Xbox and Nintendo have also been pushing consumers towards digital games. Sony has announced that PlayStation is going all digital, with physical game disc production being discontinued starting January 2028. After this date, you'll only be able to purchase new games digitally on the PlayStation Store and in retailers. Sony says its decision is a response to shifting trends in consumer preference, with digital sales significantly outweighing physical. Last year, physical game distribution accounted for just three percent of PlayStation's revenue, and the fact that the PS5 Pro launched in 2024 without a disc drive was a pretty good indication of Sony's future direction.

The UN's independent scientific panel for AI has published its first report. Artificial intelligence development has been progressing at such a rapid pace that current governance systems are unable to keep up, the UN's Independent International Scientific Panel on Artificial Intelligence says in its preliminary report . The panel, consisting of members from around the world, will provide the information needed to stage the UN Global Dialogue on AI Governance. It will take place in Geneva, where member states will discuss how to manage the technology, and is scheduled to begin on July 6. In its report, the panel discusses how quickly AI capabilities have evolved over the past few years.

Things to Do in L.A. Tap to enable a layout that focuses on the article. I'm not sure I want to be around that long Mel Brooks, shown in January, celebrated his 100th birthday this week. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

I am on holiday later this month, so I'm pleased to find there's a really wide range of intriguing new science fiction to take with me. I'm particularly keen to get cracking on a tale by Sheila Armstrong about strange ancient things found in a bog, but I'm also excited to read a new book by one of my favourite authors, Paul Tremblay (even if it does sound very disturbing). And I'm looking forward to the high-concept thrillers and classic space-set sci-fi on offer, too - not forgetting the first new novel released in 30 years. This sounds a little -like and ideal summer reading for those of us who enjoy a good high-concept thriller. It's set in a near future where you can outsource your emotional pain thanks to a biotech company, Eudaimonia.

The UN panel said its approach to AI was'scientific, not political'. The UN panel said its approach to AI was'scientific, not political'. A new United Nations report warns that the development of artificial intelligence may exacerbate global inequality and proposes a shared framework for how to responsibly develop AI, as adoption and investment into the technology accelerates unevenly across the world. "Access to AI tools alone does not produce equal benefit," the report states. "Countries that rely on foreign models, cloud infrastructure and data pipelines may gain access to AI while losing practical control over its standards, safeguards and local fit."

Kawasaki Heavy Industries is collaborating with companies including Nvidia to integrate AI and robotics, and last month announced a development hub in Silicon Valley. Kawasaki Heavy Industries is finalizing plans to raise about ¥200 billion ($1.23 billion) by issuing new shares and convertible bonds to fund capital expenditure, according to two sources familiar with the matter. The company will decide on the details of the issuance as soon as this week, the sources said. The shares and convertible bonds will be sold mainly to overseas institutional investors, one of the sources said. The plan to raise funds has not been reported earlier. Kawasaki Heavy said in a statement that it is considering various capital strategies including issuing new shares and bonds but that nothing has been decided.

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.

Many modern Language Model (LM) pipelines return an averaged model, such as an exponential moving average of the training iterates, rather than the final iterate itself. This raises a fundamental question: given that we will return an iterate average, how should we change training to improve the performance of this average? We study this question by formulating optimizer design for the iterate-average estimator as an optimal-control problem. In a continuous-time stochastic quadratic model, we solve for the control strategy that minimizes the error of the returned average subject to a penalty on the size of the intervention. A practical approximation to this controller yields PACE, a lightweight wrapper around AdamW that pulls the live weights toward their exponential moving average with a clipped, per-coordinate control strength. We prove that a stylized version of PACE converges at the standard stochastic convex optimization rate, up to a factor depending on the averaging rule, while in the quadratic setting it can strictly improve the limiting squared error of the iterate-average estimator and can do so by an arbitrarily large factor on some instances. Empirically, our results suggest that PACE improves over AdamW and EMA-evaluated AdamW in supervised fine-tuning of 1-2B parameter LMs and in GPT-2 pretraining on FineWeb for a wide range of learning rates, decay schedules, and other hyperparameters.

Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy-git/INFUSER.

Safety classifiers deployed in production operate under a stationarity assumption that fails silently: when input distributions drift, accuracy degrades with no error signal until ground-truth labels arrive. We present an online monitor that detects distributional shift in classifier scores via a sliding-window KS statistic with empirically calibrated alarm thresholds. In a pre-registered factorial evaluation (4 classifiers $\times$ 5 shift conditions $\times$ 20 seeds $\times$ 2 window sizes; 800 cells), the monitor achieves 86.6% valid detection (mean latency 39.5 steps) across synthetic-onset, real-jailbreak, and adversarial regimes; a classifier $\times$ shift interaction ($η^2 = 0.185$) shows that monitoring must be tuned per classifier. Attempting to recover post-detection coverage via weighted conformal prediction exposes a failure mode: density-ratio estimation collapses for generative classifiers because logistic regression separates source from target perfectly in 3584-4096-dimensional embedding space, clipping all importance weights to zero; projecting to $\leq 32$ dimensions restores coverage. We then extend the framework to gradient-based evasion and give the first threat-model characterisation of score-disagreement monitoring as a canary. We falsify three assumptions: that architectural diversity drives the signal (false, $η^2 = 0.011$), that it is generic out-of-distribution detection (false, GCG-specific, $p < 10^{-12}$), and that an adaptive attacker can suppress it (false while the canary is confident). We derive the exact security boundary, a confidence-gated equilibrium at which a monitor-aware attacker stalls at gap $= 1/(2λ)$, and provide a calibration-free scan martingale achieving false-alarm rate $\leq 1\%$ across all classifiers with no per-model tuning.