4 years in a row! A pretty solid training block going into this race despite some last minute travel to Germany for work.
| 2025 NC HIM Goal Plan vs Actuals |
|---|
| – Swim: 26:00 (Actual: 25:55, 140th OA) – T1: 5:00 (Actual: 4:30) – Bike: 2:10 (235W, 245Nor AVG < 160BPM) (Actual: 2:19, 150HR, 234W Avg, 239WNor, 18th OA) – T2: 2:00 (Actual: 1:30) – Run: 1:29 (6:35 @ 168bpm) (Actual: 1:39, 8W, 159HR, 60th OA) – Finish: 04:20:00 – Result: #3 AG, 20 OA, AG Winner 4:15?, OA Winner 3:59? |
| – Training Load: – CTL: 75 → 98 (Peak) 99 (Race) – Bike Load: 46 (Peak) 45 (Race) – Run Load: 35 (Peak) 36 (Race) – Biggest Week: 13.1 Hours, 864 TSS – Recovery Week: ~9 hours – Avg Week: ~12 hours |













A Definition of AGIThe lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today’s specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly “jagged” cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.





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