100-an mobil dikendalikan dengan algoritma AI berbasis RL untuk meningkatkan efisiensi dan mengurangi kemacetan.
Artikel Populer: Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment
Berikut ini beberapa literatur online untuk mempelajari bahasa C
Belajar deep learning nggak terlalu rumit. Modal dasarnya mengerti matematika SMA, komputer juga nggak harus yang mahal-mahal. Alamat kursusnya: “Practical Deep Learning“
Ada beberapa bahan yang biasa dipakai untuk membuat alat masak, yaitu:
Stainless steel kualitasnya sangat baik. Panas dari kompor mudah menyebar merata. Dapat dipakai untuk berbagai macam cara. Tidak lengket jika sudah di’seasoning’. Perawatan mudah. Material dari wajan relatif tidak masuk ke makanan, kecuali chromium yang bisa bereaksi dengan makanan kalau dipakai memasak yang asam.
Wajan besi ada yang menggunakan besi saja, ada yang dilapis enamel. Besi bereaksi jika dipakai memasak makanan yang mengandung asam. Besi dari wajan dapat masuk ke makanan.
Wajan enamel adalah wajan besi yang dilapis dengan enamel.
Gelas bagus, namun mudah pecah. Konduktivitas panas kurang bagus dibandingkan wajan logam.
Tembaga sangat mudah menghantar panas. Kelemahannya adalah tembaga cukup reaktif dengan makanan.
Keramik tidak lengket. Kurang baik untuk panas tinggi. Tidak reaktif. Namun kadang diberi lapisan anti lengket teflon.
Teflon berpotensi melepas mikroplastik ke alam
Aluminium bersifat racun.
Carbon steel: lebih mudah berkarat dibandingkan baja stainless steel
Titanium lebih kuat dari baja dan lebih ringan. Namun harganya mahal sekali.
Sumber: https://github.com/bytedance/pasa
Q: How did DeepSeek get around export restrictions? A: They didn’t. They just tinkered around with their chips to make sure they handled memory as efficiently as possibly. They lucked out, and their perfectly optimized low-level code wasn’t actually held back by chip capacity.
Q: How did DeepSeek train so much more efficiently? A: They used the formulas below to “predict” which tokens the model would activate. Then, they only trained these tokens. They need 95% fewer GPUs than Meta because for each token, they only trained 5% of their parameters.
Q: How is DeepSeek’s inference so much cheaper? A: They compressed the KV cache. (This was a breakthrough they made a while ago.)
Q: How did they replicate o1? A: Reinforcement learning. Take complicated questions that can be easily verified (either math or code). Update the model if correct.
Reference: https://x.com/wordgrammer/status/1883712727073607859
Alamat: https://apic.id/jurnal/index.php/jsc/index
Jurnal Sistem Cerdas with eISSN: 2622-8254 is a peer-reviewed journal serving as a publication medium for research findings that support the research and development of cities, villages, sectors, and other systems. The Intelligent Systems Journal is published by the Smart Indonesia Initiative Association (APIC) and is released every four months (April, August, and December). This journal is expected to serve as a platform for publishing research findings from practitioners, academics, authorities, and related communities.
The purpose of the Intelligent Systems Journal is to contribute to the intellectual life of the nation by the mandate contained in the preamble of the 1945 Constitution. This journal also serves as a platform for the publication of innovations, technologies, and policies of the APIC community, related to education and the intelligence of large-scale system components.
The scope of the systems discussed is attached but not limited to;
