Chronological feed of everything captured from Andreessen Horowitz.
IPnP addresses partially labeled medical image segmentation by iteratively prompting a frozen foundation model with a trainable specialist network to generate and refine pseudo-labels for unlabeled organs. This collaboration progressively recovers full-organ supervision without requiring additional manual annotations. On simulated partial-label AMOS dataset, IPnP outperforms prior methods and nears fully labeled performance; on a real 210-patient head-and-neck cancer dataset, it demonstrates clinical efficacy.
In a survey experiment with 236 West Point cadets compared to a demographically similar public sample, cadets performed a target identification task and reassessed after receiving advice from either algorithms or human analysts. Cadets exhibited less automation bias and algorithm aversion, with trust in AI decision support better calibrated than the public's. Military training and AI exposure likely mitigate cognitive distortions in high-stakes military contexts.
A variational action formulation treats coordinate maps as dynamical degrees of freedom alongside propagating fields in IBVPs, preserving space-time symmetries post-discretization for exact Noether charge conservation. This dynamical adjustment of coordinates enables automatic adaptive mesh refinement guided by charge conservation residuals. Results hold independently of action or governing equation solution levels when using SBP operators, demonstrated in 1+1D scalar wave propagation.
SAR introduces a regularization scheme that analytically continues the kinetic operator power in the action, rendering the theory formally finite prior to Dyson series expansion. This approach ensures full, self-consistent regularization of φ⁴ and Yukawa theories at next-to-leading order. Demonstrated efficacy highlights its potential for higher-order perturbative computations in QFT.
pQCD calculations reveal non-trivial high-momentum particle suppression due to medium-induced energy loss in light ion collisions from 208Pb+208Pb to 3He+3He, scaling as R_AB ≈ (√(AB))^{1/3}. Symmetric systems like 10B+10B, 6Li+6Li, 4He+4He, and 3He+3He show suppression persisting to the smallest sizes, with 3He and 6Li providing clean probes of QGP formation via final-state partonic energy loss. Energy loss models predict v_2{SP} ≈ 0 in these small systems, ruling out energy loss as the source of observed positive v_2 in p+208Pb collisions.
Analysis of internal data shows 29% of Fortune 500 and 19% of Global 2000 enterprises are live paying customers of leading AI startups, having signed top-down contracts and converted pilots successfully. This marks unprecedented speed for Fortune 500 adoption, contrasting historical patterns where enterprises trailed startups by years. The surge follows ChatGPT's 2022 launch, driving enterprises to deploy AI faster than prior technologies.
The a16z essay by Christian Keil argues that America's historical edge over rival powers — from the Manhattan Project to the Internet boom — stems not from incremental optimization but from a willingness to mobilize massive capital and talent toward uncertain, paradigm-shifting bets. The core thesis is that the decisive variable in each case was belief: America outspent, out-hired, and out-built competitors because it took nascent technologies more seriously than anyone else. With AI now framed as the equivalent national project, the argument is that the outcome will hinge less on current technical standing and more on whether American institutions, capital markets, and culture commit to AI with the same credibility-defying intensity. The essay draws a direct structural parallel between past mobilizations and the current AI arms race, positioning skepticism itself as the primary strategic risk.
Hiring a Chief of Staff (CoS) requires clear definition of responsibilities and expectations, as the role