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Mariusz Kurman PRO

mkurman

AI & ML interests

AI Tech Lead | MD

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reacted to openfree's post with 🔥 about 12 hours ago

# 🧬 Protein Genesis AI: Design Proteins with Just a Prompt ## 🤔 Current Challenges in Protein Design Traditional protein design faces critical barriers: - 💰 High costs ($1M - $10M+) & long development cycles (2-3 years) - 🔬 Complex equipment and expert knowledge required - 📉 Low success rates (<10%) - ⏰ Time-consuming experimental validation ## ✨ Our Solution: Protein Genesis AI Transform protein design through simple natural language input: ``` "Design a protein that targets cancer cells" "Create an enzyme that breaks down plastic" ``` ### Key Features - 🤖 AI-powered automated design - 📊 Real-time analysis & optimization - 🔬 Instant 3D visualization - 💾 Immediate PDB file generation ## 🎯 Applications ### Medical & Industrial - 🏥 Drug development - 💉 Antibody design - 🏭 Industrial enzymes - ♻️ Environmental solutions ### Research & Education - 🔬 Basic research - 📚 Educational tools - 🧫 Experimental design - 📈 Data analysis ## 💫 Key Advantages - 👨‍💻 No coding or technical expertise needed - ⚡ Results in minutes (vs. years) - 💰 90% cost reduction - 🌐 Accessible anywhere ## 🎓 Who Needs This? - 🏢 Biotech companies - 🏥 Pharmaceutical research - 🎓 Academic institutions - 🧪 Research laboratories ## 🌟 Why It Matters Protein Genesis AI democratizes protein design by transforming complex processes into simple text prompts. This breakthrough accelerates scientific discovery, potentially leading to faster drug development and innovative biotechnology solutions. The future of protein design starts with a simple prompt! 🚀 https://huggingface.co/spaces/openfree/ProteinGenesis

reacted to singhsidhukuldeep's post with 👀 about 12 hours ago

Exciting breakthrough in e-commerce recommendation systems! Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations. >> Key Innovation The framework ingeniously combines three distinct data types: - Visual data to capture product aesthetics and context - Textual information for detailed product features - Graph data to understand complex user-item relationships >> Technical Architecture The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components: Modality Fusion Module - All-Modality Self-Attention (AMSA) for unified representation - Cross-Modality Attention (CMA) mechanism for deep feature integration - Custom FFN adapters to align different modality embeddings Advanced Training Strategy - Curriculum learning approach with three complexity levels - Parameter-Efficient Fine-Tuning using LoRA - Special token system for behavior and item representation >> Real-World Impact The results are remarkable: - 38.25% improvement in Electronics recommendations - 43.09% boost in Sports category accuracy - Significantly higher human evaluation scores compared to traditional methods Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.

new activity about 22 hours ago

mkurman/llama-3.2-MEDIT-3B-o1:space

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upvoted a collection 2 months ago

MedIT SUN

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Llama 3.2 1B upscaled to 2.5B parameters • 4 items • Updated Nov 27, 2024 • 1

upvoted an article 7 months ago

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Space secrets security update

May 31, 2024

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