This paper presents $whittlehurst$, a Python package implementing Whittle's likelihood method for estimating the Hurst exponent in fractional Brownian motion (fBm). While the theoretical foundations of Whittle's estimator are well-established, practical and computational considerations are critical for effective use. We focus explicitly on assessing our implementation's performance across several numerical approximations of the fractional Gaussian noise (fGn) spectral density, comparing their computational efficiency, accuracy, and consistency across varying input sequence lengths. Extensive empirical evaluations show that our implementation achieves state-of-the-art estimation accuracy and computational speed. Additionally, we benchmark our method against other popular Hurst exponent estimation techniques on synthetic and real-world data, emphasizing practical considerations that arise when applying these estimators to financial and biomedical data.

Online restless bandits extend classic contextual bandits by incorporating state transitions and budget constraints, representing each agent as a Markov Decision Process (MDP). This framework is crucial for finite-horizon strategic resource allocation, optimizing limited costly interventions for long-term benefits. However, learning the underlying MDP for each agent poses a major challenge in finite-horizon settings. To facilitate learning, we reformulate the problem as a scalable budgeted thresholding contextual bandit problem, carefully integrating the state transitions into the reward design and focusing on identifying agents with action benefits exceeding a threshold. We establish the optimality of an oracle greedy solution in a simple two-state setting, and propose an algorithm that achieves minimax optimal constant regret in the online multi-state setting with heterogeneous agents and knowledge of outcomes under no intervention. We numerically show that our algorithm outperforms existing online restless bandit methods, offering significant improvements in finite-horizon performance.

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that rescues food from farms and elsewhere to feed the hungry. Specifically, we developed an estimation and optimization approach for partially-observed episodic restless bandits under $k$-step transitions. The results show that our Thompson sampling with Markov chain recovery (via Stein variational gradient descent) algorithm significantly outperforms baselines for the problems of both organizations. We carried out this work in a prospective manner with the express goal of devising a flexible-enough but also useful-enough solution that can help overcome a lack of sustainable impact in data science for social good.

'The Five' co-hosts discuss new AI bot ChatGPT and the impact artificial intelligence will have on future jobs. Church leaders and volunteers will soon have access to an artificial intelligence platform that aims to shave hours off their day-to-day tasks by generating content from sermons to engage fellow Christians when they are not in the pews. Upcoming platform Pulpit AI, founded by Michael Whittle, is expected to launch later this summer and will serve as a tool for Christian leaders looking to take the tedious work out of crafting religious blog posts, devotionals and prayer guides and social media posts. "We want to help pastors of small to medium-sized churches be able to make content for their congregations to interact with throughout the week and on social media," Whittle told Fox News Digital. "We think every pastor should, if they want, have a digital signal to their congregations beyond the sermon. "Most small to medium-sized churches have small or completely volunteer staff, so they have zero operational leverage when it comes to media and resources for their church," he added. "If we can help a church media team get past the blank page, we can not only save them crazy amounts of time, we can help every church become a resourcing church for their people." 'AI JESUS' TALKS DATING, RELATIONSHIPS, MORALS -- EVEN OFFERS VIDEO-GAMING TIPS A congregant reads a referred passage from her Bible during services at Highland Colony Baptist Church in Ridgeland, Mississippi, Nov. 29, 2020. Puplit AI "doesn't and never will" generate sermons, instead it serves as a tool where the user uploads a sermon or religious podcast in order to repurpose it into "social media highlights, blog posts, discussion questions, and the other content churches use to reach their congregations and communities day in and day out," Whittle said. "Pulpit AI analyzes long form audio and video, then repurposes that into various forms of content," Whittle said. "Pulpit AI's output is taken directly from the source material.

AI in the workplace is a phrase that tends to stir up angry sentiments. Will we be replaced by machines? Will automation make us redundant? But our tendency towards revelling in dystopian rhetoric has a flaw – some people dream of a utopia instead. Some dreamers collaborate on CSIRO Data61's $12 million Collaborative Intelligence (CINTEL) Future Science Platform, which aims to shift the focus of AI in the workplace and find ways to improve it.

This paper proposes a general framework of multi-armed bandit (MAB) processes by introducing a type of restrictions on the switches among arms evolving in continuous time. The Gittins index process is constructed for any single arm subject to the restrictions on switches and then the optimality of the corresponding Gittins index rule is established. The Gittins indices defined in this paper are consistent with the ones for MAB processes in continuous time, integer time, semi-Markovian setting as well as general discrete time setting, so that the new theory covers the classical models as special cases and also applies to many other situations that have not yet been touched in the literature. While the proof of the optimality of Gittins index policies benefits from ideas in the existing theory of MAB processes in continuous time, new techniques are introduced which drastically simplify the proof.