Entitas · pikiran dan kemungkinan pikiran, diurutkan menurut bukti
Jika dunia ini simulasi, pikiran mungkin termasuk hal tersulit di dalamnya untuk dikomputasi. Panduan ini mendaftar kasus-kasus yang diketahui, dari sel tunggal sampai manusia. Panduan ini juga mencakup sistem buatan yang sudah dibangun sejauh ini, dan pikiran yang diusulkan para ilmuwan dan filsuf tetapi belum diamati siapa pun.
Setiap angka memiliki sumber dan label keyakinan. terukur adalah hitungan langsung, perkiraan dihitung dari data lain, dan tidak dihitung berarti belum ada yang mempublikasikan hitungannya. Apakah salah satu sistem ini memiliki pengalaman adalah pertanyaan yang terpisah. Buktinya ada di bawah kriteria sentiens dan indikator kesadaran.
Pikiran individual
- Paus pembunuh †43,1 miliar
- Paus pilot sirip panjang †37,2 miliar
- Manusia▲116,3 miliar
- Lumba-lumba pelabuhan †14,9 miliar
- Lumba-lumba hidung botol †12,7 miliar
- Paus berparuh Cuvier9,1 miliar
- Gorila8,5 miliar
- Orangutan8,5 miliar
- Simpanse7,4 miliar
- Lumba-lumba biasa6,7 miliar
- Anjing laut harpa6,1 miliar
- Gajah Afrika▼15,6 miliar
- Paus sirip5,2 miliar
- Paus biru5,1 miliar
- Paus minke3,2 miliar
Tampilkan angkanya
| # | Hewan | Korteks atau palium | Seluruh otak |
|---|---|---|---|
| 1 | Paus pembunuh † | 43,1 miliar | |
| 2 | Paus pilot sirip panjang † | 37,2 miliar | |
| 3 | Manusia | 16,3 miliar | 86,1 miliar |
| 4 | Lumba-lumba pelabuhan † | 14,9 miliar | |
| 5 | Lumba-lumba hidung botol † | 12,7 miliar | |
| 6 | Paus berparuh Cuvier | 9,1 miliar | |
| 7 | Gorila | 8,5 miliar | |
| 8 | Orangutan | 8,5 miliar | |
| 9 | Simpanse | 7,4 miliar | |
| 10 | Lumba-lumba biasa | 6,7 miliar | |
| 11 | Anjing laut harpa | 6,1 miliar | |
| 12 | Gajah Afrika | 5,6 miliar | 257 miliar |
| 13 | Paus sirip | 5,2 miliar | |
| 14 | Paus biru | 5,1 miliar | |
| 15 | Paus minke | 3,2 miliar | 57,4 miliar |
| 16 | Babun zaitun | 2,88 miliar | 10,95 miliar |
| 17 | Paus beluga | 2,7 miliar | |
| 18 | Paus sperma kerdil | 2,02 miliar | |
| 19 | Macaw biru-kuning | 1,92 miliar | 3,14 miliar |
| 20 | Monyet rhesus | 1,71 miliar | 6,39 miliar |
| 21 | Jerapah | 1,67 miliar | 11,21 miliar |
| 22 | Monyet tupai | 1,34 miliar | 3,25 miliar |
| 23 | Burung hantu elang Eurasia | 1,33 miliar | 1,9 miliar |
| 24 | Kea | 1,28 miliar | 2,15 miliar |
| 25 | Burung hantu salju | 1,27 miliar | 1,81 miliar |
| 26 | Gagak besar | 1,2 miliar | 2,17 miliar |
| 27 | Burung hantu cokelat | 1,15 miliar | 1,58 miliar |
| 28 | Monyet kapusin | 1,14 miliar | 3,69 miliar |
| 29 | Kakaktua jambul belerang | 1,14 miliar | 2,12 miliar |
| 30 | Beo kelabu Afrika | 850 juta | 1,57 miliar |
| 31 | Gagak rook | 820 juta | 1,51 miliar |
| 32 | Kudu besar | 793,07 juta | 6,09 miliar |
| 33 | Burung hantu bertelinga panjang | 673 juta | 991,31 juta |
| 34 | Kakaktua Goffin | 599 juta | 1,16 miliar |
| 35 | Singa | 545,24 juta | 4,67 miliar |
| 36 | Elang ekor putih | 542,93 juta | 1,65 miliar |
| 37 | Jay Eurasia | 529 juta | 1,09 miliar |
| 38 | Anjing domestik | 526 juta | |
| 39 | Hiena bergaris | 495,28 juta | 3,88 miliar |
| 40 | Gagak jackdaw barat | 492 juta | 967,99 juta |
| 41 | Burung unta biasa | 479,41 juta | 1,63 miliar |
| 42 | Pica-pica Eurasia | 443 juta | 897,27 juta |
| 43 | Elang saker | 442,95 juta | 1,2 miliar |
| 44 | Emu | 439 juta | 1,34 miliar |
| 45 | Rakun | 437,94 juta | 2,15 miliar |
| 46 | Burung hantu serak | 437 juta | 689,54 juta |
| 47 | Burung hantu kecil | 400,82 juta | 585,92 juta |
| 48 | Hering hitam | 398,9 juta | 1,01 miliar |
| 49 | Burung hantu kerdil Eurasia | 364 juta | 533,26 juta |
| 50 | Elang-buzzard biasa | 351,7 juta | 1 miliar |
| 51 | Rhea besar | 330,34 juta | 1,03 miliar |
| 52 | Angsa bisu | 323,66 juta | 1 miliar |
| 53 | Babi domestik | 293 juta | 2,22 miliar |
| 54 | Angsa greylag | 258,65 juta | 738,23 juta |
| 55 | Beruang cokelat | 250,97 juta | 9,59 miliar |
| 56 | Kucing domestik | 250 juta | 1,22 miliar |
| 57 | Marmoset biasa | 245 juta | 638 juta |
| 58 | Alap-alap kecil biasa | 237,9 juta | 624,58 juta |
| 59 | Itik teal Eurasia | 167,29 juta | 375,41 juta |
| 60 | Parkit | 149 juta | 321,82 juta |
| 61 | Angsa dada merah | 148,62 juta | 482,71 juta |
| 62 | Bebek kayu | 138,21 juta | 305,82 juta |
| 63 | Alap-alap kerdil | 131,9 juta | 350,37 juta |
| 64 | Itik marmer | 130,14 juta | 386,92 juta |
| 65 | Merak India | 129,62 juta | 570,93 juta |
| 66 | Curassow berbenjol kuning | 124,62 juta | 652,99 juta |
| 67 | Merpati mahkota Victoria | 118,45 juta | 578,7 juta |
| 68 | Itik mallard | 112,26 juta | 367,47 juta |
| 69 | Kalkun liar | 105,65 juta | 492,87 juta |
| 70 | Elang-alap Eurasia | 87,83 juta | 401,75 juta |
| 71 | Pheasant Reeves | 80,69 juta | 387,17 juta |
| 72 | Merpati batu | 72 juta | 310 juta |
| 73 | Kelinci Eropa | 71,5 juta | 513 juta |
| 74 | Ayam hutan merah | 60,7 juta | 220,84 juta |
| 75 | Tinamu Cile | 59,13 juta | 241,37 juta |
| 76 | Burung zebra | 55,2 juta | 135,98 juta |
| 77 | Tinamu jambul elegan | 51,38 juta | 217,77 juta |
| 78 | Merpati hutan biasa | 51,33 juta | 258,68 juta |
| 79 | Tinamu jambul quebracho | 48,29 juta | 235,67 juta |
| 80 | Tekukur Afrika | 40 juta | 139,27 juta |
| 81 | Bobwhite utara | 39,11 juta | 148,4 juta |
| 82 | Ayam hutan abu-abu | 36,88 juta | 170,29 juta |
| 83 | Tikus | 31 juta | 200 juta |
| 84 | Puyuh biasa | 22,57 juta | 117,76 juta |
| 85 | Puyuh raja | 20,52 juta | 80,48 juta |
| 86 | Merpati berlian | 18,21 juta | 87,88 juta |
| 87 | Tikus | 13,7 juta | 70,9 juta |
| 88 | Tikus mol telanjang | 5,31 juta | 33,62 juta |
- Neokorteks manusia1.6 × 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
Tampilkan angkanya
| # | Sistem | Semua parameter | Dipakai per token | Tahun | Jenis |
|---|---|---|---|---|---|
| 1 | Neokorteks manusia | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | sinapsis | |
| 2 | GPT-5.5 (OpenAI, 2026) * | ~4.7 × 10¹² | 2026 | perkiraan | |
| 3 | Claude Fable 5 (Anthropic, 2026) * | ~3.5 × 10¹² | 2026 | perkiraan | |
| 4 | Grok 3 (xAI, 2025) | ~3 × 10¹² | 2025 | perkiraan | |
| 5 | Grok 4 (xAI, 2025) | ~3 × 10¹² | 2025 | perkiraan | |
| 6 | Gemini 2.5 Pro (Google, 2025) * | ~3 × 10¹² | 2025 | perkiraan | |
| 7 | Kimi K3 (Moonshot, 2026) | ~2.8 × 10¹² | 2026 | perkiraan | |
| 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | arsitektur tidak disebutkan |
| 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | campuran pakar |
| 10 | Grok 4.7 (xAI, 2026) * | ~2.1 × 10¹² | 2026 | perkiraan | |
| 11 | Llama 4 Behemoth (Meta AI, 2025) | ~2 × 10¹² | 2025 | perkiraan | |
| 12 | GPT-4 (OpenAI, 2023) | ~1.8 × 10¹² | 2023 | perkiraan | |
| 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | ~1.6 × 10¹² | 2026 | perkiraan | |
| 14 | LongCat-2.0 (Meituan Inc, 2026) | ~1.6 × 10¹² | 2026 | perkiraan | |
| 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 18 | Kimi K2.5 (Moonshot, 2026) | ~1 × 10¹² | 2026 | perkiraan | |
| 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 21 | Composer 2.5 (Cursor, 2026) | ~1 × 10¹² | 2026 | perkiraan | |
| 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | arsitektur tidak disebutkan |
| 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | ~1 × 10¹² | 2026 | perkiraan | |
| 24 | M6-T (Alibaba, 2021) | ~1 × 10¹² | 2021 | perkiraan | |
| 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 26 | Qwen3-Max (Alibaba, 2025) | ~1 × 10¹² | 2025 | perkiraan | |
| 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | campuran pakar |
| 28 | Kimi K2 Thinking (Moonshot, 2025) | ~1 × 10¹² | 2025 | perkiraan | |
| 29 | Inkling (Thinking Machines, 2026) | ~9.8 × 10¹¹ | 2026 | perkiraan | |
| 30 | Tencent Hy4 preview (Tencent, 2026) | ~7.7 × 10¹¹ | 2026 | perkiraan | |
| 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | ~7.5 × 10¹¹ | 2026 | perkiraan | |
| 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | ~7.4 × 10¹¹ | 2026 | perkiraan | |
| 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | campuran pakar |
| 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | campuran pakar |
| 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | campuran pakar |
| 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | campuran pakar |
| 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | arsitektur tidak disebutkan |
| 42 | Step 5 Preview (StepFun, 2026) | ~6 × 10¹¹ | 2026 | perkiraan | |
| 43 | PaLM-E (Google, 2023) | ~5.6 × 10¹¹ | 2023 | perkiraan | |
| 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 45 | LongCat-Flash (Meituan Inc, 2025) | ~5.6 × 10¹¹ | 2025 | perkiraan | |
| 46 | Nemotron 3 Ultra (Nvidia, 2026) | ~5.5 × 10¹¹ | 2026 | perkiraan | |
| 47 | PaLM (Google Research, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | padat |
| 48 | Minerva (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | padat |
| 49 | U-PaLM (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | padat |
| 50 | LMSI-Palm (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | padat |
| 51 | Flan-PaLM 540B (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | padat |
| 52 | Megatron-Turing NLG 530B (Microsoft, 2021) | 5.3 × 10¹¹ | 5.3 × 10¹¹ | 2021 | padat |
| 53 | MegaScale (ByteDance, 2024) | ~5.3 × 10¹¹ | 2024 | perkiraan | |
| 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 55 | Doubao-pro (ByteDance, 2024) | ~5 × 10¹¹ | 2024 | perkiraan | |
| 56 | Grok 4.20 (xAI, 2026) | ~5 × 10¹¹ | 2026 | perkiraan | |
| 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | campuran pakar |
| 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | campuran pakar |
| 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | campuran pakar |
| 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | padat |
| 61 | Llama 4 Maverick (Meta AI, 2025) | ~4 × 10¹¹ | 2025 | perkiraan | |
| 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | ~4 × 10¹¹ | 2026 | perkiraan | |
| 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | perkiraan | |
| 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | perkiraan | |
| 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | campuran pakar |
| 68 | PaLM 2 (Google, 2023) | ~3.4 × 10¹¹ | 2023 | perkiraan | |
| 69 | Med-PaLM 2 (Google Research, 2023) | ~3.4 × 10¹¹ | 2023 | perkiraan | |
| 70 | Nemotron-4 340B (Nvidia, 2024) | 3.4 × 10¹¹ | 3.4 × 10¹¹ | 2024 | padat |
| 71 | GLM-5.3-Flash (Z.ai (Zhipu AI), 2026) | ~3.2 × 10¹¹ | 2026 | perkiraan | |
| 72 | Grok-1 (xAI, 2023) | ~3.1 × 10¹¹ | 2023 | perkiraan | |
| 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 74 | SB-LM (Google, 2007) | ~3 × 10¹¹ | 2007 | perkiraan | |
| 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | ~2.8 × 10¹¹ | 2026 | perkiraan | |
| 78 | Gopher (DeepMind, 2021) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2021 | padat |
| 79 | TML-Interaction-Small (Thinking Machines, 2026) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2026 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 80 | ST-MoE (Google, 2022) | ~2.7 × 10¹¹ | 2022 | perkiraan | |
| 81 | ERNIE 3.0 Titan (Baidu, 2021) | 2.6 × 10¹¹ | 2.6 × 10¹¹ | 2021 | padat |
| 82 | Solar Open2 250B (Upstage, 2026) | ~2.5 × 10¹¹ | 2026 | perkiraan | |
| 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | ~2.5 × 10¹¹ | 2024 | perkiraan | |
| 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | arsitektur tidak disebutkan |
| 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | arsitektur tidak disebutkan |
| 88 | Qwen3-235B-A22B (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | perkiraan | |
| 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | perkiraan | |
| 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | ~2.4 × 10¹¹ | 2025 | perkiraan | |
| 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | campuran pakar |
| 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 94 | HyperCLOVA 204B (NAVER, 2021) | ~2 × 10¹¹ | 2021 | perkiraan | |
| 95 | Amazon Titan (Amazon, 2023) | ~2 × 10¹¹ | 2023 | perkiraan | |
| 96 | Falcon-180B (Technology Innovation Institute, 2023) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2023 | padat |
| 97 | Jurassic-1-Jumbo (AI21 Labs, 2021) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2021 | arsitektur tidak disebutkan |
| 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | padat |
| 99 | BLUUMI (University of Turku, 2023) | ~1.8 × 10¹¹ | 2023 | perkiraan | |
| 100 | InstructGPT 175B (OpenAI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | padat |
| 101 | OPT-175B (Meta AI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | padat |
| 102 | BlenderBot 3 (McGill University, 2022) | ~1.8 × 10¹¹ | 2022 | perkiraan | |
| 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | padat |
| 104 | Palmyra X 004 (Writer, 2024) | ~1.5 × 10¹¹ | 2024 | perkiraan | |
| 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | arsitektur tidak disebutkan |
| 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | arsitektur tidak disebutkan |
| 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | arsitektur tidak disebutkan |
| 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | arsitektur tidak disebutkan |
| 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 110 | GLM-130B (Tsinghua University, 2022) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2022 | padat |
| 111 | Pixtral Large (Mistral AI, 2024) | 1.2 × 10¹¹ | 1.2 × 10¹¹ | 2024 | arsitektur tidak disebutkan |
| 112 | Mistral Large 2 (Mistral AI, 2024) | ~1.2 × 10¹¹ | 2024 | perkiraan | |
| 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | ~1.2 × 10¹¹ | 2026 | perkiraan | |
| 114 | Galactica (Meta AI, 2022) | ~1.2 × 10¹¹ | 2022 | perkiraan | |
| 115 | Nemotron 3 Super (Nvidia, 2026) | ~1.2 × 10¹¹ | 2026 | perkiraan | |
| 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | campuran pakar |
| 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | arsitektur tidak disebutkan |
| 118 | Kaiju Large (Character.ai, 2025) | ~1.1 × 10¹¹ | 2025 | perkiraan | |
| 119 | Llama 4 Scout (Meta AI, 2025) | ~1.1 × 10¹¹ | 2025 | perkiraan | |
| 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | padat |
| 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | arsitektur tidak disebutkan |
| 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | padat |
| 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | arsitektur tidak disebutkan |
| 124 | Yi-Large (01.AI, 2024) | ~1 × 10¹¹ | 2024 | perkiraan | |
| 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 127 | Neokorteks tikus | 8.1 × 10¹⁰ | 8.1 × 10¹⁰ | sinapsis | |
| 128 | Flamingo (DeepMind, 2022) | 8 × 10¹⁰ | 8 × 10¹⁰ | 2022 | padat |
| 129 | Qwen3-Coder-Next (Alibaba, 2026) | ~8 × 10¹⁰ | 2026 | perkiraan | |
| 130 | Qwen2-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | padat |
| 131 | Qwen2.5-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | padat |
| 132 | Qwen2.5 Instruct (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | padat |
| 133 | Qwen-72B (Alibaba, 2023) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2023 | padat |
| 134 | Palmyra X 003 (Writer, 2024) | ~7.2 × 10¹⁰ | 2024 | perkiraan | |
| 135 | Qwen1.5-72B (Alibaba, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | padat |
| 136 | LLaVA-OV-72B (ByteDance, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | padat |
| 137 | NVLM-X 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | perkiraan | |
| 138 | NVLM-H 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | perkiraan | |
| 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 141 | Chinchilla (DeepMind, 2022) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2022 | padat |
| 142 | Llama 2-70B (Meta AI, 2023) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2023 | padat |
| 143 | PPLX-70B-Online (Perplexity, 2023) | ~7 × 10¹⁰ | 2023 | perkiraan | |
| 144 | Llama 3-70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | padat |
| 145 | ALLaM adapted 70B (Saudi Data and Artificial Intelligence Authority, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | padat |
| 147 | Reka Core (Reka AI, 2024) | ~6.7 × 10¹⁰ | 2024 | perkiraan | |
| 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | padat |
| 149 | SNM-skip (Google, 2014) | ~6.2 × 10¹⁰ | 2014 | perkiraan | |
| 150 | PaLI-X (Google Research, 2023) | ~5.5 × 10¹⁰ | 2023 | perkiraan | |
| 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 153 | NLLB (Meta AI, 2022) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2022 | padat |
| 154 | BloombergGPT (Bloomberg, 2023) | 5.1 × 10¹⁰ | 5.1 × 10¹⁰ | 2023 | padat |
| 155 | M4-50B (Google, 2019) | 5 × 10¹⁰ | 5 × 10¹⁰ | 2019 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 156 | Mixtral 8x7B (Mistral AI, 2023) | ~4.7 × 10¹⁰ | 2023 | perkiraan | |
| 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 158 | Falcon-40B (Technology Innovation Institute, 2023) | 4 × 10¹⁰ | 4 × 10¹⁰ | 2023 | padat |
| 159 | Qwen3-Omni-30B-A3B (Alibaba, 2025) | 3.5 × 10¹⁰ | 3 × 10⁹ | 2025 | campuran pakar |
| 160 | Yi-34B (01.AI, 2023) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2023 | padat |
| 161 | Cambrian-1-34B (New York University (NYU), 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 163 | Kaiju Medium (Character.ai, 2025) | ~3.4 × 10¹⁰ | 2025 | perkiraan | |
| 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | arsitektur tidak disebutkan |
| 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | padat |
| 166 | QwQ-32B (Alibaba, 2025) | ~3.3 × 10¹⁰ | 2025 | perkiraan | |
| 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | ~3.2 × 10¹⁰ | 2025 | perkiraan | |
| 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 174 | MM1-30B (Apple, 2024) | ~3 × 10¹⁰ | 2024 | perkiraan | |
| 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | arsitektur tidak disebutkan |
| 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | arsitektur tidak disebutkan |
| 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 181 | KN-LM (Google, 2007) | ~2.1 × 10¹⁰ | 2007 | perkiraan | |
| 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 183 | RNN for 1B words (Google, 2013) | ~2 × 10¹⁰ | 2013 | perkiraan | |
| 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | padat |
| 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 187 | GPT-3.5 Turbo (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | perkiraan | |
| 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | perkiraan | |
| 189 | SPHINX (Shanghai AI Lab, 2023) | ~2 × 10¹⁰ | 2023 | perkiraan | |
| 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 191 | CogAgent (Tsinghua University, 2023) | ~1.8 × 10¹⁰ | 2023 | perkiraan | |
| 192 | Turing-NLG (Microsoft, 2020) | ~1.7 × 10¹⁰ | 2020 | perkiraan | |
| 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 194 | PaLI (Google, 2022) | ~1.7 × 10¹⁰ | 2022 | perkiraan | |
| 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 196 | StarCoder (Hugging Face, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | padat |
| 197 | ESM2-15B (Meta AI, 2022) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 198 | FunSearch (Google DeepMind, 2023) | ~1.5 × 10¹⁰ | 2023 | perkiraan | |
| 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | arsitektur tidak disebutkan |
| 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 203 | mT5-XXL (Google, 2020) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2020 | padat |
| 204 | mT0-13B (Hugging Face, 2022) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2022 | arsitektur tidak disebutkan |
| 205 | LLaVA (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | padat |
| 206 | InstructBLIP (Salesforce Research, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | ~1.3 × 10¹⁰ | 2023 | perkiraan | |
| 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 211 | LLaVA 1.5 (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | padat |
| 212 | Volcano 13B (Korea University, 2023) | ~1.3 × 10¹⁰ | 2023 | perkiraan | |
| 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | ~1.3 × 10¹⁰ | 2024 | perkiraan | |
| 214 | Kaiju Small (Character.ai, 2025) | ~1.3 × 10¹⁰ | 2025 | perkiraan | |
| 215 | ByT5-XXL (Google, 2021) | ~1.3 × 10¹⁰ | 2021 | perkiraan | |
| 216 | CoRe (Tsinghua University, 2023) | ~1.2 × 10¹⁰ | 2023 | perkiraan | |
| 217 | DALL-E (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | perkiraan | |
| 218 | Codex (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | perkiraan | |
| 219 | T5-11B (Google, 2019) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2019 | padat |
| 220 | UnifiedQA (Allen Institute for AI, 2020) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2020 | padat |
| 221 | Unicorn (Allen Institute for AI, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | arsitektur tidak disebutkan |
| 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | arsitektur tidak disebutkan |
| 223 | CoEdiT-xxl (University of Minnesota, 2023) | ~1.1 × 10¹⁰ | 2023 | perkiraan | |
| 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | arsitektur tidak disebutkan |
| 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | arsitektur tidak disebutkan |
| 226 | Llama 3.2 11B (Meta AI, 2024) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2024 | padat |
| 227 | ERNIE 3.0 (Baidu, 2021) | 1 × 10¹⁰ | 1 × 10¹⁰ | 2021 | padat |
| 228 | ERNIE-ViLG (Baidu, 2021) | ~1 × 10¹⁰ | 2021 | perkiraan | |
| 229 | Qwen-VL (Alibaba, 2023) | ~9.6 × 10⁹ | 2023 | perkiraan | |
| 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 232 | CogVideo (Tsinghua University, 2022) | ~9.4 × 10⁹ | 2022 | perkiraan | |
| 233 | eDiff-I (Nvidia, 2022) | ~9.1 × 10⁹ | 2022 | perkiraan | |
| 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | arsitektur tidak disebutkan |
| 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 236 | Qwen-Audio-Chat (Alibaba, 2023) | ~8.5 × 10⁹ | 2023 | perkiraan | |
| 237 | Megatron-LM (Nvidia, 2019) | ~8.3 × 10⁹ | 2019 | perkiraan | |
| 238 | BigSSL (Google, 2021) | ~8 × 10⁹ | 2021 | perkiraan | |
| 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | padat |
| 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 242 | Imagen (Google Brain, 2022) | ~7.8 × 10⁹ | 2022 | perkiraan | |
| 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 246 | mPLUG-Owl2 (Alibaba, 2023) | ~7.1 × 10⁹ | 2023 | perkiraan | |
| 247 | Goat-7B (National University of Singapore, 2023) | ~7 × 10⁹ | 2023 | perkiraan | |
| 248 | Llama 2-7B (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | padat |
| 249 | Llama Guard (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | padat |
| 250 | Qwen-VL-Max (Alibaba, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | padat |
| 251 | Mathstral (Mistral AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | ~7 × 10⁹ | 2025 | perkiraan | |
| 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | arsitektur tidak disebutkan |
| 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 257 | GPipe (Google, 2018) | ~6 × 10⁹ | 2018 | perkiraan | |
| 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | padat |
| 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | ~6 × 10⁹ | 2023 | perkiraan | |
| 261 | MolmoAct 2 (Allen Institute for AI, 2026) | ~5.5 × 10⁹ | 2026 | perkiraan | |
| 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 265 | Mesh-TensorFlow Transformer 4.9B (Google Brain, 2018) | 4.9 × 10⁹ | 4.9 × 10⁹ | 2018 | padat |
| 266 | W.A.L.T (Stanford University, 2023) | 4.7 × 10⁹ | 4.7 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 267 | CogView (Tsinghua University, 2021) | ~4 × 10⁹ | 2021 | perkiraan | |
| 268 | Make-A-Scene (Meta AI, 2022) | ~4 × 10⁹ | 2022 | perkiraan | |
| 269 | ONE-PEACE (Alibaba, 2023) | ~4 × 10⁹ | 2023 | perkiraan | |
| 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | padat |
| 271 | DALL·E 2 (OpenAI, 2022) | ~3.5 × 10⁹ | 2022 | perkiraan | |
| 272 | Stable Diffusion XL (Stability AI, 2023) | ~3.4 × 10⁹ | 2023 | perkiraan | |
| 273 | MusicGen (Meta AI, 2023) | ~3.4 × 10⁹ | 2023 | perkiraan | |
| 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | ~3.2 × 10⁹ | 2016 | perkiraan | |
| 278 | BASIC-L (Google, 2021) | ~3.1 × 10⁹ | 2021 | perkiraan | |
| 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | ~2.9 × 10⁹ | 2018 | perkiraan | |
| 287 | T5-3B (Google, 2019) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2019 | padat |
| 288 | DPO on Pythia-2.8B (Stanford University, 2023) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2023 | padat |
| 289 | AFM-on-device (Apple, 2024) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | ~2.7 × 10⁹ | 2022 | perkiraan | |
| 291 | Hybrid H3-2.7B (Stanford University, 2022) | ~2.7 × 10⁹ | 2022 | perkiraan | |
| 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 293 | Meena (Google Brain, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | padat |
| 294 | CPM-Large (Tsinghua University, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | padat |
| 295 | Nucleotide Transformer (Nvidia, 2023) | ~2.5 × 10⁹ | 2023 | perkiraan | |
| 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | padat |
| 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 302 | MuseNet (OpenAI, 2019) | ~2 × 10⁹ | 2019 | perkiraan | |
| 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | arsitektur tidak disebutkan |
| 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | padat |
| 305 | BEIT-3 (Microsoft, 2022) | ~1.9 × 10⁹ | 2022 | perkiraan | |
| 306 | Ankh_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 309 | Phenaki (University College London (UCL), 2022) | ~1.8 × 10⁹ | 2022 | perkiraan | |
| 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 311 | DistBelief Vision (Google, 2012) | ~1.7 × 10⁹ | 2012 | perkiraan | |
| 312 | Whisper (OpenAI, 2022) | ~1.6 × 10⁹ | 2022 | perkiraan | |
| 313 | GPT-2 (OpenAI, 2019) | ~1.5 × 10⁹ | 2019 | perkiraan | |
| 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 316 | AudioLM (Google Research, 2023) | ~1.5 × 10⁹ | 2023 | perkiraan | |
| 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 319 | Stable Diffusion (Runway, 2022) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2022 | padat |
| 320 | SEER (Facebook AI Research, 2021) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2021 | arsitektur tidak disebutkan |
| 321 | InstructGPT 1.3B (OpenAI, 2022) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2022 | padat |
| 322 | Megatron-LM (Nvidia, 2019) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2019 | padat |
| 323 | RDT-1B (Tsinghua University, 2024) | ~1.2 × 10⁹ | 2024 | perkiraan | |
| 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | arsitektur tidak disebutkan |
| 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | arsitektur tidak disebutkan |
| 329 | Unsupervised High-level Feature Learner (Google, 2012) | ~1 × 10⁹ | 2012 | perkiraan | |
| 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | padat |
| 331 | HuBERT (Facebook AI Research, 2021) | ~1 × 10⁹ | 2021 | perkiraan | |
| 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | padat |
| 333 | AudioGen (Meta AI, 2023) | ~1 × 10⁹ | 2023 | perkiraan | |
| 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | arsitektur tidak disebutkan |
| 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | ~9.4 × 10⁸ | 2013 | perkiraan | |
| 336 | ImageBind (Meta AI, 2023) | ~9.3 × 10⁸ | 2023 | perkiraan | |
| 337 | Big Transfer (Google Brain, 2019) | ~9.3 × 10⁸ | 2019 | perkiraan | |
| 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 341 | ResNeXt-101 32x48d (Facebook, 2018) | 8.3 × 10⁸ | 8.3 × 10⁸ | 2018 | padat |
| 342 | FixRes ResNeXt-101 WSL (Facebook AI, 2019) | ~8.3 × 10⁸ | 2019 | perkiraan | |
| 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 344 | Statement Curriculum Learning (OpenAI, 2022) | ~7.7 × 10⁸ | 2022 | perkiraan | |
| 345 | CodeT5-large (Salesforce, 2022) | ~7.7 × 10⁸ | 2022 | perkiraan | |
| 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | arsitektur tidak disebutkan |
| 348 | data2vec (Meta AI, 2022) | ~7.1 × 10⁸ | 2022 | perkiraan | |
| 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 350 | Word2Vec (Google, 2013) | ~6.9 × 10⁸ | 2013 | perkiraan | |
| 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | ~6.8 × 10⁸ | 2023 | perkiraan | |
| 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 354 | XLM (Facebook, 2019) | ~6.7 × 10⁸ | 2019 | perkiraan | |
| 355 | ESM1b (Facebook AI Research, 2020) | 6.5 × 10⁸ | 6.5 × 10⁸ | 2020 | padat |
| 356 | Segment Anything Model (Meta AI, 2023) | 6.4 × 10⁸ | 6.4 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 357 | ViT-Huge/14 (Google Brain, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | padat |
| 358 | Masked Autoencoders ViT-H (Facebook AI Research, 2021) | ~6.3 × 10⁸ | 2021 | perkiraan | |
| 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | padat |
| 360 | DLDL (University of Oxford, 2016) | ~5.6 × 10⁸ | 2016 | perkiraan | |
| 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | padat |
| 364 | LUKE (University of Washington, 2020) | ~4.8 × 10⁸ | 2020 | perkiraan | |
| 365 | EfficientNet-L2 (Google, 2019) | ~4.8 × 10⁸ | 2019 | perkiraan | |
| 366 | Noisy Student (Carnegie Mellon University (CMU), 2019) | 4.8 × 10⁸ | 4.8 × 10⁸ | 2019 | padat |
| 367 | Meta Pseudo Labels (Google Brain, 2021) | ~4.8 × 10⁸ | 2021 | perkiraan | |
| 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 369 | NMT Transformer 437M (Google, 2019) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2019 | padat |
| 370 | Adaptive Inputs + LayerDrop (Facebook AI Research, 2019) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2019 | padat |
| 371 | ProtBERT-BFD (Technical University of Munich, 2021) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 372 | OntoProtein (Zhejiang University (ZJU), 2022) | ~4.2 × 10⁸ | 2022 | perkiraan | |
| 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 374 | BART-large (Facebook AI, 2019) | ~4.1 × 10⁸ | 2019 | perkiraan | |
| 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 376 | BERT-Large-CAS (Amazon, 2019) | ~4 × 10⁸ | 2019 | perkiraan | |
| 377 | MoCo (Facebook AI, 2019) | ~3.8 × 10⁸ | 2019 | perkiraan | |
| 378 | SimCLR (Google Brain, 2020) | ~3.8 × 10⁸ | 2020 | perkiraan | |
| 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 380 | RoBERTa Large (Facebook, 2019) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2019 | padat |
| 381 | AbLang (University of Oxford, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | padat |
| 383 | VALL-E (Microsoft, 2023) | ~3.5 × 10⁸ | 2023 | perkiraan | |
| 384 | BERT-Large (Google, 2018) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2018 | padat |
| 385 | XLNet (Carnegie Mellon University (CMU), 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | padat |
| 386 | ERNIE-GEN (Baidu, 2020) | ~3.4 × 10⁸ | 2020 | perkiraan | |
| 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | padat |
| 388 | ELECTRA (Stanford University, 2020) | ~3.4 × 10⁸ | 2020 | perkiraan | |
| 389 | German ELECTRA Large (deepset, 2020) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2020 | padat |
| 390 | ALM 1.0 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | ~3.4 × 10⁸ | 2022 | perkiraan | |
| 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | padat |
| 392 | Fusion in Encoder (Samsung, 2022) | ~3.3 × 10⁸ | 2022 | perkiraan | |
| 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | padat |
| 394 | CPC v2 (DeepMind, 2019) | ~3 × 10⁸ | 2019 | perkiraan | |
| 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | padat |
| 396 | DDPM-IP (Utrecht University, 2023) | ~3 × 10⁸ | 2023 | perkiraan | |
| 397 | GNMT (Google, 2016) | ~2.8 × 10⁸ | 2016 | perkiraan | |
| 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 399 | YOLO (University of Washington, 2015) | ~2.7 × 10⁸ | 2015 | perkiraan | |
| 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | ~2.6 × 10⁸ | 2019 | perkiraan | |
| 401 | Transformer-XL + RMS dynamic eval (University of Edinburgh, 2019) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2019 | padat |
| 402 | Transformer-XL Large + Phrase Induction (Massachusetts Institute of Technology (MIT), 2019) | ~2.6 × 10⁸ | 2019 | perkiraan | |
| 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | padat |
| 407 | S4 (Stanford University, 2021) | ~2.5 × 10⁸ | 2021 | perkiraan | |
| 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | ~2.5 × 10⁸ | 2019 | perkiraan | |
| 409 | ERNIE-Doc (Baidu, 2020) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2020 | padat |
| 410 | Adaptive Input Transformer + RD (Microsoft Research Asia, 2021) | ~2.5 × 10⁸ | 2021 | perkiraan | |
| 411 | Transformer (Adaptive Input Embeddings) WT103 (Facebook AI Research, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | padat |
| 412 | TaLK Convolution (Carleton University, 2020) | ~2.4 × 10⁸ | 2020 | perkiraan | |
| 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | ~2.4 × 10⁸ | 2020 | perkiraan | |
| 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | arsitektur tidak disebutkan |
| 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | arsitektur tidak disebutkan |
| 417 | MetNet (Google, 2020) | ~2.3 × 10⁸ | 2020 | perkiraan | |
| 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | padat |
| 419 | CodeT5-base (Salesforce, 2021) | ~2.2 × 10⁸ | 2021 | perkiraan | |
| 420 | Transformer (Google Research, 2017) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2017 | padat |
| 421 | Sandwich Transformer (Allen Institute for AI, 2019) | ~2.1 × 10⁸ | 2019 | perkiraan | |
| 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 423 | Word2Vec (Google, 2013) | ~2.1 × 10⁸ | 2013 | perkiraan | |
| 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | padat |
| 425 | DreamerV3 (DeepMind, 2023) | ~2 × 10⁸ | 2023 | perkiraan | |
| 426 | DL scaling speech (Baidu, 2017) | ~1.9 × 10⁸ | 2017 | perkiraan | |
| 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | ~1.9 × 10⁸ | 2019 | perkiraan | |
| 428 | (NTT Communication Science Laboratories, 2018) | 1.9 × 10⁸ | 1.9 × 10⁸ | 2018 | padat |
| 429 | TrellisNet (Carnegie Mellon University (CMU), 2018) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2018 | padat |
| 430 | DL scaling LM (Baidu, 2017) | ~1.8 × 10⁸ | 2017 | perkiraan | |
| 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | arsitektur tidak disebutkan |
| 432 | OpenAI Five Rerun (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | padat |
| 433 | OpenAI Five (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | padat |
| 434 | 4 layer QRNN (Salesforce Research, 2018) | ~1.5 × 10⁸ | 2018 | perkiraan | |
| 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | ~1.5 × 10⁸ | 2020 | perkiraan | |
| 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | arsitektur tidak disebutkan |
| 437 | PermuteFormer (Peking University, 2021) | ~1.5 × 10⁸ | 2021 | perkiraan | |
| 438 | Fragment embedding (Stanford University, 2014) | ~1.4 × 10⁸ | 2014 | perkiraan | |
| 439 | OverFeat (New York University (NYU), 2013) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2013 | padat |
| 440 | VGG19 (University of Oxford, 2014) | ~1.4 × 10⁸ | 2014 | perkiraan | |
| 441 | LRCN (UT Austin, 2014) | ~1.4 × 10⁸ | 2014 | perkiraan | |
| 442 | Transformer-XL DeFINE (University of Washington, 2019) | ~1.4 × 10⁸ | 2019 | perkiraan | |
| 443 | Deep Autoencoders (University of Toronto, 2011) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2011 | padat |
| 444 | AlphaStar (DeepMind, 2019) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2019 | padat |
| 445 | VGG16 (University of Oxford, 2014) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2014 | padat |
| 446 | VGG-Face (University of Oxford, 2015) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2015 | padat |
| 447 | SAF R-CNN (Beijing Institute of Technology, 2015) | ~1.4 × 10⁸ | 2015 | perkiraan | |
| 448 | Template Adaptation (University of Oxford, 2016) | ~1.4 × 10⁸ | 2016 | perkiraan | |
| 449 | LRR-4X (UC Irvine, 2016) | ~1.4 × 10⁸ | 2016 | perkiraan | |
| 450 | CMS-RCNN (IEEE, 2016) | ~1.4 × 10⁸ | 2016 | perkiraan | |
| 451 | QRNN (Salesforce Research, 2018) | ~1.4 × 10⁸ | 2018 | perkiraan | |
| 452 | Multiresolution CNN (Google, 2014) | ~1.3 × 10⁸ | 2014 | perkiraan | |
| 453 | FTW (DeepMind, 2018) | ~1.3 × 10⁸ | 2018 | perkiraan | |
| 454 | Feedback Transformer (LORIA, 2020) | ~1.3 × 10⁸ | 2020 | perkiraan | |
| 455 | KEPLER (Tsinghua University, 2020) | 1.3 × 10⁸ | 1.3 × 10⁸ | 2020 | padat |
| 456 | DL scaling Image (Baidu, 2017) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2017 | padat |
| 457 | GloVe (Stanford University, 2014) | ~1.2 × 10⁸ | 2014 | perkiraan | |
| 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | padat |
| 459 | GPT-1 (OpenAI, 2018) | ~1.2 × 10⁸ | 2018 | perkiraan | |
| 460 | ContextNet (Google, 2020) | ~1.1 × 10⁸ | 2020 | perkiraan | |
| 461 | SciBERT (Allen Institute for AI, 2019) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2019 | padat |
| 462 | DNABERT (Northeastern University, 2021) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2021 | arsitektur tidak disebutkan |
| 463 | Contriever (Meta AI, 2021) | ~1.1 × 10⁸ | 2021 | perkiraan | |
| 464 | BERT-RBP (Waseda University, 2022) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2022 | padat |
| 465 | RoFormer (Zhuiyi Technology, 2023) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2023 | arsitektur tidak disebutkan |
| 466 | LSTM LM (RWTH Aachen University, 2012) | ~1 × 10⁸ | 2012 | perkiraan | |
| 467 | ReLU-Speech (Google, 2013) | ~1 × 10⁸ | 2013 | perkiraan | |
| 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | arsitektur tidak disebutkan |
| 469 | Support Vector Machines (AT&T, 1995) | ~1 × 10⁸ | 1995 | perkiraan | |
| 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | padat |
| 471 | MSA Transformer (Facebook AI Research, 2021) | ~1 × 10⁸ | 2021 | perkiraan | |
| 472 | YOLOX-X (Megvii Inc, 2021) | ~9.9 × 10⁷ | 2021 | perkiraan | |
| 473 | DeLighT (University of Washington, 2020) | ~9.9 × 10⁷ | 2020 | perkiraan | |
| 474 | ELMo (University of Washington, 2018) | ~9.4 × 10⁷ | 2018 | perkiraan | |
| 475 | AlphaFold 2 (DeepMind, 2020) | ~9.3 × 10⁷ | 2020 | perkiraan | |
| 476 | AlphaMissense (Google DeepMind, 2023) | ~9.3 × 10⁷ | 2023 | perkiraan | |
| 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | arsitektur tidak disebutkan |
| 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | ~9.2 × 10⁷ | 2016 | perkiraan | |
| 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | arsitektur tidak disebutkan |
| 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | padat |
| 481 | NASNet-A (Google Brain, 2017) | ~8.9 × 10⁷ | 2017 | perkiraan | |
| 482 | CLIP (OpenAI, 2021) | ~8.9 × 10⁷ | 2021 | perkiraan | |
| 483 | Detic (Meta AI, 2022) | ~8.8 × 10⁷ | 2022 | perkiraan | |
| 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | padat |
| 485 | BigBiGAN (Google, 2019) | ~8.6 × 10⁷ | 2019 | perkiraan | |
| 486 | ViT-Base/32 (Google Brain, 2020) | ~8.6 × 10⁷ | 2020 | perkiraan | |
| 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | arsitektur tidak disebutkan |
| 488 | CaLM (University of Oxford, 2022) | ~8.6 × 10⁷ | 2022 | perkiraan | |
| 489 | Tensor-Transformer (University of California (UC) Berkeley, 2020) | 8.5 × 10⁷ | 8.5 × 10⁷ | 2020 | padat |
| 490 | ResNeXt-101 (University of California San Diego, 2016) | 8.3 × 10⁷ | 8.3 × 10⁷ | 2016 | padat |
| 491 | RBM Image Classifier (University of Toronto, 2009) | ~8 × 10⁷ | 2009 | perkiraan | |
| 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | padat |
| 493 | Big-Little Net (IBM, 2018) | ~7.7 × 10⁷ | 2018 | perkiraan | |
| 494 | EfficientDet (Google Brain, 2020) | ~7.7 × 10⁷ | 2020 | perkiraan | |
| 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | arsitektur tidak disebutkan |
| 496 | RNN LM (Johns Hopkins University, 2010) | ~7 × 10⁷ | 2010 | perkiraan | |
| 497 | R-CNN (University of California (UC) Berkeley, 2013) | ~6.9 × 10⁷ | 2013 | perkiraan | |
| 498 | Variational (University of Cambridge, 2015) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2015 | padat |
| 499 | DistilBERT (Hugging Face, 2019) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2019 | padat |
| 500 | Theseus 6/768 (University of California San Diego, 2020) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2020 | padat |
| 501 | ResNet-152 (Microsoft, 2015) | 6 × 10⁷ | 6 × 10⁷ | 2015 | padat |
| 502 | AlexNet (University of Toronto, 2012) | 6 × 10⁷ | 6 × 10⁷ | 2012 | padat |
| 503 | DETR (Facebook, 2020) | 6 × 10⁷ | 6 × 10⁷ | 2020 | padat |
| 504 | YOLOv3 (University of Washington, 2018) | ~5.7 × 10⁷ | 2018 | perkiraan | |
| 505 | Inception-ResNet-V2 (Google, 2016) | ~5.6 × 10⁷ | 2016 | perkiraan | |
| 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | padat |
| 507 | Otak lalat buah | 5.5 × 10⁷ | 5.5 × 10⁷ | sinapsis | |
| 508 | NAS with base 8 and shared embeddings (Google Brain, 2016) | 5.4 × 10⁷ | 5.4 × 10⁷ | 2016 | padat |
| 509 | RetinaNet-R101 (Facebook AI Research, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | padat |
| 510 | AWD-LSTM+WT+Cache+IOG (NTT Communication Science Laboratories, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | padat |
| 511 | Hierarchical Scene Labeling (New York University (NYU), 2013) | 5.2 × 10⁷ | 5.2 × 10⁷ | 2013 | padat |
| 512 | VD-LSTM+REAL Large (Salesforce Research, 2016) | 5.1 × 10⁷ | 5.1 × 10⁷ | 2016 | padat |
| 513 | YOLOv2 (University of Washington, 2016) | ~5.1 × 10⁷ | 2016 | perkiraan | |
| 514 | Dropout-LSTM+Noise (Columbia University, 2018) | ~5.1 × 10⁷ | 2018 | perkiraan | |
| 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | ~5 × 10⁷ | 2017 | perkiraan | |
| 516 | Dropout (University of Toronto, 2012) | ~4.9 × 10⁷ | 2012 | perkiraan | |
| 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | padat |
| 518 | DistBelief Speech (Google, 2012) | ~4.7 × 10⁷ | 2012 | perkiraan | |
| 519 | AlphaGo Zero (DeepMind, 2017) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2017 | padat |
| 520 | Fine-tuned-AWD-LSTM-DOC (Samsung R&D Institute Russia, 2018) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2018 | padat |
| 521 | CT-MoS (Google, 2020) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2020 | padat |
| 522 | JFT (Google Research, 2017) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2017 | padat |
| 523 | ResNet-101 (Microsoft, 2015) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2015 | padat |
| 524 | HR-ResNet101 (Carnegie Mellon University (CMU), 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | padat |
| 525 | 3DMM-CNN (University of Southern California, 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | padat |
| 526 | LaNet-L (Brown University, 2019) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2019 | padat |
| 527 | ProBERTa (University of Illinois Urbana-Champaign (UIUC), 2020) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2020 | padat |
| 528 | PreTrans-3L-250H (University of Toronto, 2013) | ~4.3 × 10⁷ | 2013 | perkiraan | |
| 529 | Inceptionv4 (Google, 2016) | ~4.3 × 10⁷ | 2016 | perkiraan | |
| 530 | GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (Ben-Gurion University of the Negev, 2017) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2017 | padat |
| 531 | U-Net (University of Freiburg, 2015) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2015 | padat |
| 532 | NASv3 (Google Brain, 2016) | ~3.7 × 10⁷ | 2016 | perkiraan | |
| 533 | MuZero (DeepMind, 2019) | ~3.7 × 10⁷ | 2019 | perkiraan | |
| 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | ~3.5 × 10⁷ | 2017 | perkiraan | |
| 535 | AWD-LSTM-MoS+PDR + dynamic evaluation (IBM, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | padat |
| 536 | AWD-LSTM-MoS + dynamic evaluation (Peking University, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | padat |
| 537 | AWD-LSTM + MoS + Partial Shuffled (University of Texas at Austin, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | padat |
| 538 | Mogrifier (d2, MoS2, MC) + dynamic eval (DeepMind, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | padat |
| 539 | Mogrifier RLSTM (DeepMind, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | arsitektur tidak disebutkan |
| 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | arsitektur tidak disebutkan |
| 541 | DenseNet-264 (Tsinghua University, 2016) | 3.4 × 10⁷ | 3.4 × 10⁷ | 2016 | padat |
| 542 | RetinaNet-R50 (Facebook AI Research, 2017) | ~3.4 × 10⁷ | 2017 | perkiraan | |
| 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | ~3.4 × 10⁷ | 2017 | perkiraan | |
| 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | ~3.4 × 10⁷ | 2019 | perkiraan | |
| 545 | AWD-LSTM - 3-layer LSTM (Salesforce Research, 2017) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2017 | padat |
| 546 | TCAN (Nanjing University, 2020) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2020 | padat |
| 547 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~3.2 × 10⁷ | 2019 | perkiraan | |
| 548 | Paragraph Vector (Google, 2014) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2014 | padat |
| 549 | aLSTM (University of Manchester, 2018) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2018 | padat |
| 550 | StyleGAN2 (Nvidia, 2019) | 3 × 10⁷ | 3 × 10⁷ | 2019 | padat |
| 551 | UDSMProt (Fraunhofer Heinrich Hertz Institute, 2019) | ~2.8 × 10⁷ | 2019 | perkiraan | |
| 552 | SENet (Chinese Academy of Sciences, 2017) | ~2.8 × 10⁷ | 2017 | perkiraan | |
| 553 | Perceiver IO (DeepMind, 2020) | 2.8 × 10⁷ | 2.8 × 10⁷ | 2020 | padat |
| 554 | Fractional Max-Pooling (University of Warwick, 2014) | ~2.7 × 10⁷ | 2014 | perkiraan | |
| 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | arsitektur tidak disebutkan |
| 556 | RNN-WER (DeepMind, 2014) | ~2.6 × 10⁷ | 2014 | perkiraan | |
| 557 | StyleGAN (Nvidia, 2018) | 2.6 × 10⁷ | 2.6 × 10⁷ | 2018 | padat |
| 558 | PyramidNet (Korea Advanced Institute of Science and Technology (KAIST), 2017) | ~2.6 × 10⁷ | 2017 | perkiraan | |
| 559 | ResNeXt-50 (University of California San Diego, 2016) | ~2.5 × 10⁷ | 2016 | perkiraan | |
| 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | padat |
| 561 | Inception v3 (Google, 2015) | ~2.4 × 10⁷ | 2015 | perkiraan | |
| 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | arsitektur tidak disebutkan |
| 563 | WeNet (Amazon, 2019) | ~2.3 × 10⁷ | 2019 | perkiraan | |
| 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | ~2.3 × 10⁷ | 2019 | perkiraan | |
| 565 | NAS+ESS (Northeastern University (China), 2020) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2020 | padat |
| 566 | Xception (Google, 2016) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2016 | padat |
| 567 | MetaMimic (Google, 2018) | ~2.2 × 10⁷ | 2018 | perkiraan | |
| 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~2.1 × 10⁷ | 2019 | perkiraan | |
| 569 | Pointer Sentinel-LSTM (MetaMind Inc, 2016) | 2.1 × 10⁷ | 2.1 × 10⁷ | 2016 | padat |
| 570 | DiffDock (Massachusetts Institute of Technology (MIT), 2022) | ~2 × 10⁷ | 2022 | perkiraan | |
| 571 | EI-REHN-1000D (Korea Advanced Institute of Science and Technology (KAIST), 2017) | 1.9 × 10⁷ | 1.9 × 10⁷ | 2017 | padat |
| 572 | TC-DNN-BLSTM-DNN (Carnegie Mellon University (CMU), 2015) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2015 | padat |
| 573 | OR-WideResNet (Duke University, 2017) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2017 | padat |
| 574 | BP-DBN (University of Toronto, 2009) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2009 | padat |
| 575 | ALBERT (Toyota Technological Institute at Chicago, 2019) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2019 | padat |
| 576 | MedBERT (Peng Cheng Laboratory, 2021) | ~1.7 × 10⁷ | 2021 | perkiraan | |
| 577 | LCNP NORB (, 2009) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2009 | padat |
| 578 | LF-MMI (Johns Hopkins University, 2016) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2016 | padat |
| 579 | AlphaFold (DeepMind, 2020) | ~1.6 × 10⁷ | 2020 | perkiraan | |
| 580 | GNoME for crystal discovery (Google DeepMind, 2023) | ~1.6 × 10⁷ | 2023 | perkiraan | |
| 581 | ReLU (University of Toronto, 2010) | ~1.6 × 10⁷ | 2010 | perkiraan | |
| 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | arsitektur tidak disebutkan |
| 583 | Eve (Harvard Medical School, 2021) | ~1.5 × 10⁷ | 2021 | perkiraan | |
| 584 | LCNP LabelMe (University of Bonn, 2009) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2009 | padat |
| 585 | BatchNorm (Google, 2015) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2015 | padat |
| 586 | Fold2Seq (IBM, 2021) | ~1.2 × 10⁷ | 2021 | perkiraan | |
| 587 | NPLM (University of Montreal / Université de Montréal, 2003) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2003 | padat |
| 588 | LCNP MNIST (, 2009) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2009 | padat |
| 589 | ISS (Duke University, 2017) | 1.1 × 10⁷ | 1.1 × 10⁷ | 2017 | padat |
| 590 | ResNet-1001 (Microsoft, 2016) | ~1 × 10⁷ | 2016 | perkiraan | |
| 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | ~9 × 10⁶ | 2012 | perkiraan | |
| 592 | Gated HORNN (York University, 2016) | ~9 × 10⁶ | 2016 | perkiraan | |
| 593 | AlphaGo Fan (DeepMind, 2015) | ~8.2 × 10⁶ | 2015 | perkiraan | |
| 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | ~8 × 10⁶ | 2015 | perkiraan | |
| 595 | Once for All (MIT-IBM Watson AI Lab, 2020) | 7.7 × 10⁶ | 7.7 × 10⁶ | 2020 | padat |
| 596 | Multi-cell LSTM (University of Hyderabad, 2018) | ~7.2 × 10⁶ | 2018 | perkiraan | |
| 597 | Feedforward NN (University of Montreal / Université de Montréal, 2010) | 7.1 × 10⁶ | 7.1 × 10⁶ | 2010 | padat |
| 598 | True-Regularization+Finetune+Dynamic-Eval (Mobvoi, 2019) | 7 × 10⁶ | 7 × 10⁶ | 2019 | padat |
| 599 | Neural LM (University of Montreal / Université de Montréal, 2000) | 6.9 × 10⁶ | 6.9 × 10⁶ | 2000 | padat |
| 600 | GoogLeNet / InceptionV1 (Google, 2014) | 6.8 × 10⁶ | 6.8 × 10⁶ | 2014 | padat |
| 601 | HyenaDNA (Stanford University, 2023) | 6.6 × 10⁶ | 6.6 × 10⁶ | 2023 | arsitektur tidak disebutkan |
| 602 | Dropout (University of Toronto, 2012) | 5.6 × 10⁶ | 5.6 × 10⁶ | 2012 | padat |
| 603 | SimpleNet (Sensifai, 2016) | 5.5 × 10⁶ | 5.5 × 10⁶ | 2016 | padat |
| 604 | AlphaX-1 (Facebook AI Research, 2019) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2019 | padat |
| 605 | 3DDFA (Chinese Academy of Sciences, 2015) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2015 | padat |
| 606 | Deep CNN + COTS (IEEE, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | padat |
| 607 | DCNN (University of Maryland, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | padat |
| 608 | NLP from scratch (NEC Laboratories, 2011) | ~5 × 10⁶ | 2011 | perkiraan | |
| 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | ~5 × 10⁶ | 2014 | perkiraan | |
| 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | arsitektur tidak disebutkan |
| 611 | High Performance CNN (IDSIA, 2011) | ~4.9 × 10⁶ | 2011 | perkiraan | |
| 612 | MobileNet (Google, 2017) | ~4.2 × 10⁶ | 2017 | perkiraan | |
| 613 | NPLM (University of Montreal / Université de Montréal, 2003) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2003 | padat |
| 614 | LMICA (, 2004) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2004 | padat |
| 615 | PoE MNIST (University College London (UCL), 2000) | 3.9 × 10⁶ | 3.9 × 10⁶ | 2000 | padat |
| 616 | Dimensionality Reduction (University of Toronto, 2006) | ~3.8 × 10⁶ | 2006 | perkiraan | |
| 617 | GOAT (DeepMind, 2021) | ~3.5 × 10⁶ | 2021 | perkiraan | |
| 618 | MobileNetV2 (Google, 2018) | ~3.4 × 10⁶ | 2018 | perkiraan | |
| 619 | Big-Little Net (IBM, 2018) | ~3.3 × 10⁶ | 2018 | perkiraan | |
| 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | arsitektur tidak disebutkan |
| 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | padat |
| 622 | DeepStack (University of Alberta, 2017) | ~2.5 × 10⁶ | 2017 | perkiraan | |
| 623 | KataGo (Jane Street, 2019) | ~2.5 × 10⁶ | 2019 | perkiraan | |
| 624 | ShuffleNet v1 (Megvii Inc, 2017) | ~2.4 × 10⁶ | 2017 | perkiraan | |
| 625 | ADAM (University of Amsterdam, 2014) | 2.4 × 10⁶ | 2.4 × 10⁶ | 2014 | padat |
| 626 | StyleGAN3-T (Nvidia, 2021) | 2.2 × 10⁶ | 2.2 × 10⁶ | 2021 | arsitektur tidak disebutkan |
| 627 | LSTM+NeuralCache (KU Leuven, 2018) | ~2.1 × 10⁶ | 2018 | perkiraan | |
| 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | padat |
| 629 | ResNet-110 (Microsoft, 2015) | ~1.7 × 10⁶ | 2015 | perkiraan | |
| 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | padat |
| 631 | DQN-2015 (Google, 2015) | ~1.7 × 10⁶ | 2015 | perkiraan | |
| 632 | Rational DQN Average (TU Darmstadt, 2021) | ~1.7 × 10⁶ | 2021 | perkiraan | |
| 633 | IBM-5 (IBM, 1993) | ~1.7 × 10⁶ | 1993 | perkiraan | |
| 634 | Deep Belief Nets (University of Toronto, 2006) | ~1.6 × 10⁶ | 2006 | perkiraan | |
| 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | padat |
| 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | arsitektur tidak disebutkan |
| 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | ~1.5 × 10⁶ | 2008 | perkiraan | |
| 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | padat |
| 639 | CNN committee (IDSIA, 2011) | ~1.4 × 10⁶ | 2011 | perkiraan | |
| 640 | SqueezeNet (DeepScale, 2016) | ~1.2 × 10⁶ | 2016 | perkiraan | |
| 641 | Neocognitron (NHK Broadcasting Science Research Laboratories, 1980) | 1.1 × 10⁶ | 1.1 × 10⁶ | 1980 | padat |
| 642 | CURL (University of California (UC) Berkeley, 2020) | ~9.1 × 10⁵ | 2020 | perkiraan | |
| 643 | DQN (DeepMind, 2013) | 8.4 × 10⁵ | 8.4 × 10⁵ | 2013 | padat |
| 644 | EnhanceNet (Max Planck Institute for Intelligent Systems, 2016) | 8.1 × 10⁵ | 8.1 × 10⁵ | 2016 | padat |
| 645 | Image generation (University of Amsterdam, 2013) | 7.8 × 10⁵ | 7.8 × 10⁵ | 2013 | padat |
| 646 | Hanabi 4 player (DeepMind, 2019) | 7.6 × 10⁵ | 7.6 × 10⁵ | 2019 | padat |
| 647 | TA-CNN (Chinese University of Hong Kong (CUHK), 2014) | ~7.1 × 10⁵ | 2014 | perkiraan | |
| 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | arsitektur tidak disebutkan |
| 649 | LISSOM (University of Texas at Austin, 1995) | ~4.3 × 10⁵ | 1995 | perkiraan | |
| 650 | SPIDER2 (Griffith University, 2016) | ~4.1 × 10⁵ | 2016 | perkiraan | |
| 651 | Mixture of linear models (, 1994) | ~3.8 × 10⁵ | 1994 | perkiraan | |
| 652 | NPD (IEEE, 2014) | 3.1 × 10⁵ | 3.1 × 10⁵ | 2014 | padat |
| 653 | Pooling CNN (University of Bonn, 2010) | 2.9 × 10⁵ | 2.9 × 10⁵ | 2010 | padat |
| 654 | Pooling CNN (University of Bonn, 2010) | 2.7 × 10⁵ | 2.7 × 10⁵ | 2010 | padat |
| 655 | Two Stage Feature Extraction (New York University (NYU), 2009) | 2.6 × 10⁵ | 2.6 × 10⁵ | 2009 | padat |
| 656 | Vector Space Model (Stanford University, 2011) | 2.5 × 10⁵ | 2.5 × 10⁵ | 2011 | padat |
| 657 | HyperNEAT (University of Texas at Austin, 2014) | ~2.4 × 10⁵ | 2014 | perkiraan | |
| 658 | DNN EM segmentation (IDSIA, 2012) | 2.2 × 10⁵ | 2.2 × 10⁵ | 2012 | padat |
| 659 | Predictive Coding NN (Technical University of Munich, 1994) | 2.1 × 10⁵ | 2.1 × 10⁵ | 1994 | padat |
| 660 | Table Tennis Agent (Google DeepMind, 2024) | ~1.9 × 10⁵ | 2024 | perkiraan | |
| 661 | CNN Committee (IDSIA, 2011) | ~1.3 × 10⁵ | 2011 | perkiraan | |
| 662 | CNN Committee (IDSIA, 2011) | ~1.2 × 10⁵ | 2011 | perkiraan | |
| 663 | RECONTRA-uncategorized (, 1999) | ~1.1 × 10⁵ | 1999 | perkiraan | |
| 664 | BLSTM for handwriting (University of Bern, 2007) | ~1 × 10⁵ | 2007 | perkiraan | |
| 665 | SVM-CNN (New York University (NYU), 2006) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2006 | padat |
| 666 | Invariant CNN (New York University (NYU), 2004) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2004 | padat |
| 667 | SexNet compression (, 1990) | 7.3 × 10⁴ | 7.3 × 10⁴ | 1990 | padat |
| 668 | NeuroChess (, 1994) | ~7.2 × 10⁴ | 1994 | perkiraan | |
| 669 | RECONTRA-categorized (, 1999) | ~6.7 × 10⁴ | 1999 | perkiraan | |
| 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | padat |
| 671 | Swift (Intel Labs, 2023) | ~5.7 × 10⁴ | 2023 | perkiraan | |
| 672 | TCN (Carnegie Mellon University (CMU), 2018) | 4.2 × 10⁴ | 4.2 × 10⁴ | 2018 | padat |
| 673 | TRPO (University of California (UC) Berkeley, 2015) | 3.4 × 10⁴ | 3.4 × 10⁴ | 2015 | padat |
| 674 | SOM-CNN (, 1997) | 3.2 × 10⁴ | 3.2 × 10⁴ | 1997 | padat |
| 675 | NETtalk reimplementation (Oregon State University, 1990) | 2.7 × 10⁴ | 2.7 × 10⁴ | 1990 | padat |
| 676 | TD-Gammon (IBM, 1992) | ~2.5 × 10⁴ | 1992 | perkiraan | |
| 677 | Cognitron (Biological Cybernetics, 1975) | 2.2 × 10⁴ | 2.2 × 10⁴ | 1975 | padat |
| 678 | NetTalk (Princeton University, 1987) | 1.9 × 10⁴ | 1.9 × 10⁴ | 1987 | padat |
| 679 | CFSS (SenseTime, 2015) | ~1.7 × 10⁴ | 2015 | perkiraan | |
| 680 | MUSIC perceptron (, 1996) | 1.4 × 10⁴ | 1.4 × 10⁴ | 1996 | padat |
| 681 | AdaRNN (Beihang University, 2014) | 1.3 × 10⁴ | 1.3 × 10⁴ | 2014 | padat |
| 682 | Bidirectional RNN (Advanced Telecommunications Research Institute, 1997) | ~1.3 × 10⁴ | 1997 | perkiraan | |
| 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | ~1.2 × 10⁴ | 2001 | perkiraan | |
| 684 | LSTM (Technical University of Munich, 1997) | 1.1 × 10⁴ | 1.1 × 10⁴ | 1997 | padat |
| 685 | MLN-ASR (McGill University, 1988) | 1 × 10⁴ | 1 × 10⁴ | 1988 | padat |
| 686 | Hopfield network (California Institute of Technology, 1982) | ~9.9 × 10³ | 1982 | perkiraan | |
| 687 | Zip CNN (AT&T, 1989) | 9.8 × 10³ | 9.8 × 10³ | 1989 | padat |
| 688 | Hierarchical Cognitron (NHK Broadcasting Science Research Laboratories, 1984) | ~9.3 × 10³ | 1984 | perkiraan | |
| 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | padat |
| 690 | Deep Blue (IBM, 1997) | ~8 × 10³ | 1997 | perkiraan | |
| 691 | Nematoda (C. elegans) | 7 × 10³ | 7 × 10³ | sinapsis | |
| 692 | System 11 (Carnegie Mellon University (CMU), 1996) | 6.5 × 10³ | 6.5 × 10³ | 1996 | padat |
| 693 | ACF-WIDER (Chinese Academy of Sciences, 2014) | 6.1 × 10³ | 6.1 × 10³ | 2014 | padat |
| 694 | RankNet (Microsoft Research, 2005) | 5.7 × 10³ | 5.7 × 10³ | 2005 | padat |
| 695 | ANN Eye Tracker (, 1993) | 5.6 × 10³ | 5.6 × 10³ | 1993 | padat |
| 696 | JPMAX (, 1994) | ~4.4 × 10³ | 1994 | perkiraan | |
| 697 | Kohonen network (Helsinki University of Technology, 1981) | ~4.1 × 10³ | 1981 | perkiraan | |
| 698 | Speaker-independent vowel classification (University of Washington, 1989) | 3 × 10³ | 3 × 10³ | 1989 | padat |
| 699 | Pengenalan pola dan pembacaan oleh mesin (Sandia Corporation, 1959) | ~2.6 × 10³ | 1959 | perkiraan | |
| 700 | Handwritten digit recognition network (AT&T, 1989) | 2.6 × 10³ | 2.6 × 10³ | 1989 | padat |
| 701 | Boosting (Bell Laboratories, 1992) | ~2.6 × 10³ | 1992 | perkiraan | |
| 702 | Decision tree adaline (Tokyo Medical and Dental University, 1969) | 2.5 × 10³ | 2.5 × 10³ | 1969 | padat |
| 703 | LTE speaker verification system (IBM, 1966) | ~2.1 × 10³ | 1966 | perkiraan | |
| 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | ~1.9 × 10³ | 1994 | perkiraan | |
| 705 | SexNet classification (, 1990) | ~1.6 × 10³ | 1990 | perkiraan | |
| 706 | RAAM (, 1990) | 1.5 × 10³ | 1.5 × 10³ | 1990 | padat |
| 707 | Super-vector coding (University of Illinois Urbana-Champaign (UIUC), 2010) | ~1 × 10³ | 2010 | perkiraan | |
| 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | ~1 × 10³ | 1957 | perkiraan | |
| 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | ~1 × 10³ | 1960 | perkiraan | |
| 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | campuran pakar (hitungan aktif tidak dipublikasikan) |
| 711 | Truck backer-upper (Stanford University, 1989) | 8.1 × 10² | 8.1 × 10² | 1989 | padat |
| 712 | Siamese-TDNN (Bell Laboratories, 1993) | ~7.4 × 10² | 1993 | perkiraan | |
| 713 | MLP with back-propagation (University of California San Diego, 1986) | 7.2 × 10² | 7.2 × 10² | 1986 | padat |
| 714 | Prediksi mekanisme obat kanker (National Cancer Institute, 1992) | ~5.9 × 10² | 1992 | perkiraan | |
| 715 | Distributed representation NN (Carnegie Mellon University (CMU), 1986) | 4.3 × 10² | 4.3 × 10² | 1986 | padat |
| 716 | Model linear sepenggal (University of Kansas, 1973) | 3.6 × 10² | 3.6 × 10² | 1973 | padat |
| 717 | ASE+ACE (University of Massachusetts Amherst, 1983) | ~3.2 × 10² | 1983 | perkiraan | |
| 718 | LSTM with forget gates (IDSIA, 1999) | ~2.8 × 10² | 1999 | perkiraan | |
| 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | ~2.3 × 10² | 1955 | perkiraan | |
| 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | padat |
| 721 | SNARC (Harvard University, 1952) | ~4 × 10¹ | 1952 | perkiraan | |
| 722 | Bankruptcy-NN (, 1990) | 3.6 × 10¹ | 3.6 × 10¹ | 1990 | padat |
| 723 | GNN (University of Siena, 2008) | 3 × 10¹ | 3 × 10¹ | 2008 | padat |
| 724 | ADALINE (Stanford University, 1960) | 1.7 × 10¹ | 1.7 × 10¹ | 1960 | padat |
| 725 | Samuel Neural Checkers (IBM, 1959) | ~1.6 × 10¹ | 1959 | perkiraan |
Kolektif
Bagan-bagan ini mengurutkan masyarakat hewan, agama, negara, partai, dan korporasi dengan dua cara. Pertama menurut jumlah anggota. Kedua menurut ekuivalen neuron, yang membobot setiap anggota menurut bagian waktu terjaganya yang diarahkan oleh instruksi kelompok. Kitab agama, hukum, aturan partai, dan uraian pekerjaan semuanya dihitung dengan cara yang sama. Semuanya adalah kumpulan instruksi, jadi kami mengukurnya dengan aturan yang sama. Cara waktu terarah diperkirakan.
- Komunitas simpanse▲17.4 × 10¹¹
- Koloni semut pemotong daun▼16.3 × 10¹¹
- Komunitas bonobo▲24.4 × 10¹¹
- Perkemahan Hadza (manusia, sebagai skala pembanding)4.1 × 10¹¹
- Kelompok paus pembunuh Bigg▼22.6 × 10¹¹
- Garis keturunan induk paus pembunuh penetap2.4 × 10¹¹
- Rombongan babun8.6 × 10¹⁰
- Aliansi lumba-lumba hidung botol▲17.6 × 10¹⁰
- Unit sosial paus sperma▲16.4 × 10¹⁰
- Koloni lebah madu▼25.8 × 10¹⁰
- Keluarga gajah5.6 × 10¹⁰
- Kawanan jay pinyon5.3 × 10¹⁰
- Klan hiena tutul2.2 × 10¹⁰
- Kawanan anjing liar Afrika7.2 × 10⁹
- Kawanan serigala4.2 × 10⁹
- Kelompok meerkat1.7 × 10⁹
- Koloni tikus mol telanjang4.1 × 10⁸
Tampilkan angkanya
| # | Kelompok | Neuron dalam kelompok | Dalam kegiatan kelompok | Anggota | Porsi yang diarahkan | Jenis panggilan |
|---|---|---|---|---|---|---|
| 1 | Komunitas simpanse | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — |
| 2 | Koloni semut pemotong daun | 6.3 × 10¹¹ | 5 × 10¹¹ | 2,5 juta | 80% | — |
| 3 | Komunitas bonobo | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — |
| 4 | Perkemahan Hadza (manusia, sebagai skala pembanding) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — |
| 5 | Kelompok paus pembunuh Bigg | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — |
| 6 | Garis keturunan induk paus pembunuh penetap | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 |
| 7 | Rombongan babun | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 |
| 8 | Aliansi lumba-lumba hidung botol | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — |
| 9 | Unit sosial paus sperma | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — |
| 10 | Koloni lebah madu | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — |
| 11 | Keluarga gajah | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — |
| 12 | Kawanan jay pinyon | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — |
| 13 | Klan hiena tutul | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 |
| 14 | Kawanan anjing liar Afrika | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 |
| 15 | Kawanan serigala | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — |
| 16 | Kelompok meerkat | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 |
| 17 | Koloni tikus mol telanjang | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — |
- Kristen▲12,27 miliar
- Islam▼12,02 miliar
- Hinduisme1,18 miliar
- Agama rakyat400 juta
- Buddhisme324,19 juta
- Agama tradisional Afrika▲198,73 juta
- Shinto▲187,92 juta
- Sikhisme▲125 juta
- Yudaisme▼314,78 juta
- Taoisme▲18 juta
- Iman Baha’i▲18 juta
- Falun Gong▲17 juta
- Jainisme▼34,45 juta
- Spiritisme3,8 juta
- Cao Đài2,5 juta
- Tenrikyo1,12 juta
- Brahma Kumaris1 juta
Tampilkan angkanya
| # | Agama | Anggota | Mengikuti instruksinya | Porsi yang diarahkan | Jam seminggu |
|---|---|---|---|---|---|
| 1 | Kristen | 2,27 miliar | 1.7 × 10¹⁸ | 0.88% | 0.9 h |
| 2 | Islam | 2,02 miliar | 8.9 × 10¹⁸ | 5.1% | 5.4 h |
| 3 | Hinduisme | 1,18 miliar | 1.6 × 10¹⁸ | 1.6% | 1.6 h |
| 4 | Agama rakyat | 400 juta | 2 × 10¹⁷ | 0.59% (proksi) | 0.6 h |
| 5 | Buddhisme | 324,19 juta | 7.5 × 10¹⁶ | 0.27% | 0.3 h |
| 6 | Agama tradisional Afrika | 98,73 juta | 5 × 10¹⁶ | 0.59% (proksi) | 0.6 h |
| 7 | Shinto | 87,92 juta | 4.5 × 10¹⁶ | 0.59% (proksi) | 0.6 h |
| 8 | Sikhisme | 25 juta | 3.7 × 10¹⁶ | 1.7% | 1.8 h |
| 9 | Yudaisme | 14,78 juta | 5.3 × 10¹⁶ | 4.2% | 4.4 h |
| 10 | Taoisme | 8 juta | 4.1 × 10¹⁵ | 0.59% (proksi) | 0.6 h |
| 11 | Iman Baha’i | 8 juta | 4.1 × 10¹⁵ | 0.59% (proksi) | 0.6 h |
| 12 | Falun Gong | 7 juta | 3.6 × 10¹⁵ | 0.59% (proksi) | 0.6 h |
| 13 | Jainisme | 4,45 juta | 1.8 × 10¹⁶ | 4.6% | 4.8 h |
| 14 | Spiritisme | 3,8 juta | 1.9 × 10¹⁵ | 0.59% (proksi) | 0.6 h |
| 15 | Cao Đài | 2,5 juta | 1.3 × 10¹⁵ | 0.59% (proksi) | 0.6 h |
| 16 | Tenrikyo | 1,12 juta | 5.7 × 10¹⁴ | 0.59% (proksi) | 0.6 h |
| 17 | Brahma Kumaris | 1 juta | 5.1 × 10¹⁴ | 0.59% (proksi) | 0.6 h |
- India▲11,46 miliar
- Tiongkok▼11,41 miliar
- Amerika Serikat341,78 juta
- Indonesia285,72 juta
- Pakistan▲5255,22 juta
- Nigeria▲1237,53 juta
- Brasil▼2212,81 juta
- Bangladesh▲5175,69 juta
- Rusia▼3143,51 juta
- Etiopia▲7135,47 juta
- Meksiko▼2131,95 juta
- Jepang▲11123,37 juta
- Mesir▼2118,37 juta
- Filipina116,79 juta
- RD Kongo▼3112,83 juta
Tampilkan angkanya
| # | Negara | Penduduk | Mengikuti instruksinya | Porsi yang diarahkan | Jam seminggu |
|---|---|---|---|---|---|
| 1 | India | 1,46 miliar | 2.5 × 10¹⁸ | 2% | 2.1 h |
| 2 | Tiongkok | 1,41 miliar | 5 × 10¹⁸ | 4.1% | 4.3 h |
| 3 | Amerika Serikat | 341,78 juta | 1.6 × 10¹⁸ | 5.6% | 5.8 h |
| 4 | Indonesia | 285,72 juta | 1 × 10¹⁸ | 4.2% | 4.4 h |
| 5 | Pakistan | 255,22 juta | 5.7 × 10¹⁷ | 2.6% | 2.7 h |
| 6 | Nigeria | 237,53 juta | 6.6 × 10¹⁷ | 3.2% | 3.4 h |
| 7 | Brasil | 212,81 juta | 8.7 × 10¹⁷ | 4.8% | 5 h |
| 8 | Bangladesh | 175,69 juta | 4.6 × 10¹⁷ | 3% | 3.1 h |
| 9 | Rusia | 143,51 juta | 7.5 × 10¹⁷ | 6.1% | 6.4 h |
| 10 | Etiopia | 135,47 juta | 3.9 × 10¹⁷ | 3.4% | 3.5 h |
| 11 | Meksiko | 131,95 juta | 5.9 × 10¹⁷ | 5.2% | 5.5 h |
| 12 | Jepang | 123,37 juta | 2.8 × 10¹⁷ | 2.7% | 2.8 h |
| 13 | Mesir | 118,37 juta | 5.5 × 10¹⁷ | 5.4% | 5.7 h |
| 14 | Filipina | 116,79 juta | 4.4 × 10¹⁷ | 4.4% | 4.6 h |
| 15 | RD Kongo | 112,83 juta | 4.7 × 10¹⁷ | 4.8% | 5.1 h |
| 16 | Vietnam | 101,6 juta | 4.2 × 10¹⁷ | 4.8% | 5 h |
| 17 | Iran | 92,42 juta | 3.5 × 10¹⁷ | 4.3% | 4.6 h |
| 18 | Turki | 85,88 juta | 4.3 × 10¹⁷ | 5.8% | 6 h |
| 19 | Jerman | 83,49 juta | 2.9 × 10¹⁷ | 4% | 4.2 h |
| 20 | Thailand | 71,62 juta | 2.4 × 10¹⁷ | 3.9% | 4.1 h |
| 21 | Tanzania | 70,55 juta | 2.2 × 10¹⁷ | 3.6% | 3.8 h |
| 22 | Inggris Raya | 69,49 juta | 2.8 × 10¹⁷ | 4.6% | 4.8 h |
| 23 | Prancis | 68,72 juta | 3.1 × 10¹⁷ | 5.3% | 5.5 h |
| 24 | Afrika Selatan | 64,75 juta | 2.9 × 10¹⁷ | 5.1% | 5.4 h |
| 25 | Italia | 58,92 juta | 2.1 × 10¹⁷ | 4.2% | 4.4 h |
| 26 | Kenya | 57,53 juta | 2.3 × 10¹⁷ | 4.7% | 5 h |
| 27 | Myanmar | 54,85 juta | 1.9 × 10¹⁷ | 4% | 4.2 h |
| 28 | Kolombia | 53,43 juta | 1.7 × 10¹⁷ | 3.7% | 3.9 h |
| 29 | Korea Selatan | 51,68 juta | 1.4 × 10¹⁷ | 3% | 3.2 h |
| 30 | Sudan | 51,66 juta | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 31 | Uganda | 51,38 juta | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 32 | Spanyol | 49,36 juta | 1.8 × 10¹⁷ | 4.2% | 4.3 h |
| 33 | Aljazair | 47,44 juta | 3.2 × 10¹⁷ | 7.9% | 8.3 h |
| 34 | Irak | 47,02 juta | 1.1 × 10¹⁷ | 2.8% | 2.9 h |
| 35 | Argentina | 45,85 juta | 1.7 × 10¹⁷ | 4.4% | 4.6 h |
| 36 | Afganistan | 43,84 juta | 1.8 × 10¹⁷ | 4.8% | 5 h |
| 37 | Yaman | 41,77 juta | 1.3 × 10¹⁷ | 3.7% | 3.9 h |
| 38 | Kanada | 41,65 juta | 2 × 10¹⁷ | 5.5% | 5.8 h |
| 39 | Angola | 39,04 juta | 1.4 × 10¹⁷ | 4.2% | 4.4 h |
| 40 | Ukraina | 38,98 juta | 2.1 × 10¹⁷ | 6.2% | 6.5 h |
| 41 | Maroko | 38,43 juta | 1.5 × 10¹⁷ | 4.5% | 4.7 h |
| 42 | Uzbekistan | 37,05 juta | 2 × 10¹⁷ | 6.3% | 6.6 h |
| 43 | Arab Saudi | 36,97 juta | 1.4 × 10¹⁷ | 4.3% | 4.6 h |
| 44 | Polandia | 36,44 juta | 2.1 × 10¹⁷ | 6.7% | 7 h |
| 45 | Malaysia | 35,98 juta | 1.8 × 10¹⁷ | 5.8% | 6.1 h |
| 46 | Mozambik | 35,63 juta | 1.5 × 10¹⁷ | 4.8% | 5.1 h |
| 47 | Ghana | 35,06 juta | 1.2 × 10¹⁷ | 3.9% | 4.1 h |
| 48 | Peru | 34,58 juta | 1.4 × 10¹⁷ | 4.8% | 5 h |
| 49 | Madagaskar | 32,74 juta | 9.4 × 10¹⁶ | 3.3% | 3.5 h |
| 50 | Pantai Gading | 32,71 juta | 9.5 × 10¹⁶ | 3.4% | 3.5 h |
| 51 | Kamerun | 29,88 juta | 9.8 × 10¹⁶ | 3.8% | 4 h |
| 52 | Nepal | 29,62 juta | 1.1 × 10¹⁷ | 4.2% | 4.4 h |
| 53 | Venezuela | 28,52 juta | 1.5 × 10¹⁷ | 6.1% | 6.4 h |
| 54 | Niger | 27,92 juta | 6.4 × 10¹⁶ | 2.6% | 2.8 h |
| 55 | Australia | 27,61 juta | 1.1 × 10¹⁷ | 4.7% | 5 h |
| 56 | Korea Utara | 26,57 juta | 6.6 × 10¹⁷ | 29% | 30.1 h |
| 57 | Suriah | 25,62 juta | 9.8 × 10¹⁶ | 4.4% | 4.6 h |
| 58 | Mali | 25,2 juta | 4.9 × 10¹⁶ | 2.3% | 2.4 h |
| 59 | Burkina Faso | 24,07 juta | 5.7 × 10¹⁶ | 2.8% | 2.9 h |
| 60 | Taiwan | 23,31 juta | 1 × 10¹⁷ | 5% | 5.3 h |
| 61 | Malawi | 22,22 juta | 1 × 10¹⁷ | 5.4% | 5.6 h |
| 62 | Zambia | 21,91 juta | 10 × 10¹⁶ | 5.3% | 5.5 h |
| 63 | Sri Lanka | 21,76 juta | 1.1 × 10¹⁷ | 6% | 6.3 h |
| 64 | Chad | 21 juta | 4.9 × 10¹⁶ | 2.7% | 2.9 h |
| 65 | Kazakhstan | 20,84 juta | 1.4 × 10¹⁷ | 7.6% | 7.9 h |
| 66 | Cile | 19,86 juta | 6.2 × 10¹⁶ | 3.6% | 3.8 h |
| 67 | Somalia | 19,65 juta | 1.5 × 10¹⁶ | 0.88% | 0.9 h |
| 68 | Rumania | 19,02 juta | 7.5 × 10¹⁶ | 4.6% | 4.8 h |
| 69 | Senegal | 18,93 juta | 5.9 × 10¹⁶ | 3.6% | 3.8 h |
| 70 | Guatemala | 18,69 juta | 6 × 10¹⁶ | 3.7% | 3.9 h |
| 71 | Ekuador | 18,29 juta | 6.6 × 10¹⁶ | 4.2% | 4.4 h |
| 72 | Belanda | 18,09 juta | 7.6 × 10¹⁶ | 4.9% | 5.1 h |
| 73 | Kamboja | 17,85 juta | 7.4 × 10¹⁶ | 4.8% | 5.1 h |
| 74 | Zimbabwe | 16,95 juta | 6.1 × 10¹⁶ | 4.2% | 4.4 h |
| 75 | Guinea | 15,1 juta | 3.1 × 10¹⁶ | 2.4% | 2.5 h |
| 76 | Benin | 14,81 juta | 4.4 × 10¹⁶ | 3.4% | 3.6 h |
| 77 | Rwanda | 14,57 juta | 7 × 10¹⁶ | 5.6% | 5.8 h |
| 78 | Burundi | 14,39 juta | 5.2 × 10¹⁶ | 4.2% | 4.4 h |
| 79 | Bolivia | 12,58 juta | 5.8 × 10¹⁶ | 5.4% | 5.6 h |
| 80 | Tunisia | 12,35 juta | 6.4 × 10¹⁶ | 6% | 6.3 h |
| 81 | Sudan Selatan | 12,19 juta | 4.4 × 10¹⁶ | 4.2% | 4.4 h |
| 82 | Belgia | 11,94 juta | 4.5 × 10¹⁶ | 4.3% | 4.5 h |
| 83 | Haiti | 11,91 juta | 1.9 × 10¹⁶ | 1.8% | 1.9 h |
| 84 | Yordania | 11,52 juta | 4.9 × 10¹⁶ | 5% | 5.2 h |
| 85 | Republik Dominika | 11,52 juta | 4.9 × 10¹⁶ | 4.9% | 5.2 h |
| 86 | Uni Emirat Arab | 11,51 juta | 3.1 × 10¹⁶ | 3.1% | 3.2 h |
| 87 | Honduras | 11,01 juta | 3.2 × 10¹⁶ | 3.3% | 3.5 h |
| 88 | Kuba | 10,94 juta | 1.6 × 10¹⁷ | 17% | 17.4 h |
| 89 | Ceko | 10,89 juta | 5.8 × 10¹⁶ | 6.2% | 6.5 h |
| 90 | Portugal | 10,8 juta | 5.1 × 10¹⁶ | 5.5% | 5.7 h |
| 91 | Tajikistan | 10,79 juta | 5.3 × 10¹⁶ | 5.7% | 6 h |
| 92 | Papua Nugini | 10,76 juta | 4.3 × 10¹⁶ | 4.6% | 4.8 h |
| 93 | Swedia | 10,6 juta | 6.8 × 10¹⁶ | 7.5% | 7.8 h |
| 94 | Yunani | 10,41 juta | 4.9 × 10¹⁶ | 5.4% | 5.7 h |
| 95 | Azerbaijan | 10,25 juta | 6.2 × 10¹⁶ | 7% | 7.4 h |
| 96 | Israel | 10,12 juta | 6.4 × 10¹⁶ | 7.3% | 7.7 h |
| 97 | Hungaria | 9,51 juta | 5.7 × 10¹⁶ | 6.9% | 7.3 h |
| 98 | Austria | 9,21 juta | 2.5 × 10¹⁶ | 3.2% | 3.4 h |
| 99 | Swiss | 9,09 juta | 3.9 × 10¹⁶ | 5% | 5.3 h |
| 100 | Belarus | 9,09 juta | 7.9 × 10¹⁶ | 10% | 10.6 h |
| 101 | Sierra Leone | 8,82 juta | 4.9 × 10¹⁶ | 6.5% | 6.8 h |
| 102 | Togo | 8,59 juta | 3 × 10¹⁶ | 4% | 4.2 h |
| 103 | Laos | 7,87 juta | 3.2 × 10¹⁶ | 4.8% | 5 h |
| 104 | Turkmenistan | 7,62 juta | 4.2 × 10¹⁶ | 6.5% | 6.8 h |
| 105 | Libya | 7,46 juta | 3.9 × 10¹⁶ | 6.1% | 6.4 h |
| 106 | Kirgistan | 7,34 juta | 4.1 × 10¹⁶ | 6.5% | 6.8 h |
| 107 | Paraguay | 7,01 juta | 2.9 × 10¹⁶ | 4.8% | 5.1 h |
| 108 | Nikaragua | 7,01 juta | 2.8 × 10¹⁶ | 4.6% | 4.8 h |
| 109 | Serbia | 6,55 juta | 4.1 × 10¹⁶ | 7.2% | 7.5 h |
| 110 | Republik Kongo | 6,48 juta | 1.7 × 10¹⁶ | 3% | 3.1 h |
| 111 | Bulgaria | 6,43 juta | 3.3 × 10¹⁶ | 5.9% | 6.2 h |
| 112 | El Salvador | 6,37 juta | 3.2 × 10¹⁶ | 5.8% | 6.1 h |
| 113 | Singapura | 6,11 juta | 1.9 × 10¹⁶ | 3.6% | 3.7 h |
| 114 | Denmark | 6,01 juta | 3.9 × 10¹⁶ | 7.6% | 8 h |
| 115 | Lebanon | 5,85 juta | 1.5 × 10¹⁶ | 3% | 3.1 h |
| 116 | Liberia | 5,73 juta | 1.5 × 10¹⁶ | 3.1% | 3.3 h |
| 117 | Finlandia | 5,65 juta | 3.2 × 10¹⁶ | 6.6% | 6.9 h |
| 118 | Norwegia | 5,61 juta | 3.7 × 10¹⁶ | 7.6% | 8 h |
| 119 | Republik Afrika Tengah | 5,51 juta | 1.7 × 10¹⁶ | 3.5% | 3.7 h |
| 120 | Oman | 5,49 juta | 2.9 × 10¹⁶ | 6.2% | 6.5 h |
| 121 | Irlandia | 5,48 juta | 3.4 × 10¹⁶ | 7.2% | 7.6 h |
| 122 | Slovakia | 5,41 juta | 3.1 × 10¹⁶ | 6.7% | 7 h |
| 123 | Palestina | 5,41 juta | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 124 | Selandia Baru | 5,32 juta | 2.7 × 10¹⁶ | 5.9% | 6.2 h |
| 125 | Mauritania | 5,32 juta | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 126 | Kosta Rika | 5,15 juta | 2.6 × 10¹⁶ | 5.9% | 6.2 h |
| 127 | Kuwait | 4,87 juta | 2.7 × 10¹⁶ | 6.5% | 6.8 h |
| 128 | Panama | 4,57 juta | 2.3 × 10¹⁶ | 5.9% | 6.2 h |
| 129 | Georgia | 3,94 juta | 2.1 × 10¹⁶ | 6.1% | 6.4 h |
| 130 | Kroasia | 3,88 juta | 2.2 × 10¹⁶ | 6.7% | 7 h |
| 131 | Eritrea | 3,61 juta | 1.5 × 10¹⁶ | 5% | 5.2 h |
| 132 | Mongolia | 3,57 juta | 2.2 × 10¹⁶ | 7% | 7.3 h |
| 133 | Uruguay | 3,38 juta | 1.7 × 10¹⁶ | 5.9% | 6.2 h |
| 134 | Bosnia dan Herzegovina | 3,14 juta | 1.6 × 10¹⁶ | 5.9% | 6.2 h |
| 135 | Namibia | 3,09 juta | 1.8 × 10¹⁶ | 6.9% | 7.2 h |
| 136 | Armenia | 3,09 juta | 1.7 × 10¹⁶ | 6.5% | 6.8 h |
| 137 | Qatar | 2,97 juta | 9.6 × 10¹⁵ | 3.8% | 3.9 h |
| 138 | Lituania | 2,89 juta | 1.8 × 10¹⁶ | 7.4% | 7.7 h |
| 139 | Jamaika | 2,84 juta | 1.2 × 10¹⁶ | 5% | 5.2 h |
| 140 | Gambia | 2,82 juta | 1.1 × 10¹⁶ | 4.5% | 4.7 h |
| 141 | Gabon | 2,59 juta | 9.9 × 10¹⁵ | 4.4% | 4.6 h |
| 142 | Botswana | 2,56 juta | 1.3 × 10¹⁶ | 5.7% | 6 h |
| 143 | Lesotho | 2,36 juta | 9.5 × 10¹⁵ | 4.7% | 4.9 h |
| 144 | Moldova | 2,36 juta | 1.5 × 10¹⁶ | 7.3% | 7.6 h |
| 145 | Albania | 2,35 juta | 1.2 × 10¹⁶ | 5.7% | 6 h |
| 146 | Guinea-Bissau | 2,25 juta | 5.7 × 10¹⁵ | 2.9% | 3.1 h |
| 147 | Slovenia | 2,13 juta | 1.2 × 10¹⁶ | 6.6% | 6.9 h |
| 148 | Guinea Khatulistiwa | 1,94 juta | 4 × 10¹⁵ | 2.4% | 2.5 h |
| 149 | Latvia | 1,85 juta | 1.2 × 10¹⁶ | 7.6% | 7.9 h |
| 150 | Makedonia Utara | 1,82 juta | 9.2 × 10¹⁵ | 5.9% | 6.2 h |
| 151 | Bahrain | 1,6 juta | 5.9 × 10¹⁵ | 4.3% | 4.5 h |
| 152 | Kosovo | 1,58 juta | 8 × 10¹⁵ | 5.9% | 6.2 h |
| 153 | Timor Leste | 1,42 juta | 8 × 10¹⁵ | 6.6% | 6.9 h |
| 154 | Siprus | 1,37 juta | 5.5 × 10¹⁵ | 4.7% | 4.9 h |
| 155 | Trinidad dan Tobago | 1,37 juta | 9.3 × 10¹⁵ | 7.9% | 8.2 h |
| 156 | Estonia | 1,37 juta | 8.3 × 10¹⁵ | 7% | 7.4 h |
| 157 | Eswatini | 1,26 juta | 7.5 × 10¹⁵ | 6.9% | 7.2 h |
| 158 | Mauritius | 1,24 juta | 5.1 × 10¹⁵ | 4.8% | 5 h |
| 159 | Djibouti | 1,18 juta | 4.6 × 10¹⁵ | 4.5% | 4.8 h |
| 160 | Fiji | 933,154 | 5.6 × 10¹⁵ | 7% | 7.4 h |
| 161 | Komoro | 882,847 | 3.5 × 10¹⁵ | 4.6% | 4.8 h |
| 162 | Kepulauan Solomon | 838,645 | 3.2 × 10¹⁵ | 4.4% | 4.6 h |
| 163 | Guyana | 835,986 | 4.6 × 10¹⁵ | 6.4% | 6.7 h |
| 164 | Bhutan | 796,682 | 5.8 × 10¹⁵ | 8.5% | 8.9 h |
| 165 | Luksemburg | 686,970 | 2.4 × 10¹⁵ | 4% | 4.2 h |
| 166 | Suriname | 639,850 | 3.7 × 10¹⁵ | 6.7% | 7.1 h |
| 167 | Montenegro | 623,129 | 4.3 × 10¹⁵ | 8.1% | 8.5 h |
| 168 | Malta | 579,704 | 3.1 × 10¹⁵ | 6.2% | 6.5 h |
| 169 | Maladewa | 529,676 | 4.7 × 10¹⁵ | 10% | 10.8 h |
| 170 | Tanjung Verde | 527,326 | 3.3 × 10¹⁵ | 7.2% | 7.6 h |
| 171 | Brunei | 466,330 | 3.6 × 10¹⁵ | 8.9% | 9.3 h |
| 172 | Belize | 422,924 | 1.1 × 10¹⁵ | 2.9% | 3.1 h |
| 173 | Bahamas | 403,033 | 2.2 × 10¹⁵ | 6.4% | 6.7 h |
| 174 | Islandia | 392,404 | 2.5 × 10¹⁵ | 7.4% | 7.7 h |
| 175 | Vanuatu | 335,169 | 2 × 10¹⁵ | 6.9% | 7.3 h |
| 176 | Barbados | 282,623 | 1.5 × 10¹⁵ | 6% | 6.3 h |
| 177 | São Tomé dan Príncipe | 240,254 | 1.1 × 10¹⁵ | 5.4% | 5.7 h |
| 178 | Samoa | 219,306 | 1.1 × 10¹⁵ | 5.7% | 5.9 h |
| 179 | Saint Lucia | 180,149 | 9.3 × 10¹⁴ | 6% | 6.3 h |
| 180 | Kiribati | 136,488 | 7.6 × 10¹⁴ | 6.4% | 6.7 h |
| 181 | Seychelles | 122,730 | 8.9 × 10¹⁴ | 8.4% | 8.8 h |
| 182 | Grenada | 117,303 | 7.3 × 10¹⁴ | 7.2% | 7.5 h |
| 183 | Mikronesia | 113,683 | 5.7 × 10¹⁴ | 5.8% | 6.1 h |
| 184 | Tonga | 103,742 | 5.9 × 10¹⁴ | 6.6% | 6.9 h |
| 185 | Saint Vincent dan Grenadine | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h |
| 186 | Antigua dan Barbuda | 94,209 | 6.5 × 10¹⁴ | 8% | 8.3 h |
| 187 | Andorra | 82,904 | 3.9 × 10¹⁴ | 5.5% | 5.8 h |
| 188 | Dominika | 65,871 | 2.3 × 10¹⁴ | 4.1% | 4.3 h |
| 189 | Saint Kitts dan Nevis | 46,922 | 2.1 × 10¹⁴ | 5.2% | 5.5 h |
| 190 | Liechtenstein | 41,024 | 2 × 10¹⁴ | 5.7% | 5.9 h |
| 191 | Monako | 38,341 | 2.3 × 10¹⁴ | 6.9% | 7.3 h |
| 192 | Kepulauan Marshall | 36,282 | 3.3 × 10¹⁴ | 10% | 10.9 h |
| 193 | San Marino | 34,109 | 1.3 × 10¹⁴ | 4.6% | 4.8 h |
| 194 | Palau | 17,663 | 1.6 × 10¹⁴ | 10% | 11 h |
| 195 | Nauru | 12,025 | 9.3 × 10¹³ | 9% | 9.4 h |
| 196 | Tuvalu | 9,492 | 8 × 10¹³ | 9.8% | 10.2 h |
- Bharatiya Janata Party (India)▲1140 juta
- Partai Komunis Tiongkok▼1101,29 juta
- Partai Demokrat (AS)▲1344,9 juta
- Partai Republik (AS)▲1638 juta
- Kongres Nasional India▼226 juta
- AIADMK (India)▼220,04 juta
- DMK (India)▼220 juta
- Partai Kemakmuran (Etiopia)▼115,7 juta
- TVK (India)▼115 juta
- Chama Cha Mapinduzi (Tanzania)▼113 juta
- All Progressives Congress (Nigeria)▼112,9 juta
- AK Party (Turki)▼111,71 juta
- Morena (Meksiko)▼111,63 juta
- Telugu Desam Party (India)▼110,05 juta
- Cambodian People’s Party▼17 juta
Tampilkan angkanya
| # | Partai | Anggota | Mengikuti instruksinya | Porsi yang diarahkan | Jam seminggu |
|---|---|---|---|---|---|
| 1 | Bharatiya Janata Party (India) | 140 juta | 6.3 × 10¹⁶ | 0.52% (proksi) | 0.6 h |
| 2 | Partai Komunis Tiongkok | 101,29 juta | 8.2 × 10¹⁶ | 0.94% | 1 h |
| 3 | Partai Demokrat (AS) | 44,9 juta | 2.3 × 10¹⁵ | 0.06% | 0.1 h |
| 4 | Partai Republik (AS) | 38 juta | 2 × 10¹⁵ | 0.06% | 0.1 h |
| 5 | Kongres Nasional India | 26 juta | 1.2 × 10¹⁶ | 0.52% (proksi) | 0.6 h |
| 6 | AIADMK (India) | 20,04 juta | 9 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 7 | DMK (India) | 20 juta | 9 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 8 | Partai Kemakmuran (Etiopia) | 15,7 juta | 7 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 9 | TVK (India) | 15 juta | 6.7 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 10 | Chama Cha Mapinduzi (Tanzania) | 13 juta | 5.8 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 11 | All Progressives Congress (Nigeria) | 12,9 juta | 5.8 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 12 | AK Party (Turki) | 11,71 juta | 5.2 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 13 | Morena (Meksiko) | 11,63 juta | 5.2 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 14 | Telugu Desam Party (India) | 10,05 juta | 4.5 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 15 | Cambodian People’s Party | 7 juta | 3.1 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 16 | Partai Pekerja Korea | 6,18 juta | 7.6 × 10¹⁵ | 1.4% | 1.5 h |
| 17 | FRELIMO (Mozambik) | 6 juta | 2.7 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 18 | Partai Komunis Vietnam | 5,6 juta | 2.1 × 10¹⁵ | 0.44% | 0.5 h |
| 19 | Partai Demokrat Korea | 5 juta | 2.2 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 20 | Partai Kekuatan Rakyat (Korea Selatan) | 4,44 juta | 2 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 21 | PSUV (Venezuela) | 4,24 juta | 1.9 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 22 | ZANU-PF (Zimbabwe) | 4,06 juta | 1.8 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 23 | UMNO (Malaysia) | 3,25 juta | 1.5 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 24 | Justicialist Party (Argentina) | 3,1 juta | 1.4 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 25 | Colorado Party (Paraguay) | 3 juta | 1.3 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 26 | Rusia Bersatu | 2,6 juta | 1.2 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 27 | UzLiDeP (Uzbekistan) | 2,31 juta | 1 × 10¹⁵ | 0.52% (proksi) | 0.6 h |
| 28 | PLD (Republik Dominika) | 2,18 juta | 9.7 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 29 | MDB (Brasil) | 2,02 juta | 9 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 30 | CHP (Turki) | 1,88 juta | 8.4 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 31 | Radical Civic Union (Argentina) | 1,79 juta | 8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 32 | Partai Pekerja, PT (Brasil) | 1,67 juta | 7.5 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 33 | PRM (Republik Dominika) | 1,55 juta | 6.9 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 34 | Authentic Radical Liberal Party (Paraguay) | 1,55 juta | 6.9 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 35 | Partido Federal ng Pilipinas | 1,5 juta | 6.7 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 36 | PAS (Malaysia) | 1,3 juta | 5.8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 37 | PRD (Brasil) | 1,29 juta | 5.8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 38 | Progressistas (Brasil) | 1,29 juta | 5.8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 39 | PSDB (Brasil) | 1,26 juta | 5.6 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 40 | PKR (Malaysia) | 1,15 juta | 5.2 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 41 | Economic Freedom Fighters (Afrika Selatan) | 1,09 juta | 4.9 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 42 | PDT (Brasil) | 1,07 juta | 4.8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 43 | União Brasil | 1,07 juta | 4.8 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 44 | CPI(M) (India) | 1,02 juta | 4.6 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 45 | Partai Demokrat Liberal (Jepang) | 1 juta | 4.5 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 46 | FLN (Aljazair) | 1 juta | 4.5 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 47 | Partai Liberal, PL (Brasil) | 952,881 | 4.3 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 48 | Partai Komunis Kuba | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h |
| 49 | African National Congress | 688,454 | 3.1 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 50 | Partai Konservatif Kanada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h |
| 51 | CDU (Jerman) | 364,202 | 1.6 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 52 | SPD (Jerman) | 357,116 | 1.6 × 10¹⁴ | 0.52% (proksi) | 0.6 h |
| 53 | Partai Buruh (Inggris) | 333,235 | 1.6 × 10¹⁴ | 0.55% | 0.6 h |
| 54 | Partai Liberal Kanada | 163,836 | 6.5 × 10¹³ | 0.46% | 0.5 h |
| 55 | Partai Hijau (Jerman) | 155,296 | 7 × 10¹³ | 0.52% (proksi) | 0.6 h |
| 56 | CSU (Jerman) | 125,153 | 5.6 × 10¹³ | 0.52% (proksi) | 0.6 h |
| 57 | Partai Konservatif (Inggris) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h |
| 58 | Partai Demokrat Baru (Kanada) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h |
| 59 | AfD (Jerman) | 51,335 | 2.3 × 10¹³ | 0.52% (proksi) | 0.6 h |
- Walmart2,1 juta
- Amazon▲11,58 juta
- State Grid▼11,36 juta
- China National Petroleum1,03 juta
- JD.com951,420
- BYD▲1869,622
- Accenture▼1779,000
- Allied Universal▲3770,000
- China Post Group▼1719,448
- Kereta Api Rusia (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
Tampilkan angkanya
| # | Korporasi | Karyawan | Mengikuti instruksinya | Jam seminggu | Porsi yang diarahkan |
|---|---|---|---|---|---|
| 1 | Walmart | 2,1 juta | 6.3 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 2 | Amazon | 1,58 juta | 4.7 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 3 | State Grid | 1,36 juta | 5.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 4 | China National Petroleum | 1,03 juta | 4.1 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 5 | JD.com | 951,420 | 3.8 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 6 | BYD | 869,622 | 3.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 7 | Accenture | 779,000 | 3.7 × 10¹⁶ | 57.58 h (India) | 55% |
| 8 | Allied Universal | 770,000 | 2.3 × 10¹⁶ | 36.52 h (Amerika Serikat) | 35% |
| 9 | China Post Group | 719,448 | 2.9 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 10 | Kereta Api Rusia (RZD) | 677,363 | 2.2 × 10¹⁶ | 39.96 h (Rusia) | 38% |
| 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (Jerman) | 34% |
| 12 | Foxconn (Hon Hai) | 633,167 | 2.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 13 | Schwarz Group | 604,000 | 1.6 × 10¹⁶ | 31.49 h (Jerman) | 30% |
| 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (Amerika Serikat) | 30% |
| 15 | Tata Consultancy Services | 584,510 | 2.8 × 10¹⁶ | 58.23 h (India) | 56% |
| 16 | DHL Group | 583,998 | 1.7 × 10¹⁶ | 35.81 h (Jerman) | 34% |
| 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (Rusia) | 39% |
| 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (Amerika Serikat) | 38% |
| 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (Amerika Serikat) | 38% |
| 23 | Agricultural Bank of China | 457,835 | 1.8 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 24 | Concentrix | 455,000 | 1.7 × 10¹⁶ | 46.71 h (Filipina) | 45% |
| 25 | Teleperformance | 446,716 | 2.2 × 10¹⁶ | 58.89 h (India) | 56% |
| 26 | Jardine Matheson | 443,000 | 1.5 × 10¹⁶ | 41.93 h (Indonesia) | 40% |
| 27 | Sodexo | 426,464 | 1.1 × 10¹⁶ | 31.7 h (Amerika Serikat) | 30% |
| 28 | Capgemini | 423,400 | 2 × 10¹⁶ | 58.23 h (India) | 56% |
| 29 | Reliance Industries | 419,911 | 2.1 × 10¹⁶ | 59.57 h (India) | 57% |
| 30 | Edeka | 417,500 | 1.1 × 10¹⁶ | 31.49 h (Jerman) | 30% |
| 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (Amerika Serikat) | 30% |
| 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (Jerman) | 34% |
| 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (Rusia) | 38% |
| 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (Jepang) | 38% |
| 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (Amerika Serikat) | 35% |
| 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (Rusia) | 38% |
| 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (Amerika Serikat) | 36% |
| 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (Amerika Serikat) | 30% |
| 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (Jerman) | 30% |
| 48 | China Construction Bank | 378,344 | 1.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 49 | TJX Companies | 377,000 | 1.1 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 50 | China State Construction Engineering | 370,272 | 1.5 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 51 | FEMSA | 368,776 | 1.4 × 10¹⁶ | 47.09 h (Meksiko) | 45% |
| 52 | Japan Post Holdings | 360,000 | 1.2 × 10¹⁶ | 41.6 h (Jepang) | 40% |
| 53 | Tesco | 354,744 | 9.3 × 10¹⁵ | 31.81 h (Inggris Raya) | 30% |
| 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (India) | 56% |
| 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (Jepang) | 38% |
| 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (Amerika Serikat) | 35% |
| 57 | Infosys | 328,594 | 1.6 × 10¹⁶ | 58.23 h (India) | 56% |
| 58 | ISS | 325,000 | 1.6 × 10¹⁶ | 58.89 h (India) | 56% |
| 59 | Securitas | 322,000 | 9.7 × 10¹⁵ | 36.52 h (Amerika Serikat) | 35% |
| 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (Amerika Serikat) | 35% |
| 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (Rusia) | 39% |
| 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (Amerika Serikat) | 38% |
| 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (Jerman) | 34% |
| 64 | Bank of China | 313,746 | 1.3 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 65 | Walgreens Boots Alliance | 312,000 | 9.3 × 10¹⁵ | 36.3 h (Amerika Serikat) | 35% |
| 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (Amerika Serikat) | 39% |
| 67 | China Railway Construction (CRCC) | 304,927 | 1.2 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 68 | CHN Energy (China Energy Investment) | 303,418 | 1.2 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 69 | China Railway Group (CREC) | 302,527 | 1.2 × 10¹⁶ | 49 h (Tiongkok) | 47% |
| 70 | CK Hutchison | 300,000 | 8.6 × 10¹⁵ | 35.08 h (Inggris Raya) | 33% |
| 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (Amerika Serikat) | 35% |
Hewan dan organisme lain
Dari sel tunggal tanpa neuron hingga otak manusia. Jumlah neuron diukur jika ada.
Tanpa neuron
- Escherichia coliEscherichia coli0 terukurSebuah sel tunggal tanpa neuron. Ia bergerak menuju makanan dan punya ingatan kimiawi yang bertahan beberapa detik.
- Jamur lendirPhysarum polycephalum0 terukurSebuah sel raksasa tunggal tanpa neuron. Ia memecahkan labirin dan menumbuhkan jaringan transportasi yang efisien.
- Putri maluMimosa pudica0 terukurTanaman yang daunnya menutup saat disentuh. Satu studi melaporkan bahwa ia belajar mengabaikan sentuhan yang tak berbahaya. Analisis ulang kemudian membantahnya.
- SponsPorifera (mis. Amphimedon queenslandica)0 terukurHewan tanpa neuron atau sinapsis yang tetap mengerutkan seluruh tubuhnya secara terkoordinasi.
Jaring saraf, tanpa otak
- HydraHydra vulgarissekitar 3.000 sampai 5.000 perkiraanCnidaria air tawar yang kecil. Ia dikendalikan jaring saraf difus dan tak punya otak.
- Ubur-ubur kotakTripedalia cystophoraTidak ada hitungan yang dipublikasikanUbur-ubur kotak tak punya otak pusat, namun ia menunjukkan pembelajaran asosiatif.
Invertebrata berotak
- Cacing gelangCaenorhabditis elegans302 terukurCacing sepanjang sekitar satu milimeter dengan tepat 302 neuron. Ia hewan pertama yang diagram pengawatan lengkapnya dipetakan.
- Lalat buahDrosophila melanogaster139,255 terukurDiagram pengawatan otak lengkap terbesar yang dipetakan sejauh ini, mencakup sekitar 139.000 neuron.
- Lebah maduApis melliferasekitar 1 juta perkiraanSekitar sejuta neuron, dan lebah bisa mempelajari konsep abstrak seperti sama dan berbeda.
- Semut (Camponotus / Formica)Camponotus spp.Tidak ada hitungan yang dipublikasikanOtak kecil yang menangani navigasi jarak jauh dengan menghitung langkah (integrasi jalur) dan mengingat penanda lanskap.
- Laba-laba pelompat PortiaPortia fimbriata / labiataTidak ada hitungan yang dipublikasikanLaba-laba pelompat yang merencanakan jalan memutar. Ia mengambil rute yang menjauhi mangsanya dan keluar dari pandangan.
- Kepiting pantai / lobster air tawarCarcinus maenas; Procambarus / Astacus spp.Tidak ada hitungan yang dipublikasikanKepiting dan lobster air tawar. Tinjauan pemerintah 2021 menemukan bukti kuat bahwa kepiting sejati adalah makhluk sentien.
- Gurita biasaOctopus vulgaris500 juta perkiraanSebagian besar neuronnya ada di lengan. Tinjauan 2021 menemukan bukti yang sangat kuat bahwa ia sentien.
Ikan dan amfibi
- Ikan zebraDanio rerioTidak ada hitungan yang dipublikasikanIkan kecil yang banyak dipakai dalam neurosains. Neuronnya belum dihitung sepenuhnya. Otak dewasanya punya sekitar 10 juta sel.
- Ikan wrasse pembersihLabroides dimidiatusTidak ada hitungan yang dipublikasikanIkan karang yang lolos versi uji tanda cermin. Orang tak sepakat tentang apa yang ditunjukkannya.
- KatakAnura (mis. Xenopus / Rana)Tidak ada hitungan yang dipublikasikanDeklarasi New York 2024 menyatakan ada kemungkinan nyata pengalaman sadar pada amfibi.
Burung
- Merpati batuColumba livia310 juta terukurRatusan juta neuron dalam otak kecil. Merpati bisa memilah gambar kompleks ke dalam kategori.
- Gagak besarCorvus corax2,17 miliar terukurLebih dari dua miliar neuron, padat berjejal. Gagak mengingat saingan secara individual.
- Gagak Kaledonia BaruCorvus moneduloidesTidak ada hitungan yang dipublikasikanIa membuat alat berkait dari ranting dan kawat. Neuronnya belum dihitung.
- Beo kelabu AfrikaPsittacus erithacus1,57 miliar terukurSekitar 1,6 miliar neuron. Puluhan tahun studi laboratorium tentang numerositas dan kategorisasi.
Mammals
- TikusMus musculus70,9 juta terukurSekitar 71 juta neuron. Mencit menunjukkan ekspresi wajah kesakitan yang bisa diukur.
- TikusRattus norvegicus200 juta terukurSekitar 200 juta neuron. Tikus membebaskan teman sekandang yang terperangkap.
- Kucing domestikFelis catus1,22 miliar terukurSekitar 250 juta neuron di korteks serebral.
- Anjing domestikCanis familiaris1,8 sampai 2,6 miliar, tergantung anjingnya terukurHingga sekitar 600 juta neuron korteks, tergantung anjingnya. Itu lebih banyak daripada kucing.
- Babi domestikSus scrofa domesticus2,22 miliar terukurBabi belajar apa yang ditunjukkan cermin dan memakainya untuk menemukan makanan.
- Lumba-lumba hidung botolTursiops truncatusTidak ada hitungan yang dipublikasikanLolos uji pengenalan diri di cermin. Tak ada hitungan neuron terukur untuk lumba-lumba hidung botol.
- Gajah AfrikaLoxodonta africana257 miliar terukur257 miliar neuron, tiga kali lipat manusia, hampir semuanya di otak kecil.
- SimpansePan troglodytessekitar 28 miliar (diprediksi dari aturan penskalaan, tidak dihitung) perkiraanSatu dari dua kerabat hidup terdekat kita (bersama bonobo). Ia lolos uji cermin.
- ManusiaHomo sapiens86,1 miliar terukur86 miliar neuron, 16 miliar di antaranya di korteks serebral. Kita satu-satunya spesies yang anggotanya bisa memberi laporan lisan tentang pengalamannya.
Sistem kolektif
Kelompok yang membuat keputusan yang tak dibuat anggota mana pun sendirian. Mereka dipelajari sebagai kecerdasan kolektif, bukan sebagai pikiran.
- Koloni semutKoloni memilih sarang yang lebih baik karena tiap semut mengikuti ambang kuorum. Tak ada semut yang membandingkan seluruh lokasi.
- Kawanan lebah maduKawanan memilih rongga terbaik lewat persaingan antarpenjelajah dan kuorum. Sinyal henti memberi penghambatan silang, yang memecah kebuntuan.
- Kawanan jalakSetiap burung melacak sejumlah tetangga yang tetap (sekitar 6-7), bukan setiap burung dalam jarak tertentu. Ini disebut interaksi topologis, bukan metrik.
- Kawanan ikanKawanan memakai aturan kuorum untuk memutuskan. Kelompok besar bisa merasakan gradien lingkungan yang tak bisa dirasakan satu ikan pun.
Sistem buatan
Jenis-jenis kecerdasan mesin, dikelompokkan menurut cara kerjanya. Tak ada sistem saat ini yang dianggap kandidat kuat untuk kesadaran.
- Sistem pakar berbasis aturanProgram yang mengodekan pengetahuan pakar bidang sebagai aturan jika-maka yang eksplisit. Mesin inferensi menerapkan aturan secara berurutan untuk mencapai kesimpulan, biasanya disertai penjelasan penalarannya.
- Mesin telusur pohon permainan (catur)Program yang menghitung urutan langkah yang mungkin hingga kedalaman tertentu dan menilai posisi yang dihasilkan dengan fungsi evaluasi buatan tangan. Mereka memangkas cabang yang tak bisa mengubah pilihan (minimax dengan pemangkasan alfa-beta).
- Agen pembelajaran penguatan mendalamJaringan saraf yang dilatih lewat coba-coba untuk memaksimalkan sinyal imbalan. Mereka mempelajari kebijakan atau fungsi nilai lewat berinteraksi dengan lingkungan, sering kali yang tersimulasi seperti permainan.
- Model bahasa besarJaringan saraf Transformer yang dilatih memprediksi token berikutnya dalam korpus teks yang sangat besar, lalu sering disetel dengan umpan balik manusia.
- Robot berjasadMesin dengan sensor dan aktuator yang bertindak di dunia fisik. Mereka sering memakai kendali reaktif berlapis alih-alih model dunia pusat, sehingga perilaku muncul dari lingkar umpan balik antara tubuh dan lingkungan.
- Perangkat keras neuromorfikCip yang sirkuitnya meniru neuron dan sinapsis yang melepaskan impuls. Cip ini digerakkan peristiwa dan hemat daya, tidak seperti rancangan prosesor-dan-memori konvensional.
Pikiran hipotetis
Pikiran yang diusulkan dalam literatur ilmiah dan filosofis. Belum ada yang mengamati satu pun.
- Emulasi seluruh otakbelum teramatiMemindai otak dengan detail halus dan menjalankan model perangkat lunaknya.
- Pikiran tersimulasi dalam simulasi leluhurbelum teramatiTrilema Bostrom: hampir tak ada peradaban yang mencapai tahap pascamanusia, atau hampir tak ada yang menjalankan simulasi leluhur, atau kita hampir pasti hidup di dalam satu simulasi.
- Otak Boltzmannbelum teramatiPengamat mirip otak yang muncul dari fluktuasi termal atau kuantum yang langka.
- Pengunggahan pikiran lewat penggantian bertahapbelum teramatiMengganti neuron satu per satu dengan komponen buatan yang identik secara fungsi.
- Superinteligensibelum teramatiIntelek hipotetis yang jauh melampaui kinerja manusia di hampir semua bidang. Buku Bostrom menganalisis jalur menuju ke sana (emulasi seluruh otak, kognisi biologis, AI), bentuknya, dan masalah kendalinya.
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