Our organs age at a different pace intra-individual, as tracked through plasma proteins. Now confirmed for female reproductive organs @NatureAging https://
nature.com/articles/s4358
7-026-01098-y
…
RESEARCH
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Organs Age Differently: Plasma Proteins Track Individual Aging Rates
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AI Model Learns to Taste from Recipe Patterns Alone
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KAIKAKU AI just trained an AI to taste.
— The Rundown AI (@TheRundownAI) 29 avril 2026
Their team fed the model nothing but recipes from existing cookbooks. No nutrition data or chemistry.
The model worked out what was sweet, salty, spicy, and bitter from ingredient pairings alone.
It even picked up texture (chewy vs.… pic.twitter.com/DBywifYj0AKAIKAKU AI just trained an AI to taste. Their team fed the model nothing but recipes from existing cookbooks. No nutrition data or chemistry. The model worked out what was sweet, salty, spicy, and bitter from ingredient pairings alone. It even picked up texture (chewy vs.
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AI Accelerates Learning Cycles and Educational Development
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He shares my belief that AI can help significantly reduce these learning cycles.
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Interview Martin Advisory Board Member CUSP AI
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Watch the whole interview with Martin, a valued member of the @cusp_ai advisory board, on YouTube now:
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Iterative Material Design Process and Composition Analysis
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Why not more frequently? The process for designing new materials can take many iterative learning cycles, where materials’ compositions are analysed, and resulting effects are closely scrutinised.
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ASML CTO on Materials Compatibility in Semiconductor Manufacturing
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Above I share another clip from my recent interview with former @ASMLcompany President and CTO Martin van den Brink, where we discussed the process to ensure new materials are compatible with processing in the semiconductor industry.
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AI in Medicine: Burial and Excavation Commentary
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The burial and the excavation @JAMA_current commentary today https://
jamanetwork.com/journals/jama/
fullarticle/2848376
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(what I wrote about in Deep Medicine, 2019) -
Embedding Layers in Small Models: Architecture and Training Optimization
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Did you know that the embedding layer can contain 63% of total model parameters? In this talk, I present unique challenges of small models from architecture (don't build giant embedding layers) to post-training (how to fix doom looping) ↓ Slides in the comments ↓
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AI Transparency: Validating and Disclosing AI-Generated Content
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My new policy: if someone asks me to read something, I ask them how they used AI in creating it, and what validation/processing they applied to the AI outputs. (I disclose the same.) Been burned by giving too much attention to (undisclosed) slop folks have sent me…
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Self-Improving AI Loops: Why Verifiers Matter Most
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I went deeper on this in the full video: how self-improving AI actually works, where these loops break, and why the verifier matters more than most people think.
Watch it here: