Chronological feed of everything captured from Dwarkesh Patel.
LHCb reports the first measurement of forward-backward asymmetry A_FB and flat term F_H in the angular distribution of B+ → π+ μ+ μ- decays using 9 fb^{-1} of pp collision data from 2011-2018. Measurements are performed in two q^2 intervals: one below and one above the J/ψ and ψ(2S) charmonium resonances. SM predictions fall within the 68% CL interval in the high-q^2 region and 99% CL in the low-q^2 region.
Memanto introduces a typed semantic memory layer for long-horizon agents using 13 predefined categories, automated conflict resolution, and temporal versioning, powered by Moorcheh's information-theoretic search engine for sub-90ms deterministic retrieval without indexing or ingestion delays. It outperforms hybrid graph and vector systems on LongMemEval (89.8%) and LoCoMoMo (87.1%) benchmarks using single-query retrieval and lower complexity. A five-stage ablation validates each component's impact, enabling scalable agentic deployments.
This study evaluates open-source image simulation tools for generating synthetic lunar images using real DEM and terrain data from Chandrayaan-2 OHRC and NASA WAC/NAC instruments. It focuses on the impact of camera models and illumination conditions on image quality to support planetary mission planning, hazard detection, and navigation validation. The work aims to enhance reliability of synthetic imagery for autonomous systems in lunar exploration.
A graph-theoretic approach maps fermion mass terms in latticized theory-space models to bipartite graphs, with fields as vertices and mass terms as edges. The number of massless fermion modes equals the cardinality of the graph's maximum matching. Wave-function support of these modes is confined to fields reachable via even-length maximum-matching-alternating paths from unmatched vertices, per Dulmage-Mendelsohn decomposition. Results rely solely on theory-space topology, enabling parameter-independent model design with specified massless mode counts and localizations.
Researchers identified 86 platforms hosting scientific posters, but only 43 allow counting, revealing just 150k posters shared as of 2024—a low volume relative to scholarly output. Platforms like Zenodo and Figshare often lack structured metadata support for discovery (e.g., conference details), and researchers rarely provide it when available. While posters garner views and downloads, citations are uncommon, hindering FAIR compliance and reuse; guidelines are recommended to promote sharing.
DigitsOnTurbo (DoT) overcomes SIMD adoption barriers in large-number arithmetic by restructuring computations into independent data-parallel operations, bypassing dependencies in conventional algorithms. It delivers 1.85x speedups for addition/subtraction and 2.3x for multiplication versus prior SIMD methods. Library integration yields up to 4x gains for add/sub, 2x for multiplication, boosting scientific throughput by 19.3% and cryptographic latency/throughput by 7.9%/5.9%.
Current AI development, heavily reliant on pre-training and supervised learning with extensive human input, struggles with genuine on-the-job learning and generalization. The economic impact remains limited because models lack the continuous, self-directed learning capabilities inherent in human intelligence, which is crucial for navigating real-world complexities and diverse job requirements. Achieving true AGI necessitates a breakthrough in continual learning, allowing AI to acquire and adapt skills efficiently across various and evolving contexts.
This content explores how the brain's learning and steering subsystems operate, hypothesizing that evolution hardwired specific, complex loss functions into the steering subsystem to guide learning in the cortex. It contrasts this with current LLMs, which primarily use simple next-token prediction, and introduces the concept of omnidirectional inference as a more generalized learning capability present in the brain. The discussion also touches on the potential for AI to leverage similar architectural and algorithmic principles for more advanced and sample-efficient learning.
The Renaissance period, particularly in Italy, witnessed a profound transformation in intellectual thought and societal structures, moving from an initial phase of seeking to restore Roman virtues to a more critical and empirical approach to knowledge. This evolution, spurred by figures like Petrarch and Machiavelli, coupled with the advent of the printing press and the changing political landscape of city-states like Florence, ultimately created a fertile environment for the Scientific Revolution. The persistent, though often self-serving, drive for knowledge and its dissemination, along with unintended consequences of technological and political shifts, were key catalysts.
The US Department of Defense
The scalability of AI compute faces significant bottlenecks, shifting from power and data centers to semiconductor manufacturing, specifically EUV tools and memory. The lead times for building new fabs and the specialized nature of ASML's supply chain limit rapid expansion. This scarcity drives up memory prices and reshapes investment strategies in the tech industry, favoring early commitment to compute and larger models.
AI is fundamentally reshaping the scientific method by lowering the cost of idea generation, shifting the bottleneck from hypothesis formulation to rigorous verification and validation. This enables a vast exploration of potential solutions ("breadth") in contrast to traditional human-driven "depth." While current AI excels at automating established techniques and identifying "low-hanging fruit" in problem-solving, its true impact lies in complementing human ingenuity by rapidly mapping out new fields and providing data-driven insights, necessitating a re-evaluation of scientific workflows and the very definition of progress.
Scientific progress transcends simplistic verification loops and falsification, evident in historical scientific revolutions. Major shifts, like the adoption of special relativity over Lorentz's ether theory, often predate definitive experimental proof, reflecting a complex interplay of aesthetic preferences, theoretical parsimony, and the gradual accumulation of varied evidence. Consequently, the advancement of science requires fostering diverse research programs and accepting that fundamental breakthroughs may not always emerge from direct, linear problem-solving approaches.