Channel
@on_artificial_intelligence
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
99subscribers
+0 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001097302019 |
|---|---|
| Type | Channel |
| Username | @on_artificial_intelligence |
| Description | If you want to know more about Science, specially Artificial Intelligence, this is the right place for you Admin Contact: @Oriea |
| Created | Between 1 September 2016 and 30 September 2017— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 9 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/on_artificial_intelligence |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Aug 2026, 11:17 | 99 | no change |
| 8 Aug 2026, 16:16 | 99 | first reading |
20 posts held, back to 12 June 2020 — the 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 20 posts for this entry, the most recent from 9 February 2025. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 9 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
The owner of this channel has been inactive for the last 17 months. If they remain inactive for the next 27 days, they may lose their account and admin rights in this channel. The contents of the channel will remain accessible for all users.
From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence Abstract: Despite the recent progress in AI-powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding th…
New Deep Learning Course by Yann LeCun & Alfredo Canziani (Recommended) Course Intro: This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Additional Info: This co…
Machine Learning & Computational Statistics Course Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. https://davidrosenberg.github.io/ml2016/#home #machine_learning …
What is an agent? Intro: A thought-provoking essay which sheds new light on the agent-environment boundary and philosophy behind the current definition of agent, especially in the field of reinforcement learning. http://anna.harutyunyan.net/wp-content/uploads/2020/09/What_is_an_agent.pdf #reinforcement_learning #philosophy
Meta Reinforcement Learning: An Introduction Intro: a good meta-learning model is expected to generalize to new tasks or new environments that have never been encountered during training. The adaptation process, essentially a mini learning session, happens at test with limited exposure to the new configurations. Even without any explicit fine-tuning (no gradient backpropagation on trainable variables), the meta-lear…
Is a good representation sufficient for sample efficient reinforcement learning? Abstract: Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic programming…
Backward Feature Correction: How Deep Learning Performs Deep Learning Summary: How does a 110-layer ResNet learn a high-complexity classifier using relatively few training examples and short training time? We present a theory towards explaining this in terms of hierarchical learning. We refer hierarchical learning as the learner learns to represent a complicated target function by decomposing it into a sequence of s…
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020) Abstract: In this tutorial article, we aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcement learning algorithms that utilize previously collected data, without additional online data collection. Offline reinforcement learning algorithm…
Yann LeCuN advice for an undergraduate student who aspires to become a Machine Learning Scientist in the field of Deep Learning (0) take all the continuous math and physics class you can possibly take. If you have the choice between “iOS programming” and “quantum mechanics”, take “quantum mechanics”. In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses …
An Introduction to Deep Reinforcement Learning Abstract: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decisionmaking tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. Th…
Neural Architecture Search without Training Abstract: The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be extremely slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be remedied if we c…
Showing the 12 most recent of 20 posts we hold for @on_artificial_intelligence. 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.
Citation-graph rank — 737,568 of 1,480,944entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
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 9 August 2026 — this entry's latest reading, not the date you are reading this.
“On Artificial Intelligence” (@on_artificial_intelligence), 99 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/on_artificial_intelligence.
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.