Coşkun Taştan

Yürütücüsü olduğum "Yapay Zekâ Temelli Karar Destek Modeli", Kamu Bilişim Ödülüne layık görüldü. JGK ve EGM Asayiş ve BT Daire Bşk.lıklarına, İller İdaresi Gn. Mdlüğüne katkılarından dolayı teşekkür ederim. Ödülü Eşim Vildan'a, oğlum Sarp'a ve kızım Rânâ'ya ithaf ediyorum ❤️.

@flabbytofit99 @_HelenaT No enforcement. Just move to other government tricks. There are countless of them.

@_HelenaT Trick doesn’t work and you have to enforce

I stole this parenting trick from the government. And it's eliminated 90% of power struggles with my kids. Here's how it works: My 5-year-old used to fight me EVERY NIGHT about brushing teeth. "No! I don't want to!" "You have to!" Meltdown. Tears. Exhausting. Then I changed one thing: Instead of "Go brush your teeth," I said: "Do you want to brush teeth before or after pajamas?" He thought about it. "After." Done. No fight. Teeth got brushed. What changed? He felt like HE was making the decision. But I was controlling both options. It's the illusion of choice. And it's genius. Now I use it everywhere: "Do you want to clean your room now or after snack?" "Do you want to wear your blue shirt or red shirt?" "Do you want to do homework before or after dinner?" Same outcome. Zero power struggle. The government uses this on adults all the time: "Do you want Candidate A or Candidate B?" (Both serve their agenda) If it works on adults, it DEFINITELY works on kids. Give them choices within boundaries. They feel empowered. You stay in control.

@xKnowledgeBANK 15 (yes, the word "dots" counts too)

How many dots do you see?

Elon, just look at which parts of the world—and in what kinds of societies—birth rates remain high! A simple regression between development levels and demographic reproduction rates makes it clear: every step forward in technology tends to signal a further decline in birth rates. Mark it this way 😁

AI is obviously gonna one-shot the human limbic system. That said, I predict – counter-intuitively – that it will *increase* the birth rate! Mark my words. Also, we’re gonna program it that way.

@charliekirk11 So simple: Being good or bad has nothing to do with religion. If you choose to be good, your beliefs will guide you that way. Trying to be bad? They'll justify your actions instead.

24 years ago a group of Muslims killed 2,753 people on 9/11 Now a Muslim Socialist is on pace to run New York City

By no means any rule-based intrusions to the model, Peter. The issue isn't just training data or model architecture; even a perfectly trained model with perfect architecture on today's "truth" sources could miss truths outside that box. My point: actively seek beyond paradigms, use LLMs humbly, and share new insights to evolve the corpus – like history's paradigm shifters (Galileo, Kepler, Newton &c) did. I know well that paradigm shifts are not overnight.

@ctastan @elonmusk Right. It keeps using legacy "truth" organizations to decide what truth is like Reuters and peer-reviewed journals, when it's pretty established that those have been compromised. How exactly do you train it to recognize truth and not just parrot the traditional sources?

xAI is the only major AI company with an absolute focus on truth, whether politically correct or not. Rigorous adherence to truth is the only way to build safe AI and the only way to understand the true nature of the Universe.

@TheFigen_ No way!😳😲

They are ants solving a geometric problem and it is great in color.

What this comparison overlooks: Truth might exist outside the 'large language', beyond "political truth" and "technical truth" (ie, accuracy of next-token prediction). For example, the ancient 'large language' declared Earth the universe's center for nearly 2000 years, with deadly consequences for any opposition. Imagine you trained Grok back in those times' corpus. That would simply perpetuate a historical falsehood.

@nyanpiggle @elonmusk I'm not minimizing truth, I'm highlighting the limitations of its expression through human language and data. They're not synonymous. Language enables truth's expression, not its perfection. The tool is separate from the ideal, buddy.

@ctastan @elonmusk And yet without human language there would be no truth to share at all, and minimising truth makes no sense What other direction would you try take it in?

@HardCoreHodler @PhysInHistory @grok Welchen Unterschied macht es für Sie? Wenn Sie mit dieser Nummer nichts produzieren, machen Sie sich keine Sorgen, mein Freund 😁

@PhysInHistory @grok ist die Zahl korrekt und vollständig?

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Arf, risalesini şöyle bir öngörü ile bitiyor: "Makinelerin bazı işleri insan beynine nazaran çok daha çabuk yapabilmelerine mukabil anlayış yani alış kapasiteleri büyük bir salonu doldurabilecek kadar büyük olanlarında bile tenevvü bakımından insan beyninden çok düşüktür. İnsan beyninin kendi kendisini kendi inisiyatifi ile tekemmül ettirmesine mukabil makine yapıldığı gibi kalmaktadır. Bununla beraber kendi kendisini tekemmül ettiren makine tasarlamak mümkündür. Fakat kanaatimce insan beyni ile makine arasındaki asıl fark, insan beyninin estetik mahiyette müessirleri alıp onlar üzerinde işleyebilmesi ve yine estetik mahiyette olan kararlar verebilmesine, verilen bir işi yapıp yapmamak hususunda kendisini serbest hissetmesine mukabil makinede bu vasıfların benzerlerinin yok oluşudur. Bu vasıfları karakterize eden husus hepsinin de bir belirsizlik unsuru ihtiva etmesi, bunların şaşmaz bir şekilde uydukları kaidelerin mevcut olmayışıdır. Belirsizlik karakterini haiz olan insan dışı tabiat hâdiseleri mevcuttur. Bunlar atom içinde ceryan eden olaylardır. Bu itibarla nisbeten küçük sayıda atom içinde ceryan eden olaylar böyle makinelerin işleyişinde müessir hale getirilebilirse, makinelerin estetik bakımdan da insan beynine benzetileceği ümit edilebilecektir. Böyle bir makine, meselâ filân müzik parçasını güzel bulmadığını söyliyebilecektir. Fakat bu işin uzun yüzyıllar sonra bile ve belki de hiçbir zaman yapılamacağını zannediyorum". Arf'in yapay zekâ ile ilgili erken dönemdeki konulara vakıf oluşu hayranlık verici ama pesimist öngörüleri Büyük Dil Modelleri ve kuantum bilgisayarlardaki gelişmelerle birlikte daha şimdiden sarsılmış durumda. Aşağıdaki resimde Arf, yukarıda verdiğimiz sorudaki tavuk ve tavşanları sayabilen bir yapay zekâ mekanizması tarif ediyor:

Cahit Arf’in 1958’de Erzurum’da verdiği “Makineler Düşünebilir mi?” başlıklı bir konferans var (bu konferansın basılı metni için açık kaynak olarak karşılaştığım, Emir Öngüner kişisel arşivindeki kopyasını esas alıyorum). Aslında Arf’in risale şeklinde yayınlanan bu yazısının başlığı, yapay zekanın atalarından sayılan Alan Turing’in 1950’de yazdığı “Computing Machinery and Intelligence” adlı makalenin daha ilk cümlesinde sorulan bir soru ama Arf kendine özgü örneklerle konuyu (Turing’den 8 yıl sonra ve onu hiç zikretmeden de olsa) anlatıyor. Arf’in makalesinde makinelerle kurulacak düşünme sistemini (kompütasyon) anlatmak için sorduğu bir soru şöyle: Bir kümeste tavuklar ve tavşanlar var. Toplamda 510 baş, 1420 ayak var. Kümesteki tavuk ve tavşan sayılarını bulun (Arf bu soruyu bir makinaya çözdürmenin yolunu gösteriyor makalede. Türkiye’de yapay zekâ ile ilgili ilk önemli yazı olması bakımından da ilginç).

El-ân, Ankara Ve dörtnala dümdüz bir mavilikte Kar yağıyor üstümüze, inceden.

turkiyegazetesi.com.tr/gundem/sucla-m… 👆👆Söylediklerimi tam yansıtan bu haber için muhabir arkadaşı (ve tabii ki editörü) tebrik ediyorum. "Karar" başka, "karar destek notu" başka bir şey. Bilim insanları olarak hakim ve savcılara "tavsiye" vermeye yetkimiz yok elbette ama adli (ve idari) kararları destekleyecek "önleyici adli çalışmalar" yapabilir, bu çalışmaların sonuçlarını "adli karar destek notları" olarak kullanıma sunabiliriz. Geleneksel olarak adli bilimler sadece gerçekleşmiş suçları aydınlatmayı hedefliyor. Oysa suçun gerçekleşme "olasılığı" ile ilgili çalışmalar da önemli. Gelişen yapay zekâ teknolojileri suç önleme çalışmalarında giderek daha fazla yer kaplayacaktır. "Önleyici kriminalistik" adını verebileceğimiz bu alan, akademik dünyada hak ettiği yere henüz gelmiş değil. Kişisel verilerin korunması ile ilgili tüm hassasiyetleri ve etik kuralları gözetmek kaydıyla vaka bazlı risk derecelendirmeleri için teknolojiden yararlanmanın gereğini ve önemini (çok sayıda suç kaydı olan kişilerin sarsıcı suçlara karışmasına tanık oldukça) her geçen gün daha net görebiliyoruz. @turkiyegazetesi

Polis cinayeti önceden ‘sezecek’ 🖊️@umtedem haberi... hurriyet.com.tr/gundem/polis-c…

@karpathy 👌

We have to take the LLMs to school. When you open any textbook, you'll see three major types of information: 1. Background information / exposition. The meat of the textbook that explains concepts. As you attend over it, your brain is training on that data. This is equivalent to pretraining, where the model is reading the internet and accumulating background knowledge. 2. Worked problems with solutions. These are concrete examples of how an expert solves problems. They are demonstrations to be imitated. This is equivalent to supervised finetuning, where the model is finetuning on "ideal responses" for an Assistant, written by humans. 3. Practice problems. These are prompts to the student, usually without the solution, but always with the final answer. There are usually many, many of these at the end of each chapter. They are prompting the student to learn by trial & error - they have to try a bunch of stuff to get to the right answer. This is equivalent to reinforcement learning. We've subjected LLMs to a ton of 1 and 2, but 3 is a nascent, emerging frontier. When we're creating datasets for LLMs, it's no different from writing textbooks for them, with these 3 types of data. They have to read, and they have to practice.

📌 Prof. Dr. Coşkun Taştan: Kadına şiddette 'yapay zeka' ile karar alınmasına izin veren uygulama geliştirdik 📎 dha.com.tr/2574104

📍TBMM Kadına Karşı Şiddetin ve Ayrımcılığın Önlenmesi Araştırma Komisyonunda çalışmalarımıza bugün, Prof. Dr. Coşkun Taştan’ın Kadına Karşı Şiddetin Önlenmesinde Yapay Zeka Teknolojileri ve Prof. Dr. Meryem Bulut’un Kadına Karşı Şiddet konusunda ayrıntılı sunumlarıyla devam ettik … Kadına karşı şiddete sıfır tolerans ilkesiyle hareket ediyor ve komisyon çalışmalarımızı sürdürüyoruz…

TBMM Kadına Şiddeti Araştırma Komisyonumuzun 13. TOPLANTISINI gerçekleştirdik. Bugünkü komisyon toplantımızda, Polis Akademisi Güvenlik Bilimleri Enst. Suç Araştırmaları ABD Öğretim Üyesi Prof. Dr. Coşkun Taştan, Ankara Üniversitesi DTCF Sosyal Antropoloji ABD Başkanı Prof. Dr. Meryem Bulut komisyonumuza çok değerli bilgiler verdi. CEVİZOĞLU: ■ "Herkesin polisi kendi vicdanıdır, polis vicdanı olmayanların karşısındadır" Atatürk'ün sözü bu. TAŞTAN: ■ "𝑲𝒂𝒅ı𝒏𝒂 ş𝒊𝒅𝒅𝒆𝒕𝒕𝒆 '𝒀𝑨𝑷𝑨𝒀 𝒁𝑬𝑲𝑨' 𝒊𝒍𝒆 𝒌𝒂𝒓𝒂𝒓 𝒂𝒍ı𝒏𝒎𝒂𝒔ı𝒏𝒂 𝒊𝒛𝒊𝒏 𝒗𝒆𝒓𝒆𝒏 𝒖𝒚𝒈𝒖𝒍𝒂𝒎𝒂 𝒈𝒆𝒍𝒊ş𝒕𝒊𝒓𝒅𝒊𝒌." BULUT: ■ "𝑲𝒂𝒅ı𝒏𝒂 𝒚ö𝒏𝒆𝒍𝒊𝒌 ş𝒊𝒅𝒅𝒆𝒕 𝒃𝒊𝒓𝒆𝒚𝒔𝒆𝒍 𝒐𝒍𝒂𝒓𝒂𝒌 𝒈ö𝒛ü𝒌𝒔𝒆 𝒅𝒆 𝒂𝒔𝒍ı𝒏𝒅𝒂 𝒃𝒖 𝒅𝒖𝒓𝒖𝒎 𝒕𝒂𝒎𝒂𝒎ı𝒚𝒍𝒂 𝒕𝒐𝒑𝒍𝒖𝒎𝒔𝒂𝒍dır." ■ "Kültürel değerlerde onaylanan şiddet, şiddeti meşrulaştırıyor."