Mike Lawler, Ro Khanna, and Bill Cassidy campaigns have the top individual spending on AI. Mike Lawler, Ro Khanna, and Bill Cassidy campaigns have the top individual spending on AI. New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them. Candidates', parties', and committees' spending reveals that AI is fast becoming an essential tool of politics. The candidates themselves are quiet about how they are using the technology in their own campaigns It's a sensitive issue that we have been tracking closely since we started writing our book, Rewiring Democracy, which examined how AI is beginning to influence politics. A September 2025 Pew survey of Americans found that more than 70% would think less of a candidate if they used AI to help write a speech.

Despite their problems, robot delivery likely isn't going to go away any time soon; on the contrary, it's predicted to vastly expand. A recent study by Transforma Insights suggests that the world will have 559,000 automated urban delivery vehicles by 2035, compared to 28,000 in 2025. Considering this prediction, this robot revolution could have a big impact on gig workers. For companies like Uber Eats and DoorDash, both of which have faced scrutiny when it comes to paying their drivers ethically, robots are an easy way to increase overall company profits. However, the distance limitations of current robot models -- about two miles -- tend to limit their usage to more urban areas, meaning that, at least for the time being, humans cannot be completely taken out of the equation.

Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Redditors say Anthropic's expensive Claude plans offer far less usage than advertised A viral thread on r/Anthropic accuses the company of misleading users about the benefits of its higher-priced subscription tiers. Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . A popular Reddit post in r/Anthropic titled More Drama has drawn significant attention this week, amplifying claims that Anthropic has been misleading customers about how much usage they actually get with its higher-priced Claude subscription plans.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. When Josh Parker joined Nvidia in 2023, there was just one other person on a team responsible for leading the sustainability strategy and public policy initiatives. It didn't deter him from his belief that they could make a difference.

Last September, after it was sued for allegedly enabling the tragic death of 16-year-old Adam Raine, OpenAI announced it was working on a system that would automatically identify teens and restrict their usage of ChatGPT. Nearly a year later, the company is putting that system to work as part of a new user experience it calls ChatGPT for Teens. "There's no need for a teen to create a new account or change anything; if we predict you're under 18, or you've told us so, this becomes your default experience," says Lauren Jonas, OpenAI's head of youth and families. ChatGPT for Teens is broadly built around two pillars: learning and safety. Starting with the former, it brings together a selection of features OpenAI has released over the last year to make ChatGPT a better teacher.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Claude's Record-a-Skill cut my research from hours to 30 minutes - but the magic has limits I used Claude Cowork to automate a workflow, and the result was remarkably effective, but it exposed four drawbacks. Claude turns narrated screen recordings into reusable skills. Last month, Anthropic announced a new Claude Cowork feature: the ability to narrate a screen recording and turn it into a Claude skill . I tried it and, well, as they say, All magic comes with a price. Let's set the stage by defining what we're after here. A skill is a very detailed canned prompt that can be run like a program from within Claude (and many other AIs).

OpenAI is making a major change to how it operates ChatGPT free and Go accounts. Starting next week, the company will no longer enforce rate limits on users of those accounts for prompts that involve only text. In effect, that change will allow you to talk with ChatGPT as much as you want. The company will continue to enforce separate limits for other forms of usage. For instance, adding files and images to your prompts will see you eventually hit a limit as a free or Go tier user, as will making use of image generation and ChatGPT's recently updated voice mode.

This position paper challenges the "scaling fundamentalism" dominating AI research, where unbounded growth in model size and computation has led to unsustainable environmental impacts and widening resource inequality. We argue that LLM development should be fundamentally reoriented toward capability-perresource rather than capability alone. We present a theoretical framework demonstrating that resource-allocation decisions guided by gradient influence patterns can dramatically improve efficiency throughout the AI lifecycle. Our analysis shows that in transformer-based models, where a small fraction of parameters exert outsized influence (following heavy-tailed distributions), three critical insights emerge: (1) updating only high-influence parameters strictly outperforms full-parameter tuning on a performance-per-resource basis; (2) simple gradient norms provide computationally efficient proxies for identifying these high-influence components; and (3) coordinated parameter and data selection yields multiplicative efficiency gains, potentially reducing resource requirements by orders of magnitude. Building on these theoretical foundations, we propose a two-stage paradigm--marginalreturn pretraining for foundation developers and influence-guided adaptation for downstream users--bridged by gradient blueprints, metadata describing which parameters matter most for various tasks. This capability-per-resource perspective transforms what were once considered pragmatic hardware workarounds into theoretically optimal strategies, democratizing access to cutting-edge AI capabilities while significantly reducing environmental impact. By embedding resource consciousness into how we develop, adapt, and evaluate models, we can reshape AI progress toward a more sustainable and equitable future.

The following section is answers to questions listed in datasheets for datasets.858 A.1 Motivation859 Question: For what purpose was the dataset created? Was there a specific task in mind?860 Was there a specific gap that needed to be filled? Answer: To evaluate the linguistic robustness of language models across diverse English862 varieties by transforming Standard American English (SAE) datasets.863 Question: Who created the dataset (e.g., which team, research group) and on behalf of864 which entity (e.g., company, institution, organization)?865 Answer: The authors of this paper.866 Question: Who funded the creation of the dataset? If there is an associated grant, please867 provide the name of the grantor and the grant name and number.868

Large Language Models (LLMs) are predominantly evaluated on Standard American English (SAE), often overlooking the diversity of global English varieties. This narrow focus may raise fairness concerns as degraded performance on nonstandard varieties can lead to unequal benefits for users worldwide. Therefore, it is critical to extensively evaluate the linguistic robustness of LLMs on multiple non-standard English varieties. We introduce Trans-EnV, a framework that automatically transforms SAE datasets into multiple English varieties to evaluate the linguistic robustness. Our framework combines (1) linguistics expert knowledge to curate variety-specific features and transformation guidelines from linguistic literature and corpora, and (2) LLM-based transformations to ensure both linguistic validity and scalability. Using Trans-EnV, we transform six benchmark datasets into 38 English varieties and evaluate seven state-of-the-art LLMs. Our results reveal significant performance disparities, with accuracy decreasing by up to 46.3% on non-standard varieties.