semazem 🍐

AI graphics; photography, mobile phone wallpapers.

[semazem 🍐 @semazem]

@seyda__aytekin Steelpan de öyle mesela. Tahammülfersa bir sesi var benim için. Karayipler’den soğudum onun yüzünden; Trinidad ve Tobago’ya karşı acayip mesafeliyim.

[semazem 🍐 @semazem]

@seyda__aytekin Turuncu olsa yine sevmezdim. 😊

[semazem 🍐 @semazem]

Bugün çok iş yaptım. Nicedir, ilgimi çeken bazı konularda videolar hazırlama niyetim vardı. Bugün birden ilham mı neyse artık bir şey geldi. Öğlenden beri 6-7 video içerik hazırladım. Instagram ve Youtube da mevcut. İşiniz yoksa, baksanız keşke. Hatta beğenirseniz paylaşsanız.🫣

[semazem 🍐 @semazem]

@usludurkizim 2

[semazem 🍐 @semazem]

NETFLIX Çöktü!

[Today Years Old @todayyearsold]

Hollywood has spent nearly $600 million trying to bring Matt Damon home.

Have they considered maybe he just doesn’t want to come back?

[Ihtesham Ali @ihteshamali]

A Berkeley philosopher published a book in 1972 warning that AI would never understand the world the way humans do, got laughed off campus for it, then watched the entire AI research community spend 50 years slowly proving him right.

His name was Hubert Dreyfus. The book was called What Computers Can't Do.

And the story of what happened to him before, during, and after he wrote it is one of the most important things nobody tells you about the history of AI.

It started in 1965. RAND Corporation hired Dreyfus to study artificial intelligence. He turned in a 90-page report comparing AI research to alchemy. Not as a compliment.

He argued that AI researchers had made the same mistake over and over for a decade. They would get a narrow system working, predict it was the first step toward general machine intelligence, and watch it hit a wall nobody predicted. Simon said by 1967 computers would be world chess champion. They were not even close. Dreyfus called the whole thing a pattern. Early wins, massive promises, quiet collapse.

The AI community did not take it well.

Herbert Simon called the paper "garbage." Dreyfus taught at MIT at the time and later wrote that his colleagues "dared not be seen having lunch with me." The entire building avoided him.

Then they challenged him to a chess match against a computer.

Dreyfus had never claimed to be good at chess. He had only claimed that AI chess was weak, which it was. But MIT researchers organized a public game between Dreyfus and MacHack VI in 1967. He lost. The Association for Computing Machinery newsletter ran the headline: "A Ten-Year-Old Can Beat the Machine. But the Machine Can Beat Dreyfus."

The entire field celebrated. They had not answered his argument. They had just beaten him at chess. Nobody seemed to notice the difference.

Dreyfus expanded the paper into a full book in 1972. What Computers Can't Do laid out something deeper than chess criticism.

His argument was philosophical, not technical. He said human intelligence was not symbolic manipulation. It was not rules and logic trees. It was something more fundamental that no one had cracked: the ability to understand context, to act in the world through a body, to make judgments that depended on being alive and embedded in a situation.

He called it know-how versus know-that. A doctor who can feel something is wrong in a patient before naming what it is. A chess grandmaster who sees the right move before calculating it. A person who walks into a room and understands the social dynamics in four seconds without running a single algorithm.

These were not tasks you could formalize. Not because they were mysterious. But because they were rooted in physical embodiment and years of embedded experience in the world. A machine sitting inside a server rack had none of that. It had never touched anything. It had no body. It had never been afraid or hungry or confused in the middle of a city.

The AI community kept dismissing him for 20 more years. Then quietly, things started shifting.

The symbolic AI approach he had criticized started breaking down exactly where he said it would. Language was too ambiguous. Common sense was impossibly hard to encode. Systems that worked in narrow domains failed completely the moment the real world showed up.

By the 1990s, the field had largely abandoned the approach Dreyfus had attacked. When MIT Press published a new edition in 1992 with a long introduction updating his position, historians of AI started writing sentences like "time has proven the accuracy and perceptiveness of Dreyfus's comments."

In 2007, a journalist asked him whether he thought he had won the argument. He said: "I figure I won and it's over. They've given up."

He was right and wrong at the same time. Symbolic AI did collapse. But something else rose in its place: deep learning, trained on massive amounts of human-generated data, learning patterns from the bottom up instead of the top down. Systems that were not programmed with rules but that absorbed something about language and images and tasks from raw experience.

That is where the argument gets genuinely interesting and genuinely unresolved.

Dreyfus spent his last years thinking carefully about whether deep learning addressed his critique or just circumvented it. He had always said the problem was not intelligence as pattern recognition. He had always said the hard part was something else. Situatedness. Meaning. The ability to care about outcomes in a way that comes from having skin in the game.

A model trained on a trillion tokens of human text knows a great deal about what humans say. Whether it knows what humans mean is a different question. Whether it can act in the world the way a human acts, with a body and a history and stakes in what happens, is the question he spent 50 years trying to make people take seriously.

He died in 2017. GPT-2 was released two years later. GPT-4 was released six years after that.

The question he raised is still open. We build systems now that would have seemed miraculous in 1972. Systems that write, reason, code, argue, compose, translate, and explain. And the AI researchers at every major lab spend an enormous amount of time trying to figure out exactly what these systems are missing.

Dreyfus spent 50 years on a campus where people refused to eat lunch with him for saying that question mattered.

The man who was wrong about chess was right about almost everything else.

[rezalet iyi şeyler @rezaletiyisey]

Hande Erçel’den daha iyi oynamış

[semazem 🍐 @semazem]

@EjderTurizm

[semazem 🍐 @semazem]

@EjderTurizm

[semazem 🍐 @semazem]

@EjderTurizm

[Tiraj @tirajnews]

Spor spikeri Cüneyt Kıran, yumuşak G’nin nasıl telaffuz edilmesi gerektiğini anlatıyor.

[𝔜𝔞𝔳𝔲𝔷 𝔈𝔰𝔢𝔯 🇹🇷🏁 @yavuzeser64]

Biraz bilgi herkese iyi gelir...

[semazem 🍐 @semazem]

All Johnny Depp

[semazem 🍐 @semazem]

@sivriikalemler Sefaletin acizliği

[İnkılâp Kitabevi @inkilapkitabevi]

TÜM KİTAPÇILARDA! 🎉

"2010'la ilgili birçok bilgi gazete kupürlerinden şuradan buradan elde edilebilir denebilir. Doğru. Ancak unutulmamalıdır ki roman gelecek için yapılan bir şey aslında. Dostoyevski'yi okuyoruz, Tolstoy'u okuyoruz, Cervantes okuyoruz, Shakespeare okuyoruz. Bunlar kimisi beş yüz, kimisi iki yüz yıl, yüz elli yıl önceki yazarlar. Demek ki roman gelecek için yazılmış bir mektuptur aynı zamanda. Bunu unutmamak lazım. Bu çerçeveden bakılırsa "2010" bir anlamda tarihe not düşmek gibi de adlandırılabilir."

Mehmet Eroğlu'nun kaleminden "2010" romanı,

https://t.co/k8IHm4c4YP 'da ve kitap satan her yerde!

#MehmetEroğlu #2010 #İnkılapKitabevi

[THIERRY D "ardéchois et français" @ThierryTout]

🇫🇷🎨
L'I.A. permet quelques fois d'obtenir des choses magnifiques.
Par exemple, d'avoir une vue intégrale de "La Liberté guidant le peuple" d'Eugène Delacroix réalisée en 1830, sans dénaturer cette œuvre grandiose ! 👇🤩

[@jurij0001 @jurijkovalenok1]

The approximation

[Nelly; @nrqa__]

Youtube Channels to Accelerate Your Learning:

1. Science & Tech
👉 https://t.co/EDNeha8fU9

2. Philosophy & Thinking
👉 https://t.co/s6rcsfcBdS

3. Physics
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👉 https://t.co/TYflyxEbhu

5. Biology & Nature
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7. History
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20. Astronomy & Space
👉 https://t.co/2xwLOLD5OQ