When Anna Funder stood before a pack of journalists at Parliament House this month, she presented herself not just as a writer but also a "victim of crime". The Stasiland author was using the analogy to illustrate how technology companies have flagrantly "hoovered up" her literary works for their own profit. Authors, artists, musicians and media organisations were last year assured those laws wouldn't be watered down when the federal government ruled out granting a legal exemption for artificial intelligence companies to mine content to train their large language models, which include ChatGPT, Gemini and Claude. But continual lobbying from tech giants and a whistleblower's tipoff to the independent senator David Pocock have ignited fears that the Albanese government might go back on its word - even as it continues to insist that it won't. The stoush has exposed splits within Labor about how to respond to AI and raised questions about how far the government should bend - if at all - to big tech to capture the supposed riches of the datacentre boom.

Apple has sued OpenAI and two former employees, alleging misappropriation of its trade secrets as the artificial intelligence company seeks to build its own hardware for ChatGPT, a major rupture in a partnership between the iPhone maker and the AI giant. The complaint, filed in a California federal court on Friday, alleges a coordinated effort to steal Apple's confidential information, including product designs, manufacturing processes and supply chain strategies. The lawsuit names Chang Liu, a former senior system electrical engineer, and Tang Yew Tan, a former vice president of product design for the iPhone and Apple Watch, as defendants, along with the OpenAI Foundation, OpenAI Group PBC and io Products. Neither defendant immediately responded to a request for comment. Apple alleged that Liu failed to return a company-issued work laptop and later used an authentication bug to access Apple's internal network, downloading "dozens of Apple's confidential hardware-related files".

Image caption, Apple CEO Tim cook is leaving the role later this year. Apple has accused OpenAI of gaining access to valuable inside information through the hiring of its former employees. In a federal lawsuit filed on Friday, Apple sued the artificial intelligence (AI) company, two of its employees, as well as io Products, claiming it has engaged in a pattern of theft of Apple's confidential product development and related work. At least two long-time Apple workers who left the company to join OpenAI allegedly took part in this pattern by, in part, emailing themselves internal Apple information. Drew Pusateri, a spokesman for OpenAI, told the BBC: We have no interest in other companies' trade secrets.

Apple filed a lawsuit against OpenAI on Friday alleging the artificial intelligence firm stole company trade secrets in a move to create its own hardware device. The suit claims OpenAI poached Apple employees, coaxing them to hand over confidential material, product designs and other tightly held information. "Recently, significant evidence has emerged suggesting individuals employed by OpenAI wrongfully took Apple's secret and confidential information regarding our unreleased technologies, processes, and products," an Apple spokesperson said in an email. Drew Pusateri, a spokesperson for OpenAI, said the company was reviewing the court filing. "We have no interest in other companies' trade secrets," he added.

Apple calls OpenAI's hardware business'rotten to its core' in trade secret theft lawsuit Apple calls OpenAI's hardware business'rotten to its core' in trade secret theft lawsuit The lawsuit also names io Products, the hardware company led by Jony Ive. Apple is suing OpenAI and two of its former employees who currently work at the AI company, for theft of its trade secrets. In a lawsuit filed in federal court Friday, Apple alleges extensive misconduct by the company it once partnered with, describing its hardware business as rotten to its core. The lawsuit also names io Products, the Jony Ive-led hardware startup acquired by OpenAI last year, as complicit in the trade secret theft. It doesn't mention Ive by name, but described the organization as complicit in a coordinated pattern of misconduct at an institutional level within OpenAI.

Guardian Australia has been told an industry proposal has been presented to cabinet that would grant AI companies special exemptions to mine creative content. In exchange, the companies would bankroll the artists' fund and commit more than $50bn worth of investment in datacentres. Australia'sleepwalking' into AI crisis and'tech bro free-for-all', says Greens senator The independent senator David Pocock said the proposal was the "ultimate dirty deal" as he demanded the government categorically rule it out. The potential adoption of a text and data mining exemption would represent a major reversal from the federal government, which last year ruled it out after criticism from artists, authors and media groups. Amid fears the government could capitulate to big tech, a delegation of creatives staged a press conference in parliament house on Wednesday to urge the government to hold the line.

Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set of 3,799,695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR.

Public model repositories now contain millions of models, yet most remain undocumented and effectively lost: their capabilities, provenance, and constraints cannot be reliably determined. As a result, the field wastes training time and compute, propagates hidden biases, faces intellectual-property risks, and misses opportunities for model reuse and transfer. In this position paper, we advocate charting the world's model population in a unified structure we call the Model Atlas: a graph that captures models, their attributes, and the weight transformations connecting them. The Model Atlas enables applications in model forensics, meta-ML research, and model discovery, challenging tasks given today's unstructured model repositories. However, because most models lack documentation, large atlas regions remain uncharted. Addressing this gap motivates new machine learning methods that treat models themselves as data and infer properties such as functionality, performance, and lineage directly from their weights. We argue that a scalable path forward is to bypass the unique parameter symmetries that plague model weights. Charting all the world's models will require a community effort, and we hope its broad utility will rally researchers toward this goal.

The increasing deployment of Large Vision-Language Models (LVLMs) raises safety concerns under potential malicious inputs. However, existing multimodal safety evaluations primarily focus on model vulnerabilities exposed by static image inputs, ignoring the temporal dynamics of video that may induce distinct safety risks. To bridge this gap, we introduce Video-SafetyBench, the first comprehensive benchmark designed to evaluate the safety of LVLMs under video-text attacks.

As language models scale, their performance improves dramatically across a wide range of tasks, but so does their tendency to memorize and regurgitate parts of their training data verbatim. This tradeoff poses serious legal, ethical, and safety concerns, especially in real-world deployments. Existing mitigation techniques, such as differential privacy or model unlearning, often require retraining or access to internal weights making them impractical for most users. In this work, we introduce TOKENSWAP, a lightweight, post-hoc defense designed for realistic settings where the user can only access token-level outputs. Our key insight is that while large models are necessary for high task performance, small models (e.g., DistilGPT-2) are often sufficient to assign fluent, grammatically plausible probabilities to common function words - and crucially, they memorize far less. By selectively swapping token probabilities between models, TOKENSWAP preserves the capabilities of large models while reducing their propensity for verbatim reproduction.