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Jamir Nazir, the controversial winner of the Commonwealth award, tells his side of the story. Jamir Nazir has become the face of the AI-writing crisis. In May, the largely unknown 62-year-old Trinidadian writer was named a regional winner of the prestigious Commonwealth Prize for his short story " The Serpent in the Grove " But after it was published in the literary magazine, signs began to emerge that the story--about a cocoa farmer who cheated on his wife, and then tried to kill her--may have been AI-generated. Inscrutable lines plucked from Nazir's dense prose were mocked and memed. A young woman in the story "had the kind of walking that made benches become men."

Hiroo Ota (center left), CEO of Kioxia Holdings, and others unveil Kioxia's new 3D flash memory chip at its Kitakami plant in Kitakami, Iwate Prefecture, on Friday. Kioxia Holdings has started shipping samples of its next-generation flash memory chips to artificial-intelligence data center operators, seeking to gain ground in the lucrative business against rivals. The Tokyo-based chipmaker's latest high-density 3D flash memory chips aim to better meet AI data center needs with better efficiency and transmission speeds. The 332-layer 10th-generation chips pack more data into silicon and can store 59% more data compared with its previous flagship 8th-generation chip, the company said Friday. Production will take place at the company's second manufacturing facility at its Kitakami plant in Iwate Prefecture, which began operating in September last year.

Follow the day's latest updates Court approves $23.5m fine and costs order against ASX Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' Albanese defends gambling reforms, says he's'not against someone having a punt' Pocock says it's'tragic' gambling reforms don't go nearly far enough Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' If AI companies want to use Australian creative work, they should do what everyone else does: ask permission and pay for it. Australian creativity is one of our greatest national assets - not a free resource for multinational tech companies. The Coalition will always back the right of artists to control their work and be fairly compensated when others profit from it. This is about consent, fairness and respect for Australian creativity. Court approves $23.5m fine and costs order against ASX Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' Albanese defends gambling reforms, says he's'not against someone having a punt' Pocock says it's'tragic' gambling reforms don't go nearly far enough Court approves $23.5m fine and costs order against ASX A federal court judge has ordered the ASX operator to pay $23.5m in penalties and costs after the company admitted to making a misleading statement about a troubled upgrade for technology required to run the stock exchange.

OpenAI proposes handing U.S. government a 5% stake, report says OpenAI has discussed giving the U.S. government a 5% stake as artificial intelligence firms face scrutiny in Washington. OpenAI has discussed giving the U.S. government a 5% stake, the Financial Times reported on Thursday, as artificial intelligence firms face scrutiny in Washington over the likely misuse of advanced models and whether Americans would benefit from the industry's massive valuations. The ChatGPT creator has proposed that other U.S. AI firms also give Washington similar stakes, although it is unclear whether they would agree, the report said, citing two people familiar with the talks. The move follows growing public backlash in the U.S. over AI's potential to cause economic upheaval, including layoffs, and could help OpenAI sweeten ties with an administration that is increasingly taking an active role in regulating the technology. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

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In such settings, the inverse scattering transform (IST) of Ablowitz, Kaup, Newell, and Segur [2] has enjoyed a rich and successful history, and is now the standard theoretical framework for deriving reduced-order evolution equations for soliton dynamics. Although these derivations are traditionally of an analytical - rather than data-driven - nature, recent work has employed the IST formalism as a tool for experimental data analysis, using the technique to analyze soliton content from empirical measurements [8, 15, 24]. Moreover, recent approaches using alternative parameterization techniques have demonstrated that the learning of reduced-order, interpretable equations of motion for solitons is tenable in a data-driven setting [6, 26, 27]. Despite the success of this recent work, however, little effort has been devoted to developing a data-driven modeling approach based on the IST itself, most likely due to the fact that the framework is fundamentally problem-specific. In this paper, we address the question of whether effective soliton dynamics can be inferred directly from observed scattering data (as opposed to being derived or approximated analytically).

Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive. We study CIT and CIF as top-$k$ feature-ranking methods for downstream prediction using real-data benchmarks, runtime ablations, and synthetic feature-recovery experiments. At a fixed node, if the features and permutation budget do not depend on the node responses, Bonferroni-corrected $+1$ Monte Carlo permutation $p$-values control nodewise rejection under the complete permutation null. CIF ranks 4th among 17 classification methods on 22 datasets and 3rd among 18 regression methods on 8 datasets. With Bonferroni correction held fixed, the CIF runtime ablations indicate that adaptive stopping and the number of thresholds searched have the largest measured effect on runtime: turning off adaptive stopping and using exact threshold search increase fitting time by 4.0--8.4$\times$ and 1.9--10.8$\times$, respectively, while downstream score changes are at most 0.011. Sparse high-$p$ simulations indicate that forest feature sampling can leave informative features out of many split decisions. Overall, the results support CIF as a top-$k$ feature-ranking method in the evaluated downstream prediction benchmarks.

We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training steps and batch size (called three-term law). Fitting the proposed law on a large set of training runs, we find that it correctly recovers the scaling of the optimal batch size. Moreover, because it makes use of training runs with suboptimal batch size, our proposed law can be robustly fit with a significantly smaller amount of training runs. We further show that the three-term law can be used to derive scaling laws for suboptimal batch sizes, and that it matches previous empirical findings related to the critical batch size.

Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets. This mismatch can limit reconstruction accuracy, reduce sample quality, and constrain the expressive power of the learned latent space. We propose the eXact-Prior Variational Autoencoder (X-VAE), a framework that replaces the conventional standard normal prior with a Gaussian prior derived from the latent representations of a pretrained autoencoder (AE). Specifically, the empirical mean and standard deviation of the AE latent codes are used to parameterize a data-adaptive prior that more closely reflects the underlying structure of the training data. During generation, X-VAE introduces a latent scaling factor that enables explicit control over the variance of the sampled latent vectors, providing a simple mechanism for balancing sample diversity and fidelity. This flexibility makes the proposed approach particularly well suited for applications such as industrial and engineering design, where generated solutions must satisfy strict structural or functional constraints while still permitting meaningful design exploration. We present the mathematical formulation of well-suited X-VAE, derive the corresponding KL divergence objective for the proposed prior, and evaluate the method on standard benchmark datasets. Experimental results demonstrate that X-VAE preserves reconstruction quality while producing latent representations that better align with the empirical data distribution, leading to improved controllability and more realistic generated samples.