Say More Mashable Selects Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Safety Net Versus Gift Ideas For Everyone On Your List All Series The Tetris Company was not involved in the creation of'Build the Wall'. Matt Binder joined Mashable's tech vertical in 2018, where he covers social media, tech policy, cybersecurity, online scams, cryptocurrency, AI, creator news, weird tech, and other related tech beats. Tetris is speaking out against the anti-immigration puzzle game clone released by Trump's White House. The team behind Tetris, the classic 1984 puzzle video game, has responded to the White House over a knockoff game that the Trump administration released this week. The statement from Tetris hits back against Trump's White House over its recently released Tetris clone, Build the Wall.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Switch Off Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series The lawsuit claims the AI company is engaging in the most blatant ongoing thefts of intellectual property in history. Sony Music and Warner just filed a lawsuit alleging that Anthropic developed Claude models using stolen music. Music giants Sony Music Publishing and Warner Chappell Music have just filed a potentially multibillion-dollar lawsuit against Anthropic, and the publishers aren't mincing words. The lawsuit, filed Friday evening in the U.S. District Court for the Northern District of California, calls Anthropic the culprits behind one of the largest and most blatant ongoing thefts of intellectual property in history. Sony and Warner also named Anthropic CEO and cofounder Dario Amodei and cofounder Benjamin Mann in the lawsuit.

Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Even though ByteDance just released Seedance 2.0 on Thursday, it's already earned praise, but also indignation from Hollywood studios, when it comes to its AI-generating capabilities. With the strong early momentum, Seedance has already found itself in hot water with one of the largest media companies in the world. However, it's not the first time that Disney has threatened legal action against an AI company, since Character.AI received a cease-and-desist letter for the same offense in September. On the other hand, Disney partnered with OpenAI in a three-year licensing agreement that allows the AI giant to generate images and videos using that highly sought-after intellectual property. By subscribing, you are agreeing to Engadget's Terms and Privacy Policy .

As vehicles grow more software-dependent, repairing them has become harder than ever. A bill in the US House called the Repair Act would ease those restrictions, but it comes with caveats. Every time you get behind the wheel, your car is collecting data about you. Where you go, how fast you're driving, how hard you brake, and even how much you weigh. All of that data is not typically available to the vehicle owner.

As AI-generated video platforms rapidly advance, ethical challenges such as copyright infringement emerge. This study examines how users make sense of AI-generated videos on OpenAI's Sora by conducting a qualitative content analysis of user comments. Through a thematic analysis, we identified four dynamics that characterize how users negotiate authenticity, authorship, and platform governance on Sora. First, users acted as critical evaluators of realism, assessing micro-details such as lighting, shadows, fluid motion, and physics to judge whether AI-generated scenes could plausibly exist. Second, users increasingly shifted from passive viewers to active creators, expressing curiosity about prompts, techniques, and creative processes. Text prompts were perceived as intellectual property, generating concerns about plagiarism and remixing norms. Third, users reported blurred boundaries between real and synthetic media, worried about misinformation, and even questioned the authenticity of other commenters, suspecting bot-generated engagement. Fourth, users contested platform governance: some perceived moderation as inconsistent or opaque, while others shared tactics for evading prompt censorship through misspellings, alternative phrasing, emojis, or other languages. Despite this, many users also enforced ethical norms by discouraging the misuse of real people's images or disrespectful content. Together, these patterns highlighted how AI-mediated platforms complicate notions of reality, creativity, and rule-making in emerging digital ecosystems. Based on the findings, we discuss governance challenges in Sora and how user negotiations inform future platform governance.

The primary goal of traditional federated learning is to protect data privacy by enabling distributed edge devices to collaboratively train a shared global model while keeping raw data decentralized at local clients. The rise of large language models (LLMs) has introduced new challenges in distributed systems, as their substantial computational requirements and the need for specialized expertise raise critical concerns about protecting intellectual property (IP). This highlights the need for a federated learning approach that can safeguard both sensitive data and proprietary models. To tackle this challenge, we propose FedQSN, a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters. Consequently, the server transmits only a privacy-preserving proxy of the global model to clients during each communication round, thus enhancing the model's confidentiality. Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning settings but also achieves enhanced protection of model parameters compared to baseline methods.

Farquhar, the Tech Council of Australia CEO, told ABC's 7.30 program on Tuesday: "all AI usage of mining or searching or going across data is probably illegal under Australian law and I think that hurts a lot of investment of these companies in Australia". Farquhar's claim overlooks that this is not a settled issue in the US, and could have devastating effects on creative industries. Farquhar's argument is that it is not theft of people's work unless the AI is used to "copy an artist directly" such as creating a song in their style. "I do think people would say that, hey, if people are going to sit down with a digital companion, an AI song creator and they collaboratively work with an AI to create something new to the world, that's probably fair use." Farquhar said the benefits of large language models outweigh the issues raised by AI training its data on other people's work for free.

AI risks entrenching racism and sexism in Australia, the human rights commissioner has warned, amid internal Labor debate about how to respond to the emerging technology. Lorraine Finlay says the pursuit of productivity gains from AI should not come at the expense of discrimination if the technology is not properly regulated. Finlay's comments follow Labor senator Michelle Ananda-Rajah breaking ranks to call for all Australian data to be "freed" to tech companies to prevent AI perpetuating overseas biases and reflect Australian life and culture. Ananda-Rajah is opposed to a dedicated AI act but believes content creators should be paid for their work. Media and arts groups have warned of "rampant theft" of intellectual property if big tech companies can take their content to train AI models.

--With the increasing importance of protecting intellectual property in machine learning, watermarking techniques have gained significant attention. As advanced models are increasingly deployed in domains such as social network analysis, the need for robust model protection becomes even more critical. While existing watermarking methods have demonstrated effectiveness for conventional deep neural networks, they often fail to adapt to the novel architecture, Kolmogorov-Arnold Networks (KAN), which feature learnable activation functions. KAN holds strong potential for modeling complex relationships in network-structured data. However, their unique design also introduces new challenges for watermarking. Therefore, we propose a novel watermarking method, Discrete Cosine Transform-based Activation W atermarking ( DCT-AW), tailored for KAN. Leveraging the learnable activation functions of KAN, our method embeds watermarks by perturbing activation outputs using discrete cosine transform, ensuring compatibility with diverse tasks and achieving task independence. Experimental results demonstrate that DCT-AW has a small impact on model performance and provides superior robustness against various watermark removal attacks, including fine-tuning, pruning, and retraining after pruning.

Recent years have witnessed growing interest in adopting large language models (LLMs) for Register Transfer Level (RTL) code optimization. While powerful cloud-based LLMs offer superior optimization capabilities, they pose unacceptable intellectual property (IP) leakage risks when processing proprietary hardware designs. In this paper, we propose a new scenario where Verilog code must be optimized for specific attributes without leaking sensitive IP information. We introduce the first IP-preserving edge-cloud collaborative framework that leverages the benefits of both paradigms. Our approach employs local small LLMs (e.g., Qwen-2.5-Coder-7B) to perform secure comparative analysis between paired high-quality target designs and novice draft codes, yielding general design principles that summarize key insights for improvements. These principles are then used to query stronger cloud LLMs (e.g., Deepseek-V3) for targeted code improvement, ensuring that only abstracted and IP-safe guidance reaches external services. Our experimental results demonstrate that the framework achieves significantly higher optimization success rates compared to baseline methods. For example, combining Qwen-2.5-Coder-7B and Deepseek-V3 achieves a 66.67\% optimization success rate for power utilization, outperforming Deepseek-V3 alone (49.81\%) and even commercial models like GPT-4o (55.81\%). Further investigation of local and cloud LLM combinations reveals that different model pairings exhibit varying strengths for specific optimization objectives, with interesting trends emerging when varying the number of comparative code pairs. Our work establishes a new paradigm for secure hardware design optimization that balances performance gains with IP protection.