AHS UI menyelenggarakan kegiatan bertajuk “Workshop Perumusan Program AHS UI Untuk Renstra 2020-2024” pada hari Rabu, 5 Februari 2020, bertempat di Ruang Rapat Gedung Alt. 2, Rumpun Ilmu Kesehatan UI Depok.
Acara ini dihadiri oleh perwakilan dari seluruh Fakultas yang tergabung dalam Rumpun Ilmu Kesehatan UI yaitu Fakultas Kedokteran, Fakultas Kedokteran Gigi, Fakultas Kesehatan Masyarakat, Fakultas Ilmu Keperawatan, dan Fakultas Farmasi, serta perwakilan dari Rumah Sakit Pendidikan jejaring yang tergabung dalam AHS UI. Kegiatan ini bertujuan untuk menentukan kegiatan dan program AHS UI selanjutnya yang akan tercantum pada Rencana Strategis (Renstra) periode 2020 – 2024. Workshop dibuka dengan sambutan dan overview mengenai AHS UI 2019 oleh Dekan FKUI yang sekaligus merupakan Koordinator AHS UI (Prof. Dr. dr. Ari Fahrial Syam, SpPD-KGEH, MMB). Prof. Dr. dr. Budi Iman Santoso, SpOG (K),MPH selaku Wakil Koordinator AHS UI kemudian memberi paparan mengenai Program AHS UI 2020 untuk Persiapan Renstra AHS UI 2020 – 2024. Selanjutnya, dr. Anis Karuniawati, PhD, SpMK(K) selaku Wakil Dekan Bidang Sumber Daya, Ventura, dan Administrasi Umum memberikan presentasi mengenai pengembangan SDM AHS UI. Workshop dilanjutkan dengan presentasi singkat mengenai prioritas program oleh perwakilan dari masing-masing fakultas dan rumah sakit. Sesi selanjutnya adalah diskusi dan tanya jawab dimana para peserta workshop memberikan feedback terkait rencana program yang telah disampaikan dan juga klarifikasi mengenai proses pengembangan SDM AHS UI.
Acara ditutup oleh Prof. Budi dengan merangkum hasil diskusi yang telah dilakukan, serta menetapkan bahwa workshop selanjutnya akan dilakukan pada bulan April tahun 2020 di RIK UI, Depok untuk menyepakati program pada Renstra AHS UI 2020-2024.
Melihat luaran yang dapat dicapai melalui program-program yang akan dimasukan dalam Renstra 2020-2024, AHS UI dapat memberi dampak positif tidak hanya bagi anggota yang terlibat di dalamnya, namun juga bagi masyarakat luas.
Pada akhirnya, diharapkan bahwa AHS UI dapat meningkatkan kualitas pendidikan, memberikan pelayanan kesehatan yang maksimal, serta memperoleh manfaat dari hasil penelitian di bidang kesehatan.
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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning ability. DeepSeek-R1 attains results on par with OpenAI’s o1 design on a number of standards, consisting of MATH-500 and SWE-bench.
DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs outshine larger models, including GPT-4, on mathematics and coding benchmarks.
[DeepSeek-R1 is] the very first step toward enhancing language model thinking abilities utilizing pure reinforcement learning (RL). Our goal is to explore the potential of LLMs to develop reasoning capabilities with no monitored information, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … excels in a large range of tasks, including imaginative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on jobs needing long-context understanding, significantly surpassing DeepSeek-V3 on long-context standards.
To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also released. This model shows strong thinking efficiency, however” powerful thinking behaviors, it deals with a number of concerns. For example, DeepSeek-R1-Zero has problem with challenges like poor readability and language mixing.”
To resolve this, the group utilized a short stage of SFT to avoid the “cold start” problem of RL. They gathered numerous thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered more SFT data utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled models from Llama and Qwen.
DeepSeek assessed their design on a range of reasoning, mathematics, and coding criteria and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the criteria, consisting of AIME 2024 and MATH-500.
DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report
Within a few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was likewise connected for # 1 with o1 in “Hard Prompt with Style Control” category.
Django framework co-creator Simon Willison blogged about his try outs among the DeepSeek distilled Llama models on his blog:
Each response begins with a … pseudo-XML tag containing the chain of thought utilized to help create the response. [Given the timely] “a joke about a pelican and a walrus who run a tea space together” … It then believed for 20 paragraphs before outputting the joke! … [T] he joke is awful. But the process of arriving was such an interesting insight into how these new designs work.
Andrew Ng’s newsletter The Batch wrote about DeepSeek-R1:
DeepSeek is rapidly becoming a strong contractor of open models. Not just are these designs excellent entertainers, but their license allows usage of their outputs for distillation, potentially pressing forward the state of the art for language designs (and multimodal models) of all sizes.
The DeepSeek-R1 designs are available on HuggingFace.
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