Highlights: In this post we try to answer one practical question: can we score the visual complexity of an image without humans in the loop? We grab the SAVOIAS dataset (1,420 images with human-rated complexity), focus on the 200 advertisements, and try five different recipes — zero-shot CLIP prompts, Ridge regression on CLIP embeddings, pairwise ranking on embedding differences, kNN retrieval on cosine similarity, and finally Gemini 2.5 Pro as a chain-of-thought judge. The headline:…
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Highlights: Jason Weston traces the arc from early neural language models to self-improving LLMs that generate their own training data and evaluate their own reasoning System 1 vs System 2: fixed-compute pattern-matching vs deliberate multi-step reasoning — and why the same LLM implements both Chain-of-Thought prompting: adding “Let’s think step by step” jumps GSM8K accuracy from ~10% to 40–50%; few-shot CoT hits 90%+ on MultiArith CoVe + S2A: Chain-of-Verification reduces hallucinations 3× on knowledge list…
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Highlights: Can we measure spatial composition in living room photographs? We score 100 interior images using saliency-based rule-of-thirds alignment, Gemini Vision layout ratings, and CLIP composition prompts — then cross-correlate with color scores from #018 to find rooms that nail both color and layout. Method 1 — Rule of Thirds + Balance: gradient saliency → Gaussian-weighted power point alignment + visual balance index Method 2 — Gemini Vision Layout: send each image to Gemini 2.5…
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