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Channel

هوش مصنوعی و علم داده به فارسی

@DataPlusScience

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry

6,064subscribers

+18 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001219053374
TypeChannel
Username@DataPlusScience
CreatedBetween 1 April 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live10 November 2026
Measurements held25
Confirmed unchanged2 times, most recently 10 November 2026
On Telegramt.me/DataPlusScience

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 12 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.

Growth

6,0416,0716,0566 August 2026 — 6,046 subscribers6 August 2026 — 6,044 subscribers10 August 2026 — 6,046 subscribers13 August 2026 — 6,060 subscribers16 August 2026 — 6,069 subscribers19 August 2026 — 6,064 subscribers22 August 2026 — 6,071 subscribers25 August 2026 — 6,063 subscribers28 August 2026 — 6,059 subscribers31 August 2026 — 6,049 subscribers4 September 2026 — 6,041 subscribers9 September 2026 — 6,049 subscribers13 September 2026 — 6,054 subscribers25 September 2026 — 6,056 subscribers4 October 2026 — 6,062 subscribers11 October 2026 — 6,068 subscribers14 October 2026 — 6,066 subscribers17 October 2026 — 6,069 subscribers20 October 2026 — 6,066 subscribers23 October 2026 — 6,065 subscribers26 October 2026 — 6,063 subscribers29 October 2026 — 6,070 subscribers1 November 2026 — 6,066 subscribers4 November 2026 — 6,065 subscribers7 November 2026 — 6,064 subscribers6,0646 August 20267 November 2026
25 measurements spanning 93 days, net +18. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 6,037–6,076 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 25
Measured (UTC)SubscribersChange
7 Nov 2026, 07:586,064-1
4 Nov 2026, 12:066,065-1
1 Nov 2026, 03:146,066-4
29 Oct 2026, 09:456,070+7
26 Oct 2026, 02:086,063-2
23 Oct 2026, 06:066,065-1
20 Oct 2026, 06:556,066-3
17 Oct 2026, 15:576,069+3
14 Oct 2026, 18:286,066-2
11 Oct 2026, 06:546,068+6
4 Oct 2026, 19:166,062+6
25 Sept 2026, 00:576,056+2
13 Sept 2026, 01:386,054+5
9 Sept 2026, 02:586,049+8
4 Sept 2026, 03:226,041-8
31 Aug 2026, 20:346,049-10
28 Aug 2026, 23:436,059-4
25 Aug 2026, 19:086,063-8
22 Aug 2026, 22:356,071+7
19 Aug 2026, 07:416,064first reading

Engagement

106 posts held, back to 5 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 13 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
3.33%
avg views ÷ 6,064 subscribers
Avg views / post
202
24 posts measured
Reaction rate
0.391%
reactions ÷ views · ER floor
Posts in window
24
of 106 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 1 of 24 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 5 November 2026
Posts held106 (5 August 2026 – 5 November 2026)
Views total4,852
Reactions total1
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken5 Nov 2026, 17:08 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Video runtime
3h 17m
Average length
10m 25s

Measured directly from 19 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Reaction mix

11 reactions across 11 posts, in 1 kind.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤11100.0%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 14 of the 106 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 11 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 106 most recent posts we hold, published 5 August 2026 to 5 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

Telegram’s own ‘edited’ marker: 0 of 24 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

5 Nov 2026, 16:07 UTC67 viewsread 5 November 2026
Photo

‏🤖 سیستم Agile Deliberation گوگل برای یادگیری مفاهیم انتزاعی ‏گوگل ریسرچ سیستم جدیدی به نام Agile Deliberation معرفی کرده است که به مدل‌های هوش مصنوعی اجازه می‌دهد مفاهیم ذهنی و مبهم (مانند تعریف دقیق غذای سالم) را بدون نیاز به بازآموزی (Retraining) یاد بگیرند. ‏در این روش، مدل به جای تکیه بر دستورالعمل‌های ایستا، تصاویر یا داده‌های بحث‌برانگیز را شناسایی کرده و از انسان می‌خواهد تکلیف آن‌ها را مشخص کند. سپس یک LLM…

5 Nov 2026, 13:43 UTC123 viewsread 5 November 2026
Photo

‏🚀 افزایش سرعت استنتاج مدل‌های MoE توسط انویدیا ‏انویدیا سه راهکار برای افزایش سرعت استنتاج مدل‌های Mixture-of-Experts (MoE) روی سخت‌افزار DGX Spark معرفی کرده است. این روش‌ها با استفاده از مدل Ling-3.0-flash (با ۱۲۴ میلیارد پارامتر) آزمایش شده‌اند. ‏از آنجا که در batch size برابر با ۱، سرعت استنتاج محدود به حافظه (Memory-bound) است، انویدیا بر کاهش انتقال داده تمرکز کرده است: ‏* استفاده از هسته‌های بهینه‌شده Hummi…

5 Nov 2026, 12:03 UTC151 viewsread 5 November 2026
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‏⚙️ انتشار بنچمارک OpenAI برای ارزیابی مدل‌ها در مهندسی فیزیکی ‏OpenAI در مطلب جدیدی به بررسی نقش مدل‌های GPT در فرآیندهای مهندسی دنیای فیزیکی پرداخته است. ‏در میان داده‌های منتشرشده، نموداری از بنچمارک BenchCAD 2.0 دیده می‌شود که عملکرد یک مدل داخلی را در برابر GPT-6 Astra مقایسه می‌کند. طبق این نمودار، حالت استدلال پایین (low reasoning mode) در مدل داخلی، هزینه‌ای معادل حالت حداکثری Astra دارد اما امتیاز بالاتری …

5 Nov 2026, 12:00 UTC136 viewsread 5 November 2026
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‏📊 به‌روزرسانی فرم S-1 شرکت Anthropic و انتشار آمار مالی سه ماهه سوم ‏شرکت Anthropic فرم S-1 خود در سازمان بورس و اوراق بهادار آمریکا را به‌روزرسانی کرد و آمار مالی سه ماهه سوم سال ۲۰۲۶ را به آن افزود. بر اساس این گزارش، درآمد این شرکت در ۹ ماه نخست سال ۲۰۲۶ به ۳۳ میلیارد دلار رسیده است. ‏برخلاف ادعاهای مطرح‌شده مبنی بر سوزاندن شدید سرمایه، گزارش جدید نشان می‌دهد ذخایر نقدی و سرمایه‌گذاری Anthropic در اوراق خزانه‌د…

5 Nov 2026, 07:11 UTC180 viewsread 5 November 2026
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‏🛡 عرضه ابزار FlashProve توسط Anthropic ‏شرکت Anthropic ابزار FlashProve را به صورت متن‌باز منتشر کرد. این پروتکل رمزنگاری به کاربران امکان می‌دهد بدون دسترسی به وزن‌های مدل، تأیید کنند که خروجی ارائه‌شده دقیقاً توسط همان مدل ادعاشده تولید شده است. ‏این فناوری برای اطمینان از صحت مدل در سرویس‌های روتینگ یا محیط‌های سازمانی کاربرد دارد تا از جایگزینی مدل با نمونه‌های ارزان‌تر یا دستکاری توسط مهاجمان جلوگیری شود. ‏ن…

5 Nov 2026, 06:25 UTC184 viewsread 5 November 2026
Video

‏🎥 DeepSeek امکان طراحی هَـرنِس (Harness) توسط کاربر را فراهم کرد ‏DeepSeek ماه گذشته هَـرنِس متن‌باز خود را منتشر کرد که بر پایه ایده «همه‌چیز یک پلاگین است» بنا شده. اکنون حالت «Creator Mode» به آن افزوده شده است. در این حالت، شما ایده خود را توصیف می‌کنید و یک ایجنت (agent) به طور خودکار پلاگینی برای آن نوشته و مستقیماً در رابط کاربری برنامه (interface) ادغام می‌کند. ‏این بدان معناست که رابط کاربری هَـرنِس دیگر …

4 Nov 2026, 18:15 UTC229 viewsread 5 November 2026
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‏📊 معماری‌های Retrieval-Augmented Generation (RAG) ‏معماری‌های Retrieval-Augmented Generation (RAG) با ترکیب بازیابی اطلاعات و تولید متن توسط مدل‌های زبانی بزرگ (LLMs)، پاسخ‌های دقیق‌تر و مبتنی بر داده‌های خارجی ارائه می‌دهند. در اینجا انواع مختلف RAG معرفی شده‌اند: ‏* Naive RAG: ساده‌ترین رویکرد که شامل بازیابی اولیه و سپس تولید متن است. ‏* Retrieve-and-Rerank RAG: بازیابی چندین نتیجه و سپس رتبه‌بندی مجدد آن‌ها بر…

4 Nov 2026, 15:21 UTC222 viewsread 5 November 2026
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‏🌐 تغییر دسترسی مدارس به هوش مصنوعی گوگل ‏گوگل اعلام کرده است که از اوایل سال آینده میلادی، ابزارهای هوش مصنوعی به‌صورت پیش‌فرض برای حساب‌های کاربری دانش‌آموزی (K-12) غیرفعال خواهند شد و مدارس برای استفاده از آن‌ها باید خودشان اقدام به فعال‌سازی کنند. ‏طبق گزارش نیویورک‌تایمز، این تغییر محدودیت‌های جدیدی را ایجاد می‌کند؛ مدارسی که هوش مصنوعی را کاملاً مسدود کنند، دسترسی دانش‌آموزان به «جستجوی گوگل» (Google Search) …

4 Nov 2026, 13:14 UTC277 viewsread 5 November 2026
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‏🧐 کاهش نرخ توهم و افزایش تمایل به تملق در مدل‌های جدید ‏گروه پژوهشی دانشگاه استنفورد با بررسی ۵۰ یافته شناخته‌شده در ۲۰ مدل هوش مصنوعی جدید متوجه شدند که ۴۴ درصد این یافته‌ها دیگر صحت ندارند و ۳۴ درصد دیگر نیز بسیار ضعیف شده‌اند. این نتایج نشان می‌دهد که عمر مفید بسیاری از باورها درباره نقاط ضعف مدل‌های زبانی به حدود ۱۵ ماه کاهش یافته است. ‏تغییرات کلیدی در رفتار مدل‌ها: ‏- توهم منبع: در حالی که در سال ۲۰۲۳ حدود ۵…

4 Nov 2026, 12:14 UTC198 viewsread 5 November 2026
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‏🤖 تولید مستقیم وزن‌های یک شبکه عصبی توسط مدل‌های جدید AI ‏پژوهشگران MIT نشان دادند مدل‌های جدید هوش مصنوعی می‌توانند وزن‌های یک مدل دیگر را بدون نیاز به آموزش و به‌صورت مستقیم بنویسند. الکس کرنتسل، پژوهشگر دکتری دانشگاه برکلی، این آزمون را روی مدل‌های مختلف تکرار کرد. ‏در این آزمایش، مدل‌ها باید تمام ۲۰٬۴۸۶ پارامتر یک شبکه عصبی کوچک برای بازی Atari Breakout را تنها در یک پاسخ تولید می‌کردند. معیار مقایسه، شبکه‌ای …

4 Nov 2026, 10:30 UTC197 viewsread 5 November 2026

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4 Nov 2026, 10:08 UTC190 viewsread 5 November 2026
Video

‏🧊 مدل متن‌باز Idol برای تبدیل یک عکس به مدل سه‌بعدی ‏مجموعه Speridlabs مدل متن‌باز Idol را برای ساخت اشیای سه‌بعدی از یک تصویر تک منتشر کرد. این مدل زاویه‌های ناپیدای تصویر را بازسازی می‌کند و خروجی را با روش 3D Gaussian Splatting تا سقف ۱ میلیون گاسین می‌سازد. ‏مدل Idol به صورت مستقل آموزش دیده و کدهای آن با مجوز Apache 2.0 در گیت‌هاب قرار گرفته است. فرمت خروجی آن PLY است و مصرف حافظه ویدئویی (VRAM) آن زیر ۷.۲ گی…

Showing the 12 most recent of 106 posts we hold for @DataPlusScience. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Posts edited after publishing

@DataPlusScience edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
10 August 2026
Most recent edit
10 August 2026

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Named by

Channels on the register whose posts name this channel's handle.

Names

Channels on the register whose handles appear in this channel's posts.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

هوش مصنوعی در پژوهش
@AI_in_Research · 305,648
Telegram ranks this channel #12 of 92 here — alongside 91 others — read 19 August 2026
آقای هوش مصنوعی
@MrArtificialintelligence · 20,689
Telegram ranks this channel #15 of 94 here — alongside 93 others — read 26 September 2026
روش تحقیق و مقاله نویسی
@Raveshtahghighh · 39,853
Telegram ranks this channel #15 of 84 here — alongside 83 others — read 31 August 2026
پایتون | Data Science | Machine Learning
@Python4all_pro · 24,119
Telegram ranks this channel #18 of 91 here — alongside 90 others — read 16 September 2026
اخبار هوش مصنوعی
@Ai_NewsTv · 27,210
Telegram ranks this channel #20 of 88 here — alongside 87 others — read 10 September 2026
Data Science | علم داده
@DataScience_ir · 50,222
Telegram ranks this channel #22 of 92 here — alongside 91 others — read 25 August 2026
مغز آینده |NextBrain
@NextBrain_ir · 19,745
Telegram ranks this channel #23 of 90 here — alongside 89 others — read 29 September 2026
هوش مصنوعی |یادگیری ماشین| علم داده
@Ai_Tv · 26,535
Telegram ranks this channel #25 of 94 here — alongside 93 others — read 12 September 2026
کتابخانه مهندسی کامپیوتر و پایتون
@programmers_street · 30,945
Telegram ranks this channel #26 of 87 here — alongside 86 others — read 6 September 2026
IELTS Reading 📑
@improveReading · 20,417
Telegram ranks this channel #29 of 80 here — alongside 79 others — read 26 September 2026
مجله هوش مصنوعی
@HomeAI · 32,870
Telegram ranks this channel #32 of 91 here — alongside 90 others — read 5 September 2026
آموزش مقاله و پایان‌ نامه‌ نویسی
@dr_amaniiii · 61,968
Telegram ranks this channel #32 of 79 here — alongside 78 others — read 21 August 2026
DLeX: AI Python
@ai_python · 21,294
Telegram ranks this channel #39 of 94 here — alongside 93 others — read 23 September 2026
Power BI | تحلیل داده | SQL
@PowerBI_ir · 22,001
Telegram ranks this channel #41 of 89 here — alongside 88 others — read 22 September 2026
Machine Learning | یادگیری ماشین
@MachineLearning_ir · 34,710
Telegram ranks this channel #42 of 90 here — alongside 89 others — read 3 September 2026
اقتصاد و قانون در آنتاریو
@ontario_360 · 21,734
Telegram ranks this channel #49 of 80 here — alongside 79 others — read 21 September 2026
Programming Resources
@pythony · 19,711
Telegram ranks this channel #54 of 95 here — alongside 94 others — read 30 September 2026
آموزش پایان نامه و مقاله نویسی پژوهه
@pajoohehgroup · 59,230
Telegram ranks this channel #68 of 87 here — alongside 86 others — read 22 August 2026
آیت الله فاطمی نیا(رحمه الله)
@fateminia · 25,023
Telegram ranks this channel #84 of 92 here — alongside 91 others — read 15 September 2026

This channel appears in 19 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 November 2026 — this entry's latest reading, not the date you are reading this.

“هوش مصنوعی و علم داده به فارسی” (@DataPlusScience), 6,064 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/DataPlusScience.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.