مستقبل واعد يتشكل بأيدي سعوديات مبدعات
أفكار وابتكارات وحلول رقمية تؤكد أن حضور المرأة السعودية في التقنية والذكاء الاصطناعي أصبح واقعًا يصنع الأثر ويلهم العالم.
اليوم لا نتحدث عن مشاركة فقط، بل عن قيادة في صناعة المستقبل من الذكاء الاصطناعي وتحليل البيانات، إلى تطوير الحلول وتصميم التقنيات الحديثة
فخورين بالقصص التي وثقناها اليوم، وبقدرة بنات الوطن على تحويل المعرفة إلى منتجات، والأفكار إلى ابتكارات، والفرص إلى أثر ملموس في المجتمع والاقتصاد الرقمي.
جيل جديد من المبدعات السعوديات يصنعن مشاريع نوعية، وأثرًا يتجاوز الحدود.
We are entering the era of new AI models becoming worse than older ones ( under some setups ) … which is a big sign for saturation ( or what @ilyasut called it the “ age of research “ ) I think Gemini model 3 and Opus 4.5 are the only two exceptions strictly better than previous
This year’s chemistry laureate Omar Yaghi was born in Amman, Jordan, in 1965 to parents who were refugees from Palestine. When we spoke to him he shared his story:
“I grew up in a very humble home, we were a dozen of us in one room, sharing it with the cattle that we used to raise. I was born in a family of refugees, and my parents could barely read or write. My father finished sixth grade and my mother couldn’t read or write. It’s quite a journey. Science allows you to do it. Science is the greatest equalising force in the world.
Smart people, talented people, skilled people exist everywhere. That’s why we really should focus on unleashing their potential through providing them with opportunity.”
Today Yaghi shared the 2025 Nobel Prize in Chemistry with Susumu Kitagawa and Richard Robson for their work developing metal–organic frameworks.
Learn more about the prize: https://t.co/4nmszg1ZIR
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2025 #NobelPrize in Chemistry to Susumu Kitagawa, Richard Robson and Omar M. Yaghi “for the development of metal–organic frameworks.”
The most important skill for a researcher is not technical ability. It's taste. The ability to identify interesting and tractable problems, and recognize important ideas when they show up.
This can't be taught directly. It's cultivated through curiosity and broad reading.
This is my will and my final message. If these words reach you, know that Israel has succeeded in killing me and silencing my voice. First, peace be upon you and Allah’s mercy and blessings.
Allah knows I gave every effort and all my strength to be a support and a voice for my people, ever since I opened my eyes to life in the alleys and streets of the Jabalia refugee camp. My hope was that Allah would extend my life so I could return with my family and loved ones to our original town of occupied Asqalan (Al-Majdal). But Allah’s will came first, and His decree is final. I have lived through pain in all its details, tasted suffering and loss many times, yet I never once hesitated to convey the truth as it is, without distortion or falsification—so that Allah may bear witness against those who stayed silent, those who accepted our killing, those who choked our breath, and whose hearts were unmoved by the scattered remains of our children and women, doing nothing to stop the massacre that our people have faced for more than a year and a half.
I entrust you with Palestine—the jewel in the crown of the Muslim world, the heartbeat of every free person in this world. I entrust you with its people, with its wronged and innocent children who never had the time to dream or live in safety and peace. Their pure bodies were crushed under thousands of tons of Israeli bombs and missiles, torn apart and scattered across the walls.
I urge you not to let chains silence you, nor borders restrain you. Be bridges toward the liberation of the land and its people, until the sun of dignity and freedom rises over our stolen homeland. I entrust you to take care of my family. I entrust you with my beloved daughter Sham, the light of my eyes, whom I never got the chance to watch grow up as I had dreamed.
I entrust you with my dear son Salah, whom I had wished to support and accompany through life until he grew strong enough to carry my burden and continue the mission.
I entrust you with my beloved mother, whose blessed prayers brought me to where I am, whose supplications were my fortress and whose light guided my path. I pray that Allah grants her strength and rewards her on my behalf with the best of rewards.
I also entrust you with my lifelong companion, my beloved wife, Umm Salah (Bayan), from whom the war separated me for many long days and months. Yet she remained faithful to our bond, steadfast as the trunk of an olive tree that does not bend—patient, trusting in Allah, and carrying the responsibility in my absence with all her strength and faith.
I urge you to stand by them, to be their support after Allah Almighty. If I die, I die steadfast upon my principles. I testify before Allah that I am content with His decree, certain of meeting Him, and assured that what is with Allah is better and everlasting.
O Allah, accept me among the martyrs, forgive my past and future sins, and make my blood a light that illuminates the path of freedom for my people and my family. Forgive me if I have fallen short, and pray for me with mercy, for I kept my promise and never changed or betrayed it.
Do not forget Gaza… And do not forget me in your sincere prayers for forgiveness and acceptance.
Anas Jamal Al-Sharif
06.04.2025
This is what our beloved Anas requested to be published upon his martyrdom.
كتبتها أمس سريعًا، وما راحت عن بالي.
بغض النظر عن أي ظروف وتفاصيل، لطالما أنك سعودي فكل فرص الدنيا مفتوحة لك. بلادنا ليست كغيرها، فروح قائدها تنعكس على شبابها وشعبها.. هذه الروح تجعل الكل يؤمن أن المستحيل ممكنًا، وأننا نستطيع منافسة العالم في كل مجال… لا لا، بل نصنع ما لم يُصنع بعد.
أن تعيش في هذا المكان وهذا الزمان، والله أنها نعمة عظيمة. فلله الحمد من قبل ومن بعد على نعمة السعودية ونعمة قدوتنا سيدي أبو سلمان ❤️🇸🇦
في إطار الزيارة الرسمية لفخامة الرئيس دونالد ترامب رئيس الولايات المتحدة الأمريكية..
شراكة سعودية أمريكية بين #كاكست وجامعة بيركلي لتعزيز التعاون البحثي والتقني وتأهيل الكوادر البشرية في مجالات المواد المتقدمة وتطبيقات الذكاء الاصطناعي.
#الرئيس_الأمريكي_في_المملكة
https://t.co/rzO7NW4j0T
NEWS: Saudi Arabia and NVIDIA will build AI factories powering the next wave of intelligence.
NVIDIA will help deploy AI infrastructure, strengthen the regional ecosystem, and train developers and scientists—fueling growth and prosperity in the region. ➡️https://t.co/v1rlz6xBXM
We're missing (at least one) major paradigm for LLM learning. Not sure what to call it, possibly it has a name - system prompt learning?
Pretraining is for knowledge.
Finetuning (SL/RL) is for habitual behavior.
Both of these involve a change in parameters but a lot of human learning feels more like a change in system prompt. You encounter a problem, figure something out, then "remember" something in fairly explicit terms for the next time. E.g. "It seems when I encounter this and that kind of a problem, I should try this and that kind of an approach/solution". It feels more like taking notes for yourself, i.e. something like the "Memory" feature but not to store per-user random facts, but general/global problem solving knowledge and strategies. LLMs are quite literally like the guy in Memento, except we haven't given them their scratchpad yet. Note that this paradigm is also significantly more powerful and data efficient because a knowledge-guided "review" stage is a significantly higher dimensional feedback channel than a reward scaler.
I was prompted to jot down this shower of thoughts after reading through Claude's system prompt, which currently seems to be around 17,000 words, specifying not just basic behavior style/preferences (e.g. refuse various requests related to song lyrics) but also a large amount of general problem solving strategies, e.g.:
"If Claude is asked to count words, letters, and characters, it thinks step by step before answering the person. It explicitly counts the words, letters, or characters by assigning a number to each. It only answers the person once it has performed this explicit counting step."
This is to help Claude solve 'r' in strawberry etc. Imo this is not the kind of problem solving knowledge that should be baked into weights via Reinforcement Learning, or least not immediately/exclusively. And it certainly shouldn't come from human engineers writing system prompts by hand. It should come from System Prompt learning, which resembles RL in the setup, with the exception of the learning algorithm (edits vs gradient descent). A large section of the LLM system prompt could be written via system prompt learning, it would look a bit like the LLM writing a book for itself on how to solve problems. If this works it would be a new/powerful learning paradigm. With a lot of details left to figure out (how do the edits work? can/should you learn the edit system? how do you gradually move knowledge from the explicit system text to habitual weights, as humans seem to do? etc.).