Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1DdEJZaHVzWl9YcnkzajdFbVBvV3hRS051MXhuN1VfOGw2dHMtRlM0cm5qaVoxSXZjRGJmR3FkQ1dGam1FcEU1emNBVF8xMjczS1VyM2JnOHYwbXo4NjRR?oc=5" target="_blank">Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis</a> <font color="#6f6f6f">Nature</font>
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Per-Layer Embeddings: A simple explanation of the magic behind the small Gemma 4 models
Many of you seem to have liked my recent post "A simple explanation of the key idea behind TurboQuant" . Now I'm really not much of a blogger and I usually like to invest all my available time into developing Heretic, but there is another really cool new development happening with lots of confusion around it, so I decided to make another quick explainer post. You may have noticed that the brand-new Gemma 4 model family includes two small models: gemma-4-E2B and gemma-4-E4B . Yup, that's an "E", not an "A". Those are neither Mixture-of-Experts (MoE) models, nor dense models in the traditional sense. They are something else entirely, something that enables interesting new performance tradeoffs for inference. What's going on? To understand how these models work, and why they are so cool, let'
Positional Restructuring of System Prompts: Mitigating Transformer Attention Bias in Sub-Frontier Models
I built a sovereign AI system on a Mac Mini that kept forgetting facts written in its own system prompt. Instead of upgrading hardware, I figured out why — and found some things I was not expecting. The obvious part: moving critical facts from the middle to the beginning and end of the system prompt fixes recall (2.0 to 7.0 on a verification battery). This builds on Liu et al.'s lost-in-the-middle work. The less obvious part: a model with 83.4% IFBench scored 3.4/10 on fact recall while a model with 23.9% IFBench scored 7.5/10 after restructuring. Instruction-following and fact recall appear to be independent capabilities. I have not seen this documented elsewhere. The paper also covers a behavioral rule methodology that took a 32B model from 6.2 to 9.4 across seven dimensions with cold re
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What is the effect on the Human mind from AI?
I am suggesting this topic because I know first hand that LLM-AI has changed how I process. My Story: At first I attempted to have GPT help write C code. Then the frustration between how it wrote code and how I learned to write code became an issue. I relented and decided to use what it gave me. I then allowed GPT to design the code and there I became lost and now fear dependency. In the end I see a need to go back to my own logic ,reasoning and design skills. So this is an issue that is advancing in the public realm and it has credibility. I thought to see what my HF peers think. -Ernst 1 post - 1 participant Read full topic
90% людей используют нейросети как поисковик. И проигрывают.
Сидел я как-то в своем любимом кафе в центре Киева, потягивая капучино и глядя на бесконечную ленту кода на экране. Было 15 сентября 2023 года, и я уже потратил 12 часов на борьбу с проектом для клиента из США. Попытка использовать ChatGPT для аналитики больших данных только затянула процесс. Заплатить за это много часов, но результат оставлял желать лучшего. Нервно подперев голову рукой, я нащупал кнопку 'обновить' в поисках очередного ответа. Проблема была не в инструменте, а в подходе. Я попытался использовать универсальное решение для задачи, которая требовала чего-то более специализированного. Когда клиенты платят за результат, а не за процесс, времени на эксперименты просто нет. Я собрал промпты по этой теме в PDF. Забери бесплатно: https://t.me/airozov_bot В тот же вечер, разочарова
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