#趨勢預測
Live, measured metrics for the hashtag #趨勢預測 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #趨勢預測
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Day-by-day usage
measured · mastodon.social (Mastodon public tags API) · fetched 2026-08-22 17:55 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.social (Mastodon public search API) · fetched 2026-08-22 17:55 UTCNo related tags with measured usage found for #趨勢預測.
Live pulse
measured · mastodon.social (Mastodon tag timeline) · fetched 2026-08-22 17:55 UTCEverything below is measured over the latest 4 public posts (spanning ~10154 hours).
Posting hours (UTC)
Languages: Chinese (Taiwan) (4)
Avg boosts / post: 0
Top of the latest posts
🌘 外推量子質因數分解的進度 ➤ 從有限數據點預測量子質因數分解的遙遠未來 ✤ https://www.johndcook.com/blog/2025/09/28/extrapolating-quantum-factoring/ 本文探討了量子計算在質因數分解方面的進展,並透過兩種外推模型——線性及指數——預測未來發展。作者指出,儘管數據點有限,仍嘗試從 2001 年(分解 15)和 2012 年(分解 21)的量子計算能力來預測。線性外推顯示量子電腦在可預見的未來無法分解
🌗 S形曲線救不了你 ➤ 莫將預測模型當作阻擋現實的防護盾 ✤ https://www.astralcodexten.com/p/the-sigmoids-wont-save-you 「所有指數增長最終都會演變成 S 形曲線」是探討 AI 發展時常見的論調,用以規避對超級 AI 的擔憂。然而,作者指出這種論點常被誤用。雖然物理極限必然導致增長放緩,但過早斷定趨勢將進入「平臺期」往往會犯下預測錯誤。作者透過回顧聯合國對出生率的誤判、能源組織對太陽能部署的低估,以及 AI 能力
🌕 衡量人工智慧完成長時程任務的能力 ➤ AI任務完成時程的指數級增長趨勢 ✤ https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ 這份研究提出了一個新指標,用以衡量人工智慧(AI)代理人完成任務的時程長度。研究發現,此指標在過去六年呈現指數級增長,大約每七個月翻倍。根據此趨勢推算,十年內AI將能獨立完成現今需時數天至數週的大部分軟體任務。此研究方法透過比較AI在不同長度
#趨勢預測 across platforms
every network with a public tag surfaceFollow #趨勢預測 straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/趨勢預測