Look Up Versus Trending Now Safety Net Say More Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Selects Getting Technical with Mashable Gift Ideas For Everyone On Your List In My Bag Mashable @Home All Series It's called the AI Deceptive Advertising Act and it's casting a very wide net. Across the nation, there appears to be growing frustration with the explosion of AI-generated advertising plaguing small-business storefronts and restaurant interiors. Last Thursday, Pennsylvania Governor Josh Shapiro signed the Artificial Intelligence Deceptive Advertising Act into law, mandating that ads, from million-dollar television commercials to printed flyers passed out on the street, contain a disclosure label in the same format as the ad itself. SEE ALSO: Hackers find a way to turn users' own AI agents and Downloads folders against them The purpose of the bill is to combat false or misleading advertising, which is much easier to engage in with the help of AI-generated content. An advertiser may not create and disseminate or cause to be created and disseminated an advertisement that uses synthetic content in a manner that creates a false consumer perception about the nature, origin or authenticity of the goods or services being advertised, without clear and conspicuous disclosure.

If you image search on Google for'cultural heritage Australia', near the top of the results is a photograph of giant carved stone heads rising from a red desert. They look ancient and real. But the image is a generative artificial intelligence (GenAI) fabrication that never existed, and it is circulating the internet as though it is a real record of Australia's history. While GenAI, the technology behind chatbots and image generators, has the potential to broaden access to knowledge, it can also popularise false histories and strip Indigenous Knowledge of its context. It's something that can undermine both scientific expertise and Indigenous authority.

Long before Donald Trump entered national politics, I wrote a book published by Oxford University Press making the historical case against popular constitutionalism and departmentalism -- an originalist defense of judicial finality. But that scholarly position does not mean President Trump is ignoring the rule of law or behaving like an authoritarian when he advances a theory of judicial review that other presidents and scholars have likewise advanced.

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These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{ö}dinger bridge and flow matching.

Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies their local-mass behaviour that is not directly captured by such objectives. We introduce and use two mathematical tools: (1) Mass Index for recording the polynomial and logarithmic decay scales of local mass, and (2) regularised extended KL (RE-KL), a set-localised divergence that can be formulated in the presence of singular components. Mass Indices help characterise how Bayesian updating changes local mass: (1) power-log likelihood factors shift it explicitly, and (2) parameter-dependent supports, or their smooth softenings, may change the local scale through the amount of mass that remains near the parameter value. Using local RE-KL, we prove absolute, relative, and directional inequalities for comparing local small-ball masses under the two KL directions. Together, these results provide a local theoretical account of local mass behaviour. Experiments provide controlled illustrations of the local behaviour. Code is available at https://github.com/Forsythia0604/Local-Mass-Framework.

Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of impurity measures have been proposed for different statistical models, these criteria are typically introduced on a case-by-case basis and analyzed separately. In this paper, we study CART through the lens of Bregman divergences. This perspective places the classical least-squares criterion, Poisson deviance, Kullback-Leibler-type losses, and other impurity measures associated with exponential-family models within a common framework. As a result, key ingredients of the CART methodology -- including node representatives, impurity measures, and split selection rules -- can be expressed and analyzed through general properties of convex functions rather than through separate model-specific constructions. Beyond the algorithmic formulation, we investigate theoretical properties of Bregman-based CART procedures. In particular, we analyze how geometric properties of the generating convex function influence impurity reductions and stability of recursive partitions. We also establish consistency results within the proposed framework, providing a unified theoretical treatment for a broad family of CART type procedures. Our results provide a geometric interpretation of impurity-based tree construction and show that many classical CART impurity criteria admit a common interpretation within a Bregman framework.

Textual data are often collected alongside structured response variables, but prediction and interpretation are commonly treated as separate tasks. This paper studies rating prediction as an initial case of interpretable text-response modelling, where the aim is to learn textual representations that are both semantically meaningful and aligned with an external response. We propose a joint non-negative matrix factorisation and binomial regression model, in which the document-topic representation is learned from both text reconstruction and rating prediction. Simulation experiments and a real-world review dataset show that the model can recover stable response-relevant textual signals and achieve competitive performance against linear and ridge regression baselines. The framework provides a practical step towards interpretable modelling of text-linked outcomes, with potential extensions to other response types beyond bounded ratings.

The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post-hoc explainability methods and inherently interpretable approaches shed light on model behavior, yet they primarily reveal how models exploit correlations to maximize performance in prediction tasks. However, many decisions require causal insights and the possibility of using models for what-if scenario evaluation. To address this, we propose the integration of causal machine learning with inherently interpretable models for cross-sectional data. We evaluate these methods in terms of predictive accuracy and interpretability. Our findings show that the proposed approach achieves competitive performance in prediction and what-if analysis while offering transparency on the system structure, causal relationships among variables, and the functional forms that connect them. This work contributes to research on causality, machine learning interpretability, and data-driven decision support by offering informed, transparent, and causally grounded decisions.

Text-to-Visualization (Text2VIS) enables users to create visualizations from natural language queries, making data insights more accessible. However, Text2VIS faces challenges in interpreting ambiguous queries, as users often express their visualization needs in imprecise language. To address this challenge, we introduce nvBench 2.0, a new benchmark designed to evaluate Text2VIS systems in scenarios involving ambiguous queries.