Tag: Machine Learning
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Vision-Language Models: Architecture and the Benchmark Gap
How CLIP, SigLIP, Q-Former, and MLP adapters work in vision-language models. Why Qwen2.5-VL compresses visual tokens 4x, and what current VLMs still cannot do.
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Chinchilla Scaling Laws: Three Methods and Why Labs Ignore Them
Chinchilla proved GPT-3 was undertrained. The 20:1 rule is a training-compute floor. Three methods, their disagreements, and why frontier labs now exceed it.
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LoRA and QLoRA: Fine-Tuning Large Models on One GPU
LoRA fine-tunes 70B models on one GPU using low-rank weight updates. The intrinsic dimension proof, rsLoRA scaling fix, and where LoRA falls short.
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Speculative Decoding: How LLMs Generate 3x Faster
Speculative decoding achieves 3-4x LLM speedup with zero output quality loss. The math proof, EAGLE-2’s 4.26x result, and when it does not help.
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LLMs in Veterinary Clinical Practice: What the Evidence Actually Shows
ChatGPT-4.5 scored 90% on feline eye disease cases vs 96.7% for experienced veterinary ophthalmologists and significantly outperformed novices (56-67%). Where LLMs add clinical value in veterinary practice, where…
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AI-Assisted Zoonotic Disease Detection: From SARS to H5N1
H5N1 in US dairy cattle is the live test of AI-assisted zoonotic detection. NGS with AI flags novel pathogens before specific assays exist. What AI surveillance can and…
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One Health and Machine Learning: How AI Bridges Human and Animal Disease Surveillance
Machine learning now integrates electronic health records, social media, wearable sensors, and environmental data to detect outbreaks earlier than traditional systems. The AI4MPOX-SN initiative in Senegal and the…
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Generative AI for Small Molecule Drug Discovery: How It Works and What the Evidence Shows
Generative AI is producing novel molecules from VAEs, GANs, and diffusion models. Machine learning virtual screening shows 75% hit validation rates against 106M-compound libraries. Why no AI-designed drug…
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AI in Digital Pathology: What Computational Pathology Can and Cannot See
An NIH multi-institution study in Lancet Oncology classified 52 CNS tumor types from tissue images at 80% accuracy across 5,516 test samples. A Cancer Science paper simultaneously documented…
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FDA Clearance for AI Medical Devices: What 510(k), De Novo, and PMA Actually Mean
The FDA has cleared 700+ AI medical devices through 510(k), De Novo, and PMA pathways. A March 2026 European Radiology review documents how the EU AI Act, FDA…
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AI-Driven ADMET Prediction: What the Blind Challenge Results Actually Show
Deep learning beat classical methods for ADME prediction in a 65-team blind challenge at the 2025 OpenADMET competition. An AI-PBPK platform predicted full human pharmacokinetic curves from molecular…
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RFdiffusion and ProteinMPNN: How AI Now Designs Proteins From Scratch
RFdiffusion generates protein backbones. ProteinMPNN designs the sequences that fold into them. Together they achieved sub-Angstrom accuracy at influenza binding interfaces. How the two-step pipeline works, why AI-generative…
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DNA Synthesis Screening Cannot Keep Up With AI-Designed Sequences
The IGSC DNA synthesis screening standard was built on sequence homology to known pathogens. AI-designed sequences achieve dangerous functions through novel sequences that homology checks cannot recognize. What…
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Evo 2: The Genomic Foundation Model Trained on 9.3 Trillion DNA Bases
Evo 2 from Arc Institute is a 40B-parameter genomic foundation model trained on 9.3 trillion DNA bases spanning all domains of life. How the 128K context architecture works,…
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ESM3: The Protein Language Model That Unifies Sequence, Structure and Function
ESM3 from EvolutionaryScale is a 98B-parameter generative protein language model that reasons across sequence, structure, and function simultaneously. How the VQ-VAE structural tokenization works, what the GFP design…
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AlphaFold 3 in Drug Discovery: Where It Works and Where It Fails
AlphaFold 3 predicts protein backbones well but fails significantly on ligand-binding poses in GPCR drug targets, which represent 33% of approved drugs. Five PubMed-sourced studies covering what works,…
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Radiology Foundation Models: What Merlin, the 22% Hallucination Rate, and ED Fracture Data Tell Us
Stanford published Merlin in Nature: a CT foundation model tested on 44,098 scans across 3 institutions. Meanwhile 22% of AI radiology reports contain factual errors and LLMs miss…
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AI in Radiology: Three Phases and What the Clinical Evidence Shows
Radiology AI has moved through three phases: rule-based CAD, the deep learning benchmark era, and clinical deployment validation. A 556-paper bibliometric analysis and a multicenter thymus CT validation…
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AI in Veterinary Medicine: What the Clinical Evidence Actually Shows
Veterinary AI is producing measurable results in canine radiology, equine PET imaging, gait analysis, and dairy herd monitoring. Six PubMed-indexed studies from 2024-2026 with specific accuracy numbers, and…
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How Protein Language Models Learned to Design Dangerous Proteins
Researchers used three open-source protein design models to bypass DNA synthesis screening in 2025. Here is how protein language models work, why training data exclusion fails as a…
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