probability

该论文提出有界续写分析(BCA),通过概率模型检查量化自回归语言模型中隐藏的有害生成概率,以补充传统的攻击成功率(ASR)指标,更好地揭示表面拒绝之下的分布脆弱性。

Nokia Applied Research开源了AnyJev库，可将任意LLM转化为Jev风格的决策模型，通过旋转预算和闭式头拟合实现无微调的概率校准输出，大幅降低order-flip率并提升自动决策比例。

The article explains the quality system in Factorio's Space Age extension and introduces an online calculator tool to help players optimize factory planning with probability-based quality upgrades.

Eliezer Yudkowsky critiques the P(doom) concept for conflating P(ruin|ASI) and P(ASI), advocating for focused discussion on specific causal links in AI risk rather than broad, vague estimates.

In an experiment, the AI model Jev completes 100 trolley problems, choosing to sacrifice one person to save five with 99% probability in the first round.

This paper introduces a data-free universal prior over syntactic structures from a cognitive model of language production, demonstrating it assigns higher probabilities to attested trees and correlates with language-specific data across 138 languages.

The article introduces 'p(despair)' as a broader measure of AI risks compared to 'p(doom)', emphasizing scenarios like mass unemployment and falling living conditions that may be more probable than total extinction.

The article proposes a Drake equation-inspired framework to decompose AI extinction risk into conditional probabilities, enabling more precise discussion and estimation of various outcomes.

In 2025, experts estimated a 10% chance that AI would solve or substantially assist in solving a Millennium Prize Problem by 2027.

Stanford professor Chris Piech announces a free 'Probability for AI' course with a volunteer teaching model, aiming for a 1:10 teacher-student ratio and open applications for learners and teachers.

Explorations in Monte Carlo Methods is a hands-on textbook that teaches Monte Carlo methods through realistic problems and programming exercises, suitable for students in engineering, sciences, and mathematics.

Google DeepMind's VP Research discusses how incorporating uncertainty and probability into AI systems can lead to safer, more reliable real-world decision-making, covering Bayesian thinking and historical perspectives.

A 7-day crash course to learn essential probability concepts for machine learning using Python, designed for developers to deepen their ML understanding.

The article discusses the mathematical equivalence between compression and prediction in information theory, referencing classical foundations and modern applications like language models.

An insightful explainer on the true meaning of entropy, contrasting the common 'disorder' metaphor with a probabilistic interpretation using dice rolling analogies.

A paper presenting a novel computer Scrabble engine based on probability that achieves championship-level performance.

Discusses the trade-offs of using Kullback-Leibler divergence in quantitative analysis, framing it as a Hamlet-like dilemma for quants.

This paper develops a quantum-like extension of the Tug-of-War decision-making model, using a qutrit internal state to model context dependence and decision dynamics, and argues that contextual probability is a resource signature of minimal decision dynamics.

The paper introduces Telescope Perplexity, a metric that measures token repetition probability to detect LLM-generated text in a zero-shot manner, achieving state-of-the-art or competitive performance across diverse datasets.

This article details the learning path for an ordinary person to become a quantitative trader, covering five stages: probability, statistics, linear algebra, calculus, and stochastic calculus. It also explains the industry's compensation structure, interview requirements, and the rapid growth of AI/ML positions.