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Говорит AI

@govorit_ai

On this record: Growth · Engagement · Reactions · Posts · Cite this entry

855subscribers

-1 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001413699800
TypeChannel
Username@govorit_ai
Created28 December 2018measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded8 August 2026
Last confirmed live28 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 28 August 2026
On Telegramt.me/govorit_ai

Growth

8558578567 August 2026 — 856 subscribers8 August 2026 — 856 subscribers20 August 2026 — 857 subscribers28 August 2026 — 855 subscribers7 August 202628 August 2026
4 measurements spanning 21 days, net -1. 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 855–857 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
28 Aug 2026, 10:13855-2
20 Aug 2026, 22:24857+1
8 Aug 2026, 01:18856no change
7 Aug 2026, 15:41856first reading

Engagement

13 posts held, back to 19 January 2021the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 13 posts for this entry, the most recent from 29 June 2022. An engagement rate over an empty window would be a number about nothing.

Reaction mix

39 reactions across 7 posts, in 4 distinct kinds. The most used accounts for 69.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍2769.2%
🔥820.5%
🤔37.69%
🤯12.56%

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 8 of the 13 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 39reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 13 most recent posts we hold, published 19 January 2021 to 29 June 2022, 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

29 Jun 2022, 07:40 UTC≈2,160 views0 reactionsread 8 August 2026
Photo

Photo, posted without a caption

Signed Denis

29 Jun 2022, 07:39 UTC≈2,080 views21 reactionsread 8 August 2026

Typical Decoding for Natural Language Generation https://arxiv.org/abs/2202.00666v2 TL;DR Предложили новый способ сэмплирования из языковых моделей, основанный на концепциях теории информации и предположениях о том, как люди оперируют естественным языком в реальной жизни. Суть подхода Как известно, современные языковые модели способны очень хорошо оценивать вероятности текстов, выдавая низкие значения перплексии дл

👍15🤔3🔥2🤯1

Signed Denis

21 Apr 2022, 09:37 UTC≈1,960 views3 reactionsread 8 August 2026
Photo

Photo, posted without a caption

👍3

Signed Denis

21 Apr 2022, 09:37 UTC≈1,740 views2 reactionsread 8 August 2026
Photo

Photo, posted without a caption

🔥2

Signed Denis

21 Apr 2022, 09:36 UTC≈1,490 views1 reactionsread 8 August 2026
Photo

Photo, posted without a caption

👍1

Signed Denis

21 Apr 2022, 09:36 UTC≈1,540 views6 reactionsread 8 August 2026

Red Teaming Language Models with Language Models https://arxiv.org/abs/2202.03286 TL;DR Предложили оригинальный способ поиска оскорбительного поведения в языковых моделях: благодаря способностям к zero-shot генерации можно создавать «провокационные» контексты с помощью других языковых моделей. Метод позволяет выявлять bias в модели практически без ручного вмешательства, причём как в режиме «вопрос-ответ», так и при

🔥4👍2

Signed Denis

13 Apr 2022, 11:11 UTC≈1,400 viewsread 8 August 2026

Результаты Применяемый подход сравнивали с обучением со “стандартными” параметрами, а также с двумя работами конкурентов - 1) с дискретным 2-ступенчатым увеличением длины последовательности (“2-stage CL“) и 2) плавным увеличением размера батча (“Bsz Warmup 45”). Метрики меряли по перплексии на валидации, перплексии на WikiText и accuracy на LAMBADA. Для анализа “нестабильности” обучения предложили метрику “loss ratio

Signed Renat

13 Apr 2022, 11:11 UTC≈2,120 views5 reactionsread 8 August 2026

Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training https://arxiv.org/abs/2108.06084 TL;DR Microsoft показали, что возможно делать pre-training больших (1.5B) GPT-модели с большим размером батча (bs 4К), более стабильно (без скачков лосса) и без потери в качестве - если плавно увеличивать максимальную длину последовательности в ходе обучения. Суть подхода Для

👍5

Signed Renat

19 May 2021, 07:23 UTC≈3,670 views1 reactionsread 8 August 2026

Google презентовал свою новую наработку в Conversational AI под названием LaMDA. Blogpost | Video Судя по посту первого автора GShard сеть представляет из себя огромный MoE transformer на сотни миллиардов (триллионы?) параметров. Тренировали скорее всего на своем внутреннем огромном диалоговом датасете, про который гугл упоминал в Meena - The Meena model has 2.6 billion parameters and is trained on 341 GB of text, f

👍1

Signed Artem R

17 Mar 2021, 10:27 UTC≈3,360 viewsread 8 August 2026

Всем привет! На этот раз без пейпера: Думаю, в этом канале сидит много талантливых ML инженеров с интересом к NLP — а мы в нашу замечательную AI команду Replika ищем Senior NLP Research Engineer развивать наш open-domain диалог на миллионах пользователей! Все подробности по ссылке — https://www.notion.so/Senior-NLP-Research-Engineer-Replika-fa43826e6e0f4dc1a13e2b69c3c6f6ef Пишите мне напрямую в телеграм @nikitospr

Signed Nikita

Showing the 12 most recent of 13 posts we hold for @govorit_ai. 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.

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 28 August 2026 — this entry's latest reading, not the date you are reading this.

“Говорит AI” (@govorit_ai), 855 subscribers as measured 28 August 2026. Telegram Register, tgregister.com/channel/govorit_ai.

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.