውሻው እስከ 600 ሚሊዮን የሚደርሱ የአንጎል ቅርፊት ነርቮች። ይህ ከድመቶች ይበልጣል።
- የቤት አሳማSus scrofa domesticus2.22 ቢሊዮን የተለካአሳማዎች መስታወት የሚያሳየውን ይማራሉ፣ ምግብ ለማግኘትም ይጠቀሙበታል።
- ቦትልኖዝ ዶልፊንTursiops truncatusየታተመ ቆጠራ የለምየመስታወት ራስን የማወቅ ፈተናዎችን ያልፋል። ለቦትልኖዝ ዶልፊን የተለካ የነርቭ ቆጠራ የለም።
- የአፍሪካ ዝሆንLoxodonta africana257 ቢሊዮን የተለካ257 ቢሊዮን ነርቮች፣ ከሰው ሦስት እጥፍ፣ ሁሉም ማለት ይቻላል በሴሬቤለም ውስጥ።
- ቺምፓንዚPan troglodytesወደ 28 ቢሊዮን (ከማስፋፊያ ደንቦች የተተነበየ፣ ያልተቆጠረ) ግምትከሁለቱ የቅርብ ሕያው ዘመዶቻችን አንዱ (ከቦኖቦ ጋር)። የመስታወት ፈተናውን ያልፋል።
- ሰውHomo sapiens86.1 ቢሊዮን የተለካ86 ቢሊዮን ነርቮች፣ 16 ቢሊዮን የሚሆኑት በአንጎል ቅርፊት ውስጥ። አባሎቻቸው ስለ ልምዳቸው በቃል ሪፖርት ማድረግ የሚችሉ ብቸኛ ዝርያ እኛ ነን።
የጋራ ሥርዓቶች
ማንም ነጠላ አባል ብቻውን የማይወስናቸውን ውሳኔዎች የሚወስኑ ቡድኖች። እንደ አእምሮ ሳይሆን እንደ የጋራ ብልህነት ይጠናሉ።
- የጉንዳን ማኅበረሰቦችየጉንዳን ማኅበረሰቦች የተሻለውን ጎጆ ይመርጣሉ፤ ምክንያቱም እያንዳንዱ ጉንዳን የምልአተ ጉባኤ ደፍ ይከተላል። ማንም ጉንዳን ቦታዎቹን በጠቅላላ አያወዳድርም።
- የማር ንብ መንጋዎችመንጋዎች በስካውቶች ፉክክርና በምልአተ ጉባኤ ምርጥ ጉድጓድ ይመርጣሉ። የማቆሚያ ምልክቶች እርስ በርስ መገደብ ያቀርባሉ፤ ይህም ውሳኔን የሚያስቆሙ እኩል ክፍፍሎችን ይሰብራል።
- የስታርሊንግ መንጋዎችእያንዳንዱ ወፍ በተወሰነ ርቀት ውስጥ ያለውን ወፍ ሁሉ ሳይሆን የተወሰነ ቁጥር ጎረቤቶችን (ወደ 6-7) ይከታተላል። ይህ ሜትሪክ ሳይሆን ቶፖሎጂካል መስተጋብር ይባላል።
- የዓሣ መንጋዎችመንጋዎች ለመወሰን የምልአተ ጉባኤ ደንቦችን ይጠቀማሉ። ትልልቅ ቡድኖች ማንም ነጠላ ዓሣ ሊያውቅ የማይችለውን የአካባቢ ቀስ በቀስ ለውጦች ሊሰማቸው ይችላል።
ሰው ሠራሽ ሥርዓቶች
የማሽን ብልህነት ዓይነቶች፣ በአሠራራቸው የተደረደሩ። ዛሬ ያለ ማንም ሥርዓት ለንቃተ ህሊና ጠንካራ ዕጩ ተደርጎ አይቆጠርም።
- በደንብ ላይ የተመሠረቱ ኤክስፐርት ሥርዓቶችየአንድ ዘርፍ ባለሙያ ዕውቀትን እንደ ግልጽ ከሆነ-ከዚያ ደንቦች የሚያስቀምጡ ፕሮግራሞች። የመደምደሚያ ሞተር ደንቦቹን በቅደም ተከተል ይተገብራል፣ ብዙ ጊዜ የአስተሳሰቡን ማብራሪያ ያቀርባል።
- የጨዋታ ዛፍ ፍለጋ ሞተሮች (ቼስ)እስከ ተወሰነ ጥልቀት ድረስ ሊሆኑ የሚችሉ የእንቅስቃሴ ቅደም ተከተሎችን የሚዘረዝሩና የተገኙትን አቋሞች በእጅ በተሠራ የግምገማ ተግባር የሚመዝኑ ፕሮግራሞች። ምርጫውን መለወጥ የማይችሉ ቅርንጫፎችን ይቆርጣሉ (ሚኒማክስ ከአልፋ-ቤታ ቁረጣ ጋር)።
- ጥልቅ የማጠናከሪያ ትምህርት ወኪሎችየሽልማት ምልክትን ከፍ ለማድረግ በሙከራና በስህተት የሰለጠኑ የነርቭ መረቦች። ከአካባቢ ጋር በመገናኘት፣ ብዙ ጊዜ እንደ ጨዋታ ካለ የተመሰለ፣ ፖሊሲ ወይም የእሴት ተግባር ይማራሉ።
- ትልልቅ የቋንቋ ሞዴሎችበጣም ትልልቅ የጽሑፍ ስብስቦች ውስጥ ቀጣዩን ቶከን ለመተንበይ የሰለጠኑ የትራንስፎርመር የነርቭ መረቦች፣ ከዚያ ብዙ ጊዜ በሰው ግብረ መልስ የተስተካከሉ።
- አካል ያላቸው ሮቦቶችዳሳሾችና አንቀሳቃሾች ያሏቸው በአካላዊው ዓለም የሚሠሩ ማሽኖች። ብዙ ጊዜ ከማዕከላዊ የዓለም ሞዴል ይልቅ ተደራቢ ምላሽ ሰጪ ቁጥጥር ይጠቀማሉ፤ ስለዚህ ባህሪ ከአካልና ከአካባቢ መካከል ካለው ዑደት ይወጣል።
- ኒውሮሞርፊክ ሃርድዌርወረዳዎቻቸው የሚንቀለቀሉ ነርቮችንና ሲናፕሶችን የሚመስሉ ቺፖች። ከተለመደው ፕሮሰሰርና ሜሞሪ ንድፍ በተለየ በክስተት የሚነዱና ጥቂት ኃይል የሚጠቀሙ ናቸው።
መላምታዊ አእምሮዎች
በሳይንሳዊና ፍልስፍናዊ ጽሑፎች የተጠቆሙ አእምሮዎች። ማንም አንዱንም አልተመለከተም።
- የሙሉ አንጎል ኢሙሌሽንያልታየአንጎልን በጥልቅ ዝርዝር መቃኘትና ሶፍትዌር ሞዴሉን ማስኬድ።
- በቅድመ አያቶች ማስመሰሎች ውስጥ የተመሰሉ አእምሮዎችያልታየየቦስትሮም ሦስት አማራጭ፦ ወይ ከድኅረ ሰው ደረጃ የሚደርሱ ሥልጣኔዎች የሉም ማለት ይቻላል፣ ወይ የቅድመ አያቶች ማስመሰል የሚያስኬዱ የሉም ማለት ይቻላል፣ ወይም እኛ በእርግጠኝነት ማለት ይቻላል በአንዱ ውስጥ እንኖራለን።
- የቦልትስማን አንጎሎችያልታየከብርቅ የሙቀት ወይም የኳንተም መለዋወጥ የሚነሱ አንጎል መሰል ተመልካቾች።
- አእምሮን በደረጃ በመተካት መጫንያልታየነርቮችን አንድ በአንድ በተግባር ተመሳሳይ በሆኑ ሰው ሠራሽ ክፍሎች መተካት።
- ልዕለ ብልሃትያልታየከሰው አፈጻጸም ሁሉ ማለት ይቻላል በሁሉም ዘርፍ እጅግ የሚበልጥ መላምታዊ አእምሮ። የቦስትሮም መጽሐፍ ወደ እሱ የሚወስዱ መንገዶችን (የሙሉ አንጎል ኢሙሌሽን፣ ባዮሎጂያዊ ዕውቀት፣ AI)፣ ቅርጾቹንና የቁጥጥር ችግሮቹን ይተነትናል።
የውሂብ ስሪት 2.0.0። አውርድ (JSON)።