Science in telegram
Science that matters: AI, space, biotech, physics, future tech — explained sharply
Ko'proq ko'rsatish📈 Telegram kanali Science in telegram analitikasi
Science in telegram (@science) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 121 433 obunachidan iborat bo'lib, Maʼlumotlar toifasida 104-o'rinni va AQSH mintaqasida 179-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 121 433 obunachiga ega bo‘ldi.
07 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -864 ga, so‘nggi 24 soatda esa -39 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 9.05% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.38% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 10 987 marta ko‘riladi; birinchi sutkada odatda 2 887 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 102 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent medicine, cell, researcher, scientist, u.s kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Science that matters: AI, space, biotech, physics, future tech — explained sharply”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Maʼlumotlar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
This story matters far beyond the technical achievement. First, if this approach scales, it could change the economics of AI training. A 100B-parameter model trained on geographically distributed A100 GPUs at roughly 65% of comparable datacenter efficiency is not yet a replacement for hyperscaler infrastructure — but it is a serious signal. It suggests that large-scale AI training may not always require a single billion-dollar GPU cluster. Second, the Bittensor layer is important. This is not just a distributed computing experiment; it is an incentive system. GPU owners can be rewarded for contributing compute, which creates the foundation for a market around idle hardware. In simple terms, this could become something like “Airbnb for AI training”: monetizing unused GPU capacity the way Airbnb monetized unused rooms. Third, the uncomfortable part: decentralized AI training has often been dismissed by the mainstream AI community as impractical. Orion-100B does not prove that decentralized training will beat datacenters tomorrow. But it does prove that the idea deserves to be taken much more seriously. The next phase — permissionless participation from consumer hardware — will be the real test. If that works, the AI infrastructure map could become much more distributed than many people expected.Original report: https://macrocosmosai.substack.com/p/orion-100b-distributed-pretraining Summary: https://www.tao.media/macrocosmos-unveils-orion-100b-a-100b-parameter-distributed-ai-training-run/ #AI #DecentralizedAI #Bittensor #LLM #DeepLearning @science
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