1. Home
  2. Detail

@arab_doctors: إحمرار العين له درجات مختلفة، من الخفيف إلى الشديد، وقد يكون سببه الجفاف أو الحساسية أو الالتهابات أو دخول جسم غريب للعين. في حال كان الاحمرار شديدًا أو مصحوبًا بألم أو تشوش في الرؤية، ننصحكم بزيارة طبيب العيون للاطمئنان. للحجز والاستفسار: 📞 065544414 | 0542790301 📍 الشارقة – التعاون – برج المنصور – الطابق الأول

Arab Doctors Specialists
Arab Doctors Specialists
Open In TikTok:
Region: AE
Wednesday 29 April 2026 16:33:29 GMT
20243
34
0
1

Music

Download

No Watermark .mp4 (5.84MB) No Watermark(HD) .mp4 (5.42MB) Watermark .mp4 (3.63MB) Music .mp3

Comments

There are no more comments for this video.
To see more videos from user @arab_doctors, please go to the Tikwm homepage.

Other Videos
#foryourpage🥰🥰
#foryourpage🥰🥰
#catalog #fyp #greymarket #skincare #peps
#catalog #fyp #greymarket #skincare #peps
Nhìn vào bầu trời có riêng ta ~~ #xinhdep #dethuomg
Nhìn vào bầu trời có riêng ta ~~ #xinhdep #dethuomg
➡️ ML news part 1 An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL),
➡️ ML news part 1 An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL), "Sparks of Weight-Space Programming", blog post, November 2, 2026, https://rikiyat.github.io/blog/weight-space-programming/ (early report, not peer reviewed). Screenshots from the report are shown as quotes. Model names are given only to name the source. #machinelearning #ai #claude #neuralnetworks #llm
banner idea black version #black #banner #discord #night #sky @᥆rᥲᥒgᑲᥱsᥙ𝗍
banner idea black version #black #banner #discord #night #sky @᥆rᥲᥒgᑲᥱsᥙ𝗍

About

Legal