Tag: Machine Learning
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How AI Detectors Actually Work, and Why They Fail
AI text detectors run on perplexity, trained classifiers, and watermarks. Peer-reviewed testing shows where each mechanism breaks, and who pays.
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Single-Cell Foundation Models Keep Losing to Linear Baselines
Seven benchmarks tested scGPT, Geneformer, UCE, TranscriptFormer and Arc State against simple baselines. The baselines won. Here is the mechanism.
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Data Leakage in Machine Learning: Why Your Metrics Lie
Six classes of data leakage inflate ML metrics silently. New measurements across 2,047 datasets show the textbook villain barely matters.
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Is an AI Edit a New Use? The Omni Hotels Likeness Suit
Two models say Omni Hotels ran an AI-altered holiday ad they never shot. The complaint tests where a photo license ends and a new AI use begins.
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Embedding Inversion: Your Vector Database Isn’t Anonymous
Vec2Text recovers 92% of text from embeddings alone. How inversion attacks work, why geometry predicts risk, and what actually defends a RAG pipeline.
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GGUF vs GPTQ vs AWQ: Which Quantization to Use
GGUF, GPTQ, AWQ, NF4, FP8 and MXFP4 solve different problems. How each format works, where each one wins, and which fits your deployment.
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Why a 1M-Token Model Only Reasons Over 200K
Models advertise 1M-token windows but reason reliably over far less. The positional-encoding reason why, and how to measure your real ceiling.
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The Jailbreak Hiding in Your JSON Schema
A CCS 2026 paper hides jailbreaks in JSON schemas, hitting 94-99% success against GPT-5 and Gemini. Why prompt filters never see it.
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Ghost Vectors: Deleted Embeddings Stay Recoverable
Researchers tested three vector databases and found deleted embeddings stay intact on disk, recoverable at rates that break GDPR and HIPAA.
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How Model Merging Actually Combines Separate LLMs
Some top open-weight models are merged, not trained. The math behind task vectors, TIES, DARE, and why the technique works at all.
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Why AI Has Not Yet Found RNA’s AlphaFold Moment
AlphaFold solved protein folding. RNA structure prediction remains unsolved. Here is the specific chemistry and data gap that makes it harder.
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How Model Quantization Actually Works: INT8 to INT4
A 70B model needs 140GB at full precision, 35GB at INT4. The rounding math, why naive quantization breaks, and how GPTQ and AWQ fix it.
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How LLM Tokenization Actually Works: BPE Explained
The algorithm behind every LLM tokenizer was built in 1994 to compress files, not language. The mechanism, and why it breaks on math and non-English text.
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How Mixture-of-Experts Actually Routes Every Token
DeepSeek V4 holds 1.6 trillion parameters and uses a fraction per token. The routing math, why naive versions collapse, and the modern fix.
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Why R Still Beats Python in Clinical Biostatistics
SAS still dominates regulatory submissions, but R runs the survival models, mixed-effects analysis, and increasingly the FDA-facing tables behind it.
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ONNX Explained: How One Format Runs Any AI Model
ONNX lets a model trained in PyTorch run on a phone, browser, or edge chip without rewriting it. The graph, the opset system, and where conversions break.
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Same Model, 20-Point Gap: Why Coding Benchmarks Mislead
Claude Opus 4.6 scores 58% or 80% on the same benchmark depending only on which harness wraps it. Here is why coding agent scores mislead.
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NeuroGolf 2026: Kaggle’s Race to Build the Tiniest AI
A Kaggle contest that closed today scores neural networks by size, not accuracy. Here is the formula, the constraints, and what teams built to win.
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The Annotation Underground: Who Trains AI for So Little
Every frontier model, Claude included, runs on labelers paid under $2 an hour in countries picked for weak labor law. The full wage chain, documented.
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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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