probability

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.

A tweet showcases a visualization of 8 LLMs' reasoning traces on a probability question, highlighting moments of self-correction and pivoting.

A visualization that shows how 8 different LLMs reason through a tricky probability question, with branches representing moments of self-correction like 'But, wait...'.

Recommend an interactive visualization website called Seeing Theory to help users intuitively understand core concepts of probability and statistics, covering basic probability, distributions, inference, regression, etc., suitable for beginners and those reviewing.

Nathan Lambert shares a video lecture covering prerequisites for his book, including language model basics, probabilities, and training pipelines, using GLM 5.2.

Mathematicians have extended the classic 1992 proof about card shuffling to less precise shuffles, showing that a 'cutoff phenomenon' still occurs even with uneven deck splits.

This paper argues that probability theory is a historically evolving form of rationality, tracing its development from combinatorial games to Bayesian inference and contrasting it with fuzzy logic and deep learning.

KL Zero is an interactive browser game where players draw a probability distribution to match a target KL divergence value, helping users intuitively understand the concept of KL divergence in machine learning.

A tweet shares a lecture at MIT by David Shirokoff covering the fundamentals of Markov Chains, including transition probabilities, Markov matrices, eigenvalues, and long-term steady state.