Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Trending Now Gift Ideas For Everyone On Your List Safety Net Mashable @Home In My Bag Versus Creator Playbook Getting Technical with Mashable All Series Kalshi's claim to be a commodities trader is being rejected by courts California tribes won the nation's first injunction to block Kalshi sporting contracts. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. States square off against the federal government in a push to subject Kalshi to gambling laws. State courts have struck another blow against online prediction market Kalshi, which has sought to remain outside of gaming law, claiming it's a trading -- not gambling -- platform. On Wednesday, a US District judge granted a preliminary injunction to California tribes, prohibiting Kalshi from offering sports contracts (AKA trades or swaps) on tribal land .

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Gift Ideas For Everyone On Your List Mashable @Home In My Bag Safety Net Versus Trending Now Say More Creator Playbook Mashable Selects Getting Technical with Mashable All Series What is happening with Kalshi in Utah? The site's founders face numerous felony charges. Neal joined Mashable's Social Good team in 2024, editing and writing stories about digital culture and its effects on the environment and marginalized communities. He is the former editorial director of The Advocate and Out magazines, has contributed to the Los Angeles Times, Curbed, and Los Angeles magazine, and is a recipient of the Sarah Pettit Memorial Award for LGBTQ Journalist of the Year Award from the National Gay and Lesbian Journalists Association (NLGJA). He lives in Los Angeles with his family.

Paddy Power-owner Flutter has announced plans to cut up to 1,400 jobs as its boss claimed the company cannot fully absorb tax rises imposed on the gambling industry. Staff based in the UK and Ireland, as well as British and Irish workers at Flutter's sites in Portugal and Romania will be affected, but what roles are at risk has not been disclosed. The government announced increased taxes on online casino and sports betting, with an exemption for horse racing, in the Budget last November. Flutter boss Kevin Harrington said the tax hikes had fundamentally changed the economic model of the UK betting and gaming industry, arguing it had helped fuel the unlicensed, unregulated market. We continue to expect these tax increases to have a very significant impact on the overall market, Flutter said on Tuesday.

Look Up Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series Polymarket and Kalshi face possible regulation as Supreme Court'monitors the situation' Prediction markets regulated as gambling platforms by individual states? Shannon Connellan is Mashable's Senior Editor, General Assignments, based in London. She has been Mashable's UK Editor (and still manages the illustrious UK team) and Australia Editor, but emotionally, she lives searching for . A Tomatometer-approved critic, Shannon writes about entertainment, tech, social good, science, culture, and Australian horror, and loves to nerd out with movie stars, filmmakers, and TV creators . SCOTUS is the focus for state regulators aimed at Kalshi.

Say More Look Up Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Prediction markets like Kalshi violate sports betting laws, ruling says. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness. Rebecca's experience prior to Mashable includes working as a staff writer, reporter, and editor at NBC News Digital and as a staff writer at Forbes.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about AI for science, delve into world models, research transparent and trustworthy AI, and hear about the lottery ticket hypothesis. The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants featured Ximing Wen who is researching transparent and trustworthy AI systems. We found out more about her work, her experience as a research intern, and what inspired her to study AI. In this wide-ranging conversation, Jonathan Frankle delves into empiricism versus theoretical proofs, how the approach to computer science has changed (even if the fundamental problems haven't), how younger researchers are rapidly adapting to a world that values impact above all else, and what it means to be a researcher.

Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time $O(D)$ in the data dimension $D$, whereas a naive classical implementation relies on handling exponentially many candidate nodes and hence takes $\exp[O(D)]$ time. In this work, we construct and analyze a quantum-inspired fully classical algorithm for the same sampling task. We show that our algorithm runs in time $O(\operatorname{poly}(D))$, thereby removing the exponential dependence on $D$ from the previous classical approach. Numerical simulations show that the proposed sampler achieves empirical risk comparable to exact sampling from the optimized distribution and substantially lower than sampling from the non-optimized uniform distribution, while also exhibiting exponentially improved runtime scaling compared with the conventional classical implementation. These successful dequantization results show that sparse subnetwork selection via optimized sampling can be achieved classically with polynomial data-dimension scaling on conventional computers without quantum hardware, providing an alternative to the existing quantum algorithm.

The lottery ticket hypothesis (LTH) [20] states that learning on a properly pruned network (the winning ticket) improves test accuracy over the original unpruned network. Although LTH has been justified empirically in a broad range of deep neural network (DNN) involved applications like computer vision and natural language processing, the theoretical validation of the improved generalization of a winning ticket remains elusive. To the best of our knowledge, our work, for the first time, characterizes the performance of training a pruned neural network by analyzing the geometric structure of the objective function and the sample complexity to achieve zero generalization error. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned, indicating the structural importance of a winning ticket. Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer. With a fixed number of samples, training a pruned neural network enjoys a faster convergence rate to the desired model than training the original unpruned one, providing a formal justification of the improved generalization of the winning ticket. Our theoretical results are acquired from learning a pruned neural network of one hidden layer, while experimental results are further provided to justify the implications in pruning multi-layer neural networks.

Neural Information Processing Systems http://nips.cc/